Search

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Inferior vena cava (IVC) filters are commonly used in China to prevent pulmonary embolisms in patients with deep vein thrombosis. However, IVC filter removal is complicated when the filter has penetrated the IVC wall and endovascular techniques usually fail. The purpose of this study was to evaluate the effectiveness and safety of retroperitoneal laparoscopic-assisted retrieval of wall-penetrating IVC filters after endovascular techniques have failed. Patients and Methods: We retrospectively evaluated a series of 8 patients who underwent retroperitoneal laparoscopic-assisted retrieval of a wall-penetrating IVC filter between December 2017 and November 2019. All patients had experienced at least 1 failure with endovascular retrieval before the study. The filters were slanted and the proximal retrieval hooks penetrated the posterior lateral IVC wall in all patients on computed tomography. Demographic information, operation parameters, and complications were recorded and analyzed. All patients were followed up for at least 12 months. Results: The procedure was successful in all patients. The median surgery time was 53.6 ± 12.7 min and the average blood loss was 45.0 ± 13.5 ml. No serious complication occurred during the patients' hospitalization, which was an average of 6.4 days. The median follow-up time was 15.1 months, and no patient had deep vein thrombosis recurrence. Conclusions: Retroperitoneal laparoscopic-assisted retrieval is a feasible and effective technique, particularly when proximal retrieval hooks penetrate the posterior lateral wall of the IVC after endovascular techniques have failed. To some extent, the development of this technique at our institution has increased the success rate of filter removal and improved patient satisfaction. [ABSTRACT FROM AUTHOR]

• The goal of this paper is to create a sketch dataset that is suitable for developing deep learning-based solutions to the problem of search and retrieval in 3D CAD models. • A sketch dataset of query images, called 'CADSketchNet' has been built using the available CAD datasets. For the 58,696 CAD models of the MCB Dataset, we obtain computer-generated sketches. We call this as Dataset-A. We obtain hand-drawn sketch data for all the 801 CAD models from ESB dataset. This is the Dataset-B. • For obtaining Dataset-A, many computer sketch generation methods, including the current state-of-the art methods are experimented. We justify the proposed weighted canny technique based on the comparison. • An analysis of the quality of the hand-drawn sketches in Dataset-B is also performed. • Various deep learning architectures have been tested on the developed CADSketchNet dataset, and a detailed analysis of their performance have been reported. • Among the many experiments conducted, it is shown that a Siamese Network approach yielded the best results as compared to other features/networks. The class-wise results of the approach on both Datasets - A & B. • We also justify the need for a dedicated dataset for CAD over the regular 3D graphical shapes by testing the existing methods on the developed CADSketchNet. [Display omitted] Ongoing advancements in the fields of 3D modelling and digital archiving have led to an outburst in the amount of data stored digitally. Consequently, several retrieval systems have been developed depending on the type of data stored in these databases. However, unlike text data or images, performing a search for 3D models is non-trivial. Among 3D models, retrieving 3D Engineering/CAD models or mechanical components is even more challenging due to the presence of holes, volumetric features, presence of sharp edges etc., which make CAD a domain unto itself. The research work presented in this paper aims at developing a dataset suitable for building a retrieval system for 3D CAD models based on deep learning. 3D CAD models from the available CAD databases are collected, and a dataset of computer-generated sketch data, termed 'CADSketchNet', has been prepared. Additionally, hand-drawn sketches of the components are also added to CADSketchNet. Using the sketch images from this dataset, the paper also aims at evaluating the performance of various retrieval system or a search engine for 3D CAD models that accepts a sketch image as the input query. Many experimental models are constructed and tested on CADSketchNet. These experiments, along with the model architecture, choice of similarity metrics are reported along with the search results. [ABSTRACT FROM AUTHOR]

• The problem of cross-time 3D registration is formally defined and a framework for cross-time 3D registration is proposed. • A down-sampling methodology that detects the most valuable points for cross-time registration is proposed. • A new benchmark for evaluating both traditional and cross-time registration algorithms was created (ECHO). • An extensive evaluation of geometry-based and deep learning state-of-the-art approaches on 3D cross-time registration is performed. [Display omitted] Registration is a ubiquitous operation in visual computing and constitutes an important pre-processing step for operations such as 3D object reconstruction, retrieval and recognition. Particularly in cultural heritage (CH) applications, registration techniques are essential for the digitization and restoration pipelines. Cross-time registration is a special case where the objects to be registered are instances of the same object after undergoing processes such as erosion or restoration. Traditional registration techniques are inadequate to address this problem with the required high accuracy for detecting minute changes; some are extremely slow. A deep learning registration framework for cross-time registration is proposed which uses the DeepGMR network in combination with a novel down-sampling scheme for cross-time registration. A dataset especially designed for cross-time registration is presented (called ECHO) and an extensive evaluation of state-of-the-art methods is conducted for the challenging case of cross-time registration. [ABSTRACT FROM AUTHOR]

• We propose a novel MHFP architecture, which can attain better multi-view features. • We design a 3D attention module for multi-view to construct the graph in our MHFP. • The results on ModelNet40 show the superiority of our multi-view based method. 3D shape recognition has received widespread attention in the field of computer vision. Since the 3D model contains much geometric information, which is difficult to extract but important to feature learning, effective description of 3D shape is still facing great challenges. With the rapid development of deep learning, a large number of methods have been proposed. However, these approaches always focus on the learning of view features but ignore the multi-view information protection in the process of feature fusion. In this work, we propose a novel Multi-view based Hierarchical Fusion Pooling Method (MHFP) for 3D Model Recognition, which hierarchically fuses the features of multi-view into a compact descriptor. Our approach considers the correlation between views, it can powerfully remove redundant information and retain a large amount of essential information. Meanwhile, we design a 3D attention module to dig out the correlation between the views, which prepares for the graph construction. To verify the effectiveness of our MHFP, we conduct experiments on the ModelNet40 dataset and compare it with some state-of-the-art methods. The final results demonstrate the superiority of our proposed approach in 3D shape recognition. [ABSTRACT FROM AUTHOR]

Two cases are reported to present the "Burrow platform" (BP) technique. The BP technique uses a partial platform for retrieving instrument fragments, thereby reducing the loss of radicular dentin.The BP technique is a microscope-aided approach implementing coated ultrasonic tips to create an access to instrument fragments in the middle and apical thirds of the root canal. The technique consists of 4 steps: (1) coronal access, (2) radicular access, (3) partial platform, and (4) exposure of the fragment and retrieval. A precise, angulated access pathway is specific to the BP technique. In the coronal half, the radicular access pathway extends toward the outer wall of the curvature. In the apical half, the radicular access pathway is oriented toward the inside of the curvature. One and 4 instrument fragments were successfully retrieved in the respective cases. At follow-up, all teeth were asymptomatic and had responded favorably to the treatment.The BP technique may present a suitable alternative to fragment removal from curved canals and slender roots to avoid ledge formation, transportation, and perforation. Instrument retrieval using the BP technique can be performed using commonly available armamentarium. [ABSTRACT FROM AUTHOR]

Purpose: To compare the Celect and Denali filters in terms of complex filter retrieval and indwelling complications after a 2-month indwelling time.Materials and Methods: In this prospective, randomized trial, 153 subjects were assessed for eligibility between May 2016 and July 2018. A total of 136 participants were randomly assigned to receive either Celect (n = 68) or Denali (n = 68) filter placement in the infrarenal inferior vena cava. Tilt angles at placement and retrieval and rates of overall filter retrieval, indwelling complication, and complex retrieval were compared.Results: Of 136 participants (mean age, 62 ± 12.8 years, 55 male), 24 (17.6%) were lost to follow-up. The mean indwelling time of filter was 60.4 ± 7 days and there was no significant difference in the baseline characteristics between the two groups. Filter retrieval was successful in all participants (112/112, 100%). Significantly higher rates of filter tilt > 15° (n = 8) and strut penetration (n = 14) were found with the Celect filter than with the Denali filter (1 significant tilt and 1 penetration) (P = 0.033 and 0.001, respectively). No filter fractures were observed and there was no significant difference in tip embedment, filter fracture, filter migration, or mean fluoroscopy times. There were 3 cases of complex retrieval (1 for Denali vs. 2 for Celect, P = 0.500), for which the loop-snare technique was used.Conclusion: Denali filters demonstrated significantly lower rates of tilt angle > 15° and strut penetration. However, there was no significant difference in the complex filter retrieval rate between the Celect and Denali filters. [ABSTRACT FROM AUTHOR]

AIM: To evaluate the knowledge about instrument separation and retrieval treatment options among postgraduate students and practising dentists. MATERIALS AND METHODS: A self-administered questionnaire was given to dentists and postgraduate students. RESULTS: There exists difference in knowledge and practices among postgraduate students and practitioners. CONCLUSION: Prevention is the best antidote for a separated file in the canal. Adhering to proven concepts with combining the best strategies and making safe techniques during preparation, will eliminate the separated instrument procedural mishaps. [ABSTRACT FROM AUTHOR]

Abnormality of fear memory is one of the important pathogenic factors leading to post-traumatic stress disorder (PTSD), anxiety disorder, and other mental disorders. Clinically, although exposure therapy, which is based on the principle of fear memory extinction, has a certain effect on these diseases, it still relapses frequently in some cases. These troubles can be effectively solved by retrieving the memory in a certain time window before the extinction of fear memory. Therefore, it is generally believed that the extinction of fear memory is the result of forming new safe memory to competitively inhibit the original fear memory, while the retrieval-extinction operation is the updating or erasure of the original fear memory, thus, which has greater clinical therapeutic potential. However, what are the detailed molecular networks, specifically the circular RNAs (circRNAs), involved in fear memory updating, and the differences with fear extinction, are still unknown. In this study, we systematically observed the expression of mRNAs, microRNAs (miRNA), long non-coding RNAs (lncRNAs), and circRNAs in the basolateral amygdala of mice after fear memory formation, extinction, and updating by whole-transcriptional sequencing, then a variety of inter-group comparison and bioinformatics analysis were used to find the differential expressed RNAs, enrich the function of them, and construct the molecular interaction networks. Moreover, competing endogenous RNA (ceRNA) molecular networks and transcriptional regulatory networks for the candidate circRNAs were constructed. Through these analyses, we found that about 10% of molecules were both involved in the fear memory extinction and formation, but the molecules and their signaling pathways were almost completely different between fear memory extinction and updating. This study describes a relatively detailed molecular network for fear memory updating, which might provide some novel directions for further mechanism research, and help to develop a specific physical method for fear memory intervention, based on the regulation of these key molecules.

Radiation transmitted through sea ice and snow has an important impact on the energy partitioning at the atmosphere-ice-ocean interface. Snow depth and ice thickness are crucial in determining its temporal and spatial variations. Under-ice surveys using autonomous robotic vehicles to measure transmitted radiation often lack coincident snow depth and ice thickness measurements so that direct relationships cannot be investigated. Snow and ice imprint distinct features on the spectral shape of transmitted radiation. Here, we use those features to retrieve snow depth. Transmitted radiance was measured underneath landfast level first-year ice using a remotely operated vehicle in the Lincoln Sea in spring 2018. Colocated measurements of snow depth and ice thickness were acquired. Constant ice thickness, clear water conditions, and low in-ice biomass allowed us to separate the spectral features of snow. We successfully retrieved snow depth using two inverse methods based on under-ice optical spectra with 1) normalized difference indices and 2) an idealized two-layer radiative transfer model including spectral snow and sea ice extinction coefficients. The retrieved extinction coefficients were in agreement with previous studies. We then applied the methods to continuous time series of transmittance and snow depth from the landfast first-year ice and from drifting, melt-pond covered multiyear ice in the Central Arctic in autumn 2018. Both methods allow snow depth retrieval accuracies of approximately 5 cm. Our results show that atmospheric variations and absolute light levels have an influence on the snow depth retrieval.

Purpose: Recent studies using the Montreal Cognitive Assessment (MoCA) suggest delayed recall is challenging for cochlear implant (CI) users. To better understand the underlying processes associated with delayed recall in CI users, we administered the MoCA and the California Verbal Learning Test, Third Edition (CVLT-3), which provides a more comprehensive assessment of delayed recall ability.Methods: The MoCA and CVLT-3 were administered to 18 high-performing CI users. For the CVLT-3, both the traditional scoring and a newer scoring method, the Item-Specific Deficit Approach (ISDA), were employed.Results: The original MoCA score and MoCA delayed recall subtest score did not relate to performance on any CVLT-3 measures regardless of scoring metric applied (i.e., traditional or ISDA). Encoding performance for both the CVLT-3 and ISDA were related. Consolidation, which is only distinctly defined by the ISDA, related to CVLT-3 cued delay recall performance but not free delay recall performance. Lastly, ISDA retrieval only related to CVLT-3 measures when modified.Conclusion: Performance on the MoCA and CVLT-3 in a high performing CI patient population were not related. We demonstrate that the ISDA can be successfully applied to CI users for the quantification and characterization of delayed recall ability; however, future work addressing lower performing CI users, and comparing to normal hearing controls is needed to determine the extent of potential translational applications. Our work also indicates that a modified ISDA retrieval score may be beneficial for evaluating CI users although additional work addressing the clinical relevance of this is still needed.

Different cloud-top heights (CTHs) have different degrees of atmospheric heating, which is an important factor for weather forecasting and aviation safety. AHIs (Advanced Himawari Imagers) on the Himawari-8 satellite are a new generation of visible and infrared imaging spectrometers characterized by a wide observation range and a high temporal resolution. In this paper, a cloud-top height retrieval algorithm based on XGBoost is proposed. The algorithm comprehensively utilizes AHI L1 multi-channel radiance data and calculates the input parameters of the generated model according to the characteristics of the cloud phase, texture, and the local brightness temperature change of the cloud. In addition, the latitude, longitude, solar zenith angle and satellite zenith angle are input into the model to further constrain the influence of the geographical and spatial factors such as the sea and land location, on CTH. Compared with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud-top height data (CTHCAL), the results show that: the algorithm retrieved the cloud-top height (CTHXGB) with a mean error (ME) of 0.3 km, a standard deviation (Std) of 1.72 km, and a root mean square error (RMSE) of 1.74 km. Additionally, it improves the problem of the large systematic deviation in the cloud-top height products released by the Japan Meteorological Agency (CTHJMA), especially for ice clouds and multi-layer clouds with ice clouds on the top layer. For water clouds below 2 km and multi-layer clouds with water clouds at the top, the algorithm solves the systematically serious CTHJMA problem. XGBoost can effectively distinguish between different cloud scenarios within the model, which is robust and suitable for CTH retrieval.

Satellite remote sensing of aerosol optical depth (AOD) is essential for detection, characterization, and forecasting of wildfire smoke. In this work, we evaluate the AOD (550 nm) retrievals during the extreme wildfire events over the western U.S. in September 2020. Three products are analyzed, including the Moderate-resolution Imaging Spectroradiometers (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) product collections C6.0 and C6.1, and the NOAA-20 Visible Infrared Imaging Radiometer (VIIRS) AOD from the NOAA Enterprise Processing System (EPS) algorithm. Compared with the Aerosol Robotic Network (AERONET) data, all three products show strong linear correlations with MAIAC C6.1 and VIIRS presenting overall low bias (

The Fengyun-4A/B (FY-4A/B) geostationary satellite carries the Geostationary Interferometric Infrared Sounder (GIIRS). The instrument parameters of the GIIRS on FY-4A and FY-4B are not exactly the same, which is crucial for the atmospheric temperature and humidity measurements. The objective of this paper is to discuss the influence of spectral range on the retrieval for the FY-4A/B GIIRS. Firstly, we performed channel selection to choose the appropriate channels for retrieval. Then, the multiple cycling utilization of the physical retrieval method is proposed and conducted for improving the accuracy, and the retrieval results of FY-4A/B GIIRS are compared. Finally, perturbation analysis is performed to discuss the sensitivity of the retrieval to temperature perturbations due to the difference in spectral range between the two GIIRS. The results show the retrieval method can realize the improvement of the average accuracy by more than 0.9 K for temperature and 3.0% for humidity. As the spectral range widens, the retrieval accuracy of FY-4B GIIRS is superior to that of FY-4A GIIRS from 130 hPa to 400 hPa. Furthermore, perturbation analysis also shows the extension of the spectral range is beneficial to the retrieval. This study could offer the usefulness of current GIIRS instruments with observed on-orbit bias, and a reference for the parameter design of the subsequent instruments.

Flexible retrieval mechanisms that allow us to infer relationships across events may also lead to memory errors or distortion when details of one event are misattributed to the related event. Here, we tested how making successful inferences alters representation of overlapping events, leading to false memories. Participants encoded overlapping associations (‘AB’ and ‘BC’), each of which was superimposed on different indoor and outdoor scenes that were pre-exposed prior to associative learning. Participants were subsequently tested on both the directly learned pairs (‘AB’ and ‘BC’) and inferred relationships across pairs (‘AC’). We predicted that when people make a correct inference, features associated with overlapping events may become integrated in memory. To test this hypothesis, participants completed a final detailed retrieval test, in which they had to recall the scene associated with initially learned ‘AB’ pairs (or ‘BC’ pairs). We found that the outcome of inference decisions impacted the degree to which neural patterns elicited during detailed ‘AB’ retrieval reflected reinstatement of the scene associated with the overlapping ‘BC’ event. After successful inference, neural patterns in the anterior hippocampus, posterior medial prefrontal cortex, and our content-reinstatement region (left inferior temporal gyrus) were more similar to the overlapping, yet incorrect ‘BC’ context relative to after unsuccessful inference. Further, greater hippocampal activity during inference was associated with greater reinstatement of the incorrect, overlapping context in our content-reinstatement region, which in turn tracked contextual misattributions during detailed retrieval. These results suggest recombining memories during successful inference can lead to misattribution of contextual details across related events, resulting in false memories.

Human memory can be unreliable, and when reading a sentence with a pragmatic implication, such as “the karate champion hit the cinder block,” people often falsely remember that the karate champion “broke” the cinder block. Yet, research has shown that encoding instructions affect the false memories we form. On the one hand, instructing participants to imagine themselves manipulating the to-be-recalled items increase false memories (imagination inflation effect). But on the other hand, instructions to imagine have reduced false memories in the DRM paradigm (imagination facilitation effect). Here, we explored the effect of imaginal encoding with pragmatic inferences, a way to study false memories for information about everyday actions. Across two experiments, we manipulated imaginal encoding through the instructions given to participants and the after-item filler task (none vs. math operations). In Experiment 1, participants were either assigned to the encoding condition of imagine+no filler; pay attention+math; or memorize+math. In Experiment 2, the encoding instructions (imagine vs. memorize) and the filler task (none vs. math) were compared across four separate conditions. Results from the two experiments showed that imagination instructions lead to better memory, by showing a higher proportion of correct responses and better performance in a memory benefit index. Similarly, a significant reduction of false memories was observed across both experiments, even though a complementary Bayesian analysis only supported this conclusion for Experiment 1. The findings show that imaginal encoding improves memory, suggesting the engagement of a distinctiveness heuristic and source-monitoring process.

Abstract The Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (re), ice water content (IWC), number density (N0), and distribution width parameter (w). First we examine the algorithm feasibility using simulated W‐band radar reflectivity. The retrievals locate in a solution space around the exact results. Two methods are applied to produce a unique solution with maximum likelihood function, that is, the mean value of all acceptable sets of parameters in the chain and the first convergence sample. The results of the second method have a better performance compared with that of the first method. The algorithm has better performance when one particle size distribution (PSD) parameter is known than that when three PSD parameters are unknown. Then we apply the algorithm for CloudSat Cloud Profiling Radar (CPR) observations. Four habits, including column, plate, bullet rosette and sphere, are considered to analyze the influence on the retrieval accuracy. The averaged deviation of res and IWCs obtained from MCMC algorithm among different habit assumptions has a maximum of about 10% and 5%, respectively. It appears clearly that the MCMC algorithm retrieval for short hexagonal column (HEXS) are the closest to that of the official products (2B‐CWC‐RO), while thick hexagonal plate (HEXF) has the largest bias. Retrieved re, IWC, N0, and w from radar reflectivity are in reasonable agreement with 2B‐CWC‐RO product, which indicate that the MCMC algorithm can produce reliable cloud properties from radar observations.

In this paper, we focus on learning semantic representations for large-scale highly abstract sketches that were produced by the practical sketch-based application rather than the excessively well dawn sketches obtained by crowd-sourcing. We propose a dual-branch CNN-RNN network architecture to represent sketches, which simultaneously encodes both the static and temporal patterns of sketch strokes. Based on this architecture, we further explore learning the sketch-oriented semantic representations in two practical settings, i.e., hashing retrieval and zero-shot recognition on million-scale highly abstract sketches produced by practical online interactions. Specifically, we use our dual-branch architecture as a universal representation framework to design two sketch-specific deep models: (i) We propose a deep hashing model for sketch retrieval, where a novel hashing loss is specifically designed to further accommodate both the abstract and messy traits of sketches. (ii) We propose a deep embedding model for sketch zero-shot recognition, via collecting a large-scale edge-map dataset and proposing to extract a set of semantic vectors from edge-maps as the semantic knowledge for sketch zero-shot domain alignment. Both deep models are evaluated by comprehensive experiments on million-scale abstract sketches produced by a global online game QuickDraw and outperform state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

The present study investigated age-related differences in retrieval from long-term memory using a semantic fluency task in which participants recalled animals during a 5-minute retrieval period. We evaluated the relative uniqueness of items and their output position within the retrieval process to further elucidate how younger and older adults access and retrieve semantic knowledge in long-term memory. Although older (n = 96, aged 56-79, M = 62.44) and younger adults (n = 98, aged 18-27, M = 23.44) scored similarly for retrieval fluency and originality, these abilities tended to decline when we analyzed age as a continuous variable, indicating some preservation in earlier adulthood, but impairment in older age. Additionally, participants tended to recall common, more easily accessible items before unique, less accessible items, and this pattern was more prominent in older adults. Thus, there are both similarities and differences in semantic fluency in older age. [ABSTRACT FROM AUTHOR]

Rainfall is triggered and mainly dominated by atmospheric thermo-dynamics and rich water vapor. Nonetheless, turbulence is also considered as an important factor influencing the evolution of rainfall microphysical parameters. To study such an influence, the present study utilized boundary layer wind profiler radar measurements. The separation point of the radar power spectral density data was carefully selected to classify rainfall and turbulence signals; the turbulent dissipation rate ε and rainfall microphysical parameters can be retrieved to analyze the relationship between ε and microphysical parameters. According to the retrievals of two rainfall periods in Beijing 2016, it was observed that (1) ε in the precipitation area ranged from 10-3.5 to 10-1 m2 s-3 and was positively correlated with the falling velocity spectrum width; (2) interactions between turbulence and raindrops showed that small raindrops got enlarge through collision and coalescence in weak turbulence, but large raindrops broke up into small drops under strong turbulence, and the separation value of ε being weak or strong varied with rainfall attributes; (3) the variation of rainfall microphysical parameters(characteristic diameters, number concentration, rainfall intensity, and water content) in the middle stage were stronger than those in the early and the later stages of rainfall event; (4) unlike the obvious impacts on raindrop size and number concentration, turbulence impacts on rain rate and LWC were not significant because turbulence did not cause too much water vapor and heat exchange. [ABSTRACT FROM AUTHOR]

It is very pleasing for human health that medical knowledge has increased and the technological infrastructure improves medical systems. The widespread use of medical imaging devices has been instrumental in saving lives by allowing early diagnosis of many diseases. These medical images are stored in large databases for many purposes. These datasets are used when a suspicious diagnostic case is encountered or to gain experience for inexperienced radiologists. To fulfill these tasks, images similar to one query image are searched from within the large dataset. Accuracy and speed are vital for this process, which is called content-based image retrieval (CBIR). In the literature, the best way to perform a CBIR system is by using hash codes. This study provides an effective hash code generation method based on feature selection-based downsampling of deep features extracted from medical images. Firstly, pre-hash codes of 256-bit length for each image are generated using a pairwise siamese network architecture that works based on the similarity of two images. Having a pre-hash code between -1 and 1 makes it very easy to generate hash code in hashing algorithms. For this reason, all activation functions of the proposed convolutional neural network (CNN) architecture are selected as hyperbolic tanh. Finally, neighborhood component analysis (NCA) feature selection methods are used to convert pre-hash code to binary hash code. This also downsamples the hash code length to 32-bit, 64-bit, or 96-bit levels. The performance of the proposed method is evaluated using NEMA MRI and NEMA CT datasets. [ABSTRACT FROM AUTHOR]

Students' engagement is key to their success. How can instructors "hook" students from the very beginning of the class? We propose, following the Jumpstart lesson planning model and Universal Design for Learning (UDL) best practices, to include a Connection Activity at the onset of every lesson. The Connection Activity connects students with content and engages several important cognitive processes for learning, including elaboration and retrieval. [ABSTRACT FROM AUTHOR]

The ability to bind together the contextual details associated with an event undergoes dramatic improvement during childhood. However, few studies have examined the neural correlates of memory binding encoding and retrieval during middle childhood. We examined age‐related encoding and retrieval differences using continuous electroencephalogram (EEG) measures in a sample of 6‐ and 8‐year‐olds. For the memory binding task, children were tested on memory for individual items (i.e., objects and backgrounds only) and combined object–backgrounds pairings (combination condition). Memory for individual item information was comparable across both age groups. However, younger children experienced greater difficulty (i.e., higher false alarm rate) in the combination condition. Theta (4–7 Hz) neuronal oscillations were analyzed to compare memory encoding and retrieval processes. Widespread retrieval‐related increases in theta band EEG power (compared with baseline and encoding‐related activation) were evident in both 6‐ and 8‐year‐olds. Regression analyses revealed that parietal theta EEG power during retrieval accounted for variability in memory binding performance. These findings suggest that theta rhythms are intricately linked to memory binding processes during middle childhood. [ABSTRACT FROM AUTHOR]

Transmission of maternal behavior across generations occurs, but less is known about paternal behavior. In biparental species like the California mouse (Peromyscus californicus), paternal care contributes to the well‐being of offspring with lasting consequences on the brain and behavior. Paternal huddling/grooming behavior can be passed on to future generations, but whether paternal retrieval, which removes young from potential harm, is transmitted independently is unclear. We manipulated paternal retrieval experience through pup displacement manipulations, then examined whether males exposed to higher levels of paternal retrieval in development altered their adult retrieval behavior with their offspring. Males exposed to heightened paternal retrievals, as compared to reduced retrievals, retrieved their offspring more often but huddled/groomed offspring less during undisturbed natural observations. No differences were observed following a pup displacement challenge. The high paternal retrieval group also exhibited more physical activity and stereotypy. Our results are consistent with the hypothesis that paternal retrieval levels are transmitted across generations and may function via mechanisms separate from huddling/grooming. One modifying factor may be anxiety because increased activity and stereotypy occurred in the high retrieval group. We speculate how the transmission of paternal retrievals may inform a protective parenting style. [ABSTRACT FROM AUTHOR]

The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information. [ABSTRACT FROM AUTHOR]

Human memory can be unreliable, and when reading a sentence with a pragmatic implication, such as " the karate champion hit the cinder block ," people often falsely remember that the karate champion " broke " the cinder block. Yet, research has shown that encoding instructions affect the false memories we form. On the one hand, instructing participants to imagine themselves manipulating the to-be-recalled items increase false memories (imagination inflation effect). But on the other hand, instructions to imagine have reduced false memories in the DRM paradigm (imagination facilitation effect). Here, we explored the effect of imaginal encoding with pragmatic inferences, a way to study false memories for information about everyday actions. Across two experiments, we manipulated imaginal encoding through the instructions given to participants and the after-item filler task (none vs. math operations). In Experiment 1, participants were either assigned to the encoding condition of imagine+no filler; pay attention+math; or memorize+math. In Experiment 2, the encoding instructions (imagine vs. memorize) and the filler task (none vs. math) were compared across four separate conditions. Results from the two experiments showed that imagination instructions lead to better memory, by showing a higher proportion of correct responses and better performance in a memory benefit index. Similarly, a significant reduction of false memories was observed across both experiments, even though a complementary Bayesian analysis only supported this conclusion for Experiment 1. The findings show that imaginal encoding improves memory, suggesting the engagement of a distinctiveness heuristic and source-monitoring process. [ABSTRACT FROM AUTHOR]

Designers usually retrieve and review existing relevant product images to draw inspiration when designing a new proposal. Obtaining reasonable retrieval results of a given target is a challenging task due to the large-scale, low-correlation searching process in the product image field. In this paper, an end-to-end skip-connected network is introduced to improve the accuracy of retrieval by directly addressing the above problems. In more detail, skip-connections are employed to combine the multi-resolution feature maps produced by a pre-trained backbone network, which provides important improvements to address the presence of large scale variance problem. And the second-order information of the multi-resolution feature maps is obtained by using gram matrix operation, which allows the network to exploit more representative features and their correlations. Experimental results indicate that the proposed method achieves better retrieval performance when compared to current state-of-the-art techniques on there normal benchmark datasets as well as the product image data. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to retrieve and study the highly cited papers as well as the correlation between the citation frequency and the download frequency of the 20 traditional Chinese medicine journals in China, in order to provide the guidance for improving the influence and academic quality of these journals. Bibliometric analyses were conducted on 1103 papers of 20 traditional Chinese medicine journals from 2011 to 2020 by retrieving for the China Academic Journal Network Publishing Database (CAJD) in China National Knowledge Infrastructure (CNKI). SPSS 17.0 software was used to analyze the correlation between the citation frequency and the download frequency via conducting regression fitting and establishing the mathematical models. The results showed that the total citations of the 1103 papers were 93051 times and the average citations were 84.36 times per paper. The total downloads of the 1103 papers were 2058442 times, and the average downloads were 1866.22 times per paper. China Journal of Chinese Materia Medica ranked first according to the number of papers, total citations and total downloads. The citations of Journal of Chinese Medicinal Materials ranked first based on the number of citations per paper. One of Li's paper had been cited the most (983 times). There were 629 (57.03%) papers whose first author was from universities. The scopes of the first authors were distributed in 29 regions and 2 special administrative regions (Macao, Hong Kong) in China. The authors from Beijing published 283 (25.66%) papers, ranking number one. The number of papers supported by funds was 882 (79.96%). The research results of correlation showed that the citation frequency and the download frequency of the highly cited papers had a highly positive correlation from both journal and paper level for whether the sample data of journals was normally distributed or nonnormally distributed. The correlation coefficients of the 20 journals at journal level and that at paper level were 0.9765 and 0.6677, respectively. The correlation was better at journal level than that at paper level, while the optimal regression fitting was all cubic polynomial. Among the 1103 papers, there were 684 (62.01%) research papers and 419 (37.99%) review papers. The main citation period of the top 15 papers was from the 2nd year to the 6th year after publication, accounting for 78.39%. Papers on clinical therapeutics research, papers on the pharmacological effects and its mechanism of traditional Chinese medicine, and papers on traditional Chinese medicine and natural medicine were the main source of the highly cited papers of the traditional Chinese medicine journals. Editors of the journals should focus on the above-mentioned research areas to select manuscripts for exploiting the excellent sources extensively, while paying attention to review papers, focusing on national major or key projects, paying attention to network spreading, stabilizing authors with quality services, in order to improve the influence and the academic quality of journals. [ABSTRACT FROM AUTHOR]

The Partition of 1947 remains an important reference point for Hindu nationalist political discourse in India, defining its relationship with Pakistan and Muslims in South Asia. This article investigates the use of the label 'India's daughters' by the BJP-led NDA government (2014–15), a term originally used to describe Hindu and Sikh women retrieved from Pakistan in 1947. It offers an example of the emotional power wielded by Partition metaphors and demonstrates the BJP's attempts to insert itself into the emotional history of Partition by transforming the meanings of its terms. The case of Uzma Ahmad's retrieval from Pakistan acts as a case in point, illustrating the BJP's increasing attempts to harness Partition semantics. [ABSTRACT FROM AUTHOR]

The Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (re), ice water content (IWC), number density (N0), and distribution width parameter (w). First we examine the algorithm feasibility using simulated W‐band radar reflectivity. The retrievals locate in a solution space around the exact results. Two methods are applied to produce a unique solution with maximum likelihood function, that is, the mean value of all acceptable sets of parameters in the chain and the first convergence sample. The results of the second method have a better performance compared with that of the first method. The algorithm has better performance when one particle size distribution (PSD) parameter is known than that when three PSD parameters are unknown. Then we apply the algorithm for CloudSat Cloud Profiling Radar (CPR) observations. Four habits, including column, plate, bullet rosette and sphere, are considered to analyze the influence on the retrieval accuracy. The averaged deviation of res and IWCs obtained from MCMC algorithm among different habit assumptions has a maximum of about 10% and 5%, respectively. It appears clearly that the MCMC algorithm retrieval for short hexagonal column (HEXS) are the closest to that of the official products (2B‐CWC‐RO), while thick hexagonal plate (HEXF) has the largest bias. Retrieved re, IWC, N0, and w from radar reflectivity are in reasonable agreement with 2B‐CWC‐RO product, which indicate that the MCMC algorithm can produce reliable cloud properties from radar observations. Plain Language Summary: Ice clouds are globally widespread and their microphysical properties play an important role in the radiative balance of Earth, which are still not well understood and poorly represented. Thus, the algorithm that drive the microphysical properties of ice clouds precisely from radar observations is necessary. Here, we address this issue using Markov chain Monte Carlo algorithm for deriving ice cloud microphysical properties. The feasibility of Markov chain Monte Carlo (MCMC) algorithm has been tested using simulated W‐band radar reflectivity and CloudSat Cloud Profiling Radar observations. The retrievals are in reasonable agreement with CloudSat 2B‐CWC‐RO product. The study will be very useful for analyzing the role that ice clouds play in climate. Key Points: The Markov chain Monte Carlo algorithm has been validated with simulated W‐band radar reflectivity and CloudSat Cloud Profiling Radar (CPR) observationsRetrieved ice cloud microphysical properties are in reasonable agreement with CloudSat 2B‐CWC‐RO productThe crystal habit assumption has significant influence on the accuracy of retrievals [ABSTRACT FROM AUTHOR]

Background: This study examined a cohort of retrieved rotating hinge (RH) total knee arthroplasty implants of four different designs with emphasis on the surface damage observed on the polyethylene components. Our purpose was to determine if differences in polyethylene damage existed among the designs, and if those differences could be explained by differences in design characteristics.Materials and Methods: Seventy-two RH implants from four manufacturers (DePuy LPS/SROM, Zimmer NexGen, Stryker Howmedica MRH, and Biomet Finn-OSS) removed at the time of revision performed between 2002 and 2017 were identified in our institutional retrieval registry. Damage to the surfaces of the polyethylene was assessed using a subjective grading system and evaluated in multiple zones. Design characteristics that were evaluated included the following: location of the dwell point on the polyethylene component, posterior position of the axle, and amount of hyperextension and rotation allowed by the implant.Results: There were no differences in total damage scores between the four implant groups (P = .45). The Stryker Howmedica MRH group showed the least backside wear of all implants but significantly more articular-sided wear compared with two of the other three groups. All implants except NexGen showed increased total damage scores in implants revised for mechanical (vs nonmechanical) reasons and in implants with a longer duration of implantation.Conclusion: No single implant design emerged as superior in terms of minimizing polyethylene wear damage. Polyethylene damage existed in various locations but was not different in severity across designs, suggesting that there is no clear superior RH design that minimizes overall articular surface wear compared with other designs. [ABSTRACT FROM AUTHOR]

Copyright of Umm Al-Qura University Journal for Languages & Literature is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The current artificial intelligence retrieval algorithm ignores the diversity of the text data from multiple data sources, resulting in poor retrieval accuracy, and the problem of low retrieval efficiency is caused by the diversity of the text data from multiple data sources. In this paper, an artificial intelligence retrieval algorithm based on correlation computation is proposed. The documents with different length in the text data from multiple data sources are normalized, the format markers, invalid characters, disused words and affixes in the text data are eliminated, and the words with strong representation ability are selected as the feature items to realize the feature selection and dimension reduction of the text data from multiple data sources. This method improves the inefficient retrieval efficiency caused by different length of text data. According to the descriptive query statements of text data from multiple data sources, the text correlation degree is calculated, and the retrieval result is obtained based on the mean value of correlation degree. The experimental results show that the proposed algorithm can not only realize the diversified query of the input text data from multiple data sources, but also can retrieve the data associated with the input descriptive query words accurately and quickly, and get a better comprehensive evaluation value. [ABSTRACT FROM AUTHOR]

Four‐dimensional (4D) wind fields were derived from radiance measurements of the Geosynchronous Interferometric Infrared Sounder (GIIRS) onboard the FengYun‐4A satellite with 15‐min temporal resolution during Typhoon Maria (2018). Results are evaluated with independent ERA5 reanalysis, Global Data Assimilation System (GDAS) analysis and dropsonde wind profiles, and show a statistical root mean squared error less than 2 m/s for U and V components in troposphere against ERA5 and GDAS. The temporal variation of the wind fields from GIIRS at 15‐min intervals is consistent with that of the hourly ERA5. The added value of wind profiles over the numerical weather predictions (NWP) background field is also revealed. Further experiments confirm that higher temporal resolution from geostationary infrared (IR) sounder measurements could provide better dynamic information. 4D dynamic information can be extracted from high temporal resolution geostationary hyperspectral IR radiances in a consistent and continuous manner that can be used together with the thermodynamic information for various quantitative applications such as NWP data assimilation, near real‐time weather monitoring, situational awareness and nowcasting. Plain Language Summary: A geostationary hyperspectral infrared (IR) sounder not only provides continuous observations of atmospheric thermodynamic information, but also provides continuous dynamic information critical for weather monitoring, warning, and forecast. The atmospheric motion vectors (AMVs) based on the geostationary imager observations using tracking techniques have been widely used in weather analysis and forecast. However, the retrieval of three‐dimensional atmospheric wind fields from geostationary hyperspectral sounder measurements have not been well studied although a strong argument in favor of the realization of such an instrument has been the derivation of wind fields from moisture structures. The main reason is the lack of real observations from space. Using 15‐min Geosynchronous Interferometric Infrared Sounder observations, an algorithm has been developed for deriving atmospheric four‐dimensional (4D) wind fields, besides, the impacts of temporal resolution, cloudiness, and temporal information on 4D wind fields, respectively, are also studied. It indicates that both temporal and spatial information contributes to the 4D wind fields, and higher temporal resolution provides better 4D wind fields than the lower temporal resolution. Key Points: Hyperspectral infrared radiances with high temporal resolution from geostationary (GEO) satellite provide four‐dimensional (4D) wind fieldsThe 4D wind fields can be extracted quantitatively with root mean squared error less than 2 m/s for U and V components of wind in troposphere against ERA5Higher temporal resolution from GEO measurements provides better wind profile information than lower temporal resolution [ABSTRACT FROM AUTHOR]

Due to the wide applications in a rapidly increasing number of different fields, 3D shape recognition has become a hot topic in the computer vision field. Many approaches have been proposed in recent years. However, there remain huge challenges in two aspects: exploring the effective representation of 3D shapes and reducing the redundant complexity of 3D shapes. In this paper, we propose a novel deep-attention network (DAN) for 3D shape representation based on multiview information. More specifically, we introduce the attention mechanism to construct a deep multiattention network that has advantages in two aspects: 1) information selection, in which DAN utilizes the self-attention mechanism to update the feature vector of each view, effectively reducing the redundant information, and 2) information fusion, in which DAN applies attention mechanism that can save more effective information by considering the correlations among views. Meanwhile, deep network structure can fully consider the correlations to continuously fuse effective information. To validate the effectiveness of our proposed method, we conduct experiments on the public 3D shape datasets: ModelNet40, ModelNet10, and ShapeNetCore55. Experimental results and comparison with state-of-the-art methods demonstrate the superiority of our proposed method. Code is released on https://github.com/RiDang/DANN. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to identify relevant data across different modalities. In this work, we are dedicated to cross-modal retrieval between images and text sentences, which is formulated into similarity measurement for each image-text pair. To this end, we propose a Cross-modal Relation Guided Network (CRGN) to embed image and text into a latent feature space. The CRGN model uses GRU to extract text feature and ResNet model to learn the globally guided image feature. Based on the global feature guiding and sentence generation learning, the relation between image regions can be modeled. The final image embedding is generated by a relation embedding module with an attention mechanism. With the image embeddings and text embeddings, we conduct cross-modal retrieval based on the cosine similarity. The learned embedding space well captures the inherent relevance between image and text. We evaluate our approach with extensive experiments on two public benchmark datasets, i.e., MS-COCO and Flickr30K. Experimental results demonstrate that our approach achieves better or comparable performance with the state-of-the-art methods with notable efficiency. [ABSTRACT FROM AUTHOR]