Outlier-aware Inlier Modeling and Multi-scale Scoring for Anomalous Sound Detection via Multitask Learning

This paper proposes an approach for anomalous sound detection that incorporates outlier exposure and inlier modeling within a unified framework by multitask learning. While outlier exposure-based methods can extract features efficiently, it is not robust. Inlier modeling is good at generating robust features, but the features are not very effective. Recently, serial approaches are proposed to combine these two methods, but it still requires a separate training step for normal data modeling. To overcome these limitations, we use multitask learning to train a conformer-based encoder for outlier-aware inlier modeling. Moreover, our approach provides multi-scale scores for detecting anomalies. Experimental results on the MIMII and DCASE 2020 task 2 datasets show that our approach outperforms state-of-the-art single-model systems and achieves comparable results with top-ranked multi-system ensembles.Comment: accepted at INTERSPEECH 202

Towards an Open-Source Industry CAD: A Review of System Development Methods

Due to the industry knowledge barrier, general computer aided design (CAD) software cannot do everything in digital manufacturing by itself, and industry CAD, therefore, occupies a crucial position in the CAD industry. To develop industry CAD smoothly, open-source is the best choice. We analyzed recent examples of industry CAD development and divided the development methods into four types: development based on the graphics development environment, development based on geometric modelling kernel, secondary development based on general CAD, and hybrid development. We analyzed the characteristics of various methods and believe that the method based on the hybrid development of the geometric modelling kernel and the graphics development environment is the best open-source industry CAD development method. We proposed a system architecture of open-source industry CAD for reference and conducted a preliminary exploration of the reference architecture to verify its feasibility

The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022

This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.Comment: arXiv admin note: substantial text overlap with arXiv:2109.0200

Prediction of initial objective response to drug-eluting beads transcatheter arterial chemoembolization for hepatocellular carcinoma using CT radiomics-based machine learning model

Objective: A prognostic model utilizing CT radiomics, radiological, and clinical features was developed and validated in this study to predict an objective response to initial transcatheter arterial chemoembolization with drug-eluting beads (DEB-TACE) for hepatocellular carcinoma (HCC).Methods: Between January 2017 and December 2022, the baseline clinical characteristics and preoperative and postoperative follow-up imaging data of 108 HCC patients who underwent the first time treatment of DEB-TACE were analyzed retrospectively. The training group (n = 86) and the validation group (n = 22) were randomly assigned in an 8:2 ratio. By logistic regression in machine learning, radiomics, and clinical-radiological models were constructed separately. Finally, the integrated model construction involved the integration of both radiomics and clinical-radiological signatures. The study compared the integrated model with radiomics and clinical-radiological models using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA).Results: The objective response rate observed in a group of 108 HCC patients who received initial DEB-TACE treatment was found to be 51.9%. Among the three models, the integrated model exhibited superior predictive accuracy in both the training and validation groups. The training group resulted in an area under the curve (AUC) of 0.860, along with sensitivity and specificity values of 0.650 and 0.913, respectively. Based on the findings from the validation group, the AUC was estimated to be 0.927. Additionally, it was found that values of sensitivity and specificity were 0.875 and 0.833, respectively. In the validation group, the AUC of the integrated model showed a significant improvement when contrasted to the clinical-radiological model (p = 0.042). Nevertheless, no significant distinction was observed in the AUC when comparing the integrated model with the radiomics model (p = 0.734). The DCA suggested that the integrated model demonstrates advantageous clinical utility.Conclusion: The integrated model, which combines the CT radiomics signature and the clinical-radiological signature, exhibited higher predictive efficacy than either the radiomics or clinical-radiological models alone. This suggests that during the prediction of the objective responsiveness of HCC patients to the first DEB-TACE treatment, the integrated model yields superior outcomes

Improving Methane Production During the Anaerobic Digestion of Waste Activated Sludge: Cao-ultrasonic Pretreatment and Using Different Seed Sludges

AbstractThree individual seed sludges, which domesticated by filter paper (SS1), food waste (SS2) and grease (SS3), respectively, were used for enhancing the methane production of waste activated sludge (WAS). Also CaO-ultrasonic pretreatment was performed on WAS to evaluate the effectiveness on improving efficient anaerobic digestion (AD). The results showed that WAS being acidated for 24h after CaO-ultrasonic pretreatment was an effective method for increasing initial methane production rate. The daily concentration of volatile fatty acids (VFAs) during the AD course showed that the propionic was easier to be reduced after adding seed sludge. The optimum seed sludge for improving methane production and biodegradability of WAS was SS3, which led to an increase in the methane production of 68.92% and VS reduction of 69.20% higher than the control. This pretreatment combined with adding optimum seed sludge can greatly improve clean energy generation from WAS

Cytotoxicity of hydroxydihydrobovolide and its pharmacokinetic studies in Portulaca oleracea L. extract

Hydroxydihydrobovolide (HDB) was for the first time isolated from Portulaca oleracea L. and then its cytotoxicity against SH-SYTY cells was studied. Moreover, a rapid and sensitive ultra-high performance liquid chromatographic (UHPLC) method with bergapten as internal standard (IS) was developed and validated to investigate the pharmacokinetics of HDB in rats after intravenous and oral administrations of extract (POE). The UHPLC analysis was performed on a Diamonsil C18 analytical column, using acetonitrile-water (35:65, v/v) as the mobile phase with UV detection at 220 nm. The calibration curve was linear over the range of 0.2-25 µg/mL in rat plasma. The average extraction recovery was from 90.1 to 98.9%, and the relative standard deviations (RSDs) of the intra- and inter-day precisions were less than 4.7 and 4.1%, respectively. The results showed that 50 µM HDB had significant cytotoxicity on the SH-SY5Y cells, which was rapidly distributed with a Tmax of 11 min after oral administration and presented a low absolute bioavailability, 4.12%

Tissue distribution and excretion of the five components of Portulaca oleracea L. extract in rat assessed by UHPLC

The aim of the present study was to investigate the tissue distribution and excretion of five components of Portulaca oleracea L. extract (POE) in rat following oral administration. A rapid, sensitive and specific ultra-high performance liquid chromatography (UHPLC) method with puerarin as the internal standard was used for the quantitative analysis of five components of POE, including caffeic acid (CA), p-coumaric acid (p-CA), ferulic acid (FA), quercitrin (QUER) and hesperidin (HP) in rat tissues including the liver, intestine, stomach, muscle, heart, lung, brain, kidney and spleen, urine and feces. The results show that onlyp-CA and FA were found in nearly all tissues with low cumulative ratios, and CA was higher in the intestine and stomach with a slightly higher cumulative ratio in the urine and feces after 24 h. HP and QUER were found at low levels in the tissues with low cumulative ratios.O objetivo do presente estudo foi investigar a distribuição tecidual e excreção de cinco componentes de extrato Portulaca oleracea L. (POE) em ratos após administração oral. Um método analítico rápido, sensível e específico para quantificação de cinco componentes de POE (ácido cafeico (CA), ácidop-cumárico (p-CA), ácido ferúlico (FA), quercitrina (QUER) e hesperidina (HP)) por cromatografia líquida de ultra eficiência (UHPLC), empregando puerarina como padrão interno de referência. Os compostos foram quantificados em diferentes tecidos dos animais, sendo eles fígado, intestino, estômago, músculo, coração, pulmão, cérebro, rim e baço, urina e fezes. Os resultados mostraram que apenas p-CA e FA foram encontradas em todos os tecidos com baixas taxas cumulativas e CA apresentou níveis mais altos no intestino e estômago com a taxa cumulativa um pouco mais elevada na urina e nas fezes após 24 h. HP e QUER apresentaram baixas concentrações nos tecidos com baixas taxas cumulativas