Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-Reflectionless Wireless Transmission into the Body with Cascaded Metasurfaces

The ability to transmit wireless signals into the body is important for medical communication, sensing, and powering technologies. However, the efficiency of signal transmission is limited by the large reflection encountered at the interface between air and biological tissue. Here, we demonstrate an approach to overcome this reflection using cascaded metasurfaces with deeply subwavelength thickness to transform the wave impedance at the air-tissue interface. We develop a procedure to systematically synthesize these metasurfaces using multilayer tissue model that can account for different body compositions. Using this procedure, we design a three-layer metasurface operating at 2.45 GHz with λ/24.5 thickness. Full-wave simulations in a computational human body model show that the metasurface provides transmission equivalent to replacing the background with a tissue-matched medium, while experimental measurements demonstrate 12.1 dB enhancement of transmission into a multilayer tissue phantom.
Recognition of Rural Residential Areas in Mountainous Regions Using Deep Learning Method

Improving rural living levels in mountainous regions is one of the most important tasks in developing countries. The spatial distribution of rural residential areas is the basis of scientific evaluation of development potential and rural optimal distribution. In recent years, the application of high-resolution remote sensing images and the deep learning technologies helps to efficiently obtain and update the spatial form of rural residential areas. This paper presents a recognition method of rural residential areas in mountainous regions using the U2-Net model with high-resolution images. The results show that the recognition accuracy of U2-Net model is 88%, which is better than the FCN, U-Net, and DeepLabV3 models. The effectiveness and efficiency of the recognition method of high-resolution remote sensing images using the deep learning network would be helpful for current rural development.
TASI Lectures on the Conformal Bootstrap

These notes are from courses given at TASI and the Advanced Strings School in summer 2015. Starting from principles of quantum field theory and the assumption of a traceless stress tensor, we develop the basics of conformal field theory, including conformal Ward identities, radial quantization, reflection positivity, the operator product expansion, and conformal blocks. We end with an introduction to numerical bootstrap methods, focusing on the 2d and 3d Ising models.
Sub-Nyquist SAR Imaging Based on Pseudo-Random Space-Time Modulation under Different Compressive Sensing Algorithms

A novel sub-Nyquist SAR based on pseudo-random space-time modulation has been proposed to increase swath width for the sparse scene while preserving the azimuthal resolution. Comparing to the traditional high-resolution wide-swath (HRWS) system, e.g., the azimuthal multi-channels SAR and multi-input multi-output (MIMO) SAR, with large antenna and amount of data, it applies single-channel and overcomes the limitation of Nyquist theorem based on compressive sensing (CS) theorem. CS algorithms are important to sub-Nyquist SAR imaging, and include three algorithms, i.e., greedy algorithm, $\mathcal{L}_{1}$-norm optimization algorithm and Bayesian-based method. This paper presents the comparative work of sub-Nyquist SAR imaging based on pseudo-random space-time modulation under different CS algorithms by simulation of real SAR images.
Enhance Essential Features for Road Extraction from Remote Sensing Images

In deep learning based road extraction from remote sensing images, the network often learns some features that are not essential to road discrimination, such as trees, buildings, etc. In fact, there is no causal relationship between these features and road discrimination, which will lead to error and omission in final results. In this paper, we propose a novel road extraction network to enhance essential features, including local and global line features and geometric features along the road direction. Multi-scale Line Enhancement Module utilize hough transform to enhance line featues of different scales. Neighboring road prediction branch make the network pre-dict the distance and direction of each pixel to the neighboring road, which helps the network to focus on geometric features along the road direction. Experimental results on the deepglobe dataset show that the network is able to obtain bet-ter road extraction results by enhancing essential features that have a causal relationship with the task. Codes are available at https://github.com/zaoyifan/EssentialFeatures.
Multisource Remote Sensing Data Classification Using Fractional Fourier Transformer

Focusing on joint classification of Hyperspectral image (HSI) and Light detection and ranging (LiDAR) data, a fractional Fourier image transformer (FrIT) is proposed as a backbone network in this paper. In the proposed FrIT, HSI and LiDAR data are firstly fused at pixel-level. Both multi-source and HSI feature extractors are utilized to capture local contexts. Then, a plug-and-play image transformer FrIT is explored for global contexts and sequential feature extraction. Unlike the attention-based representations in classic visual image transformer (VIT), FrIT is capable of speeding up the transformer architectures massively. To reduce the information loss from shallow to deep layers, FrIT is devised to connect contextual features in multiple fractional domains. At last, to evaluate the performance of FrIT, a new HSI and LiDAR benchmark is provided for extensive experiments, on which the proposed FrIT gains an improvement of 3% over state-of-the-art methods.
Towards GNSS-R Hybrid Compact Polarimetry: Introducing the Stokes Parameters for SMAP-R Dataset

The Soil Moisture Active Passive - Reflectometry (SMAPR) is the first polarimetric L2C GNSS-R dataset collected from space. Given that the dataset consists of I/Q raw data, different processing techniques can be applied. In this paper we explore the calculation of the full polarization Stokes derived from the horizontal (H) and vertical (V) polarization measurements of the transmitted right-hand circularly polarized (RHCP) Global Positioning System (GPS) signal. This configuration is known as hybrid compact polarimetry (HCP) radar, in this case HCP of a bistatic radar or a GNSSR instrument. This work describes how to use the collected data to obtain the full Stokes parameters and presents a glimpse to its value in the GNSS-R field, in particular the value of the phase of the cross-correlation signal between the H and V polarization measurements.
The Spaceborne Imaging Spectrometer Desis: Data Access, Outreach Activities, and Scientific Applications

The DLR Earth Sensing Imaging Spectrometer (DESIS) [1] is a spaceborne instrument installed and operated on the International Space Station (ISS). The German Aerospace Center (DLR) has developed the instrument and the software for data processing [2], while the US company Teledyne Brown Engineering (TBE) provided the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operations and data tasking [3]. The main parameters of the DESIS instrument are summarized in Table 1. DESIS is equipped with an on-board calibration unit and a rotating pointing mirror (POI). The POI can change the line of sight ±15° in the forward/backward direction (independently of the MUSES orientation), allowing BRDF measurements of the same area on ground within an overflight.
Recent High Spatiotemporal-Resolution Observations and Evolution of Ice-Flow Fields Over the ROI Baudouin Ice Shelf, East Antarctica

In contact with the ocean, the Antarctic Ice Sheet (AIS) starts to float and forms ice shelves. These ice shelves have an important role in the AIS stability, acting as active regulators of the upward ice streams. In this article, we extract from four years of Sentinel-1 SAR images ice velocity fields. Using speckle tracking, we determine the ice velocities at high spatial and temporal resolution over the particular case of the Roi Baudouin Ice Shelf - RBIS. The results show the strong influence of ice rises, as well as intra-annual temporal variability, a part being attributable to tides and inverse barometer effect. Apart from these variations, we observe no significant trend in any regions of the RBIS. Nevertheless, and following the recent RBIS calving event, we recommend continuing to monitor the region and observing any potential changes.
[O i] λλ6300, 6364 IN THE NEBULAR SPECTRUM OF A SUBLUMINOUS TYPE Ia SUPERNOVA

In this Letter, a late-phase spectrum of SN 2010lp, a subluminous Type Ia supernova (SN Ia), is presented and analyzed. As in 1991bg-like SNe Ia at comparable epochs, the spectrum is characterized by relatively broad [Fe ii] and [Ca ii] emission lines. However, instead of narrow [Fe iii] and [Co iii] lines that dominate the emission from the innermost regions of 1991bg-like supernovae (SNe), SN 2010lp shows [O i] λλ6300, 6364 emission, usually associated with core-collapse SNe and never previously observed in a subluminous thermonuclear explosion. The [O i] feature has a complex profile with two strong, narrow emission peaks. This suggests that oxygen is distributed in a non-spherical region close to the center of the ejecta, severely challenging most thermonuclear explosion models discussed in the literature. We conclude that, given these constraints, violent mergers are presently the most promising scenario to explain SN 2010lp.
Limited Times of Data Access Based on SGX in Cloud Storage*

It is straightforward to encrypt the outsourced data to protect its confidentiality using symmetric cryptography in cloud storage. How to control and restrict the use of the encryption key in data users’ devices becomes one of the critical issues. In most of existing time-based and policy-based schemes, the key cannot be stored locally in data users’ devices, making the traffic cost linear with the data access times as the key should be retrieved in each data access. In this paper, we propose a times-based scheme with Intel SGX to restrict the times the key can be used in the data user’s device to restrict the times of data access. The basic idea is to compare the current used times with the specified maximal times to determine whether the key can be used or not. To this end, we use a monotonic counter to count the times the key has been used. When the use condition is not satisfied, we destroy the key securely and generate public proof so that (i) the encrypted data cannot be accessed anymore, i.e., the data is deleted assuredly, (ii) the deletion can be verified. In addition, a hash-based integrity check approach is utilized to detect and prevent replay attacks. The experimental results on the implemented prototype show our scheme is feasible in practice.
Universal phase diagrams for the quantum spin Hall systems

We describe how the three-dimensional (3D) quantum spin Hall phase arises from the insulator phase by changing an external parameter. In 3D systems without inversion symmetry, a gapless phase should appear between the two phases with a bulk gap. The gapless points are monopoles and antimonopoles (in $\mathbf{k}$ space), whose topological nature is the source of this gapless phase. In general, when the external parameter is changed from the ordinary insulator phase, two monopole-antimonopole pairs are created and the system becomes gapless. The gap-closing points (monopoles and antimonopoles) then move in the $\mathbf{k}$ space as the parameter is changed further. They eventually annihilate in pairs, with changing partners from the pair creations, and the system opens a gap again entering into the quantum spin Hall phase.
Non-Local Proposal Dynamic Enhancement Learning for Few-Shot Object Detection in Remote Sensing Images

Deep neural networks have underpinned much of recent progress in few-shot object detection (FSOD) in remote sensing images. The key lies in accurately inferring the object categories and bounding boxes depending on the feature of each proposal region. However, due to lack of sufficient labeled samples for training model well-fitting, the feature of each proposal fails to be discriminative and informative enough for accurate inference, thus limiting the generalization capacity. To mitigate this problem, we propose a non-local proposal dynamic enhancement learning (NPDEL) methods for FSOD in remote sensing images. In contrast to directly utilizing the proposal features extracted from the backbone, we propose to enhance them before inference using a non-local dynamic enhancement module which first carries out a non-local graph convolution on all proposal features and then dynamically fuses the convolved results with the original features for enhancement. By doing this, the enhanced proposal features can adaptively aggregate the related semantic information from the whole image, thus improving their discriminability as well as the generalization capacity in FSOD. Experiments results on different FSOD tasks demonstrate the efficacy of the proposed method.
An unfitted interior penalty discontinuous Galerkin method for incompressible Navier–Stokes two‐phase flow

A discontinuous Galerkin method for the solution of the immiscible and incompressible two‐phase flow problem based on the nonsymmetric interior penalty method is presented. Therefore, the incompressible Navier–Stokes equation is solved for a domain decomposed into two subdomains with different values of viscosity and density as well as a singular surface tension force. On the basis of a piecewise linear approximation of the interface, meshes for both phases are cut out of a structured mesh. The discontinuous finite elements are defined on the resulting Cartesian cut‐cell mesh and may therefore approximate the discontinuities of the pressure and the velocity derivatives across the interface with high accuracy. As the mesh resolves the interface, regularization of the density and viscosity jumps across the interface is not required. This preserves the local conservation property of the velocity field even in the vicinity of the interface and constitutes a significant advantage compared with standard methods that require regularization of these discontinuities and cannot represent the jumps and kinks in pressure and velocity. A powerful subtessellation algorithm is incorporated to allow the usage of standard time integrators (such as Crank–Nicholson) on the time‐dependent mesh. The presented discretization is applicable to both the two‐dimensional and three‐dimensional cases. The performance of our approach is demonstrated by application to a two‐dimensional benchmark problem, allowing for a thorough comparison with other numerical methods. Copyright © 2012 John Wiley & Sons, Ltd.
Building Type Classification with Incomplete Labels

Buildings can be distinguished by their form or function and maps of building types can be used by authorities for city planning. Training models to perform this classification requires appropriate training data. OpenStreetMap (OSM) data is globaly available and partly provides information on building types. However, this data can be incomplete or wrong. In this work a U-Net is trained to group buildings into one of the three major function classes (commercial/industrial, residential and other) using incomplete OSM data or ground-truth cadastral data. The model achieves overall accuracies of 72 and 75 percent. Given the OSM data has only around 20 percent of the ground truth labels this shows the incomplete data can be used to train for the building classification task.
Extreme Cyclones and Storm Surges in the Past and Future Climates: A Sensitivity Study for the Baltic Sea Region

The purpose of the paper is to analyse the possible parameters of extratropical cyclones (ETCs) as they would occur in the Baltic Sea area at the end of the 21st century and in the past during the Holocene thermal maximum. Using a modelling system composed of the Weather Research and Forecasting (ARW-WRF) atmospheric model, Finite Volume Community Ocean Model (FVCOM), and surrogate background conditions, the results showed that changes in cyclone parameters are more profound under steeper climate change scenarios (RCP8.5) and under higher air temperatures. Most likely, the North Atlantic ETCs will become slightly stronger, but also the uncertainty increases. Nonlinear interactions between ice melt, North Atlantic current systems and atmospheric parameters probably induce an uncertainty both into future and past storminess reconstructions.
Ai-Based Downscaling of Ocean Subsurface Salinity from Remote Sensing Perspective

Accurate and detailed ocean interior salinity data are fundamental to the studies of hydrological cycle, ocean current, and global climate change. This study adopted multisource satellite-based sea surface parameters and Argo float data for downscaling the subsurface salinity with higher spatial resolution based on the Light Gradient Boosting Machine (LightGBM) and the Convolutional Neural Networks (CNN) algorithms. Moreover, we utilized the sparse EN4 profiles gridded salinity data with quarter degree (EN4-GOSQ) for the accuracy validation of downscaling results. The model performance was well measured by the determination coefficient (R2), root mean square error (RMSE), and relative RMSE (RRMSE). The R2 and RRMSE indicated the advanced LightGBM model is more preferable for downscaling global subsurface salinity than the CNN model.
An Optimizer based on Spiking Neural-oscillator Networks

Spiking neural networks (SNNs) are one of the human brain-inspired neural networks, which have been applied to various applications. SNNs can be implemented on hardware and can realize low-costs, low-power consumption and high-parallelism computation. There, however, are few researches to solve continuous optimization problems using SNNs. Swarm intelligence (SI) algorithm is a population-based metaheuristics, which is effective in solving those problems. SI algorithm requires many search agents to search for good quality of solutions. Since SNNs can provide high-parallelism computation, SNNs and SI algorithm are well suited. In this paper, we propose a new swarm intelligence algorithm called an optimizer based on spiking neural-oscillator networks (OSNN). In OSNN, each particle consists of plural spiking neural-oscillators which search one-dimensional search space. These spiking neuraloscillators are networked (i.e. SNNs), networks of which affect search dynamics and performances of OSNN. We performed numerical experiments to investigate basic search dynamics of OSNN and evaluated search performances of OSNN compared to OPRC and PSO. As the results, network topology of OSNN affects spike timing for spiking neural-oscillator, search dynamics and search performances. In addition, OSNN shows better search performances than OPRC and PSO.
Initial Mode Design Framework of Sparse Tops SAR System

Compared with traditional synthetic aperture radar (SAR), sparse SAR system can obtain higher swath with reduced pulse repetition frequency (PRF). Terrain observation by progressive scan (TOPS) is a novel and promising mode of wide-swath SAR application. It can achieve the same swath coverage as ScanSAR, but greatly reduces the scalloping. To achieve the high-resolution and wide-swath SAR imaging, this paper introduces a novel mode design framework of sparse TOPS SAR system. Combining the advantages of sparse imaging and TOPS, the proposed mode can obtain wider swath by sparse sampling in azimuth direction without changing other SAR hardware parameters, and simultaneously obtains the high-resolution sparse SAR image. The effect on DTAR from steering angle is also suppressed in this mode.
Interference Alignment for Joint Phased Array Radar and Communication Systems

In this paper, we propose an interference alignment (IA) method for interference elimination in co-existing Phased Array (PA) radar and multi-user multiple-input multiple-output (MIMO) communication systems. Although ergodic IA method has been proposed to cancel the mutual interferences, it will definitely reduce the degrees-of-freedom (DoFs) of communication systems. Differently, our proposed IA method allows to improve the DoFs. It is examined by the Neyman-Pearson (NP) test for radar functionality, while the communication performance is evaluated by DoFs and bit error rate (BER) analysis. Both theory analysis and numerical results are provided to validate the effectiveness of the proposed IA method.
Evaluation of Imputation Models Based on the Enhancement to Yield Forecasting*

Market price and yield forecasting models for Fresh Produce (FP) are crucial to protect retailers and consumers from overpriced FP. However, utilizing the data for forecasting is obstructed by the occurrence of missing values. Therefore, it is imperative to impute the encountered missing instances to enable effective forecasting. Most of the work found in literature tackles imputation of missing values when they are randomly scattered in the dataset while very little work is found tackling both: consecutive occurrence of missing data, i.e. missing data chunks, as well as those randomly missing. In this work, the data used for forecasting has missing values in chunks as well as at random points. Therefore, various comprehensive imputation models are used to impute both random as well as chunks of missing values. Since the imputed time series are incomplete, the only way to evaluate those imputation models is to analyze their effect on forecasting performance. The ensemble of two compound deep learning (DL) models, namely Attention Convolutional Neural Networks Long Short Term Memory (Att-CNN-LSTM) and SeriesNet with Gated Recurrent Unit (GRU), is used for forecasting. For imputation, three DL models are tested: The Ensemble imputation model which is a Voting Regressor of two DL submodels, Residual GRU and LSTM-Deep-GRU. Another deep learning imputation model is used which is a Transfer Learning (TL) model. Finally, a Hybrid model of both DL models is designed to take the pros of each of its integrated models by using the Ensemble model in case of random missing data and the Transfer Learning model in case of missing data chunks. It is observed that, in general, imputing the missing values improves the forecasting result as compared to eliminating the instances with missing values. The Hybrid model improves the overall forecasting performance by up to 60% compared to the case of using the second-best Transfer Learning model and around 64% as compared to the case of imputation using the Ensemble model.
3DCADFusion: Tracking and 3D Reconstruction of Dynamic Objects without External Motion Information

In this paper, we propose 3DCADFusion, an end-to-end object reconstruction pipeline. Our system is capable of producing dense high-fidelity reconstructions of objects by simultaneously tracking and reconstructing their incrementally improving 3D geometry in a sequence of frames captured by a depth-enabled camera setup. In this process, no external information about the object motion is required as long as the camera is stationary. This is accomplished by removing irrelevant scene elements such as the background or the operator hands holding the object to track and fuse its shape over time only using pixels associated with the object mask. Previous approaches either target static scene reconstruction therefore ignoring dynamic elements, or they make strong assumptions about the scanning platform or required scanning sessions. In contrast, we propose a fast and flexible scanning method similar to how humans visually inspect objects, enabling users to easily acquire a true-to-scale 3D CAD models ready for use in other computer vision tasks such as object tracking or pose estimation. We demonstrate the performance of our approach on a real-world dataset and quantitatively analyze the resulting reconstruction accuracy. Demo is available at github.com/4ri4Slr/3DCADFusion