A Normalization Model for Analyzing Multi-Tier Millimeter Wave Cellular Networks

Based on the distinguishing features of multi-tier millimeter wave (mmWave) networks such as different transmit powers, different directivity gains from directional beamforming alignment and path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links, we introduce a normalization model to simplify the analysis of multi-tier mmWave cellular networks. The highlight of the model is that we convert a multi-tier mmWave cellular network into a single-tier mmWave network, where all the base stations (BSs) have the same normalized transmit power 1 and the densities of BSs scaled by LOS or NLOS scaling factors respectively follow piecewise constant function which has multiple demarcation points. On this basis, expressions for computing the coverage probability are obtained in general case with beamforming alignment errors and the special case with perfect beamforming alignment in the communication. According to corresponding numerical exploration, we conclude that the normalization model for multi-tier mmWave cellular networks fully meets requirements of network performance analysis, and it is simpler and clearer than the untransformed model. Besides, an unexpected but sensible finding is that there is an optimal beam width that maximizes coverage probability in the case with beamforming alignment errors.Comment: 7 pages, 4 figure

Methyl eucomate

The crystal structure of the title compound [systematic name: methyl 3-carboxy-3-hydr­oxy-3-(4-hydroxy­benz­yl)propanoate], C12H14O6, is stabilized by inter­molecular O—H⋯O and C—H⋯O hydrogen bonds. The mol­ecules are arranged in layers, parallel to (001), which are inter­connected by the O—H⋯O hydrogen bonds

Hypericin enhances β-lactam antibiotics activity by inhibiting sarA expression in methicillin-resistant Staphylococcus aureus.

Bacteremia is a life-threating syndrome often caused by methicillin-resistant Staphylococcus aureus (MRSA). Thus, there is an urgent need to develop novel approaches to successfully treat this infection. Staphylococcal accessory regulator A (SarA), a global virulence regulator, plays a critical role in pathogenesis and β-lactam antibiotic resistance in Staphylococcus aureus. Hypericin is believed to act as an antibiotic, antidepressant, antiviral and non-specific kinase inhibitor. In the current study, we investigated the impact of hypericin on β-lactam antibiotics susceptibility and mechanism(s) of its activity. We demonstrated that hypericin significantly decreased the minimum inhibitory concentrations of β-lactam antibiotics (e.g., oxacillin, cefazolin and nafcillin), biofilm formation and fibronectin binding in MRSA strain JE2. In addition, hypericin significantly reduced sarA expression, and subsequently decreased mecA, and virulence-related regulators (e.g., agr RNAⅢ) and genes (e.g., fnbA and hla) expression in the studied MRSA strain. Importantly, the in vitro synergistic effect of hypericin with β-lactam antibiotic (e.g., oxacillin) translated into in vivo therapeutic outcome in a murine MRSA bacteremia model. These findings suggest that hypericin plays an important role in abrogation of β-lactam resistance against MRSA through sarA inhibition, and may allow us to repurpose the use of β-lactam antibiotics, which are normally ineffective in the treatment of MRSA infections (e.g., oxacillin)

TiC: Exploring Vision Transformer in Convolution

While models derived from Vision Transformers (ViTs) have been phonemically surging, pre-trained models cannot seamlessly adapt to arbitrary resolution images without altering the architecture and configuration, such as sampling the positional encoding, limiting their flexibility for various vision tasks. For instance, the Segment Anything Model (SAM) based on ViT-Huge requires all input images to be resized to 1024$\times$1024. To overcome this limitation, we propose the Multi-Head Self-Attention Convolution (MSA-Conv) that incorporates Self-Attention within generalized convolutions, including standard, dilated, and depthwise ones. Enabling transformers to handle images of varying sizes without retraining or rescaling, the use of MSA-Conv further reduces computational costs compared to global attention in ViT, which grows costly as image size increases. Later, we present the Vision Transformer in Convolution (TiC) as a proof of concept for image classification with MSA-Conv, where two capacity enhancing strategies, namely Multi-Directional Cyclic Shifted Mechanism and Inter-Pooling Mechanism, have been proposed, through establishing long-distance connections between tokens and enlarging the effective receptive field. Extensive experiments have been carried out to validate the overall effectiveness of TiC. Additionally, ablation studies confirm the performance improvement made by MSA-Conv and the two capacity enhancing strategies separately. Note that our proposal aims at studying an alternative to the global attention used in ViT, while MSA-Conv meets our goal by making TiC comparable to state-of-the-art on ImageNet-1K. Code will be released at https://github.com/zs670980918/MSA-Conv

3D Model-free Visual Localization System from Essential Matrix under Local Planar Motion

Visual localization plays a critical role in the functionality of low-cost autonomous mobile robots. Current state-of-the-art approaches for achieving accurate visual localization are 3D scene-specific, requiring additional computational and storage resources to construct a 3D scene model when facing a new environment. An alternative approach of directly using a database of 2D images for visual localization offers more flexibility. However, such methods currently suffer from limited localization accuracy. In this paper, we propose an accurate and robust multiple checking-based 3D model-free visual localization system to address the aforementioned issues. To ensure high accuracy, our focus is on estimating the pose of a query image relative to the retrieved database images using 2D-2D feature matches. Theoretically, by incorporating the local planar motion constraint into both the estimation of the essential matrix and the triangulation stages, we reduce the minimum required feature matches for absolute pose estimation, thereby enhancing the robustness of outlier rejection. Additionally, we introduce a multiple-checking mechanism to ensure the correctness of the solution throughout the solving process. For validation, qualitative and quantitative experiments are performed on both simulation and two real-world datasets and the experimental results demonstrate a significant enhancement in both accuracy and robustness afforded by the proposed 3D model-free visual localization system

A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of the high power li-ion batteries.

To ensure the reliability and sustainability of the energy storage system, it is important to accurately estimate the state of charge of the battery management system. The Li-ion battery is established based on fractional-order model, and the model parameters are identified online using particle swarm optimization combined with the forgetting factor recursive least square method. On this basis, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification is proposed. This method can update the parameter model of Li-ion battery in real-time, which not only improves the accuracy of the battery model but also improves the accuracy of SOC estimation. Finally, to verify the accuracy and superiority of the method, the integral order extended Kalman filter, fractional-order extended Kalman filter are compared with the proposed method under the BBDST test schedule. Experimental results show that the algorithm has the highest SOC estimation accuracy and the smallest estimation error (1.5 %.). The results indicate that the fractional-order model can better describe the dynamic characteristics of Li-ion battery, and the adaptive scheme can significantly suppress noise measurement errors and battery model errors. The algorithm realizes online parameter identification and can be used in engineering applications