Effects of Different Grazing Intensities on Grasshopper Communities in \u3cem\u3eStipa breviflora\u3c/em\u3e Desert Steppe

As a common method of grassland management, grazing plays an important role in grassland conservation. Grasshoppers and large herbivores are integral parts of grassland biodiversity and food webs. However, to some extent there is still room for further research on how grasshoppers cope with grazing by large herbivores. A field grazing experiment in Stipa breviflora desert steppe in Inner Mongolia, China to investigate the effects of sheep grazing intensity on the abundance and richness of locust population and community. The grazing experiment started in 2004, and the grasshopper population and community were investigated in 2021. The results showed that: Grazing results in the change of dominant species of grasshopper in desert steppe from the C.abbreviatus to the O.asiaticus. Grazing intensity increases the abundance of dominant species, thus increasing the risk of grasshopper outbreaks. Our results suggest that light grazing by sheep is a beneficial management practice to maintain locust species diversity, but at the same time keep the abundance lo

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency

Look at Adjacent Frames: Video Anomaly Detection without Offline Training

We propose a solution to detect anomalous events in videos without the need to train a model offline. Specifically, our solution is based on a randomly-initialized multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information. Based on the information shifts between adjacent frames, an incremental learner is used to update parameters of the multilayer perceptron after observing each frame, thus allowing to detect anomalous events along the video stream. Traditional solutions that require no offline training are limited to operating on videos with only a few abnormal frames. Our solution breaks this limit and achieves strong performance on benchmark datasets.Comment: Accepted in ECCV 2022 RW

Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urgency, heterogeneous meteorological sensors across countries and regions, inevitably causing multivariate heterogeneity and data exposure, become the main barrier. This paper develops a foundation model across regions capable of understanding complex meteorological data and providing weather forecasting. To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data. Moreover, a novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints. The effectiveness of the proposed method has been demonstrated on classical weather forecasting tasks using three meteorological datasets with multivariate time series.Comment: 10 pages, 4 figures, 8 table

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms

Recognition of protein/gene names from text using an ensemble of classifiers

This paper proposes an ensemble of classifiers for biomedical name recognition in which three classifiers, one Support Vector Machine and two discriminative Hidden Markov Models, are combined effectively using a simple majority voting strategy. In addition, we incorporate three post-processing modules, including an abbreviation resolution module, a protein/gene name refinement module and a simple dictionary matching module, into the system to further improve the performance. Evaluation shows that our system achieves the best performance from among 10 systems with a balanced F-measure of 82.58 on the closed evaluation of the BioCreative protein/gene name recognitiontask (Task 1A)

Localizing noncooperative receiver through full-duplex amplify-and-forward relay

Localizing noncooperative transmitter (Tx) and receiver (Rx) that belong to another system is important in many scenarios, e.g., interference management in cognitive radio systems and user behavior learning in ad hoc wireless networks. However, obtaining the locations of these nodes in particular in frequency-division duplex systems is challenging, since the localization network usually does not know the spectrum that the Rx uses for backward transmission. In this paper, we propose to use the full-duplex relay technique to localize a noncooperative Rx, which does not require the knowledge of the Rx’s backward transmission spectrum. In the proposed method, localization sensors alternatively act as a full-duplex amplify-and-forward relay to trigger the power control of the Tx–Rx link. Then, by detecting the power adjustment of the Tx, each localization sensor can estimate the time difference of arrival between the direct and relay signals. Finally, the Rx location can be calculated from triangulation. Simulation results show that the proposed method can effectively localize the Rx, which validates its potential for receiver-aware applications and services