Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Delivery of Chloroquine to Plasmacytoid Dendritic Cells Enhances Inhibition of the Type I Interferon Response

Systemic lupus erythematosus (SLE) causes damaging inflammation in multiple organs via the accumulation of immune complexes. These complexes activate plasmacytoid DCs (pDCs) via TLR7 and TLR9, contributing to disease pathogenesis by driving secretion of inflammatory type I IFNs. Antimalarial drugs, such as chloroquine (CQ), are TLR antagonists used to alleviate inflammation in SLE. However, they require ~3 months of continuous use before achieving therapeutic efficacy and can accumulate in the retinal pigment epithelium with chronic use resulting in retinopathy. We hypothesized that poly(ethylene glycol)-b-poly(propylene sulfide) (PEG-b-PPS) filamentous nanocarriers, filomicelles (FMs) could improve drug activity and reduce toxicity by directly delivering CQ to pDCs via passive, morphology-based targeting. Healthy human PBMCs were treated with soluble CQ or CQ-loaded FMs, stimulated with TLR agonists or SLE patient sera, and type I IFN secretion was quantified via multi-subtype IFN-α ELISA and MX1 gene expression using real-time RT-qPCR. Our results showed that 50 µg CQ/mg FM decreased MX1 expression and IFN-α production after TLR activation with either synthetic nucleic acid agonists or immune complex rich sera from SLE patients. Cellular uptake and biodistribution studies showed that FMs preferentially accumulate in human pDCs in vitro and in tissues frequently damaged in SLE patients (i.e., liver and kidneys) while sparing the eye in vivo. These results showed that nanocarrier morphology enables drug delivery, and CQ-FMs may be equally effective and more targeted than soluble CQ at inhibiting SLE-relevant pathways.
Decoupled Feature-Temporal CNN: Explaining Deep Learning-Based Machine Health Monitoring

Machine learning, especially deep learning, has been extensively applied and studied in the area of machine health monitoring. For machine health monitoring systems (MHMS), major efforts have been put into designing and deploying more and more complex machine learning models. Those black-box models are nontransparent toward their working mechanism. However, this research trend brings huge potential risks in real life. Since machine health monitoring itself belongs to high stake decision applications, the outputs of the autonomous monitoring systems should be trustworthy and reliable, which refers to obtain explainability. Then, it comes to the following key question: why the deployed MHMS predicts what they predict. In this article, we shed some light on this meaningful research direction: explainable MHMSs (EMHMS). In EMHMS, the machine doctor could act like a real doctor who can not only make a diagnosis but also describe the patient’s symptoms. First, we propose a specific convolutional neural network (CNN) structure, named DecouplEd Feature-Temporal CNN (DEFT-CNN), to balance precision–explainability tradeoff. Specifically, feature information and temporal information have been encoded in different stages of our model. The spatial attention module is added to boost the performance of the model. Then, to explain the decision of the model, we adopt gradient-based methods to generate features and temporal saliency maps highlighting which kinds of features and time steps are keys for the model’s predictions. Finally, we conduct the experimental studies on two real datasets to verify the effectiveness of our proposed framework.
Protonated 1,10-Phenanthroline as a Fluorescent Sensor for Fe(II) at Micro Molar Level Detections

Pristine 1,10-phenanthroline is widely known for the formation of non-fluorescent complexes with Fe(II). Although lot of studies have been done on 1,10-phenanthroline and Fe(II), studies on Fe(II) with a pronated core of phenanthroline are very scarce. Thus, in this work, Fe(II)’s effect on the absorption and emission spectra of protonated 1,10-phenanthroline was investigated. Protonated 1,10-phenanthroline was developed as a sensitive and selective turn off fluorescent sensor for Fe(II) quantification. The excitation and emission wavelengths of protonated 1,10-phenanthroline are 273 nm and 415 nm, respectively. The intensity of the emission peak at 415 nm was remarkably decreased upon the addition of Fe(II). Temperature studies along with charge transfer spectra were evidence for a static quenching process. The limit of detection and the limit of quantification of the probe at 3.3 σ and 25 °C was 0.31 μmol/L and 0.65 μmol/L respectively. The sensitivity of the protonated fluorescent sensor was increased in acidic media while the highest sensitivity was observed at pH 2.81. The interference from Ni(II) and Cu(II) at this pH was at a minute level.
Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images

Object detection is challenging in high spatial resolution (HSR) remote sensing images that have a complex background and irregular object locations. To minimize manual annotation cost in supervised learning methods and achieve advanced detection performance, we proposed a point-based weakly supervised learning method to address the object detection challenge in HSR remote sensing images. In the study, point labels are introduced to guide candidate bounding box mining and generate pseudobounding boxes for objects. Then, pseudobounding boxes are applied to train the detection model. A progressive candidate bounding box mining strategy is proposed to refine object detection. Experiments are conducted on a comprehensive HSR dataset which contains four categories. Results indicate the proposed method achieves competitive performance compared to YOLOv5 which is trained on manual bounding box annotations. In comparison to the state-of-the-art weakly supervised learning method, our method outperforms WSDDN method with 0.62 mean average precision score.
Complementary Value of Intra- and Peri-Tumoral PET/CT Radiomics for Outcome Prediction in Head and Neck Cancer

To investigate the prognostic value of peri-tumoral radiomics features of pre-treatment PET/CT images in patients with head and neck cancer. 166 patients from 4 centers (111 for training and 55 for external independent testing) were retrospectively analyzed. 11 regions were used for feature extraction, (1) Intra-tumoral region (Intra) was first dilated radially along the edge by 3, 6, 9, 12 and 15 mm to obtain (2) 5 solid combined regions (noted as Comb_3, 6, 9, 12, and 15, respectively), and (3) 5 hollow annular regions with equal ring width of 3 mm were then generated as peri-tumoral regions (noted as Peri_3, 6, 9, 12 and 15, respectively). 92 individual/integrated models were constructed by using features from Clinical alone, CT or PET alone, Clinical+PET, Clinical+CT, PET+CT, Clinical+PET+CT, Intra+Peri, Clinical+Intra+Peri and Clinical+PET+CT (Intra+Peri). In individual models, only 4 models showed p<0.05 (PET_Peri_3 and PET_Comb_6 for distant metastasis (DM) prediction, Clinical and PET_Peri_6 for death prediction). In integrated models, Clinical+CT (Intra+Peri_6), PET (Intra+Peri_3) and Clinical+PET_Peri_6 achieved the best performance for the prediction of local recurrence (LR), DM and death with AUC of 0.75, 0.80 and 0.87, C-index of 0.71, 0.80 and 0.83, p-value of 0.003, 0.008 and 0.001, respectively. Peri-tumoral regions that located closer to the intra-tumoral region (Peri_3 and Peri_6) showed better performance compared to those located further. The integration of intra-tumoral and peri-tumoral radiomics features achieved better performance than either of them alone, PET and CT radiomics features also provided complementary information to clinical features.
Density–Temperature Constraint From Fixed-Bias Spherical Langmuir Probes

In a recent paper, we showed that a combination of analytic and multivariate regression models can be used to infer a satellite floating potential, from currents collected by a pair of fixed-bias spherical Langmuir probes. In this article, we show that such an instrument can also be used to infer the ratio of the plasma density and the square root of the electron temperature. This simple function, in turn, can provide a useful constraint and improve accuracy when density and temperature are measured independently.
Fast, Accurate Processor Evaluation Through Heterogeneous, Sample-Based Benchmarking

Performance evaluation is a key task in computing and communication systems. Benchmarking is one of the most common techniques for evaluation purposes, where the performance of a set of representative applications is used to infer system responsiveness in a general usage scenario. Unfortunately, most benchmarking suites are limited to a reduced number of applications, and in some cases, rigid execution configurations. This makes it hard to extrapolate performance metrics for a general-purpose architecture, supposed to have a multi-year lifecycle, running dissimilar applications concurrently. The main culprit of this situation is that current benchmark-derived metrics lack generality, statistical soundness and fail to represent general-purpose environments. Previous attempts to overcome these limitations through random app mixes significantly increase computational cost (workload population shoots up), making the evaluation process barely affordable. To circumvent this problem, in this article we present a more elaborate performance evaluation methodology named BenchCast. Our proposal provides more representative performance metrics, but with a drastic reduction of computational cost, limiting app execution to a small and representative fraction marked through code annotation. Thanks to this labeling and making use of synchronization techniques, we generate heterogeneous workloads where every app runs simultaneously inside its Region Of Interest, making a few execution seconds highly representative of full application execution.
Investigation on Dynamic Diffusion Behavior of Furfural in Oil-Pressboard Insulation under Partial Oil Replacement Condition

Furfural, as a typical chemical indicator, is used to evaluate the aging state of the cellulose insulation in the transformer. However, partial oil replacement will lead to the loss of furfural in oil and disturb the equilibrium distribution of furfural between oil and cellulose insulation, which could affect the evaluation results of the aging state of the cellulose insulation. Unfortunately, the influence of partial oil replacement has not been focused on. Given this issue, this work studies the influence of partial oil replacement on dynamic diffusion behavior of furfural in oil-pressboard insulation. First, the dynamic diffusion behavior of furfural between insulating pressboard and oil after partial oil replacement is discussed, and the theoretical diffusion model of furfural is established. Second, based on the mass gain curve of furfural in oil, the diffusion coefficients under different oil replacement ratios are obtained. Finally, a formula for calculating the diffusion equilibrium time is proposed. This paper attempts to report a method for analyzing the impact of the oil replacement ratio on the dynamic diffusion of furfural. These findings are expected to improve the accuracy of the aging assessment results by using furfural analysis.
Sea Ice Concentration Estimation: Using Passive Microwave and SAR Data With a U-Net and Curriculum Learning

Ice concentration estimates are typically acquired from algorithms using passive microwave satellite data, and from image analysis charts, but these have limitations. Estimates acquired from passive microwave data have coarse spatial resolution, may have errors due to atmospheric contamination, and often perform poorly in marginal ice zones. Image analysis charts are not as precise, subject to analyst interpretation, and only available over specific geographic areas. We have implemented a U-net with synthetic aperture radar images as inputs and use ice concentration estimates retrieved from passive microwave data as training labels. The U-net, due to not being sensitive to patch size, is shown to be an improvement over previous work with convolutional neural networks that use fully connected layers at the output. Data augmentation and an L1 loss function were applied along with a novel training scheme that leverages curriculum learning. In this training scheme, the model is first trained with samples from open water and consolidated ice regions before incorporating samples from marginal ice regions. In a tenfold cross validation experiment, we achieve 3–4% mean absolute error comparing to estimates using passive microwave data and observe curriculum learning models having more stable training. Predictions on four with-held SAR scenes with difficult ice conditions were evaluated with image analysis charts. A mean absolute error of 7.18% is achieved, which is lower than errors associated with passive microwave data alone. Qualitative improvements in marginal ice zone estimates are achieved, while still preserving smooth consolidated ice regions, and openings in ice cover.
Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation

Radar echo extrapolation is a basic but essential task in meteorological services. It could provide radar echo prediction results with high spatiotemporal resolution in a computationally efficient way, and effectively enhance the operational system's forecasting capability for meteorological hazards. Traditional methods perform extrapolation by estimating echo motions between contiguous radar data. This strategy is difficult to characterize complex nonlinear meteorological processes effectively, and it is difficult to benefit from large historical data. Recently, machine learning (ML) models have been used for radar echo extrapolation. These methods have effectively improved extrapolation quality in a data-driven way and from the statistical perspective. Although the ML-based methods show excellent performance, they usually produce blurry extrapolations. This leads to underestimating radar echo intensity and making echo lack small-scale details. Moreover, it makes models difficult to predict severe convective hazards. To solve this problem, a two-stage extrapolation model based on 3-D convolutional neural network and conditional generative adversarial network is proposed. These two models form the “pre-extrapolation” and “postprocessing” paradigm. The pre-extrapolation model is trained in the traditional way and performs rough extrapolation. The postprocessing model uses the pre-extrapolation result as input and is trained with the adversarial strategy. It could correct the echo intensity and increase the echo's details. In the experiment, our model could provide more precise radar echo extrapolations than other methods, especially for intense echoes and convective systems, in the data of North China from 2015 to 2016.
A New Lightweight In Situ Adversarial Sample Detector for Edge Deep Neural Network

The flourishing of Internet of Things (IoT) has rekindled on-premise computing to allow data to be analyzed closer to the source. To support edge Artificial Intelligence (AI), hardware accelerators, open-source AI model compilers and commercially available toolkits have evolved to facilitate the development and deployment of applications that use AI at its core. This paradigm shift in deep learning computations does not, however, reduce the vulnerability of deep neural networks (DNN) against adversarial attacks but introduces a difficult catch-up. This is because existing methodologies rely mainly on off-line analysis to detect adversarial inputs, assuming that the deep learning model is implemented on a 32-bit floating-point graphical processing unit (GPU) instance. In this paper, we propose a new hardware-oriented approach for in-situ detection of adversarial inputs feeding through a spatial DNN accelerator architecture or a third-party DNN Intellectual Property (IP) implemented on the edge. Our method exploits controlled glitch injection into the clock signal of the DNN accelerator to maximize the information gain for the discrimination of adversarial and benign inputs. A light gradient boosting machine (lightGBM) is constructed by analyzing the prediction probability of unmutated and mutated models and the label change inconsistency between the adversarial and benign samples in the training dataset. With negligibly small hardware overhead, the glitch injection circuit and the trained lightGBM detector can be easily implemented alongside with the deep learning model on a Xilinx ZU9EG chip. The effectiveness of the proposed detector is validated against four state-of-the-art adversarial attacks on two different types and scales of DNN models, VGG16 and ResNet50, for a thousand-class visual object recognition application. The results show a significant increase in true positive rate and a substantial reduction in false positive rate on the Fast Gradient Sign Method (FGSM), Iterative-FGSM (I-FGSM), C&W and universal perturbation attacks compared with modern software-oriented adversarial sample detection methods.
Narrow River Extraction From SAR Images Using Exogenous Information

Monitoring of rivers is of major scientific and societal importance due to the crucial resource they provide to human activities and the threats caused by flood events. Rapid revisit synthetic aperture radar (SAR) sensors such as Sentinel-1 or the future surface water and ocean topography (SWOT) mission are indispensable tools to achieve all-weather monitoring of water bodies at the global scale. Unfortunately, at the spatial resolution of these sensors, the extraction of narrow rivers is extremely difficult without resorting to exogenous knowledge. This article introduces an innovative river segmentation method from SAR images using a priori databases such as the global river widths from Landsat (GRWL). First, a recently proposed linear structure detector is used to produce a map of likely line structures. Then, a limited number of nodes along the prior river centerline are extracted from the exogenous database and used to reconstruct the full river centerline from the detection map. Finally, an innovative conditional random field approach is used to delineate accurately the river extent around its centerline. The proposed method has been tested on several Sentinel-1 images and on simulated SWOT data. Both visual and qualitative evaluations demonstrate its efficiency.
PWM Switching Delay Correction Method for High-Speed Brushless DC Drives

For high-speed brushless DC (BLDC) drives, the switching-to-fundamental frequency ratio is usually insufficient. As a result, the commutation errors caused by PWM switching delay are significant, which means the power switches will take action at inaccurate instants. This will cause low-frequency current oscillations and produce additional losses. In addition, the conventional commutation PWM method will cause accumulative errors in the sensorless operation and will further lead to instability issues in the high-speed region. In order to suppress these influences, a novel carrier-synchronized commutation PWM strategy is proposed in this paper. It shows that the proposed strategy can eliminate the commutation error caused by PWM switching delay. In addition, the instability phenomenon can be avoided and the current control performance is greatly improved. Finally, the proposed strategy is verified by simulation and experiments on a 78 000-r/min BLDC drive platform.
Unified Description of Isothermal Endoreversible Chemical Cycles with Linear Mass Transfer Law

An isothermal endoreversible four-reservoir chemical pump cycle operating between a finite potential capacity high-chemical-potential mass reservoir and three infinite potential capacity mass reservoirs is established in this paper. Optimal control theory is applied to determine the optimal cycle configuration corresponding to the maximum energy output per cycle for the fixed total cycle time and transferred energy of high-chemical-potential mass reservoir in which the mass transfer between working fluid and mass reservoirs obey linear mass transfer law. The optimal cycle configuration is an isothermal endoreversible four-reservoir chemical pump cycle, in which the chemical potential (the concentration) of the key component in the finite potential capacity high-chemical-potential mass reservoir and that in the working fluid change nonlinearly with time and the difference between the chemical potential (the ratio of the concentration) of the key component in the finite potential capacity mass reservoir and (to) that in the working fluid is a constant, and the chemical potentials (the concentration) of the key component in the working fluid at the infinite potential capacity mass reservoir sides are also constants. Moreover, the numerical example is provided to reveal the influences of concentration and chemical potential change of the finite potential capacity high-chemical-potential mass reservoir on the optimal configuration of the four-reservoir chemical pump cycle. Then, a unified description of various isothermal endoreversible chemical cycles with linear mass transfer law is obtained. They include ten type of isothermal endoreversible chemical cycles: four-reservoir chemical pumps with finite potential capacity mass reservoirs and infinite potential capacity mass reservoirs, four-reservoir chemical potential transformers with finite potential capacity mass reservoir and infinite potential capacity mass reservoirs, three- reservoir chemical pumps with finite potential capacity mass reservoirs and infinite potential capacity mass reservoirs, three-reservoir chemical potential transformers with finite potential capacity mass reservoir and infinite potential capacity mass reservoirs, two-reservoir chemical pump with infinite potential capacity mass reservoirs, and chemical engine with infinite potential capacity mass reservoirs. The results can provide some guidelines for optimal design and operation of real chemical cycles and devices.
Review of Dynamic and Transient Modeling of Power Electronic Converters for Converter Dominated Power Systems

In response to national and international carbon reduction goals, renewable energy resources like photovoltaics (PV) and wind, and energy storage technologies like fuel-cells are being extensively integrated in electric grids. All these energy resources require power electronic converters (PECs) to interconnect to the electric grid. These PECs have different response characteristics to dynamic stability issues compared to conventional synchronous generators. As a result, the demand for validated models to study and control these stability issues of PECs has increased drastically. This paper provides a review of the existing PEC model types and their applicable uses. The paper provides a description of the suitable model types based on the relevant dynamic stability issues. Challenges and benefits of using the appropriate PEC model type for studying each type of stability issue are also presented.