Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks.

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are often unstructured, nonstatic, and ambiguous, requires extensive computer resources and data specialists. Emerging neuromorphic computing technologies that mimic the data processing properties of biological neural networks offer a promising solution for handling overflowing data. Here, the development of an electrolyte-gated organic transistor featuring a selective transition from short-term to long-term plasticity of the biological synapse is presented. The memory behaviors of the synaptic device were precisely modulated by restricting ion penetration through an organic channel via photochemical reactions of the cross-linking molecules. Furthermore, the applicability of the memory-controlled synaptic device was verified by constructing a reconfigurable synaptic logic gate for implementing a medical algorithm without further weight-update process. Last, the presented neuromorphic device demonstrated feasibility to handle biometric information with various update periods and perform health care tasks.

LMGAN: Linguistically Informed Semi-Supervised GAN with Multiple Generators.

Semi-supervised learning is one of the active research topics these days. There is a trial that solves semi-supervised text classification with a generative adversarial network (GAN). However, its generator has a limitation in producing fake data distributions that are similar to real data distributions. Since the real data distribution is frequently changing, the generator could not create adequate fake data. To overcome this problem, we present a novel approach for semi-supervised learning for text classification based on generative adversarial networks, Linguistically Informed SeMi-Supervised GAN with Multiple Generators, LMGAN. LMGAN uses trained bidirectional encoder representations from transformers (BERT) and the discriminator from GAN-BERT. In addition, LMGAN has multiple generators and utilizes the hidden layers of BERT. To reduce the discrepancy between the distribution of fake data and real data distribution, LMGAN uses fine-tuned BERT and the discriminator from GAN-BERT. However, since injecting fine-tuned BERT could induce incorrect fake data distribution, we utilize linguistically meaningful intermediate hidden layer outputs of BERT to enrich fake data distribution. Our model shows well-distributed fake data compared to the earlier GAN-based approach that failed to generate adequate high-quality fake data. Moreover, we can get better performances with extremely limited amounts of labeled data, up to 20.0%, compared to the baseline GAN-based model.

UAT: Universal Attention Transformer for Video Captioning.

Video captioning via encoder-decoder structures is a successful sentence generation method. In addition, using various feature extraction networks for extracting multiple features to obtain multiple kinds of visual features in the encoding process is a standard method for improving model performance. Such feature extraction networks are weight-freezing states and are based on convolution neural networks (CNNs). However, these traditional feature extraction methods have some problems. First, when the feature extraction model is used in conjunction with freezing, additional learning of the feature extraction model is not possible by exploiting the backpropagation of the loss obtained from the video captioning training. Specifically, this blocks feature extraction models from learning more about spatial information. Second, the complexity of the model is further increased when multiple CNNs are used. Additionally, the author of Vision Transformers (ViTs) pointed out the inductive bias of CNN called the local receptive field. Therefore, we propose the full transformer structure that uses an end-to-end learning method for video captioning to overcome this problem. As a feature extraction model, we use a vision transformer (ViT) and propose feature extraction gates (FEGs) to enrich the input of the captioning model through that extraction model. Additionally, we design a universal encoder attraction (UEA) that uses all encoder layer outputs and performs self-attention on the outputs. The UEA is used to address the lack of information about the video's temporal relationship because our method uses only the appearance feature. We will evaluate our model against several recent models on two benchmark datasets and show its competitive performance on MSRVTT/MSVD datasets. We show that the proposed model performed captioning using only a single feature, but in some cases, it was better than the others, which used several features.

Enhancing Targeted Minority Class Prediction in Sentence-Level Relation Extraction.

Sentence-level relation extraction (RE) has a highly imbalanced data distribution that about 80% of data are labeled as negative, i.e., no relation; and there exist minority classes (MC) among positive labels; furthermore, some of MC instances have an incorrect label. Due to those challenges, i.e., label noise and low source availability, most of the models fail to learn MC and get zero or very low F1 scores on MCs. Previous studies, however, have rather focused on micro F1 scores and MCs have not been addressed adequately. To tackle high mis-classification errors for MCs, we introduce (1) a minority class attention module (MCAM), and (2) effective augmentation methods specialized in RE. MCAM calculates the confidence scores on MC instances to select reliable ones for augmentation, and aggregates MCs information in the process of training a model. Our experiments show that our methods achieve a state-of-the-art F1 scores on TACRED as well as enhancing minority class F1 score dramatically.

Effective Pre-Training Method and Its Compositional Intelligence for Image Captioning.

With the increase in the performance of deep learning models, the model parameter has increased exponentially. An increase in model parameters leads to an increase in computation and training time, i.e., an increase in training cost. To reduce the training cost, we propose Compositional Intelligence (CI). This is a reuse method that combines pre-trained models for different tasks. Since the CI uses a well-trained model, good performance and small training cost can be expected in the target task. We applied the CI to the Image Captioning task. Compared to using a trained feature extractor, the caption generator is usually trained from scratch. On the other hand, we pre-trained the Transformer model as a caption generator and applied CI, i.e., we used a pre-trained feature extractor and a pre-trained caption generator. To compare the training cost of the From Scratch model and the CI model, early stopping was applied during fine-tuning of the image captioning task. On the MS-COCO dataset, the vanilla image captioning model reduced training cost by 13.8% and improved performance by up to 3.2%, and the Object Relation Transformer model reduced training cost by 21.3%.

Root proximity of the anchoring miniscrews of orthodontic miniplates in the mandibular incisal area: Cone-beam computed tomographic analysis.

OBJECTIVE: This outcome analysis study evaluated the actual positions of the orthodontic miniplate and miniplate anchoring screws (MPASs) and the risk factors affecting adjacent anatomic structures after miniplate placement in the mandibular incisal area. METHODS: Cone-beam computed tomographic images of 97 orthodontic miniplates and their 194 MPASs (diameter, 1.5 mm; length, 4 mm) in patients whose miniplates provided sufficient clinical stability for orthodontic treatment were retrospectively reviewed. For evaluating the actual positions of the miniplates and analyzing the risk factors, including the effects on adjacent roots, MPAS placement height (PH), placement depth (PD), plate angle (PA), mental fossa angle (MA), and root proximity were assessed using the paired t-test, analysis of variance, and generalized linear model and regression analyses. RESULTS: The mean PDs of MPASs at positions 1 (P1) and 2 (P2) were 2.01 mm and 2.23 mm, respectively. PA was significantly higher in the Class III malocclusion group than in the other groups. PH was positively correlated with MA and PD at P1. Of the 97 MPASs at P1, 49 were in the no-root area and 48 in the dentulous area; moreover, 19 showed a degree of root contact (19.6%) without root perforation. All MPASs at P2 were in the no-root area. CONCLUSIONS: Positioning the miniplate head approximately 1 mm lower than the mucogingival junction is highly likely to provide sufficient PH for the P1- MPASs to be placed in the no-root area.

HyAdamC: A New Adam-Based Hybrid Optimization Algorithm for Convolution Neural Networks.

As the performance of devices that conduct large-scale computations has been rapidly improved, various deep learning models have been successfully utilized in various applications. Particularly, convolution neural networks (CNN) have shown remarkable performance in image processing tasks such as image classification and segmentation. Accordingly, more stable and robust optimization methods are required to effectively train them. However, the traditional optimizers used in deep learning still have unsatisfactory training performance for the models with many layers and weights. Accordingly, in this paper, we propose a new Adam-based hybrid optimization method called HyAdamC for training CNNs effectively. HyAdamC uses three new velocity control functions to adjust its search strength carefully in term of initial, short, and long-term velocities. Moreover, HyAdamC utilizes an adaptive coefficient computation method to prevent that a search direction determined by the first momentum is distorted by any outlier gradients. Then, these are combined into one hybrid method. In our experiments, HyAdamC showed not only notable test accuracies but also significantly stable and robust optimization abilities when training various CNN models. Furthermore, we also found that HyAdamC could be applied into not only image classification and image segmentation tasks.

Artificial stimulus-response system capable of conscious response.

A stimulus-response system and conscious response enable humans to respond effectively to environmental changes and external stimuli. This paper presents an artificial stimulus-response system that is inspired by human conscious response and is capable of emulating it. The system is composed of an artificial visual receptor, artificial synapse, artificial neuron circuits, and actuator. By incorporating these artificial nervous components, a series of conscious response processes that markedly reduces response time as a result of learning from repeated stimuli are demonstrated. The proposed artificial stimulus-response system offers the promise of a new research field that would aid the development of artificial intelligence-based organs for patients with neurological disorders.

Effectiveness of 2D radiographs in detecting CBCT-based incidental findings in orthodontic patients.

Some craniofacial diseases or anatomical variations are found in radiographic images taken for other purposes. These incidental findings (IFs) can be detected in orthodontic patients, as various radiographs are required for orthodontic diagnosis. The radiographic data of 1020-orthodontic patients were interpreted to evaluate the rates of IFs in three-dimensional (3D) cone-beam-computed tomography (CBCT) with a large field of view (FOV) and investigate the effectiveness and accuracy of two-dimensional (2D) radiographs for detecting IFs compared to CBCT. Prevalence and accuracy in five areas was measured for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The accuracies of various 2D-radiograph were compared through a proportion test. A total of 709-cases (69.5%) of 1020-subjects showed one or more IFs in CBCT images. Nasal cavity was the most affected area. Based on the CBCT images as a gold standard, different accuracies of various 2D-radiographs were observed in each area of the findings. The highest accuracy was confirmed in soft tissue calcifications with comprehensive radiographs. For detecting nasal septum deviations, postero-anterior cephalograms were the most accurate 2D radiograph. In cases the IFs were not determined because of its ambiguity in 2D radiographs, considering them as an absence of findings increased the accuracy.

Ultrasound-guided sialo-irrigation for the treatment of chronic sialodochitis with sialolithiasis.

Sialolithiasis is one of the most common causes of salivary duct obstruction. In the last 20 years, minimally invasive procedures like sialendoscopy, extracorporeal lithotripsy, and basket snaring are increasingly being used for the treatment of salivary gland duct stones. Sialo-irrigation of the salivary gland is an effective procedure for treating inflammation and providing symptomatic relief. This procedure can be employed for the treatment of sialolithiasis using the back pressure of instilled saline. Sialo-irrigation under ultrasound (US) guidance allows for dynamic studies showing real-time images during diagnostic or surgical procedure and can be used for the removal of sialoliths. In addition, it can also be used to remove primitive sialoliths and microliths by washing out the ductal system, which prevents the recurrence of sialoliths. The aim of this study was to propose a minimally invasive technique for sialolithiasis using US-guided sialo-irrigation.

Rectifying optoelectronic memory based on WSe2/graphene heterostructures.

van der Waals heterostructures composed of two-dimensional materials vertically stacked have been extensively studied to develop various multifunctional devices. Here, we report WSe2/graphene heterostructure devices with a top floating gate that can serve as multifunctional devices. They exhibit gate-controlled rectification inversion, rectified nonvolatile memory effects, and multilevel optoelectronic memory effects. Depending on the polarity of the gate voltage pulses (V Gp), electrons or holes can be trapped in the floating gate, resulting in rectified nonvolatile memory properties. Furthermore, upon repeated illumination with laser pulses, positive or negative staircase photoconductivity is observed depending on the history of V Gp, which is ascribed to the tunneling of electrons or holes between the WSe2 channel and the floating gate. These multifunctional devices can be used to emulate excitatory and inhibitory synapses that have different neurotransmitters. Various synaptic functions, such as potentiation/depression curves and spike-timing-dependent plasticity, have been also implemented using these devices. In particular, 128 optoelectronic memory states with nonlinearity less than 1 can be achieved by controlling applied laser pulses and V Gp, suggesting that the WSe2/graphene heterostructure devices with a top floating gate can be applied to optoelectronic synapse devices.

Radiation dosimetry analyses of radiographic imaging systems used for orthodontic treatment: comparison among child, adolescent, and adult patients.

OBJECTIVES: The aim of this study was to compare the effective doses of orthodontic radiographs in children, adolescents, and adults. METHODS: We exposed a child, an adolescent (simulated by an adult female phantom), and adult male phantoms using common scanning protocols for panoramic radiography, cephalography, and cone-beam computed tomography (CBCT). Glass dosimeters were placed in the organs of the phantom to measure the absorbed doses. The effective doses were deduced using tissue weighting factors as defined in the ICRP Publication 103. RESULTS: For panoramic imaging, the parotid gland had the highest absorbed dose in the child and the submandibular glands had the highest absorbed dose in both the adolescent and adult phantoms. For cephalography, the organs and tissues located closest to the X-ray tube had the highest absorbed dose values. For CBCT, the lenses of the eyes received the highest absorbed dose. Effective doses with CBCT were the greatest in the adolescent phantom, followed by in the adult and child phantoms. CONCLUSIONS: Dental practitioners should be aware of patient age, as younger patients will incur greater risks from radiation.

Vertical organic synapse expandable to 3D crossbar array.

Recently, three-terminal synaptic devices have attracted considerable attention owing to their nondestructive weight-update behavior, which is attributed to the completely separated terminals for reading and writing. However, the structural limitations of these devices, such as a low array density and complex line design, are predicted to result in low processing speeds and high energy consumption of the entire system. Here, we propose a vertical three-terminal synapse featuring a remote weight update via ion gel, which is also extendable to a crossbar array structure. This synaptic device exhibits excellent synaptic characteristics, which are achieved via precise control of ion penetration onto the vertical channel through the weight-control terminal. Especially, the applicability of the developed vertical organic synapse array to neuromorphic computing is demonstrated using a simple crossbar synapse array. The proposed synaptic device technology is expected to be an important steppingstone to the development of high-performance and high-density neural networks.

Leiomyosarcoma of the jaw: case series.

OBJECTIVES: Leiomyosarcoma is a malignant neoplasm that affects smooth muscle tissue and it is very rare in the field of oral and maxillofcial surgery. The purpose of this study was to obtain information on diagnosis of and treatment methods for leiomyosarcoma by retrospectively reviewing of the cases. PATIENTS AND METHODS: The study included nine patients who were diagnosed with leiomyosarcoma in the Department of Oral and Maxillofacial Surgery at Seoul National University Dental Hospital. The subjects were analyzed with respect to sex, age, clinical features, primary site of disease, treatment method, recurrence, and metastasis. RESULTS: Particular clinical features included pain, edema, mouth-opening limitations, dysesthesia, and enlarged lymph nodes. All cases except one were surgically treated, and recurrence was found in two cases. Four of nine patients were followed up without recurrence and one patient underwent additional surgery due to recurrence. CONCLUSION: In our case series, notable symptoms included pain, edema, mouth-opening limitations, and dysesthesia; however, it was difficult to label these as specific symptoms of leiomyosarcoma. Considering the aggressive characteristics of the disease and poor prognosis, surgical treatment is necessary with careful consideration of postoperative radiotherapy and chemotherapy.

Oxygen-Detecting Synaptic Device for Realization of Artificial Autonomic Nervous System for Maintaining Oxygen Homeostasis.

Incorporation of various functions of a biological nervous system into electronic devices is an intriguing challenge in the realization of a human-like recognition and response system. Emerging artificial synaptic devices capable of processing electronic signals through neuromorphic functions operate such biomimetic systems similarly to biological nervous systems. Here, an oxygen-sensitive artificial synaptic device that simultaneously detects oxygen concentration and generates a synaptic signal is demonstrated. The device successfully achieves an interconversion between the excitatory and inhibitory modes of the synaptic current at various oxygen concentrations by virtue of an oxygen-sensitive trilayered organic double heterojunction. The oxygen-induced traps in the organic layer modulate the majority charge carrier from holes to electrons, and this modulation induces an interconversion between the excitatory and inhibitory modes according to the environmental oxygen condition. Finally, the proposed synaptic device is applied to the realization of a negative feedback system for regulation of oxygen homeostasis, which mimics the human autonomic nervous system. The oxygen-sensitive synaptic device proposed in this study is expected to open up new possibilities for the development of a biomimetic neural system that can respond appropriately to various environmental changes.

Cone-beam computed tomography of mandibular foramen and lingula for mandibular anesthesia.

PURPOSE: The positions of the mandibular foramen (MnF) and the lingula affect the success rate of inferior alveolar nerve block. The objective of this study was to investigate aspects of the MnF and the lingula relevant for mandibular block anesthesia using cone-beam computed tomography (CBCT). MATERIALS AND METHODS: Fifty CBCT scans were collected from a picture archiving and communications system. All scans were taken using an Alphard Vega 3030 (Asahi Roentgen Co. Ltd., Kyoto, Japan). Fifty-eight MnFs of 30 subjects were included in the study. The position of the MnF, the size of the MnF, the position of the lingula, the size of the lingula, and the shape of the lingula were measured and recorded. All data were statistically analyzed at a significance level of P<0.05. RESULTS: The position of MnF was 0.1 mm and 0.8 mm below the occlusal plane in males and females, respectively. The horizontal position of the MnF was slightly anterior to the center of the ramus in males and in the center in females (P<0.05). The vertical position of the MnF was lower in females than in males (P<0.05). The MnF was an oval shape with a longer anteroposterior dimension. The height of the lingula was 9.3 mm in males and 8.2 mm in females. The nodular type was the most common shape of the lingula, followed by the triangular, truncated, and assimilated types. CONCLUSION: CBCT provided useful information about the MnF and lingula. This information could improve the success rate of mandibular blocks.

2D MXene-TiO2 Core-Shell Nanosheets as a Data-Storage Medium in Memory Devices.

MXenes, an emerging class of 2D transition metal carbides and nitrides with the general formula Mn +1 Xn Tx (n = 1-4), have potential for application as floating gates in memory devices because of their intrinsic properties of a 2D structure, high density-of-states, and high work function. In this study, a series of MXene-TiO2 core-shell nanosheets are synthesized by deterministic control of the surface oxidation of MXene. The floating gate (multilayer MXene) and tunneling layer (TiO2 ) in a nano-floating-gate transistor memory (NFGTM) device are prepared simultaneously by a facile, low-cost, and water-based process. The memory performance is optimized via adjustment of the thickness of the oxidation layer formed on the MXene surface. The fabricated MXene NFGTMs exhibit excellent nonvolatile memory characteristics, including a large memory window (>35.2 V), high programming/erasing current ratio (≈106 ), low off-current (<1 pA), long retention (>104 s), and cyclic endurance (300 cycles). Furthermore, synaptic functions, including the excitatory postsynaptic current/inhibitory postsynaptic current, paired-pulse facilitation, and synaptic plasticity (long-term potentiation/depression), are successfully emulated using the MXene NFGTMs. The successful control of MXene oxidation and its application to NFGTMs are expected to inspire the application of MXene as a data-storage medium in future memory devices.

Rational Band Engineering of an Organic Double Heterojunction for Artificial Synaptic Devices with Enhanced State Retention and Linear Update of Synaptic Weight.

Herein, we propose an organic double heterojunction to enable a nonvolatile step modulation of the conductance of an artificial synapse; the double heterojunction is composed of N,N'-dioctyl-3,4,9,10-perylene tetracarboxylic diimide (PTCDI-C8), copper phthalocyanine (CuPc), and para-sexiphenyl (p-6P). The carrier confinement in the CuPc region present in the double-heterojunction structure enabled the nonvolatile modulation of the postsynaptic current. The proposed organic synapse exhibited an excellent conductance change, characteristic with a nonlinearity (NL) value below 0.01 in the long-term potentiation (LTP) region. Furthermore, the NL value for long-term depression (LTD) could be reduced effectively from 45 to 3.5 by a pulse modulation technique. A simple artificial neural network (ANN) was theoretically designed using the LTP/LTD characteristic curves of such organic synapses, and then, learning and recognition tasks were performed using Modified National Institute of Standards and Technology digit images. A four-amplitude weight update method enabled considerable enhancement of the recognition rate from 53 to 70%. Although the designed ANN was based on a single-layer perceptron model, a high maximum accuracy of 75% was achieved. These newly studied techniques for synaptic devices are expected to open up new possibilities for the realization of artificial synapses based on organic double heterojunctions.

Malignant transformation of fibrous dysplasia into angiosarcoma.

Fibrous dysplasia (FD) is generally considered to be a benign disease that affects the bones, but it has potential to become malignant over time, generally several decades after its initial diagnosis. Radiation therapy can induce malignant transformation of FD; however, reports have indicated a few cases of malignant transformation of FD in the absence of radiation therapy. Angiosarcoma is a particularly rare type of cancer in the oral region, which accounts for less than 1% of all soft-tissue sarcomas. Herein, we reported a case of a 62-year-old man with monostotic FD of the left maxilla of over 50 years' duration that underwent malignant transformation into an epithelioid-type angiosarcoma. To the best of our knowledge, this is the first report of such case.

Gate-Tunable Synaptic Dynamics of Ferroelectric-Coupled Carbon-Nanotube Transistors.

Artificial neural networks (ANNs) based on synaptic devices, which can simultaneously perform processing and storage of data, have superior computing performance compared to conventional von Neumann architectures. Here, we present a ferroelectric coupled artificial synaptic device with reliable weight update and storage properties for ANNs. The artificial synaptic device, which is based on a ferroelectric polymer capacitively coupled with an oxide dielectric via an electric-field-permeable, semiconducting single-walled carbon-nanotube channel, is successfully fabricated by inkjet printing. By controlling the ferroelectric polarization, synaptic dynamics, such as excitatory and inhibitory postsynaptic currents and long-term potentiation/depression characteristics, is successfully implemented in the artificial synaptic device. Furthermore, the constructed ANN, which is designed in consideration of the device-to-device variation within the synaptic array, efficiently executes the tasks of learning and recognition of the Modified National Institute of Standards and Technology numerical patterns.