An Insufficient Preoperative Diagnosis of Borrmann Type 4 Gastric Cancer in Spite of EMR

Borrmann type 4 gastric cancers are notorious for the difficulty of finding cancer cells in the biopsy samples obtained from gastrofiberscopy. It is important to obtain the biopsy results for making surgical decisions. In cases with Borrmann type 4 gastric cancer, even though the radiological findings (such as an upper gastrointestinal series, abdominal computed tomography and positron emission tomography/computed tomography) or the macroscopic findings of a gastrofiberscopy examination imply a high suspicion of cancer, there can be difficulty in getting the definite pathologic results despite multiple biopsies. In these cases, we have performed endoscopic mucosal resection under gastrofiberscopy as an alternative to simple biopsies. Here we report on a case in which no cancer cells were found even in the endoscopic mucosal resection specimen, but the radiologic evidence and clinical findings were highly suspicious for gastric cancer. The patient finally underwent total gastrectomy with lymph node resection, and she was pathologically diagnosed as having stage IV gastric cancer postoperatively

Violet-light spontaneous and stimulated emission from ultrathin In-rich InGaN/GaN multiple quantum wells grown by metalorganic chemical vapor deposition

We investigated the spontaneous and stimulated emission properties of violet-light-emitting ultrathin In-rich InGaN/GaN multiple quantum wells (MQWs) with indium content of 60%-70%. The Stokes shift was smaller than that of In-poor InGaN MQWs, and the emission peak position at 3.196 eV was kept constant with increasing pumping power, indicating negligible quantum confined Stark effect in ultrathin In-rich InGaN MQWs despite of high indium content. Optically pumped stimulated emission performed at room temperature was observed at 3.21 eV, the high-energy side of spontaneous emission, when the pumping power density exceeds ???31 kW/ cm2.open6

Disentangled dimensionality reduction for noise-robust speaker diarisation

The objective of this work is to train noise-robust speaker embeddings adapted for speaker diarisation. Speaker embeddings play a crucial role in the performance of diarisation systems, but they often capture spurious information such as noise and reverberation, adversely affecting performance. Our previous work has proposed an auto-encoder-based dimensionality reduction module to help remove the redundant information. However, they do not explicitly separate such information and have also been found to be sensitive to hyper-parameter values. To this end, we propose two contributions to overcome these issues: (i) a novel dimensionality reduction framework that can disentangle spurious information from the speaker embeddings; (ii) the use of a speech/non-speech indicator to prevent the speaker code from representing the background noise. Through a range of experiments conducted on four different datasets, our approach consistently demonstrates the state-of-the-art performance among models without system fusion.Comment: This paper was submitted to Interspeech202

Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification

In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from speaker embeddings, we introduce a novel approach using an additional embedding to represent the session information. This is achieved by training an auxiliary network appended to the speaker embedding extractor which remains fixed in this training process. This results in two similarity scores: one for the speakers information and one for the session information. The latter score acts as a compensator for the former that might be skewed due to session variations. Our extensive experiments demonstrate that session information can be effectively compensated without retraining of the embedding extractor

Large-scale learning of generalised representations for speaker recognition

The objective of this work is to develop a speaker recognition model to be used in diverse scenarios. We hypothesise that two components should be adequately configured to build such a model. First, adequate architecture would be required. We explore several recent state-of-the-art models, including ECAPA-TDNN and MFA-Conformer, as well as other baselines. Second, a massive amount of data would be required. We investigate several new training data configurations combining a few existing datasets. The most extensive configuration includes over 87k speakers' 10.22k hours of speech. Four evaluation protocols are adopted to measure how the trained model performs in diverse scenarios. Through experiments, we find that MFA-Conformer with the least inductive bias generalises the best. We also show that training with proposed large data configurations gives better performance. A boost in generalisation is observed, where the average performance on four evaluation protocols improves by more than 20%. In addition, we also demonstrate that these models' performances can improve even further when increasing capacity.Comment: 5pages, 5 tables, submitted to ICASS