Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality

Real-time occlusion handling is a major problem in outdoor mixed reality system because it requires great computational cost mainly due to the complexity of the scene. Using only segmentation, it is difficult to accurately render a virtual object occluded by complex objects such as trees, bushes etc. In this paper, we propose a novel occlusion handling method for real-time, outdoor, and omni-directional mixed reality system using only the information from a monocular image sequence. We first present a semantic segmentation scheme for predicting the amount of visibility for different type of objects in the scene. We also simultaneously calculate a foreground probability map using depth estimation derived from optical flow. Finally, we combine the segmentation result and the probability map to render the computer generated object and the real scene using a visibility-based rendering method. Our results show great improvement in handling occlusions compared to existing blending based methods

Atypical spatial frequency dependence of visual metacognition among schizophrenia patients

Although altered early stages of visual processing have been reported among schizophrenia patients, how such atypical visual processing may affect higher-level cognition remains largely unknown. Here we tested the hypothesis that metacognitive performance may be atypically modulated by spatial frequency (SF) of visual stimuli among individuals with schizophrenia, given their altered magnocellular function. To study the effect of SF on metacognitive performance, we asked patients and controls to perform a visual detection task on gratings with different SFs and report confidence, and analyzed the data using the signal detection theoretic measure meta-d′. Control subjects showed better metacognitive performance after yes- (stimulus presence) than after no- (stimulus absence) responses (‘yes-response advantage’) for high SF (HSF) stimuli but not for low SF (LSF) stimuli. The patients, to the contrary, showed a ‘yes-response advantage’ not only for HSF but also for LSF stimuli, indicating atypical SF dependency of metacognition. An fMRI experiment using the same task revealed that the dorsolateral prefrontal cortex (DLPFC), known to be crucial for metacognition, shows activity mirroring the behavioral results: decoding accuracy of perceptual confidence in DLPFC was significantly higher for HSF than for LSF stimuli in controls, whereas this decoding accuracy was independent of SF in patients. Additionally, the functional connectivity of DLPFC with parietal and visual areas was modulated by SF and response type (yes/no) in a different manner between controls and patients. While individuals without schizophrenia may flexibly adapt metacognitive computations across SF ranges, patients may employ a different mechanism that is independent of SF. Because visual stimuli of low SF have been linked to predictive top-down processing, this may reflect atypical functioning in these processes in schizophrenia

Renal mitochondrial complex in 5/6 Nx mice

Hyperglycemia induces nonconcordant regulation of renal mitochondrial respiratory complexes, increases oxidative stress, and causes diabetic nephropathy. Hypertension is a complication associated with diabetes and involves glomerular hyperfiltration, the effects of which on mitochondrial respiratory complexes are not well understood. To investigate the effect of glomerular hyperfiltration on renal mitochondrial respiratory complexes, we used the 5/6 nephrectomized BKS.Cg-Dock7m+/+Leprdb/J, Dock7m+/+Leprdb mice (db/m-5/6Nx mice) as a model for glomerular hyperfiltration. The BKS.Cg-Dock7m+/+Leprdb/J, +Leprdb/+Leprdb mice (db/db mice), a model for type 2 diabetes, was used as the positive control. We investigated the activities and protein levels of the mitochondrial complex, and themitochondrial DNA and adenosine triphosphate content in the kidneys of these models. Blood chemistry and renal histopathological examination were performed for characterization of the disease. Both models showed expansion of the mesangial matrix of the glomeruli, which is indicative of glomerular hyperfiltration. The activities of complexes I and IV and the protein levels of complexes I and III were nonconcordant in db/m-5/6Nx mice. In conclusion, we demonstrated that nonconcordant regulation of mitochondrial complexes in db/m-5/6Nx mice involved with glomerular hyperfiltration. The progression and/or severity of nephropathymight be affected through a synergistic effect of mitochondrial dysfunction in hyperglycemia and glomerular hyperfiltration

Endoscopic resection is a suitable initial treatment strategy for oxyntic gland adenoma or gastric adenocarcinoma of the fundic gland type

The aim of this study was to reveal the histological features of oxyntic gland adenomas and gastric adenocarcinoma of the fundic-gland type (GA-FG). We retrospectively examined the histological features of 126 lesions of oxyntic gland adenoma and/or GA-FG in 116 patients. The prevalence of oxyntic gland adenomas and GA-FG was approximately equal. The majority of the lesions were resected by endoscopic mucosal resection using a diathermic snare (EMR, n=42) or endoscopic submucosal dissection (ESD, n=72). Histologically, there were no lesions with invasion at the level of the muscularis propria or deeper, and lymphovascular invasion was present in 1.6%. Of the ESD and EMR specimens, there were no lesions that were positive for vertical margins. Among the eight GA-FG patients with deep (>= 500 mu m) submucosal invasion, six were treated with endoscopic resection alone, and no recurrence was documented. No patients died of the disease during the median follow-up period of 14.5 months. In conclusion, all lesions were confined to the mucosa or submucosa and were negative for vertical margins. Lymphovascular invasion was present in only 1.6% of the patients. Thus, we believe that endoscopic resection is a suitable initial treatment method for oxyntic gland adenoma and GA-FG

A Comparative Study on Transformer vs RNN in Speech Applications

Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS). This paper focuses on an emergent sequence-to-sequence model called Transformer, which achieves state-of-the-art performance in neural machine translation and other natural language processing applications. We undertook intensive studies in which we experimentally compared and analyzed Transformer and conventional recurrent neural networks (RNN) in a total of 15 ASR, one multilingual ASR, one ST, and two TTS benchmarks. Our experiments revealed various training tips and significant performance benefits obtained with Transformer for each task including the surprising superiority of Transformer in 13/15 ASR benchmarks in comparison with RNN. We are preparing to release Kaldi-style reproducible recipes using open source and publicly available datasets for all the ASR, ST, and TTS tasks for the community to succeed our exciting outcomes.Comment: Accepted at ASRU 201