META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection

For learning-based sound event localization and detection (SELD) methods, different acoustic environments in the training and test sets may result in large performance differences in the validation and evaluation stages. Different environments, such as different sizes of rooms, different reverberation times, and different background noise, may be reasons for a learning-based system to fail. On the other hand, acquiring annotated spatial sound event samples, which include onset and offset time stamps, class types of sound events, and direction-of-arrival (DOA) of sound sources is very expensive. In addition, deploying a SELD system in a new environment often poses challenges due to time-consuming training and fine-tuning processes. To address these issues, we propose Meta-SELD, which applies meta-learning methods to achieve fast adaptation to new environments. More specifically, based on Model Agnostic Meta-Learning (MAML), the proposed Meta-SELD aims to find good meta-initialized parameters to adapt to new environments with only a small number of samples and parameter updating iterations. We can then quickly adapt the meta-trained SELD model to unseen environments. Our experiments compare fine-tuning methods from pre-trained SELD models with our Meta-SELD on the Sony-TAU Realistic Spatial Soundscapes 2023 (STARSSS23) dataset. The evaluation results demonstrate the effectiveness of Meta-SELD when adapting to new environments.Comment: Submitted to DCASE 2023 Worksho

Effects of Temperature on the Quality of Vacuum Concentrated Pear Juice and Construction of Quality Evaluation Model

Objective: To compare the quality of vacuum concentrated pear juice at different temperatures and construct a quality evaluation method for concentrated pear juice. Method: Fresh pear juice was concentrated under decompression condition (vacuum degree 0.005 MPa) at concentration temperatures of 50, 60, 70 and 80 ℃, respectively. The evaluation model of concentrated pear juice was constructed based on the browning degree, pH, total phenolic content, total flavonoid content, Fe3+reducing power, soluble sugar, organic acid and volatile components of the concentrated pear juice. Results: The content of tartaric acid, fumaric acid and pH decreased significantly with the increasing temperature, while the browning degree, total phenolic content, total flavonoid content, Fe3+reducing power, quinic acid, malic acid, citric acid increased significantly. The content of alcohols was the highest in concentrated pear juice of 50 ℃ (4.753 μg/mL), the esters was the highest in concentrated pear juice of 70 ℃ (2.808 μg/mL), the aldehydes and ketones were the highest in concentrated pear juice of 70 ℃ (12.478 μg/mL). This study obtained a model for evaluating the quality of concentrated pear juice and found that 70 ℃ was best vacuum concentration temperature for pear juice. Conclusion: The vacuum concentration temperature could affect the quality of concentrated pear juice, which could be well distinguished by the quality evaluation model. This study would provide references for the quality control of vacuum concentrated pear juice

Factors in the occurrence and restoration of hypoparathyroidism after total thyroidectomy for thyroid cancer patients with intraoperative parathyroid autotransplantation

IntroductionPostoperative hypoparathyroidism (POH) is the most common and important complication for thyroid cancer patients who undergo total thyroidectomy. Intraoperative parathyroid autotransplantation has been demonstrated to be essential in maintaining functional parathyroid tissue, and it has clinical significance in identifying essential factors of serum parathyroid hormone (PTH) levels for patients with parathyroid autotransplantation. This retrospective cohort study aimed to comprehensively investigate influential factors in the occurrence and restoration of POH for patients who underwent total thyroidectomy with intraoperative parathyroid autotransplantation (TTIPA).MethodThis study was conducted in a tertiary referral hospital, with a total of 525 patients who underwent TTIPA. The postoperative serum PTH levels were collected after six months, and demographic characteristics, clinical features and associated operative information were analyzed.ResultsA total of 66.48% (349/525) of patients who underwent TTIPA were diagnosed with POH. Multivariate logistic regression indicated that Hashimoto’s thyroiditis (OR=1.93, 95% CI: 1.09-3.42), P=0.024), the number of transplanted parathyroid glands (OR=2.70, 95% CI: 1.91-3.83, P<0.001) and postoperative blood glucose levels (OR=1.36, 95% CI: 1.06-1.74, P=0.016) were risk factors for POH, and endoscopic surgery (OR=0.39, 95% CI: 0.22-0.68, P=0.001) was a protective factor for POH. Multivariate Cox regression indicated that PTG autotransplantation patients with same-side central lymph node dissection (CLND) (HR=0.50; 95% CI: 0.34-0.73, P<0.001) demonstrated a longer time for increases PTH, and female patients (HR=1.35, 95% CI: 1.00-1.81, P=0.047) were more prone to PTH increases. Additionally, PTG autotransplantation with same-side CLND (HR=0.56, 95% CI: 0.38-0.82, P=0.003) patients had a longer time to PTH restoration, and patients with endoscopic surgery (HR=1.54, 95% CI: 1.04-2.28, P=0.029) were more likely to recover within six months.ConclusionHigh postoperative fasting blood glucose levels, a large number of transplanted PTGs, open surgery and Hashimoto’s thyroiditis are risk factors for postoperative POH in TTIPA patients. Elevated PTH levels occur earlier in female patients and patients without CLND on the transplant side. PTH returns to normal earlier in patients without CLND and endoscopic surgery on the transplant side

MR Relaxation, Diffusion, and Stiffness Characterization of Engineered Cartilage Tissue

The primary goal of this thesis is to develop a combined MR relaxation (T2 and T1ρ), diffusion (ADC, apparent diffusion coefficient), elastography (shear stiffness) method to fully characterize the development of tissue-engineered cartilage in terms of the changes in its composition, structure, and mechanical properties during tissue growth. We do this for the purpose of understanding how we may better use MR-based methodologies to noninvasively monitor and optimize the cartilage tissue engineering process without sacrificing the constructs. While conventional T2 and ADC have been widely used in the studies of engineered cartilage tissues, there were few T1ρ and MRE studies related to it. We begin by demonstrating the potential capabilities of T2, T1ρ, ADC, and shear stiffness in characterization of a scaffold-free engineered cartilage tissue. We examine the correlations between MR parameters and biochemical determined macromolecule contents in tissue-engineered cartilage. We show that, in addition to the conventional T2 and ADC, T1ρ and MRE can also be used as potential biomarkers to assess the specific changes in proteoglycan content and mechanical properties of engineered cartilage during tissue growth. Secondly, to increase the efficiency of MR characterization of engineered tissues, we develop two new methodologies for simultaneous acquisition of MRI and MRE data: (1) diffusion and MRE (dMRE) and (2) T1ρ and MRE (T1ρ-MRE), respectively. Conventional T1ρ , diffusion, and MRE acquisitions are performed as separate measurements that prolong the imaging protocols. The dMRE and T1ρ-MRE are developed to overcome this problem by acquiring two pieces of information in one temporally resolved scan. This allows the simultaneous characterization of both biochemical and mechanical properties of engineered cartilage tissues. We carry out dMRE and T1ρ-MRE experiments on tissue-mimicking phantoms to show the feasibilities of two techniques. The results obtained show a good correspondence between simultaneous acquisitions and conventional separate acquisition methods. We expect that the combined MRI/MRE methods will benefit the optimal cartilage tissue engineering process

Bounding the Upper Delays of the Tactile Internet Using Deterministic Network Calculus

With the increasing popularity of time-sensitive network applications and the gradual integration of the Tactile Internet into people’s lives, how to ensure ultra-low latency has become a demand and challenge for network performance. Therefore, it is extremely important to analyze the performance of the Tactile Internet. In this paper, we propose an analytical model based on deterministic network calculus (DNC) to quantitatively derive the end-to-end performance bounds of the Tactile Internet, develop a tandem model describing the communication of the Tactile Internet network, and analyze delay-related traffic parameters, such as arrival rate and burst size. We investigate the variation of the accuracy of the DNC analytical model and the measurement model under different parameters, and verify the accuracy of the proposed DNC analytical model by theoretical derivation and analysis and comparison with the measurement model under the NS3 platform. We discuss the impact of relevant parameters on the delay boundaries to determine which network configuration enables the end-to-end delay to meet the established requirements. This will provide valuable guidance for the design of Tactile Internet architectures

Bounding the Upper Delays of the Tactile Internet Using Deterministic Network Calculus

With the increasing popularity of time-sensitive network applications and the gradual integration of the Tactile Internet into people’s lives, how to ensure ultra-low latency has become a demand and challenge for network performance. Therefore, it is extremely important to analyze the performance of the Tactile Internet. In this paper, we propose an analytical model based on deterministic network calculus (DNC) to quantitatively derive the end-to-end performance bounds of the Tactile Internet, develop a tandem model describing the communication of the Tactile Internet network, and analyze delay-related traffic parameters, such as arrival rate and burst size. We investigate the variation of the accuracy of the DNC analytical model and the measurement model under different parameters, and verify the accuracy of the proposed DNC analytical model by theoretical derivation and analysis and comparison with the measurement model under the NS3 platform. We discuss the impact of relevant parameters on the delay boundaries to determine which network configuration enables the end-to-end delay to meet the established requirements. This will provide valuable guidance for the design of Tactile Internet architectures

Magnetic Resonance in the Assessment of Tissue Engineered Cartilage

Magnetic resonance spectroscopy (MRS) and imaging (MRI) are routinely used for non-invasive monitoring and assessment of cartilage regeneration in vitro and in vivo. Cartilage tissue engineering utilizes a combination of three-dimensional porous scaffold, chondrocytes or stem cells, growth factors such as transforming growth factor-β, and growth stimulating conditions to obtain a neocartilage tissue that contains a high level of chondrogenic extracellular matrix proteins, proteoglycans and collagen, type II. Water proton (1H) parametric MRI is commonly applied for monitoring and assessing tissue-engineered cartilage growth at the bench and for in vivo settings. The change in relaxation times (T1, T2 and T1ρ) and apparent diffusion coefficient are correlated with the change in the amount of proteoglycan and collagen in tissueengineered cartilage. In stem cells and scaffold-based engineered cartilage, it has been shown that once the scaffold’s contribution is removed, both T1 and T2 correlate with the amount of matrix regeneration. The cartilage tissue’s functional properties depend on its special composition of extracellular matrix proteins. This arrangement of extracellular matrix is highly anisotropic and one that is the source of cartilage health. In engineered cartilage, tissue anisotropy can be measured using the sodium triple quantum coherence nuclear magnetic resonance-based average quadrupolar coupling (ωQ) or the diffusion tensor imaging based fractional anisotropy parameters. Using these techniques, it has been shown that the engineered cartilage tissues are less anisotropic than the natural cartilage. Glycosaminoglycan (GAG) of proteoglycan is negatively charged and sodium MRI can be used for assessing the GAG amount. The sodium MRI-based fixed charge density (FCD) is found to strongly correlate with the FCD derived from the GAG assay in a tissue-engineered matrix created from stem cell chondrogenesis in polymer–hydrogel hybrid scaffolds. In summary, magnetic resonance technologies offer tools to non-invasively assess the engineered cartilage tissue growth at all stages, in vitro and in vivo, from cell seeding to post-implantation