CheapNET: Improving Light-weight speech enhancement network by projected loss function

Noise suppression and echo cancellation are critical in speech enhancement and essential for smart devices and real-time communication. Deployed in voice processing front-ends and edge devices, these algorithms must ensure efficient real-time inference with low computational demands. Traditional edge-based noise suppression often uses MSE-based amplitude spectrum mask training, but this approach has limitations. We introduce a novel projection loss function, diverging from MSE, to enhance noise suppression. This method uses projection techniques to isolate key audio components from noise, significantly improving model performance. For echo cancellation, the function enables direct predictions on LAEC pre-processed outputs, substantially enhancing performance. Our noise suppression model achieves near state-of-the-art results with only 3.1M parameters and 0.4GFlops/s computational load. Moreover, our echo cancellation model outperforms replicated industry-leading models, introducing a new perspective in speech enhancement

Spike-driven Transformer

Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option due to their unique spike-based event-driven (i.e., spike-driven) paradigm. In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition. Together, there are only sparse addition operations in the Spike-driven Transformer. To this end, we design a novel Spike-Driven Self-Attention (SDSA), which exploits only mask and addition operations without any multiplication, and thus having up to $87.2\times$ lower computation energy than vanilla self-attention. Especially in SDSA, the matrix multiplication between Query, Key, and Value is designed as the mask operation. In addition, we rearrange all residual connections in the vanilla Transformer before the activation functions to ensure that all neurons transmit binary spike signals. It is shown that the Spike-driven Transformer can achieve 77.1\% top-1 accuracy on ImageNet-1K, which is the state-of-the-art result in the SNN field. The source code is available at https://github.com/BICLab/Spike-Driven-Transformer

Cultivation and Characterization of Pulmonary Microvascular Endothelial Cells from Chicken Embryos

To improve the understanding on the biological properties of endothelial cells (ECs), a method for the isolation and identification in vitro culture of avian pulmonary microvascular endothelial cells (PMVECs) is described. The isolated and cultured cells from chick embryos were identified by cellular morphology and immunocytochemistry. The results showed that the cultured cells exhibited typical cobblestone morphology viewed under an inverted microscope; and were bound with Bandeiraea simplicifolia lectin and stained positive for CD31 and factor VIII-related antigen. In conclusion, the findings of present study for the isolation and cultivation of PMVECs may allow more detailed analysis of their biological properties, and provide a valuable model for studying pathological processes including pulmonary hypertension, ascites and pulmonary vascular remodeling in broiler chickens

Inherent Redundancy in Spiking Neural Networks

Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs' redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in \url{https://github.com/BICLab/ASA-SNN}.Comment: Accepted by ICCV202

Effect of Total Flavonoids of Rhizoma drynariae on Tibial Dyschondroplasia by Regulating BMP-2 and Runx2 Expression in Chickens

Tibial dyschondroplasia (TD) is an abnormality of the growth cartilage that occurs in chickens and other rapidly growing avian species. This disease not only cause huge economic losses, but also greatly affects animal welfare. The total flavonoids of Rhizoma drynariae (TFRD) has been used to cure wide variety of diseases including bone fractures and osteoarthritis and osteoporosis. However, less information is available about the using of TFRD against the TD. The aim of this study was to determine the effect of TFRD on TD by regulating BMP-2 and Runx2 in chickens. A total of 200 birds were randomly divided into control, TD, TD recovery (TDR), and TFRD groups. All the groups were given standard diet with an addition of thiram (50 mg/kg) from days 3 to 7 in TD, TDR, and TFRD groups in order to induce TD in chickens. After the induction of TD, the birds of TFRD group were fed standard diet with the addition of TFRD at 20 mg/kg. Clinical results conveyed that TFRD can improve the growth performance of the TD chickens and recover normal activity, and it is more obvious than TDR. Gene expressions of BMP-2 and Runx2 were down-regulated during the development of the disease and were up-regulated obviously after TFRD treatment. In conclusion, TFRD not only decreased the mortality rate but also increased the growth performance of TD in chickens. In conclusion, TFRD plays important role in improving the growth performance, adjusting the relevant physiological indicators, and regulating BMP-2 and Runx2 in chickens

Landslide displacement prediction from on-site deformation data based on time series ARIMA model

Time series Autoregressive Integrated Moving Average (ARIMA) model is often used in landslide prediction and forecasting. However, few conditions have been suggested for the application of ARIMA models in landslide displacement prediction. This paper summarizes the distribution law of the tangential angle in different time periods and analyzes the landslide displacement data by combining wavelet transform. It proposes an applicable condition for the ARIMA model in the field of landslide prediction: when the landslide deformation is in the initial deformation to initial acceleration stage, i.e., the tangential angle of landslide displacement is less than 80°, the ARIMA model has higher prediction accuracy for 24-h landslide displacement data. The prediction results are RMSE = 4.52 mm and MAPE = 2.39%, and the prediction error increases gradually with time. Meanwhile, the ARIMA model was used to predict the 24-h displacements from initial deformation to initial acceleration deformation for the landslide in Guangna Township and the landslide in Libian Gully, and the prediction results were RMSE = 1.24 mm, MAPE = 1.34% and RMSE = 5.43 mm, MAPE = 1.67%, which still maintained high accuracy and thus verified this applicable condition. At the same time, taking the landslide of Libian Gully as an example, the ARIMA model was used to test the displacement prediction effect of the landslide in the Medium-term acceleration stage and the Imminent sliding stage (the tangential angle of landslide displacement is 80° and 85°, respectively). The relative error of displacement data prediction in the Medium-term acceleration stage is within 3%, while the relative error of the prediction value in the Imminent sliding stage is more than 3%, and the error gradually increases with time. This demonstrates that the relative error of the ARIMA model in landslide prediction and forecasting is within 3%. The relative error of the prediction value in the Imminent sliding stage is above 3%, and the error increases gradually with time. Meanwhile, the prediction results are analyzed and it is concluded that the increase in prediction time and tangential angles are the main reasons for the increase in error. The applicable conditions proposed in this study can provide a reference for the application of ARIMA model in landslide prediction and forecast

Preliminary Research on Grid-based Remote Sensing Image distributed Processing

Massive remote sensing image fast processing is one of the important tasks for remote sensing image applications, it is not only data intensive but also computation intensive, which arises our interest in distributed parallel processing by grid computing., The novel grid computing technology seeks to aggregate computing resources which are geographically distributed or heterogeneous for computational intensive applications. In this paper, a grid-based image processing testbed is established for remote sensing image distributed processing, and middleware is developed, which was applied to remote sensing image distributed deblurring processing. Preliminary experimental results show the processing efficiency is greatly improved, which demonstrate the promising potential of grid computing technology on remote sensing image distributed processing.Computer Science, Hardware & ArchitectureComputer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)

Development and application of a vegetation degradation classification approach for the temperate grasslands of northern China

To establish a unified grassland degradation assessment system, this study proposed a classification approach using a statistical method based on the percent decrease in the annual maximum leaf area index from a long time series of remote sensing data. The obtained classification thresholds for vegetation degradation were temporally stable and spatially universal and were thus well-suited as a new standard. They were specific for different temperate grassland types and applicable to large and heterogeneous areas. Meadow steppes had the lowest degradation thresholds, sensitive to changes in plant growth, desert-related steppes had the highest thresholds, and typical steppes had intermediate thresholds. We then used the obtained thresholds to assess the degradation status of temperate grasslands in northern China from 1981 to 2017. Degraded grasslands accounted for 8.2 % (1.72 × 107 ha), of which moderately, severely, and extremely severely degraded grasslands accounted for 38.6, 34.9, and 4.6 %, respectively. More than half (∼67 %) of the degraded grasslands in northern China occurred in the Inner Mongolia Autonomous Region, and these were dominated by severely degraded grasslands. About 20 % of all degraded grasslands in northern China occurred in the Xinjiang Uygur Autonomous Region, and were dominated by moderate degradation. These results were highly consistent with the Chinese national standard regarding the total degraded area. However, differences occurred in specific grassland degradation levels and grassland types. Therefore, compared with traditional approaches, the new approach provided a more accurate degradation assessment, which is crucial for protecting and restoring the different temperate grasslands of China

Plastrum Testudinis Extract Mitigates Thiram Toxicity in Broilers via Regulating PI3K/AKT Signaling

Tibial dyschondroplasia (TD) negatively affects broilers all over the world, in which the accretion of the growth plate (GP) develops into tibial proximal metaphysis. Plastrum testudinis extract (PTE) is renowned as a powerful antioxidant, anti-inflammatory, and bone healing agent. The current study was conducted to evaluate the efficacy of PTE for the treatment of thiram-induced TD chickens. Broilers (day old; n = 300) were raised for 3 days with normal feed. On the 4th day, three groups (n = 100 each) were sorted, namely, the control (normal diet), TD, and PTE groups (normal diet+ thiram 50 mg/kg). On the 7th day, thiram was stopped in the TD and PTE group, and the PTE group received a normal diet and PTE (30 mg/kg/day). Plastrum testudinis extract significantly restored (p < 0.05) the liver antioxidant enzymes, inflammatory cytokines, serum biochemicals, GP width, and tibia weight as compared to the TD group. The PTE administration significantly increased (p < 0.05) growth performance, vascularization, AKT (serine/threonine-protein kinase), and PI3K expressions and the number of hepatocytes and chondrocytes with intact nuclei were enhanced. In conclusion, PTE has the potential to heal TD lesions and act as an antioxidant and anti-inflammatory drug in chickens exposed to thiram via the upregulation of AKT and PI3K expressions

Prediction of Bioluminescent Proteins Using Auto Covariance Transformation of Evolutional Profiles

Bioluminescent proteins are important for various cellular processes, such as gene expression analysis, drug discovery, bioluminescent imaging, toxicity determination, and DNA sequencing studies. Hence, the correct identification of bioluminescent proteins is of great importance both for helping genome annotation and providing a supplementary role to experimental research to obtain insight into bioluminescent proteins’ functions. However, few computational methods are available for identifying bioluminescent proteins. Therefore, in this paper we develop a new method to predict bioluminescent proteins using a model based on position specific scoring matrix and auto covariance. Tested by 10-fold cross-validation and independent test, the accuracy of the proposed model reaches 85.17% for the training dataset and 90.71% for the testing dataset respectively. These results indicate that our predictor is a useful tool to predict bioluminescent proteins. This is the first study in which evolutionary information and local sequence environment information have been successfully integrated for predicting bioluminescent proteins. A web server (BLPre) that implements the proposed predictor is freely available