LightVessel: Exploring Lightweight Coronary Artery Vessel Segmentation via Similarity Knowledge Distillation

In recent years, deep convolution neural networks (DCNNs) have achieved great prospects in coronary artery vessel segmentation. However, it is difficult to deploy complicated models in clinical scenarios since high-performance approaches have excessive parameters and high computation costs. To tackle this problem, we propose \textbf{LightVessel}, a Similarity Knowledge Distillation Framework, for lightweight coronary artery vessel segmentation. Primarily, we propose a Feature-wise Similarity Distillation (FSD) module for semantic-shift modeling. Specifically, we calculate the feature similarity between the symmetric layers from the encoder and decoder. Then the similarity is transferred as knowledge from a cumbersome teacher network to a non-trained lightweight student network. Meanwhile, for encouraging the student model to learn more pixel-wise semantic information, we introduce the Adversarial Similarity Distillation (ASD) module. Concretely, the ASD module aims to construct the spatial adversarial correlation between the annotation and prediction from the teacher and student models, respectively. Through the ASD module, the student model obtains fined-grained subtle edge segmented results of the coronary artery vessel. Extensive experiments conducted on Clinical Coronary Artery Vessel Dataset demonstrate that LightVessel outperforms various knowledge distillation counterparts.Comment: 5 pages, 7 figures, conferenc

Heat and Mass Transfer of Droplet Vacuum Freezing Process Based on Dynamic Mesh

A numerical simulation using dynamic mesh method by COMSOL has been developed to model heat and mass transfer during vacuum freezing by evaporation of a single droplet. The initial droplet diameter, initial droplet temperature, and vacuum chamber pressure effect are studied. The surface and center temperature curve was predicted to show the effect. The mass transfer rate and radius displacement were also calculated. The results show the dynamic mesh shows well the freezing process with the radius reduction of droplet. The initial droplet diameter, initial droplet temperature, and vacuum pressure have obvious effect on freezing process. The total freezing time is about 200 s, 300 s, and 400 s for droplet diameter 7.5 mm, 10.5 mm, and 12.5 mm, respectively. The vacuum pressure less than 200 Pa is enough for the less time to freezing the droplet, that is, the key point in freezing time. The initial droplet temperature has obvious effect on freezing but little effect on freezing temperature

Heat and Mass Transfer of Vacuum Cooling for Porous Foods-Parameter Sensitivity Analysis

Based on the theory of heat and mass transfer, a coupled model for the porous food vacuum cooling process is constructed. Sensitivity analyses of the process to food density, thermal conductivity, specific heat, latent heat of evaporation, diameter of pores, mass transfer coefficient, viscosity of gas, and porosity were examined. The simulation results show that the food density would affect the vacuum cooling process but not the vacuum cooling end temperature. The surface temperature of food was slightly affected and the core temperature is not affected by the changed thermal conductivity. The core temperature and surface temperature are affected by the changed specific heat. The core temperature and surface temperature are affected by the changed latent heat of evaporation. The core temperature is affected by the diameter of pores. But the surface temperature is not affected obviously. The core temperature and surface temperature are not affected by the changed gas viscosity. The parameter sensitivity of mass transfer coefficient is obvious. The core temperature and surface temperature are affected by the changed mass transfer coefficient. In all the simulations, the end temperature of core and surface is not affected. The vacuum cooling process of porous medium is a process controlled by outside process

TrimTail: Low-Latency Streaming ASR with Simple but Effective Spectrogram-Level Length Penalty

In this paper, we present TrimTail, a simple but effective emission regularization method to improve the latency of streaming ASR models. The core idea of TrimTail is to apply length penalty (i.e., by trimming trailing frames, see Fig. 1-(b)) directly on the spectrogram of input utterances, which does not require any alignment. We demonstrate that TrimTail is computationally cheap and can be applied online and optimized with any training loss or any model architecture on any dataset without any extra effort by applying it on various end-to-end streaming ASR networks either trained with CTC loss [1] or Transducer loss [2]. We achieve 100 $\sim$ 200ms latency reduction with equal or even better accuracy on both Aishell-1 and Librispeech. Moreover, by using TrimTail, we can achieve a 400ms algorithmic improvement of User Sensitive Delay (USD) with an accuracy loss of less than 0.2.Comment: submitted to ICASSP 202

TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch

TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing well-designed, easy-to-use, and performant PyTorch components. Its contributors routinely engage with users to understand their needs and fulfill them by developing impactful features. Here, we survey TorchAudio's development principles and contents and highlight key features we include in its latest version (2.1): self-supervised learning pre-trained pipelines and training recipes, high-performance CTC decoders, speech recognition models and training recipes, advanced media I/O capabilities, and tools for performing forced alignment, multi-channel speech enhancement, and reference-less speech assessment. For a selection of these features, through empirical studies, we demonstrate their efficacy and show that they achieve competitive or state-of-the-art performance

Parameter Sensitivity of the Microdroplet Vacuum Freezing Process

The vacuum freezing process of microdroplets (<100 μm in diameter) is studied by dynamic mesh method. The mass transfer coefficient was studied using the results of related papers that considered droplet diameters exceeding 1 mm. The diameter, initial temperature, and vacuum chamber pressure effects are also discussed. To estimate parameter sensitivity, the effects of material density, specific heat, and thermal conductivity in 20% scope, as well as latent evaporation/sublimation in 5%, were simulated. The results show that the mass transfer coefficient K is essentially different between microdroplets (<100 μm) and macrodroplet (>1 mm). Pressure and droplet diameter have an effect on cooling and freezing stages, but initial temperature only affects the cooling stage. The thermal conductivity coefficient kl affected the cooling stage, whereas ki affected the freezing stage. Heat capacity Cl affected the cooling stage, but Ci has virtually no effect on all stages. The actual latent heat of freezing ΔH was also affected. Higher density corresponds to lower cooling rate in the cooling stage

The Kinetic Mechanism of the Thermal Decomposition Reaction of Small Particles of Limestone at Steelmaking Temperatures

Converter blowing limestone powder making slag steelmaking process has the advantages of low carbon and high efficiency, and can realize the resource utilization of CO2 in the metallurgical process, which is in line with the development direction of green metallurgy. Based on a thermogravimetric-differential thermal analyzer, the kinetic mechanism of decomposition of small limestone at steelmaking temperatures was investigated by a modified double extrapolation method. The results showed that with a higher rate of heating, limestone decomposition lagged, and decomposition temperature increased. Furthermore, the smaller the limestone particle size, the lower the activation energy of decomposition. Compared with N2, air, and O2, small limestone powder used for converter blowing could complete more rapid decomposition, and the time required for decomposition shortened by about 1/3, although the decomposition temperature increased in the CO2. The limestone decomposition rate increased and then decreased at low to high CO2 partial pressures. With a limiting link, the inhibition was more significant under high CO2 partial pressure, but the reaction can be fully completed by 1000 °C. The decomposition type modeled was stochastic nucleation and subsequent growth. As the partial pressure of CO2 increased from 25% to 100%, the number of reaction stages, n, increased