MDFL: Multi-domain Diffusion-driven Feature Learning

High-dimensional images, known for their rich semantic information, are widely applied in remote sensing and other fields. The spatial information in these images reflects the object's texture features, while the spectral information reveals the potential spectral representations across different bands. Currently, the understanding of high-dimensional images remains limited to a single-domain perspective with performance degradation. Motivated by the masking texture effect observed in the human visual system, we present a multi-domain diffusion-driven feature learning network (MDFL) , a scheme to redefine the effective information domain that the model really focuses on. This method employs diffusion-based posterior sampling to explicitly consider joint information interactions between the high-dimensional manifold structures in the spectral, spatial, and frequency domains, thereby eliminating the influence of masking texture effects in visual models. Additionally, we introduce a feature reuse mechanism to gather deep and raw features of high-dimensional data. We demonstrate that MDFL significantly improves the feature extraction performance of high-dimensional data, thereby providing a powerful aid for revealing the intrinsic patterns and structures of such data. The experimental results on three multi-modal remote sensing datasets show that MDFL reaches an average overall accuracy of 98.25%, outperforming various state-of-the-art baseline schemes. The code will be released, contributing to the computer vision community

Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching

Considering the computation complexity, we propose a Guided Hybrid Quantization with One-to-one Self-Teaching (GHOST}) framework. More concretely, we first design a structure called guided quantization self-distillation (GQSD), which is an innovative idea for realizing lightweight through the synergy of quantization and distillation. The training process of the quantization model is guided by its full-precision model, which is time-saving and cost-saving without preparing a huge pre-trained model in advance. Second, we put forward a hybrid quantization (HQ) module to obtain the optimal bit width automatically under a constrained condition where a threshold for distribution distance between the center and samples is applied in the weight value search space. Third, in order to improve information transformation, we propose a one-to-one self-teaching (OST) module to give the student network a ability of self-judgment. A switch control machine (SCM) builds a bridge between the student network and teacher network in the same location to help the teacher to reduce wrong guidance and impart vital knowledge to the student. This distillation method allows a model to learn from itself and gain substantial improvement without any additional supervision. Extensive experiments on a multimodal dataset (VEDAI) and single-modality datasets (DOTA, NWPU, and DIOR) show that object detection based on GHOST outperforms the existing detectors. The tiny parameters (<9.7 MB) and Bit-Operations (BOPs) (<2158 G) compared with any remote sensing-based, lightweight or distillation-based algorithms demonstrate the superiority in the lightweight design domain. Our code and model will be released at https://github.com/icey-zhang/GHOST.Comment: This article has been delivered to TRGS and is under revie

SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representations for objects separated from the background, which often results in a heavy computation burden. In this article, we propose an accurate yet fast object detection method for RSI, named SuperYOLO, which fuses multimodal data and performs high-resolution (HR) object detection on multiscale objects by utilizing the assisted super resolution (SR) learning and considering both the detection accuracy and computation cost. First, we utilize a symmetric compact multimodal fusion (MF) to extract supplementary information from various data for improving small object detection in RSI. Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy. Moreover, to avoid introducing additional computation, the SR branch is discarded in the inference stage, and the computation of the network model is reduced due to the LR input. Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO achieves an accuracy of 75.09% (in terms of mAP50 ), which is more than 10% higher than the SOTA large models, such as YOLOv5l, YOLOv5x, and RS designed YOLOrs. Meanwhile, the parameter size and GFLOPs of SuperYOLO are about 18 times and 3.8 times less than YOLOv5x. Our proposed model shows a favorable accuracy and speed tradeoff compared to the state-of-the-art models. The code will be open-sourced at https://github.com/icey-zhang/SuperYOLO.Comment: The article is accepted by IEEE Transactions on Geoscience and Remote Sensin

Cooling and Crack Suppression of Bone Material Drilling Based on Microtextured Bit Modeled on Dung Beetle

In recent years, the number of patients with orthopedic diseases such as cervical spondylosis has increased, resulting in an increase in the demand for orthopedic surgery. However, thermal necrosis and bone cracks caused by surgery severely restrict the development and progression of orthopedic surgery. For the material of cutting tool processing bone in bone surgery of drilling high temperature lead to cell death, easy to produce the problem such as crack cause secondary damage effects to restore, in this paper, a bionic drill was designed based on the micro-structure of the dung beetle’s head and back. The microstructure configuration parameters were optimized by numerical analysis, and making use of the optical fiber laser marking machine preparation of bionic bit; through drilling test, the mathematical model of drilling temperature and crack generation based on micro-structure characteristic parameters was established by infrared thermal imaging technology and acoustic emission signal technology, and the cooling mechanism and crack suppression strategy were studied. The experimental results show that when the speed is 60 m/min, the cooling effects of the bionic bit T1 and T2 are 15.31% and 19.78%, respectively, and both kinds of bits show obvious crack suppression effect. The research in this paper provides a new idea for precision and efficient machining of bone materials, and the research results will help to improve the design and manufacturing technology and theoretical research level in the field of bone drilling tools

Magnetic Tunnel Junction-Based On-Chip Microwave Phase and Spectrum Analyzer

A magnetic tunnel junction (MTJ)-based microwave detector is proposed and investigated. When the MTJ is excited by microwave magnetic fields, the relative angle between the free layer and pinned layer alternates, giving rise to an average resistance change. By measuring the average resistance change, the MTJ can be utilized as a microwave power sensor. Due to the nature of ferromagnetic resonance, the frequency of an incident microwave is directly determined. In addition, by integrating a mixer circuit, the MTJ-based microwave detector can also determine the relative phase between two microwave signals. Thus, the MTJbased microwave detector can be used as an on-chip microwave phase and spectrum analyzer

Xenotime-type high-entropy (Dy1/7Ho1/7Er1/7Tm1/7Yb1/7Lu1/7Y1/7)PO4: A promising thermal/environmental barrier coating material for SiCf/SiC ceramic matrix composites

Rare-earth phosphates (REPO4) are regarded as one of the promising thermal/environmental barrier coating (T/EBC) materials for SiCf/SiC ceramic matrix composites (SiC-CMCs) owing to their excellent resistance to water vapor and CaO–MgO–Al2O3–SiO2 (CMAS). Nevertheless, a relatively high thermal conductivity (κ) of the REPO4 becomes the bottleneck for their practical applications. In this work, novel xenotime-type high-entropy (Dy1/7Ho1/7Er1/7Tm1/7Yb1/7Lu1/7Y1/7)PO4 (HE (7RE1/7)PO4) has been designed and synthesized for the first time to solve this issue. HE (7RE1/7)PO4 with a homogeneous rare-earth element distribution exhibits high thermal stability up to 1750 ℃ and good chemical compatibility with SiO2 up to 1400 ℃. In addition, the thermal expansion coefficient (TEC) of HE (7RE1/7)PO4 (5.96×10−6 ℃−1 from room temperature (RT) to 900 ℃) is close to that of the SiC-CMCs. What is more, the thermal conductivities of HE (7RE1/7)PO4 (from 4.38 W·m−1·K−1 at 100 ℃ to 2.25 W·m−1·K−1 at 1300 ℃) are significantly decreased compared to those of single-component REPO4 with the minimum value ranging from 9.90 to 4.76 W·m−1·K−1. These results suggest that HE (7RE1/7)PO4 has the potential to be applied as the T/EBC materials for the SiC-CMCs in the future

Global epidemiology and clinical outcomes of carbapenem-resistant Pseudomonas aeruginosa and associated carbapenemases (POP): a prospective cohort study

BACKGROUND: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global threat, but the distribution and clinical significance of carbapenemases are unclear. The aim of this study was to define characteristics and outcomes of CRPA infections and the global frequency and clinical impact of carbapenemases harboured by CRPA. METHODS: We conducted an observational, prospective cohort study of CRPA isolated from bloodstream, respiratory, urine, or wound cultures of patients at 44 hospitals (10 countries) between Dec 1, 2018, and Nov 30, 2019. Clinical data were abstracted from health records and CRPA isolates were whole-genome sequenced. The primary outcome was 30-day mortality from the day the index culture was collected. We compared outcomes of patients with CRPA infections by infection type and across geographic regions and performed an inverse probability weighted analysis to assess the association between carbapenemase production and 30-day mortality. FINDINGS: We enrolled 972 patients (USA n=527, China n=171, south and central America n=127, Middle East n=91, Australia and Singapore n=56), of whom 581 (60%) had CRPA infections. 30-day mortality differed by infection type (bloodstream 21 [30%] of 69, respiratory 69 [19%] of 358, wound nine [14%] of 66, urine six [7%] of 88; p=0·0012) and geographical region (Middle East 15 [29%] of 52, south and central America 20 [27%] of 73, USA 60 [19%] of 308, Australia and Singapore three [11%] of 28, China seven [6%] of 120; p=0·0002). Prevalence of carbapenemase genes among CRPA isolates also varied by region (south and central America 88 [69%] of 127, Australia and Singapore 32 [57%] of 56, China 54 [32%] of 171, Middle East 27 [30%] of 91, USA ten [2%] of 527; p\u3c0·0001). KPC-2 (n=103 [49%]) and VIM-2 (n=75 [36%]) were the most common carbapenemases in 211 carbapenemase-producing isolates. After excluding USA patients, because few US isolates had carbapenemases, patients with carbapenemase-producing CRPA infections had higher 30-day mortality than those with non-carbapenemase-producing CRPA infections in both unadjusted (26 [22%] of 120 vs 19 [12%] of 153; difference 9%, 95% CI 3-16) and adjusted (difference 7%, 95% CI 1-14) analyses. INTERPRETATION: The emergence of different carbapenemases among CRPA isolates in different geographical regions and the increased mortality associated with carbapenemase-producing CRPA infections highlight the therapeutic challenges posed by these organisms. FUNDING: National Institutes of Health

Epidemiología mundial y resultados clínicos de Pseudomonas aeruginosa resistente a carbapenemes y carbapenemasas asociadas (POP): un estudio prospectivo de cohortes

Antecedentes: La Pseudomonas aeruginosa resistente a los carbapenemes (CRPA) es una amenaza mundial, pero la distribución y la importancia clínica de las carbapenemasas no están claras. El objetivo de este estudio fue definir las características y los resultados de las infecciones por CRPA, así como la frecuencia global y el impacto clínico de las carbapenemasas albergadas por CRPA. Métodos: Llevamos a cabo un estudio de cohortes observacional y prospectivo de CRPA aislados de cultivos de torrente sanguíneo, respiratorio, orina o heridas de pacientes en 44 hospitales (10 países) entre el 1 de diciembre de 2018 y el 30 de noviembre de 2019. Los datos clínicos se extrajeron de los registros de salud y los aislados de CRPA se secuenciaron en todo el genoma. El resultado primario fue la mortalidad a 30 días a partir del día en que se recolectó el cultivo índice. Se compararon los resultados de los pacientes con infecciones por CRPA por tipo de infección y entre regiones geográficas y se realizó un análisis ponderado de probabilidad inversa para evaluar la asociación entre la producción de carbapenemasas y la mortalidad a 30 días. Resultados: Se incluyeron 972 pacientes (EE.UU. n=527, China n=171, América del Sur y Central n=127, Oriente Medio n=91, Australia y Singapur n=56), de los cuales 581 (60%) tenían infecciones por CRPA. La mortalidad a los 30 días difería según el tipo de infección (torrente sanguíneo 21 [30%] de 69, respiratoria 69 [19%] de 358, herida nueve [14%] de 66, orina seis [7%] de 88; p=0-0012) y la región geográfica (Oriente Medio 15 [29%] de 52, América del Sur y Central 20 [27%] de 73, EE.UU. 60 [19%] de 308, Australia y Singapur tres [11%] de 28, China siete [6%] de 120; p=0-0002). La prevalencia de genes carbapenemasa entre los aislados CRPA también varió según la región (América del Sur y Central 88 [69%] de 127, Australia y Singapur 32 [57%] de 56, China 54 [32%] de 171, Oriente Medio 27 [30%] de 91, EE.UU. diez [2%] de 527; p<0-0001). KPC-2 (n=103 [49%]) y VIM-2 (n=75 [36%]) fueron las carbapenemasas más comunes en 211 aislados productores de carbapenemasas. Después de excluir a los pacientes de EE.UU., porque pocos aislados de EE.UU. tenían carbapenemasas, los pacientes con infecciones por CRPA productoras de carbapenemasas tuvieron una mayor mortalidad a los 30 días que aquellos con infecciones por CRPA no productoras de carbapenemasas, tanto en los análisis no ajustados (26 [22%] de 120 frente a 19 [12%] de 153; diferencia 9%, IC 95% 3-16) como ajustados (diferencia 7%, IC 95% 1-14). Interpretación: La aparición de diferentes carbapenemasas entre los aislados de CRPA en diferentes regiones geográficas y el aumento de la mortalidad asociada a las infecciones por CRPA productores de carbapenemasas ponen de manifiesto los retos terapéuticos que plantean estos organismos. Financiación: Institutos Nacionales de Salud.Background: Carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global threat, but the distribution and clinical significance of carbapenemases are unclear. The aim of this study was to define characteristics and outcomes of CRPA infections and the global frequency and clinical impact of carbapenemases harboured by CRPA. Methods: We conducted an observational, prospective cohort study of CRPA isolated from bloodstream, respiratory, urine, or wound cultures of patients at 44 hospitals (10 countries) between Dec 1, 2018, and Nov 30, 2019. Clinical data were abstracted from health records and CRPA isolates were whole-genome sequenced. The primary outcome was 30-day mortality from the day the index culture was collected. We compared outcomes of patients with CRPA infections by infection type and across geographic regions and performed an inverse probability weighted analysis to assess the association between carbapenemase production and 30-day mortality. Findings: We enrolled 972 patients (USA n=527, China n=171, south and central America n=127, Middle East n=91, Australia and Singapore n=56), of whom 581 (60%) had CRPA infections. 30-day mortality differed by infection type (bloodstream 21 [30%] of 69, respiratory 69 [19%] of 358, wound nine [14%] of 66, urine six [7%] of 88; p=0·0012) and geographical region (Middle East 15 [29%] of 52, south and central America 20 [27%] of 73, USA 60 [19%] of 308, Australia and Singapore three [11%] of 28, China seven [6%] of 120; p=0·0002). Prevalence of carbapenemase genes among CRPA isolates also varied by region (south and central America 88 [69%] of 127, Australia and Singapore 32 [57%] of 56, China 54 [32%] of 171, Middle East 27 [30%] of 91, USA ten [2%] of 527; p<0·0001). KPC-2 (n=103 [49%]) and VIM-2 (n=75 [36%]) were the most common carbapenemases in 211 carbapenemase-producing isolates. After excluding USA patients, because few US isolates had carbapenemases, patients with carbapenemase-producing CRPA infections had higher 30-day mortality than those with non-carbapenemase-producing CRPA infections in both unadjusted (26 [22%] of 120 vs 19 [12%] of 153; difference 9%, 95% CI 3–16) and adjusted (difference 7%, 95% CI 1–14) analyses. Interpretation: The emergence of different carbapenemases among CRPA isolates in different geographical regions and the increased mortality associated with carbapenemase-producing CRPA infections highlight the therapeutic challenges posed by these organisms. Funding: National Institutes of Health

Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery

Anomaly detection of hyperspectral imagery (HSI) identifies the very few samples that do not conform to an intricate background without priors. Despite the extensive success of hyperspectral interpretation techniques based on generative adversarial networks (GANs), applying trained GAN models to hyperspectral anomaly detection remains promising but challenging. Previous generative models can accurately learn the complex background distribution of HSI and typically convert the high-dimensional data back to the latent space to extract features to detect anomalies. However, both background modeling and feature-extraction methods can be improved to become ideal in terms of the modeling power and reconstruction consistency capability. In this work, we present a multi-prior-based network (MPN) to incorporate the well-trained GANs as effective priors to a general anomaly-detection task. In particular, we introduce multi-scale covariance maps (MCMs) of precise second-order statistics to construct multi-scale priors. The MCM strategy implicitly bridges the spectral- and spatial-specific information and fully represents multi-scale, enhanced information. Thus, we reliably and adaptively estimate the HSI label to alleviate the problem of insufficient priors. Moreover, the twin least-square loss is imposed to improve the generative ability and training stability in feature and image domains, as well as to overcome the gradient vanishing problem. Last but not least, the network, enforced with a new anomaly rejection loss, establishes a pure and discriminative background estimation