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Here, we present an EfficientNet-B0-based model to directly predict multiple properties of lithium manganese nickel oxides (LMNO) using their crystal structure images. The model is supposed to predict the energy above the convex hull, bandgap energy, crystal systems, and crystal space groups of LMNOs. In the last layer of the model, a linear function is used to predict the bandgap energy and energy above the convex hull, while a SoftMax function is used to classify the crystal systems and crystal space groups. In the test set, the percentages of coefficient of determination (R2) scores are 97.73% and 96.50% for the bandgap energy and energy above the convex hull predictions, respectively, while the percentages of accuracy are 99.45% and 99.27% for the crystal system and crystal space group classifications, respectively. The class saliency maps explain that the model pays more attention to the shape of the crystal lattices and gradients around the lattice region occupied by the larger ions. This work provides new insight into using an intelligent model to directly relate the crystal structures of LMNO materials with their properties.

This study investigates the impact of resonant acoustic powder mixing on the delay time of the W-KClO4-BaCrO4 (WKB) mixture and its potential implications for powder and material synthesis. Through thermal analysis, an inverse linear relationship was found between thermal conductivity and delay time, allowing us to use thermal conductivity as a reliable proxy for the delay time. By comparing the thermal conductivity of WKB mixtures mixed manually and using acoustic powder mixer, we found that acoustic powder mixing resulted in minimal deviations in thermal conductivity, proving more uniform mixing. Furthermore, DSC analysis and Sestak-Berggren modeling demonstrated consistent reaction dynamics with a constant activation energy as the reaction progressed in samples mixed using acoustic waves. These findings underscore the critical role of uniform powder mixing in enhancing the thermodynamic quality of the WKB mixture and emphasize the importance of developing novel methods for powder and material synthesis., Comment: 29 pages, 8 figures

Piezoresponse force microscopy (PFM) has been widely used for nanoscale analysis of piezoelectric properties and ferroelectric domains. Although PFM is useful because of its simple and nondestructive features, PFM measurements can be obscured by non-piezoelectric effects that could affect the PFM signals or lead to ferroelectric-like behaviors in non-ferroelectric materials. Many researches have addressed related technical issues, but they have primarily focused on vertical PFM. Here, we investigate significant discrepancies of lateral PFM signals between the trace and the retrace scans, which are proportional to the scan angle and the cantilever lateral tilting discrepancy. The discrepancies of PFM signals are analyzed based on intrinsic and extrinsic components, including out-of-plane piezoresponse, electrostatic force, and other factors. Our research will contribute to the accurate PFM measurements for visualization of ferroelectric in-plane polarization distributions., Comment: 38 pages, 18 figures

The primary goal of training in early convolutional neural networks (CNN) is the higher generalization performance of the model. However, as the expected calibration error (ECE), which quantifies the explanatory power of model inference, was recently introduced, research on training models that can be explained is in progress. We hypothesized that a gap in supervision criteria during training and inference leads to overconfidence, and investigated that performing label distribution learning (LDL) would enhance the model calibration in CNN training. To verify this assumption, we used a simple LDL setting with recent data augmentation techniques. Based on a series of experiments, the following results are obtained: 1) State-of-the-art KD methods significantly impede model calibration. 2) Training using LDL with recent data augmentation can have excellent effects on model calibration and even in generalization performance. 3) Online LDL brings additional improvements in model calibration and accuracy with long training, especially in large-size models. Using the proposed approach, we simultaneously achieved a lower ECE and higher generalization performance for the image classification datasets CIFAR10, 100, STL10, and ImageNet. We performed several visualizations and analyses and witnessed several interesting behaviors in CNN training with the LDL., Comment: 11 pages main manuscript + references and 11 pages supplementary materials

Here, we optimized the annealing temperature of HZO/TiN thin film heterostructure via multiscale analysis of remnant polarization, crystallographic phase, minimum ferroelectric domain size, and average grain size. We found that the remnant polarization was closely related to the relative amount of the orthorhombic phase whereas the minimum domain size was to the relative amount of the monoclinic phase. The minimum domain size was obtained at the annealing temperature of 500$^\cird$C while the optimum remnant polarization and capacitance at the annealing temperature of 600$^\circ$C. We conclude that the minimum domain size is more important than the sheer magnitude of remnant polarization considering the retention and fatigue of switchable polarization in nanoscale ferroelectric devices. Our results are expected to contribute to the development of ultra-low-power logic transistors and next-generation non-volatile memory devices., Comment: 28 pages, 11 figures, 1 table

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Here, we correlate the nanoscale morphology and chemical composition of solid electrolyte interphases (SEI) with the electrochemical property of graphite-based composite electrodes. Using electrochemical strain microscopy (ESM) and X-ray photoelectron spectroscopy (XPS), changes of chemical composition and morphology (Li and F distribution) in SEI layers on the electrodes as a function of solid electrolyte contents are analyzed. As a result, we find a strong correlation between morphological variations on the electrode, Li and F distribution in SEI layer, and Coulomb efficiency. This correlation determines the optimum composition of the composite electrode surface that can maximize the physical and chemical uniformity of the solid electrolyte on the electrode, which is a key parameter to increase electrochemical performance in solid-state batteries., Comment: 35 pages, 13 figures

Artificially induced asymmetric tribological properties of ferroelectrics offer an alternative route to visualize and control ferroelectric domains. Here, we observe the switchable friction and wear behavior of ferroelectrics using a nanoscale scanning probe where down domains having lower friction coefficient than up domains can be used as smart masks as they show slower wear rate than up domains. This asymmetry is enabled by flexoelectrically coupled polarization in the up and down domains under a sufficiently high contact force. Moreover, we determine that this polarization-sensitive tribological asymmetry is universal across ferroelectrics with different chemical composition and crystalline symmetry. Finally, using this switchable tribology and multi-pass patterning with a domain-based dynamic smart mask, we demonstrate three-dimensional nanostructuring exploiting the asymmetric wear rates of up and down domains, which can, furthermore, be scaled up to technologically relevant (mm-cm) size. These findings establish that ferroelectrics are electrically tunable tribological materials at the nanoscale for versatile applications., Comment: 72 pages, 33 figures

Dendritic microstructures are ubiquitous in nature and are the primary solidification morphologies in metallic materials. Techniques such as x-ray computed tomography (XCT) have provided new insights into dendritic phase transformation phenomena. However, manual identification of dendritic morphologies in microscopy data can be both labor intensive and potentially ambiguous. The analysis of 3D datasets is particularly challenging due to their large sizes (terabytes) and the presence of artifacts scattered within the imaged volumes. In this study, we trained 3D convolutional neural networks (CNNs) to segment 3D datasets. Three CNN architectures were investigated, including a new 3D version of FCDense. We show that using hyperparameter optimization (HPO) and fine-tuning techniques, both 2D and 3D CNN architectures can be trained to outperform the previous state of the art. The 3D U-Net architecture trained in this study produced the best segmentations according to quantitative metrics (pixel-wise accuracy of 99.84% and a boundary displacement error of 0.58 pixels), while 3D FCDense produced the smoothest boundaries and best segmentations according to visual inspection. The trained 3D CNNs are able to segment entire 852 x 852 x 250 voxel 3D volumes in only ~60 seconds, thus hastening the progress towards a deeper understanding of phase transformation phenomena such as dendritic solidification.

This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalities, among video, kinematic, and segmentation data, in order to study their added value. The PETRAW challenge provided a data set of 150 peg transfer sequences performed on a virtual simulator. This data set was composed of videos, kinematics, semantic segmentation, and workflow annotations which described the sequences at three different granularity levels: phase, step, and activity. Five tasks were proposed to the participants: three of them were related to the recognition of all granularities with one of the available modalities, while the others addressed the recognition with a combination of modalities. Average application-dependent balanced accuracy (AD-Accuracy) was used as evaluation metric to take unbalanced classes into account and because it is more clinically relevant than a frame-by-frame score. Seven teams participated in at least one task and four of them in all tasks. Best results are obtained with the use of the video and the kinematics data with an AD-Accuracy between 93% and 90% for the four teams who participated in all tasks. The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams. However, the difference in testing execution time between the video/kinematic-based and the kinematic-based methods has to be taken into consideration. Is it relevant to spend 20 to 200 times more computing time for less than 3% of improvement? The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition., Comment: Challenge report doi.org/10.1016/j.cmpb.2023.107561

The combustion delay mixture of tungsten (W), potassium perchlorate (KClO4), and barium chromate (BaCrO4), also known as the WKB mixture, has long been considered to be an integral part of military-grade ammunition. Despite its long history, however, their progressive reaction dynamics remains a question mark, especially due to the complex nature of their combustion reaction. As opposed to a one-step oxidation commonly observed in conventional combustions, the WKB mixture is associated with a multibody reaction between its solid-state components. To this end, the emergence of three combustion peaks, which we corresponded with disparate chemical reactions, was observed using thermogravimetric analysis on two separate WKB mixtures with differing mixture ratios. We applied the stepwise isoconversional method on each of the peaks to match the combustion chemistry it represents to the Sestak-Berggren model and computed the conceptual activation energy. Further plotting the logarithmic pre-exponential factor as a function of the reaction progress, we demonstrate a method of using the plot as an intuitive tool to understand the dynamics of individual reactions that compose multi-step chemical reactions. Our study provides a systematic approach in visualizing the reaction chemistry, thereby strengthening the analytical arsenal against reaction dynamics of combustion compounds in general., Comment: 10 pages, 5 figures, and 1 table

Knowledge transfer using convolutional neural networks (CNNs) can help efficiently train a CNN with fewer parameters or maximize the generalization performance under limited supervision. To enable a more efficient transfer of pretrained knowledge under relaxed conditions, we propose a simple yet powerful knowledge transfer methodology without any restrictions regarding the network structure or dataset used, namely self-supervised knowledge transfer (SSKT), via loosely supervised auxiliary tasks. For this, we devise a training methodology that transfers previously learned knowledge to the current training process as an auxiliary task for the target task through self-supervision using a soft label. The SSKT is independent of the network structure and dataset, and is trained differently from existing knowledge transfer methods; hence, it has an advantage in that the prior knowledge acquired from various tasks can be naturally transferred during the training process to the target task. Furthermore, it can improve the generalization performance on most datasets through the proposed knowledge transfer between different problem domains from multiple source networks. SSKT outperforms the other transfer learning methods (KD, DML, and MAXL) through experiments under various knowledge transfer settings. The source code will be made available to the public., Comment: Accepted at WACV 2022

Automated surgical instrument localization is an important technology to understand the surgical process and in order to analyze them to provide meaningful guidance during surgery or surgical index after surgery to the surgeon. We introduce a new dataset that reflects the kinematic characteristics of surgical instruments for automated surgical instrument localization of surgical videos. The hSDB(hutom Surgery DataBase)-instrument dataset consists of instrument localization information from 24 cases of laparoscopic cholecystecomy and 24 cases of robotic gastrectomy. Localization information for all instruments is provided in the form of a bounding box for object detection. To handle class imbalance problem between instruments, synthesized instruments modeled in Unity for 3D models are included as training data. Besides, for 3D instrument data, a polygon annotation is provided to enable instance segmentation of the tool. To reflect the kinematic characteristics of all instruments, they are annotated with head and body parts for laparoscopic instruments, and with head, wrist, and body parts for robotic instruments separately. Annotation data of assistive tools (specimen bag, needle, etc.) that are frequently used for surgery are also included. Moreover, we provide statistical information on the hSDB-instrument dataset and the baseline localization performances of the object detection networks trained by the MMDetection library and resulting analyses., Comment: https://hsdb-instrument.github.io

As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge training are required. In particular, it is difficult to guarantee generalization performance in the clinical field in the case of expert knowledge training where opinions may differ even among experts on annotations. To raise the issue of the annotation generation process for expertise training of CNNs, we verified the annotations for surgical phase recognition of laparoscopic cholecystectomy and subtotal gastrectomy for gastric cancer. We produce calibrated annotations for the seven phases of cholecystectomy by analyzing the discrepancies of previously annotated labels and by discussing the criteria of surgical phases. For gastrectomy for gastric cancer has more complex twenty-one surgical phases, we generate consensus annotation by the revision process with five specialists. By training the CNN-based surgical phase recognition networks with revised annotations, we achieved improved generalization performance over models trained with original annotation under the same cross-validation settings. We showed that the expertise data annotation pipeline for deep neural networks should be more rigorous based on the type of problem to apply clinical field., Comment: Bridging the Gap: From Machine Learning Research to Clinical Practice @ NeurIPS 2021 (Spotlight)