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A deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training and testing the network. The proposed network outperforms state-of-the-art denoising methods by a significant margin both on simulated and experimental test data. Factors contributing to the performance are identified, including most importantly (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits global and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing datasets for 6 NLG tasks, we observe that the human evaluation scores on these multiple criteria are often not correlated. For example, there is a very low correlation between human scores on fluency and data coverage for the task of structured data to text generation. This suggests that the current recipe of proposing new automatic evaluation metrics for NLG by showing that they correlate well with scores assigned by humans for a single criteria (overall quality) alone is inadequate. Indeed, our extensive study involving 25 automatic evaluation metrics across 6 different tasks and 18 different evaluation criteria shows that there is no single metric which correlates well with human scores on all desirable criteria, for most NLG tasks. Given this situation, we propose CheckLists for better design and evaluation of automatic metrics. We design templates which target a specific criteria (e.g., coverage) and perturb the output such that the quality gets affected only along this specific criteria (e.g., the coverage drops). We show that existing evaluation metrics are not robust against even such simple perturbations and disagree with scores assigned by humans to the perturbed output. The proposed templates thus allow for a fine-grained assessment of automatic evaluation metrics exposing their limitations and will facilitate better design, analysis and evaluation of such metrics.

Deep convolutional neural networks (CNNs) for video denoising are typically trained with supervision, assuming the availability of clean videos. However, in many applications, such as microscopy, noiseless videos are not available. To address this, we propose an Unsupervised Deep Video Denoiser (UDVD), a CNN architecture designed to be trained exclusively with noisy data. The performance of UDVD is comparable to the supervised state-of-the-art, even when trained only on a single short noisy video. We demonstrate the promise of our approach in real-world imaging applications by denoising raw video, fluorescence-microscopy and electron-microscopy data. In contrast to many current approaches to video denoising, UDVD does not require explicit motion compensation. This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy. A gradient-based analysis reveals that UDVD automatically adapts to local motion in the input noisy videos. Thus, the network learns to perform implicit motion compensation, even though it is only trained for denoising.

Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they produce impressive results. However, their potential has barely been explored in the context of scientific imaging. Denoising CNNs are typically trained on real natural images artificially corrupted with simulated noise. In contrast, in scientific applications, noiseless ground-truth images are usually not available. To address this issue, we propose a simulation-based denoising (SBD) framework, in which CNNs are trained on simulated images. We test the framework on data obtained from transmission electron microscopy (TEM), an imaging technique with widespread applications in material science, biology, and medicine. SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data. Apart from the denoised images, SBD generates likelihood maps to visualize the agreement between the structure of the denoised image and the observed data. Our results reveal shortcomings of state-of-the-art denoising architectures, such as their small field-of-view: substantially increasing the field-of-view of the CNNs allows them to exploit non-local periodic patterns in the data, which is crucial at high noise levels. In addition, we analyze the generalization capability of SBD, demonstrating that the trained networks are robust to variations of imaging parameters and of the underlying signal structure. Finally, we release the first publicly available benchmark dataset of TEM images, containing 18,000 examples.

A deep convolutional neural network has been developed to denoise atomic-resolution TEM image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. The network was applied to a model system of CeO2-supported Pt nanoparticles. We leverage multislice image simulations to generate a large and flexible dataset for training and testing the network. The proposed network outperforms state-of-the-art denoising methods by a significant margin both on simulated and experimental test data. Factors contributing to the performance are identified, including most importantly (a) the geometry of the images used during training and (b) the size of the network's receptive field. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. This shows that the network exploits global and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. Finally, we develop an approach based on the log-likelihood ratio test that provides a quantitative measure of the agreement between the noisy observation and the atomic-level structure in the network-denoised image.

We present a study of the Gyangarh and Anjana granitoids in the Aravalli Craton, northwestern India, with new data on their whole-rock geochemistry, U-Pb zircon and U-Th-total Pb monazite geochronology and structures and microstructures. These granitoids are monzogranites with metaluminous and calc-alkalic geochemical characters. They show negative Eu anomalies with depletions in Sr and Ti, indicating fractionation of plagioclase and Fe-Ti oxides from their parental magmas. U-Pb zircon dating of granitoids yielded crystallization ages of 1776 ± 35 Ma to 1709 ± 29 Ma, indicating that the plutons were emplaced during the late stages of the Aravalli orogeny. These plutons have been variably deformed and show shallow- to moderately dipping mylonitic foliations (<40°) with shallow (<30°) NW- to NE-plunging stretching lineations (SL1). The pole distribution of the mylonitic foliation (S1) and lineation (SL1) data indicates that the gentle mylonitic foliations have been overprinted by steep (>65°), NE-SW-striking S2 mylonitic foliations. The kinematic indicators suggest that the D1 and D2 deformations were associated with dextral-normal and sinistral-reverse senses of shearing, respectively. Monazite dating of texturally constrained grains shows that the pluton experienced intense mylonitization (D1-S1; 1653 ± 30 Ma) during the waning stages of the Aravalli orogeny. Later, these plutons experienced a second episode of mylonitization (933 ± 11 Ma to 897 ± 9 Ma) due to sinistral-reverse shearing (D2-S2) during the late stages of the Delhi orogeny. These new results show that the Gyangarh and Anjana plutons record signatures of two major orogenies that have shaped the Sandmata Complex (Aravalli Craton) in the Palaeoproterozoic.

Objectives: Work-related musculoskeletal disorders (WMSDs) are among the most common occupational diseases, affecting various sectors such as agriculture, small-scale industries, handicrafts, construction, and banking. These disorders, caused by overexertion and repetitive motion, lead to work absenteeism, productivity loss, and economic impacts. The aim of the study was to determine the magnitude of musculoskeletal disorders among different occupational workers in India.Methods: We identified studies reporting the prevalence of WMSDs using the Nordic Musculoskeletal Questionnaire in different databases between 2005 and 2023 through searches on SCOPUS, PubMed Central, and Google Scholar. The required information was then extracted. A random effects model was used to pool estimates of prevalence with 95% CIs. Publication bias was assessed by applying funnel plots.Results: The 12-month prevalence of WMSDs was reported across several occupational groups, and the meta or the pooled prevalence was estimated as 0.76 (95% CI, 0.70 to 0.82) along with substantial variability in the prevalence estimates between different industries and studies. The meta-prevalence for low back pain was estimated as 0.60 (95% CI, 0.54 to 0.66). The meta-prevalence for neck pain was estimated as 0.40 (95% CI, 0.34 to 0.47) whereas for shoulder pain it was estimated as 0.36 (95% CI, 0.30 to 0.42), respectively. The risk of bias was statistically nonsignificant, and overall publication bias was low as per visual inspections from funnel plots.Conclusions: WMSDs are prevalent across various Indian industries in significant proportions, particularly in agriculture, health care, and mining, leading to significant productivity loss and economic impact. The variation in prevalence highlights the need for sector-specific interventions. Addressing WMSDs requires comprehensive ergonomic and policy measures. Effective strategies are essential to mitigate these disorders’ widespread impact.

An organization’s success depends on its employees, and an employee’s performance decides whether the organization is successful. Employee performance enhances the productivity and output of organizations, i.e., the performance of an employee paves the way for the organization’s success. Hence, analyzing employee performance and giving performance ratings to employees is essential for companies nowadays. It is evident that different people have different skill sets and behavior, so data should be gathered from all parts of an employee’s life. This paper aims to provide the performance rating of an employee based on various factors. First, we compare various AI-based algorithms, such as random forest, artificial neural network, decision tree, and XGBoost. Then, we propose an ensemble approach, RanKer, combining all the above approaches. The empirical results illustrate that the efficacy of the proposed model compared to traditional models such as random forest, artificial neural network, decision tree, and XGBoost is high in terms of precision, recall, F1-score, and accuracy.