Perfecting the Genetic Code with an RNP Complex

In this issue of Structure, Sekine et al. (2006) present a splendid example, using glutamyl-tRNA synthetase crystal structures, of the steps potentially taken in the transition from the RNA world to the theater of proteins

Implementation of elastic prestack reverse-time migration using an efficient finite-difference scheme

Elastic reverse-time migration (RTM) can reflect the underground elastic information more comprehensively than single-component P-wave migration. One of the most important requirements of elastic RTM is to solve wave equations. The imaging accuracy and efficiency of RTM depends heavily on the algorithms used for solving wave equations. In this paper, we propose an efficient staggered-grid finite-difference (SFD) scheme based on a sampling approximation method with adaptive variable difference operator lengths to implement elastic prestack RTM. Numerical dispersion analysis and wavefield extrapolation results show that the sampling approximation SFD scheme has greater accuracy than the conventional Taylor-series expansion SFD scheme. We also test the elastic RTM algorithm on theoretical models and a field data set, respectively. Experiments presented demonstrate that elastic RTM using the proposed SFD scheme can generate better images than that using the Taylor-series expansion SFD scheme, particularly for PS images. Furthermore, the application of adaptive variable difference operator lengths can effectively improve the computational efficiency of elastic RTM

Dual Feature Augmentation Network for Generalized Zero-shot Learning

Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. However, these methods often ignore the complex entanglement among different attributes' visual features in the embedding space. Additionally, these methods employ a direct attribute prediction scheme for classification, which does not account for the diversity of attributes in images of the same category. To address these issues, we propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules, one for visual features and the other for semantic features. The visual feature augmentation module explicitly learns attribute features and employs cosine distance to separate them, thus enhancing attribute representation. In the semantic feature augmentation module, we propose a bias learner to capture the offset that bridges the gap between actual and predicted attribute values from a dataset's perspective. Furthermore, we introduce two predictors to reconcile the conflicts between local and global features. Experimental results on three benchmarks demonstrate the marked advancement of our method compared to state-of-the-art approaches. Our code is available at https://github.com/Sion1/DFAN.Comment: Accepted to BMVC202

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Performance Comparison of Air-source Heat Pumps Using Economizer Vapor Injection and Internal Heat Exchanger in Cold Regions

Air-source heat pump (ASHP) has been widely used for residential heating and domestic hot water due to its energy saving and environmental protection as well as its high efficiency. However, with ambient temperature decreasing, the heating performance of conventional ASHP degrades rapidly which restricts its application in cold regions. In order to improve the heating performance of ASHP, this paper not only analyzes two ASHP cycles using economizer vapor injection (EVI) and internal heat exchanger (IHX) theoretically and experimentally, but also compares the heating performance of both cycles with a conventional cycle. The results showed that both EVI cycle and IHX cycle have better potential for application in low ambient temperature environments. The further study indicated that the heating capacity and power consumption of EVI cycle were higher than those of IHX cycle at the same ambient temperature while the COP of EVI cycle was lower than that of ASHP with IHX due to the different refrigerant mass flow. In addition, using EVI could reduce the discharge temperature significantly while using IHX increased the discharge temperature in comparison to the conventional ASHP. Furthermore, ambient temperature range from -15°C to -10°C can be chosen as the switch range of the two cycles to satisfy the heating performance and economy simultaneously.

GRASS: Unified Generation Model for Speech Semantic Understanding

This paper explores the instruction fine-tuning technique for speech semantic understanding by introducing a unified end-to-end (E2E) framework that generates semantic labels conditioned on a task-related prompt for audio data. We pre-train the model using large and diverse data, where instruction-speech pairs are constructed via a text-to-speech (TTS) system. Extensive experiments demonstrate that our proposed model significantly outperforms state-of-the-art (SOTA) models after fine-tuning downstream tasks. Furthermore, the proposed model achieves competitive performance in zero-shot and few-shot scenarios. To facilitate future work on instruction fine-tuning for speech-to-semantic tasks, we release our instruction dataset and code

Performance Comparison of Air-source Heat Pumps Using Economizer Vapor Injection and Internal Heat Exchanger in Cold Regions

Air-source heat pump (ASHP) has been widely used for residential heating and domestic hot water due to its energy saving and environmental protection as well as its high efficiency. However, with ambient temperature decreasing, the heating performance of conventional ASHP degrades rapidly which restricts its application in cold regions. In order to improve the heating performance of ASHP, this paper not only analyzes two ASHP cycles using economizer vapor injection (EVI) and internal heat exchanger (IHX) theoretically and experimentally, but also compares the heating performance of both cycles with a conventional cycle. The results showed that both EVI cycle and IHX cycle have better potential for application in low ambient temperature environments. The further study indicated that the heating capacity and power consumption of EVI cycle were higher than those of IHX cycle at the same ambient temperature while the COP of EVI cycle was lower than that of ASHP with IHX due to the different refrigerant mass flow. In addition, using EVI could reduce the discharge temperature significantly while using IHX increased the discharge temperature in comparison to the conventional ASHP. Furthermore, ambient temperature range from -15°C to -10°C can be chosen as the switch range of the two cycles to satisfy the heating performance and economy simultaneously.

Lattice strain effects on the optical properties of MoS2 nanosheets.

"Strain engineering" in functional materials has been widely explored to tailor the physical properties of electronic materials and improve their electrical and/or optical properties. Here, we exploit both in plane and out of plane uniaxial tensile strains in MoS2 to modulate its band gap and engineer its optical properties. We utilize X-ray diffraction and cross-sectional transmission electron microscopy to quantify the strains in the as-synthesized MoS2 nanosheets and apply measured shifts of Raman-active modes to confirm lattice strain modification of both the out-of-plane and in-plane phonon vibrations of the MoS2 nanosheets. The induced band gap evolution due to in-plane and out-of-plane tensile stresses is validated by photoluminescence (PL) measurements, promising a potential route for unprecedented manipulation of the physical, electrical and optical properties of MoS2