Hop-Reservation Multiple Access with Variable Slots

AbstractHop-reservation multiple access control protocols in Ad Hoc networks are widely researched for its virtue in anti-jamming. Several typical such protocols are introduced and compared. Based on the analysis about their performance on anti-jamming and ability to serve upper protocols, a hop-reservation multiple access protocol with variable slot (HMAVS) is proposed. By the adaptation of variable length slots, the hop speed of control channel can be supported to the largest extent while diverse applications can be served without additional cost. Simulation results demonstrate the preference of HMAVS to other existing protocols

Threshold for the Outbreak of Cascading Failures in Degree-degree Uncorrelated Networks

In complex networks, the failure of one or very few nodes may cause cascading failures. When this dynamical process stops in steady state, the size of the giant component formed by remaining un-failed nodes can be used to measure the severity of cascading failures, which is critically important for estimating the robustness of networks. In this paper, we provide a cascade of overload failure model with local load sharing mechanism, and then explore the threshold of node capacity when the large-scale cascading failures happen and un-failed nodes in steady state cannot connect to each other to form a large connected sub-network. We get the theoretical derivation of this threshold in degree-degree uncorrelated networks, and validate the effectiveness of this method in simulation. This threshold provide us a guidance to improve the network robustness under the premise of limited capacity resource when creating a network and assigning load. Therefore, this threshold is useful and important to analyze the robustness of networks.Comment: 11 pages, 4 figure

Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition

Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this paper proposes a novel domain alignment framework named the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation, multitask-assisted mask disentanglement, and domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Lastly, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework

Discovering and Explaining the Non-Causality of Deep Learning in SAR ATR

In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to non-causality. Concisely, data bias leads to comparable signal-to-clutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Our code is available at https://github.com/waterdisappear/Data-Bias-in-MSTAR

Water-saving and pollution-reducing effects of different irri-gation modes in paddy fields: A case study in Pinghu, Zhejiang province

Objective To study the water-saving and pollution reduction effects of rice under different irrigation modes, and to explore the water-saving irrigation mode suitable for the plain river network area. Methods Three modes of conventional irrigation, thin dew irrigation and suitable rain irrigation were set up in Pinghu irrigation experimental station in Zhejiang Province. The irrigation amount, TN, TP, , NO-N and COD in drainage and leakage water samples were measured. Result Compared with conventional irrigation and thin dew irrigation, the irrigation amount of suitable rain irrigation was reduced by 67.4% and 43.4%, respectively, and the water-saving effect was the best. Compared with conventional irrigation and thin dew irrigation, rain-appropriate irrigation has the least drainage. TN emissions,  emissions, COD emissions and TP and  emissions are reduced by 86.9% and 90.7%, 96.7% and 98.3%, 61.5% and 62.5%, respectively. Conclusion Under the condition of this study, the water-saving and pollution reduction effect of rain irrigation is better

CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems

In large-scale recommender systems, retrieving top N relevant candidates accurately with resource constrain is crucial. To evaluate the performance of such retrieval models, Recall@N, the frequency of positive samples being retrieved in the top N ranking, is widely used. However, most of the conventional loss functions for retrieval models such as softmax cross-entropy and pairwise comparison methods do not directly optimize Recall@N. Moreover, those conventional loss functions cannot be customized for the specific retrieval size N required by each application and thus may lead to sub-optimal performance. In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases. Furthermore, we develop the Lambda method, a gradient-based method that invites more flexibility and can further boost the system performance. We evaluate the proposed CROLoss on two public benchmark datasets. The results show that CROLoss achieves SOTA results over conventional loss functions for both datasets with various choices of retrieval size N. CROLoss has been deployed onto our online E-commerce advertising platform, where a fourteen-day online A/B test demonstrated that CROLoss contributes to a significant business revenue growth of 4.75%.Comment: 9 pages, 5 figures. Accepted by by CIKM 202

Guizhi-jia-houpu-xingzi decoction attenuates ovalbumin-induced allergic asthma via regulation of Toll-like receptor signal pathway

Purpose: To study the effect of Guizhi-jia-houpu-xingzi (GHX) on ovalbumin-induced allergic asthma in rats.Methods: An animal model of allergic asthma (AA) in rats was established by intraperitoneal injection (ip) of ovalbumin (OVA). Thereafter, GHX (375 mg/kg) was administered orally for 7 days. Pulmonary function, inflammatory cells, immunoglobulin E (Ig) E, interleukin-4 (IL)-4 and interferon-γ (IFN)-γ in serum and bronchoalveolar lavage fluids (BALF) were determined. Furthermore, mRNA expressions of Toll-like receptors (TLRs) signal pathway was determined using real time polymerase chain reaction PCR (q-RT-PCR).Results: GHX (375 mg/kg) significantly decreased respiratory rate (p < 0.01) and Penh value (p < 0.05) when compared with AA rats. The inflammatory cells (p < 0.01) and levels of IL-4 (p < 0.01) and IgE (p < 0.01) were significantly decreased by GHX treatment when compared with AA rats; whereas IFN-γ (p < 0.05) was significantly increased. Furthermore, GHX significantly decreased the mRNA expressions of GATA binding protein (GATA)-3 (p < 0.01), TRL-2 (p < 0.01), TRL-4 (p < 0.01), myeloid differentiation factor 88 (MyD88) (p < 0.01), TNF receptor associated factor 6 (TRAF6) (p < 0.01) and β-arrestin (p < 0.01) in lung tissues, relative to AA rats. However, GHX treatment led to significant up-regulation of mRNA expression of T-bet (p < 0.01).Conclusion: These results demonstrate that GHX possesses a potential for treating allergic asthma via regulation of Toll-like receptor (TLR) signal pathway. They also provide a scientific basis for the probable use of GHX in clinical treatment of allergic diseases in future.Keywords: Guizhi-jia-houpu-xingzi decoction, Ovalbumin, Allergic asthma, Toll-like recepto