Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography.

PURPOSE: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). METHODS: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. RESULTS: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. SIGNIFICANCE: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.

A robust seizure detection and prediction method with feature selection and spatio-temporal casual neural network model.

Objective.Epilepsy is a fairly common condition that affects the brain and causes frequent seizures. The sudden and recurring epilepsy brings a series of safety hazards to patients, which seriously affects the quality of their life. Therefore, real-time diagnosis of electroencephalogram (EEG) in epilepsy patients is of great significance. However, the conventional methods take in a tremendous amount of features to train the models, resulting in high computation cost and low portability. Our objective is to propose an efficient, light and robust seizure detecting and predicting algorithm.Approach.The algorithm is based on an interpretative feature selection method and spatial-temporal causal neural network (STCNN). The feature selection method eliminates the interference factors between different features and reduces the model size and training difficulties. The STCNN model takes both temporal and spatial information to accurately and dynamically track and diagnose the changing of the features. Considering the differences between medical application scenarios and patients, leave-one-out cross validation (LOOCV) and cross-patient validation (CPV) methods are used to conduct experiments on the dataset collected at the Children's Hospital Boston (CHB-MIT), Siena and Kaggle competition datasets.Main results.In LOOCV-based method, the detection accuracy and prediction sensitivity have been improved. A significant improvement is also achieved in the CPV-based method.Significance.The experimental results show that our proposed algorithm exhibits superior performance and robustness in seizure detection and prediction, which indicates it has higher capability to deal with different and complicated clinical situations.

KLF4 loss in hepatocellular carcinoma: Improving prognostic prediction and correlating immune infiltrates.

Introduction: Although the molecular mechanisms of Krüpple-like factor 4 (KLF4) as a tumor suppressor in HCC tumorigenesis have been thoroughly examined, its clinical application in terms of precise prognostication and its influence on tumor immune microenvironment in patients with HCC require further investigation. Methods: Bioinformatics and immunohistochemistry (IHC) were used to validate KLF4 expressions in a tissue microarray (TMA) containing HCC samples. Using Cox regression models, independent prognostic factors were identified and employed in the development of nomograms. Decision curve analysis (DCA) demonstrated the superiority of the nomograms. GO and KEGG pathway analyses were applied to the functional study of KLF4. The GSVA program explored the link between KLF4 expression and tumor-infiltrating immune cells, and CAMOIP was used to construct KLF4 expression immune scores. Changes in immune-related gene markers were also investigated in relation to KLF4 expression. The association between immune cell infiltration and KLF4 expression was validated by IHC in TMA. Results: HCC was reported to have a notable depletion of KLF4. The absence of KLF4 was associated with advanced clinicopathological characteristics of HCC and predicted a bad prognosis for patients. Nomograms constructed using KLF4 expression, tumor differentiation, and TNM stage provided a more accurate prognostic assessment of HCC patients than TNM stage alone. KLF4 expression was associated with immunological-related functions, infiltration of macrophages, CD8+ T cells, and other immune cells, and elevation of immune checkpoints. Higher levels of CD8+ T cells and macrophage infiltration are associated with increased KLF4 expression in HCC TMA. Conclusion: KLF4 loss in HCC is a prognostic biomarker that influences the tumor immune microenvironment (TIME).

MsVRL: Self-Supervised Multiscale Visual Representation Learning via Cross-Level Consistency for Medical Image Segmentation.

Automated medical image segmentation for organs or lesions plays an essential role in clinical diagnoses and treatment plannings. However, training an accurate and robust segmentation model is still a long-standing challenge due to the time-consuming and expertise-intensive annotations for training data, especially 3-D medical images. Recently, self-supervised learning emerges as a promising approach for unsupervised visual representation learning, showing great potential to alleviate the expertise annotations for medical images. Although global representation learning has attained remarkable results on iconic datasets, such as ImageNet, it can not be applied directly to medical image segmentation, because the segmentation task is non-iconic, and the targets always vary in physical scales. To address these problems, we propose a Multi-scale Visual Representation self-supervised Learning (MsVRL) model, to perform finer-grained representation and deal with different target scales. Specifically, a multi-scale representation conception, a canvas matching method, an embedding pre-sampling module, a center-ness branch, and a cross-level consistent loss are introduced to improve the performance. After pre-trained on unlabeled datasets (RibFrac and part of MSD), MsVRL performs downstream segmentation tasks on labeled datasets (BCV, spleen of MSD, and KiTS). Results of the experiments show that MsVRL outperforms other state-of-the-art works on these medical image segmentation tasks.

Relations between physical activity and hippocampal functional connectivity: Modulating role of mind wandering.

Physical activity is critical for maintaining cognitive and brain health. Previous studies have indicated that the effect of physical activity on cognitive and brain function varies between individuals. The present study aimed to examine whether mind wandering modulated the relations between physical activity and resting-state hippocampal functional connectivity. A total of 99 healthy adults participated in neuroimaging data collection as well as reported their physical activity in the past week and their propensity to mind wandering during typical activities. The results indicated that mind wandering was negatively related to the resting-state functional connectivity between hippocampus and right inferior occipital gyrus. Additionally, for participants with higher level of mind wandering, physical activity was negatively related to hippocampal connectivity at left precuneus and right precentral gyrus. In contrast, such relations were positive at right medial frontal gyrus and bilateral precentral gyrus for participants with lower level of mind wandering. Altogether, these findings indicated that the relations between physical activity and hippocampal functional connectivity vary as a function of mind wandering level, suggesting that individual differences are important to consider when we aim to maintain or improve cognitive and brain health through increasing physical activity.

Second primary malignancies in cervical cancer and endometrial cancer survivors: a population-based analysis.

BACKGROUND: We evaluated the relative attribution and interactions of treatment and patient-related risk factors for second primary malignancies (SPMs) in cervical and endometrial cancer survivors. METHODS: Stage I-III cervical and endometrial cancer survivors' data from the Surveillance, Epidemiology, and End Results (SEER) registry between January 1988 and December 2015 were analyzed. The standardized incidence ratio (SIR), excess absolute risk (EAR), and corresponding 95% confidence interval (95% CI) values were calculated. Analyses were classified based on proxies of human papillomavirus (HPV), smoking, hormone, and radiotherapy (RT) status. Additive and multiplicative interactions were assessed. RESULTS: Cervical cancer survivors had a higher risk for developing potentially HPV and smoking-related SPMs, especially in the RT group (SIRHPV = 3.7, 95% CI: 2.9-4.6; SIRsmoking = 3.2, 95% CI: 2.8-3.6). Second vaginal cancer patients had the highest SIR (23.8, 95% CI: 14.9-36.0). There were strong synergistic interactions between RT and the proxy of smoking (Pinteraction < 0.001), accounting for 36% of potentially smoking-related SPMs in cervical cancer survivors. CONCLUSIONS: RT, HPV, and smoking promote SPMs in cervical cancer to different extents. The SPM burden in cervical cancer survivors could be mostly attributed to smoking and RT and their interactions.

A Semi-Supervised Few-Shot Learning Model for Epileptic Seizure Detection.

In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalography (EEG). However, only a small amount of labeled epileptic data is available for training because labeling requires numerous neurologists. This paper proposes a one-step semi-supervised epilepsy detection system to reduce the labeling cost by fully utilizing the unlabeled data. The proposed neural network training strategy enables a more robust and accurate decision boundary by forcing the consistency of the double predictions on the same unlabeled data. The results show that the Area Under Receiver Operating Characteristic (AUROC) curves of our proposed model are 10.3% and 4.9% higher than the supervised methods on CHB-MIT and Kaggle datasets, respectively.

Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: An extended study.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is a public health challenge and significant cause of morbidity and mortality worldwide. Early identification is crucial for disease intervention. We recently proposed a nomogram-based NAFLD prediction model from a large population cohort. We aimed to explore machine learning tools in predicting NAFLD. METHODS: A retrospective cross-sectional study was performed on 15 315 Chinese subjects (10 373 training and 4942 testing sets). Selected clinical and biochemical factors were evaluated by different types of machine learning algorithms to develop and validate seven predictive models. Nine evaluation indicators including area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, positive predictive value, sensitivity, F1 score, Matthews correlation coefficient (MCC), specificity and negative prognostic value were applied to compare the performance among the models. The selected clinical and biochemical factors were ranked according to the importance in prediction ability. RESULTS: Totally 4018/10 373 (38.74%) and 1860/4942 (37.64%) subjects had ultrasound-proven NAFLD in the training and testing sets, respectively. Seven machine learning based models were developed and demonstrated good performance in predicting NAFLD. Among these models, the XGBoost model revealed the highest AUROC (0.873), AUPRC (0.810), accuracy (0.795), positive predictive value (0.806), F1 score (0.695), MCC (0.557), specificity (0.909), demonstrating the best prediction ability among the built models. Body mass index was the most valuable indicator to predict NAFLD according to the feature ranking scores. CONCLUSIONS: The XGBoost model has the best overall prediction ability for diagnosing NAFLD. The novel machine learning tools provide considerable beneficial potential in NAFLD screening.

Relations of Creativity to the Interplay Between High-order Cognitive Functions: Behavioral and Neural Evidence.

As a high-order cognitive ability, creativity is viewed as the result of complex interplay between a set of mental processes. However, previous studies have mainly tested one-to-one mutual relations between creativity and other cognitive abilities. It lacks studies to examine whether creativity is related to the interaction between cognitive systems. The current study aimed to fill this gap by testing the relations of creativity to the interactions between cognitive control and episodic memory systems using both behavioral and neuroimaging methods. The Alternative Uses Task was used to measure the divergent component of creativity. A computer-based behavioral task was used to measure cognitive control, episodic memory, and their interactions. Additionally, the interactions between cognitive systems were characterized by computing the resting-state functional connectivity between hippocampus and prefrontal regions, which are the neural substrates for episodic memory and cognitive control, respectively. By analyzing these behavioral and neuroimaging data, the behavioral results indicated that creativity was significantly related to the effect of cognitive control induced by switching tasks or proactive cues on subsequent memories of items or sources. Additionally, neuroimaging results showed that creativity was significantly related to the connectivity from hippocampus to both left superior frontal gyrus and middle frontal gyrus. Such relations were also differentiated between anterior and posterior hippocampus. Altogether, these findings suggest that creativity is related to interactions between cognitive control and episodic memory, supporting the claim that creativity is the result of complex interplay between high-order cognitive functions.

The stage-specific roles of radiotherapy and chemotherapy in nodular lymphocyte predominant Hodgkin lymphoma patients: a propensity score-matched analysis of the SEER database.

BACKGROUND: The stage-specific roles of radiotherapy (RT) alone, chemotherapy alone, and combined RT and chemotherapy (CRT) for patients with nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) have not been adequately evaluated. METHODS: We analyzed patients with all stages of NLPHL enrolled in the Surveillance, Epidemiology, and End Results (SEER) registry from January 2000 to December 2015. Propensity score (PS) analysis with 1:1 matching (PSM) was performed to ensure the well-balanced characteristics of the comparison groups. Kaplan-Meier and Cox proportional-hazards models were used to evaluate the overall survival (OS), cancer-specific survival (CSS), hazard ratios (HRs), and corresponding 95% confidence intervals (95% CI). Restricted mean survival times (RMST) were also used for the survival analyses. RESULTS: For early-stage patients, CRT was associated with the best survival, the mean OS was significantly improved by approximately 20 months (20 m), and the risk of death was reduced by more than 80%, both before and after PSM (p < 0.05). For advanced-stage patients, none of RT alone, chemotherapy alone, or CRT had a significant effect on survival. Chemotherapy alone and CRT might be more beneficial for long-term survival (RMST120 m : neither RT nor chemotherapy vs. chemotherapy alone vs. CRT = 104 m vs. 111 m vs. 108 m). Subgroup analysis of patients with early-stage NLPHL showed that CRT was associated with better survival of elderly patients (improved OS = 43.8 m, HR = 0.14, p < 0.05). However, the survival benefits of treatments for young patients were not statistically significant. The efficacy of RT was significantly different between the age groups (pfor interaction  = 0.020). CONCLUSIONS: These results from SEER data suggest that CRT may be considered for early-stage NLPHL, especially for elderly patients. Further studies are needed to identify effective treatments in patients with advanced-stage NLPHL.

Radiotherapy for patients with stage IV classical Hodgkin lymphoma: a propensity-matched analysis of the surveillance, epidemiology, and end results database.

Background The survival advantage of radiotherapy for patients with stage IV classic Hodgkin lymphoma (HL) has not been adequately evaluated. Methods We analyzed patients with stage IV HL enrolled from the Surveillance, Epidemiology, and End Results (SEER) registry from January 2000 to December 2012. Propensity score (PS) analysis with 1:2 matching was performed to ensure well-balanced characteristics of the comparison groups. Kaplan-Meier and Cox proportional hazardous model were used to evaluate the overall survival (OS), cancer-specific survival (CSS), the hazards ratio (HR) and corresponding 95% confidence intervals (95% CI). Results Overall, for all patients with stage IV HL, receiving radiotherapy was associated with both significantly improved OS and CSS. Radiotherapy to any lesions could independently improve the OS and CSS by 30% to 36% in the multivariate analyses before and after PS matching (PSM), with the best improvement of 33% to 40% observed for patients with nodular sclerosis (P < 0.05) among all HL pathological types. In particular, radiotherapy, most likely to the residual site, was more pronouncedly associated with the improvement in survival for patients with stage IV HL who were young (age<45, P < .05) or without B symptoms (PInteraction for OS = 0.099, PInteraction for CSS = 0.255). For those patients without B symptoms, after PSM, the OS was improved by 65% (P = .021). Conclusions The large SEER results support that radiotherapy is associated with better survival of patients with stage IV HL.

Comparison of Treatment Modalities for Locally Advanced Gastric Cancer: A Propensity Score Matching Analysis.

Background: A consensus regarding optimum treatment strategies for locally advanced gastric cancer (LAGC) has not yet been reached. We aimed to evaluate the efficacy of various treatment modalities for LAGC and provided clinicians salvage options under real-world situation. Methods: Medical charts of patients with LAGC who underwent radical resection plus adjuvant chemotherapy or chemoradiotherapy from July 2003 to December 2014 were included. Validation cohort were selected from SEER database between 2004 and 2014. Kaplan-Meier and Cox proportional hazardous models were used to evaluate the overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). Propensity score matching (PSM) was used to adjust for potential baseline confounding. Results: A total of 350 patients were included and divided into D1 dissection plus chemotherapy group (D1CT, n = 74), D1 dissection plus adjuvant chemoradiotherapy group (D1CRT, n = 69), D2 dissection plus adjuvant chemotherapy group (D2CT, n = 134), and D2 dissection plus adjuvant chemoradiotherapy group (D2CRT, n = 73). PSM identified 50 patients in each group. After PSM, better DFS (P for D2CRT vs. D1CT, D1CRT, and D2CT was 0.001, 0.006, and 0.001, respectively) and OS (P for D2CRT vs. D1CT, D1CRT, and D2CT was 0.001, 0.011, and 0.022, respectively) were found for the D2CRT group (mean, OS = 110.7months, DFS = 95.2 months) than the other groups. Similar findings were further validated in the Surveillance, Epidemiology, and End Results database (SEER) cohort. In addition, patients in the D1CRT group achieved similar survival outcomes to those in the D2CT group (mean OS, 72.8 vs. 59.1 months, P = 0.86; mean DFS, 54.4 vs. 34.1 months, P = 0.460). Conclusions: The results of the study indicated the better role for D2CRT in treating the LAGC, meanwhile, the patients treated with D1CRT might achieve similar survival as that of D2CT patients.

CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city.

OBJECTIVES: To characterize the chest computed tomography (CT) findings of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) according to clinical severity. We compared the CT features of common cases and severe cases, symptomatic patients and asymptomatic patients, and febrile and afebrile patients. METHODS: This was a retrospective analysis of the clinical and thoracic CT features of 120 consecutive patients with confirmed SARS-CoV-2 pneumonia admitted to a tertiary university hospital between January 10 and February 10, 2020, in Wuhan city, China. RESULTS: On admission, the patients generally complained of fever, cough, shortness of breath, and myalgia or fatigue, with diarrhea often present in severe cases. Severe patients were 20 years older on average and had comorbidities and an elevated lactate dehydrogenase (LDH) level. There were no differences in the CT findings between asymptomatic and symptomatic common type patients or between afebrile and febrile patients, defined according to Chinese National Health Commission guidelines. CONCLUSIONS: The clinical and CT features at admission may enable clinicians to promptly evaluate the prognosis of patients with SARS-CoV-2 pneumonia. Clinicians should be aware that clinically silent cases may present with CT features similar to those of symptomatic common patients. KEY POINTS: • The clinical features and predominant patterns of abnormalities on CT for asymptomatic, typic common, and severe cases were summarized. These findings may help clinicians to identify severe patients quickly at admission. • Clinicians should be cautious that CT findings of afebrile/asymptomatic patients are not better than the findings of other types of patients. These patients should also be quarantined. • The use of chest CT as the main screening method in epidemic areas is recommended.

Surgery to the primary tumor is associated with improved survival of patients with metastatic esophageal cancer: propensity score-matched analyses of a large retrospective cohort.

The survival advantage of surgery to the primary tumor for patients with distant metastatic esophageal cancer has not been adequately evaluated. This study aims to investigate the role of surgery to the primary tumor in distant metastatic esophageal cancer and to evaluate possible different effects of surgery on survival of esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). This study included a cohort of 4,367 metastatic esophageal cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database, registered from January 2004 to December 2014. Kaplan-Meier and Cox proportional hazardous models were used to evaluate the overall survival (OS) and corresponding 95% confidence interval (CI). Propensity score matching (PSM) was used to adjust for potential baseline confounding. Both EAC (median OS for surgery group vs. no-surgery group-14.0 vs. 9.0 months, P < 0.001) and ESCC (median OS for surgery vs. no-surgery group-11.0 vs. 7.0 months, P = 0.002) experienced survival benefits from surgery. We found that surgery to the primary tumor, when combined with chemotherapy, was associated with improved survival for patients with M1b disease, both EAC and ESCC, with a greater benefit observed in younger patients, and those with EAC. While the present data indicate a potential survival benefit from surgery for some patients with metastatic esophageal cancer, it is possible that performance status and metastatic disease burden impacted patient selection, influencing these results. Further studies are needed to determine the role of surgery for patients with metastatic esophageal cancer.

Palliative Gastrectomy and Survival in Patients With Metastatic Gastric Cancer: A Propensity Score-Matched Analysis of a Large Population-Based Study.

OBJECTIVES: The role of palliative gastrectomy in the management of metastatic gastric cancer remains inadequately clarified. METHODS: We analyzed patients with metastatic gastric cancer enrolled in the Surveillance, Epidemiology, and End Results registry from January 2004 to December 2012. Propensity score (PS) analysis with 1:1 matching and the nearest neighbor matching method was performed to ensure well-balanced characteristics of the groups of patients who undergone gastrectomy and those without gastrectomy. Data were analyzed by Kaplan-Meier and Cox proportional hazards regression models to evaluate the overall survival and cancer-specific survival rates with corresponding 95% confidence intervals (CIs). RESULTS: In general, receiving any kind of gastrectomy was associated with an improvement in survival in the multivariate analyses (hazard ratio [HR]os = 0.64, 95% CI = 0.59-0.70, HRcss = 0.63, 95% CI = 0.57-0.68) and PS matching (PSM) analyses (HRos = 0.63, 95% CI = 0.56-0.70, HRcss = 0.62, 95% CI = 0.55-0.70). After PSM, palliative gastrectomy was found to be associated with remarkably improved survival for patients with stage M1 with only 1 metastasis but not associated with survival of patients with stage M1 with extensive metastasis (≥2 metastatic sites). DISCUSSION: The results obtained from the Surveillance, Epidemiology, and End Results database suggest that patients with metastatic gastric cancer might benefit from palliative gastrectomy on the basis of chemotherapy. However, a PSM cohort study of this kind still has a strong selection bias and cannot replace a properly conducted randomized controlled trial.

Highly Compact Artificial Memristive Neuron with Low Energy Consumption.

Neuromorphic systems aim to implement large-scale artificial neural network on hardware to ultimately realize human-level intelligence. The recent development of nonsilicon nanodevices has opened the huge potential of full memristive neural networks (FMNN), consisting of memristive neurons and synapses, for neuromorphic applications. Unlike the widely reported memristive synapses, the development of artificial neurons on memristive devices has less progress. Sophisticated neural dynamics is the major obstacle behind the lagging. Here a rich dynamics-driven artificial neuron is demonstrated, which successfully emulates partial essential neural features of neural processing, including leaky integration, automatic threshold-driven fire, and self-recovery, in a unified manner. The realization of bioplausible artificial neurons on a single device with ultralow power consumption paves the way for constructing energy-efficient large-scale FMNN and may boost the development of neuromorphic systems with high density, low power, and fast speed.

Radiotherapy Improves the Survival of Patients With Metastatic Cervical Cancer: A Propensity-Matched Analysis of SEER Database.

OBJECTIVE: To demonstrate whether radiotherapy has an effect on the survival of patients with stage IVb (M1) cervical cancer, as it has not been adequately clarified. METHODS: We analyzed International Federation of Gynecology and Obstetrics (FIGO) stage M1 cervical cancer diagnosed in patients between 1992 and 2013 using population-based data from the Surveillance, Epidemiology, and End Results registry. Propensity score (PS) analysis with 1:1 matching and the nearest neighbor matching method was performed to ensure well-balanced characteristics of comparison groups. Data were analyzed by Kaplan-Meier and Cox proportional hazards regression models to evaluate the overall survival (OS) and cancer-specific survival (CSS) months with corresponding 95% confidence intervals (95% CIs). RESULTS: In general, receiving radiotherapy significantly improved OS and CSS both before and after PS matching (PSM) (P < 0.001), with significantly improved OS (hazard ratio, 0.69; 95% CI, 0.62-0.76) and CSS (hazard ratio, 0.79; 95% CI, 0.70-0.89) after PSM in patients with stage M1 cervical cancer. Before PSM, radiotherapy was found to be associated with improved survival even for the patients with stage M1 cervical cancer with extensive metastasis (≥2 metastatic sites) (P < 0.001). Although P value was not significant for brain metastasis, the survival month was numerically improved before PSM (OS and CSS, 1 month vs 4 months). Overall, radiotherapy still significantly improved survival for patients with one metastatic site (ie, oligometastases) either before or after PSM (P < 0.05), with the survival month improved more than 6 months. CONCLUSIONS: The large Surveillance, Epidemiology, and End Results results support that radiotherapy might improve the survival of patients with metastatic cervical cancer. It might be prudent to carefully select suitable patients for radiation therapy for metastatic cervical cancer.

Super Nonlinear Electrodeposition-Diffusion-Controlled Thin-Film Selector.

Selector elements with high nonlinearity are an indispensable part in constructing high density, large-scale, 3D stackable emerging nonvolatile memory and neuromorphic network. Although significant efforts have been devoted to developing novel thin-film selectors, it remains a great challenge in achieving good switching performance in the selectors to satisfy the stringent electrical criteria of diverse memory elements. In this work, we utilized high-defect-density chalcogenide glass (Ge2Sb2Te5) in conjunction with high mobility Ag element (Ag-GST) to achieve a super nonlinear selective switching. A novel electrodeposition-diffusion dynamic selector based on Ag-GST exhibits superior selecting performance including excellent nonlinearity (<5 mV/dev), ultra-low leakage (<10 fA), and bidirectional operation. With the solid microstructure evidence and dynamic analyses, we attributed the selective switching to the competition between the electrodeposition and diffusion of Ag atoms in the glassy GST matrix under electric field. A switching model is proposed, and the in-depth understanding of the selective switching mechanism offers an insight of switching dynamics for the electrodeposition-diffusion-controlled thin-film selector. This work opens a new direction of selector designs by combining high mobility elements and high-defect-density chalcogenide glasses, which can be extended to other materials with similar properties.

A single-camera gaze tracking system under natural light.

Gaze tracking is a human-computer interaction technology, and it has been widely studied in the academic and industrial fields. However, constrained by the performance of the specific sensors and algorithms, it has not been popularized for everyone. This paper proposes a single-camera gaze tracking system under natural light to enable its versatility. The iris center and anchor point are the most crucial factors for the accuracy of the system. The accurate iris center is detected by the simple active contour snakuscule, which is initialized by the prior knowledge of eye anatomical dimensions. After that, a novel anchor point is computed by the stable facial landmarks. Next, second-order mapping functions use the eye vectors and the head pose to estimate the points of regard. Finally, the gaze errors are improved by implementing a weight coefficient on the points of regard of the left and right eyes. The feature position of the iris center achieves an accuracy of 98.87% on the GI4E database when the normalized error is lower than 0.05. The accuracy of the gaze tracking method is superior to the-state-of-the-art appearance-based and feature- based methods on the EYEDIAP database.