Towards a deep-learning-based framework of sentinel-2 imagery for automated active fire detection

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019-2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km(2) (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans

High‐Performance Pseudocubic Thermoelectric Materials from Non‐cubic Chalcopyrite Compounds

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/107585/1/adma201400058-sup-0001-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/107585/2/adma201400058.pd

C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference

Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer from high computational and communication costs due to the expensive multi-party computation (MPC) protocols. Existing literature has developed lighter MPC protocols to yield more efficient PI schemes. We, in contrast, propose to lighten them by introducing an empirically-defined privacy evaluation. To that end, we reformulate the threat model of PI and use inference data privacy attacks (IDPAs) to evaluate data privacy. We then present an enhanced IDPA, named distillation-based inverse-network attack (DINA), for improved privacy evaluation. Finally, we leverage the findings from DINA and propose C2PI, a two-party PI framework presenting an efficient partitioning of the neural network model and requiring only the initial few layers to be performed with MPC protocols. Based on our experimental evaluations, relaxing the formal data privacy guarantees C2PI can speed up existing PI frameworks, including Delphi [1] and Cheetah [2], up to 2.89x and 3.88x under LAN and WAN settings, respectively, and save up to 2.75x communication costs

Entropy as a Gene‐Like Performance Indicator Promoting Thermoelectric Materials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/1/adma201702712.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/2/adma201702712-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138909/3/adma201702712_am.pd

High frequency CMOS amplifier with improved linearity

In this paper, a novel amplifier linearisation technique based on the negative impedance compensation is presented. As demonstrated by using Volterra model, the proposed technique is suitable for linearising amplifiers with low open-loop gain, which is appropriate for RF/microwave applications. A single-chip CMOS amplifier has been designed using the proposed method, and the simulation results show that high gain accuracy (improved by 38%) and high linearity (IMD3 improved by 14 dB, OIP3 improved by 11 dB and adjacent channel power ratio (ACPR) improved by 44% for CDMA signal) can be achieved

Environmental drivers of the leaf nitrogen and phosphorus stoichiometry characteristics of critically endangered Acer catalpifolium

Acer catalpifolium is a perennial deciduous broad-leaved woody plant, listed in the second-class protection program in China mainly distributed on the northwest edge of Chengdu plain. However, extensive anthropogenic disturbances and pollutants emissions (such as SO2, NH3 and NOX) in this area have created a heterogeneous habitat for this species and its impacts have not been systematically studied. In this study, we investigated the leaf nitrogen (N) and phosphorus (P) content of A. catalpifolium in the natural distribution areas, and a series of simulation experiments (e.g., various water and light supply regimes, different acid and N deposition levels, reintroduction management) were conducted to analyze responses of N and P stoichiometric characteristics to environmental changes. The results showed that leaf nitrogen content (LNC) was 14.49 ~ 25.44 mg g-1, leaf phosphorus content (LPC) was 1.29~3.81 mg g-1 and the N/P ratio of the leaf (L-N/P) was 4.87~13.93. As per the simulation experiments, LNC of A. catalpifolium is found to be relatively high at strong light conditions (80% of full light), high N deposition (100 and 150 kg N ha-1), low acidity rainwater, reintroduction to understory area or N fertilizer applications. A high level of LPC was found when applied with 80% of full light and moderate N deposition (100 kg N ha-1). L-N/P was high under severe shade (8% of full light), severe N deposition (200 kg N ha-1), and reintroduction to gap and undergrowth habitat; however, low L-N/P was observed at low acidity rainwater or P fertilizer application. The nutrient supply facilitates corresponding elements uptake, shade tends to induce P limitation and soil acidification shows N limitation. Our results provide theoretical guidance for field management and nutrient supply regimes for future protection, population rejuvenation of this species and provide guidelines for conservation and nutrient management strategies for the endangered species

Model-enhanced Vector Index

Embedding-based retrieval methods construct vector indices to search for document representations that are most similar to the query representations. They are widely used in document retrieval due to low latency and decent recall performance. Recent research indicates that deep retrieval solutions offer better model quality, but are hindered by unacceptable serving latency and the inability to support document updates. In this paper, we aim to enhance the vector index with end-to-end deep generative models, leveraging the differentiable advantages of deep retrieval models while maintaining desirable serving efficiency. We propose Model-enhanced Vector Index (MEVI), a differentiable model-enhanced index empowered by a twin-tower representation model. MEVI leverages a Residual Quantization (RQ) codebook to bridge the sequence-to-sequence deep retrieval and embedding-based models. To substantially reduce the inference time, instead of decoding the unique document ids in long sequential steps, we first generate some semantic virtual cluster ids of candidate documents in a small number of steps, and then leverage the well-adapted embedding vectors to further perform a fine-grained search for the relevant documents in the candidate virtual clusters. We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions

Exploring Region-based Deep Learning to Understand Objects in Real-world Scenarios

University of Technology Sydney. Faculty of Engineering and Information Technology.One way to infer about the real scenes is by understanding the object that presents in it, involving object localization, object recognition, object tracking, etc. Despite many advances in computer vision techniques, object understanding in real-world scenarios still remains many challenging tasks. There is no universal algorithm that can solve all of the scenarios with their own practical difficulties. This dissertation focuses on exploring region-based deep learning to understand objects in three typical real-world scenarios. The first part of the dissertation studies facial landmark detection in the condition of lack of finely labeled training data. We generate weakly labeled training data to replace finely labeled data using generative adversarial networks. Then, we propose a region-based convolutional neural network to detect facial components and landmarks simultaneously. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Extensive evaluations on several datasets indicate the effectiveness of the proposed approach. In the second part, multi-player identification and tracking tasks in sports video are discussed. We build a robust multi-camera multi-player tracking with identification framework, from player detection, to identification, to tracking. To handle the identity switches, we design a distinguishable deep representation for player identity, considering pose-guided partial features, team class, and jersey number. For data association, a robust multi-player tracker incorporating with player identity is further developed to produce identity-coherent trajectories. Experiment results illustrate that our framework handles the identity switches effectively, and outperforms state-of-the-art trackers on the sports video benchmarks. Finally, we study vehicle detection in infrared images with poor texture information, low resolution and high noise levels. To deal with these difficulties, we propose a backbone network to exploit discriminative features, composing of a frequency feature extractor, a spatial feature extractor and a dual-domain feature resource allocation model. Hypercomplex Infrared Fourier Transform is developed to calculate the infrared intensity saliency, while a convolutional neural network is used to extract feature maps in the spatial domain. To efficiently integrate and recalibrate the frequency and spatial features, we propose a Resource Allocation model for Features based on the well-designed attention blocks. The experiments substantiate the merits of the proposed method through comparisons with state-of-the-art methods

Artesunate inhibits airway remodeling in asthma via the MAPK signaling pathway

Background: Artesunate (ART), is a semi-synthetic water-soluble artemisinin derivative extracted from the plant Artemisia annua, which is often used to treating malaria. In vivo and in vitro studies suggested it may help decrease inflammation and attenuate airway remodeling in asthma. However, its underlying mechanism of action is not elucidated yet. Herein, an attempt is made to investigate the ART molecular mechanism in treating asthma.Methods: The BALB/c female mice sensitized via ovalbumin (OVA) have been utilized to establish the asthma model, followed by carrying out ART interventions. Lung inflammation scores by Haematoxylin and Eosin (H&E), goblet cell hyperplasia grade by Periodic Acid-Schiff (PAS), and collagen fibers deposition by Masson trichrome staining have been utilized for evaluating how ART affected asthma. RNA-sequencing (RNA-seq) analyses were performed to identify differentially expressed genes (DEGs). The DEGs were analyzed by Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and Protein-Protein interaction (PPI) function analyses. Hub clusters were found by Cytoscape MCODE. Subsequently, Real-Time quantitative PCR (RT-qPCR) verified the mRNA expression profiles of DEGs. Finally, immunohistochemistry (IHC) and western blots have validated the relevant genes and potential pathways.Results: ART considerably attenuated inflammatory cell infiltration, mucus secretion, and collagen fibers deposition. KEGG pathway analysis revealed that the ART played a protective role via various pathways including the mitogen-activated protein kinase (MAPK) pathway as one of them. Moreover, ART could alleviate the overexpression of found in inflammatory zone 1(FIZZ1) as revealed by IHC and Western blot analyses. ART attenuated OVA-induced asthma by downregulating phosphorylated p38 MAPK.Conclusion: ART exerted a protective function in a multitarget and multi-pathway on asthma. FIZZ1 was a possible target for asthma airway remodeling. The MARK pathway was one of the key pathways by which ART protected against asthma