Low-resolution Maps Research Articles

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km2 in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management.

MARIE 2.0: A Perturbation Matrix Based Patient-Specific MRI Field Simulator.

High static field MR scanners can produce human tissue images of astounding clarity, but rely on high frequency electromagnetic radiation that generates complicated in-tissue field patterns that are patient-specific and potentially harmful. Many such scanners use parallel transmitters to better control field patterns, but then adjust the transmitters based on general guidelines rather than optimizing for the specific patient, mostly because computing patient-specific fields was presumed far too slow. It was recently demonstrated that the combination of fast low-resolution tissue mapping and fast voxel-based field simulation can be used to perform a patient-specific MR safety check in minutes. However, the field simulation required several of those minutes, making it too slow to perform the dozens of simulations that would be needed for patient-specific optimization. In this paper we describe a compressed-perturbation-matrix technique that nearly eliminates the computational cost of including complex coils (or coils and shields) in voxel-based field simulation of tissue, thereby reducing simulation time from minutes to seconds. The approach is demonstrated on a wide variety of head+coil and head+coil+shield configurations, using the implementation in MARIE 2.0, the latest version of the open-source MR field simulator MARIE.

Single-particle cryo-EM analysis of the shell architecture and internal organization of an intact α-carboxysome

Carboxysomes are proteinaceous bacterial microcompartments that sequester the key enzymes for carbon fixation in cyanobacteria and some proteobacteria. They consist of a virus-like icosahedral shell, encapsulating several enzymes, including ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO), responsible for the first step of the Calvin-Benson-Bassham cycle. Despite their significance in carbon fixation and great bioengineering potentials, the structural understanding of native carboxysomes is currently limited to low-resolution studies. Here, we report the characterization of a native α-carboxysome from a marine cyanobacterium by single-particle cryoelectron microscopy (cryo-EM). We have determined the structure of its RuBisCO enzyme, and obtained low-resolution maps of its icosahedral shell, and of its concentric interior organization. Using integrative modeling approaches, we have proposed a complete atomic model of an intact carboxysome, providing insight into its organization and assembly. This is critical for a better understanding of the carbon fixation mechanism and toward repurposing carboxysomes in synthetic biology for biotechnological applications.

High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks. Unlike previous studies, in this study, we utilized the high-resolution network (HRNet) design style by replacing the convolutional layers with Transformer blocks, continuously exchanging feature map information with different resolutions generated by the Transformer blocks. The proposed Transformer-based network is named the high-resolution Swin Transformer network (HRSTNet). Extensive experiments demonstrated that the HRSTNet can achieve performance comparable with that of the state-of-the-art Transformer-based U-Net-like architecture on the 2021 Brain Tumor Segmentation dataset, the Medical Segmentation Decathlon's liver dataset, and the BTCV multi-organ segmentation dataset.

Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability

Image animation aims to transfer the posture change of a driving video to the static object of the source image, and has potential applications in various domains, such as film and game industries. The essential part in this task is to generate a video by learning the motion from the driving video while preserving the appearance from the source image. As a result, a new object with the same motion will be generated in the animated video. However, it is a significant challenge if the object pose shows large-scale change. Even the most recent method failed to achieve this correctly with good visual effects. In order to solve the problem of poor visual effects in the videos with the large-scale pose change, a novel method based on an improved first-order motion model (FOMM) with enhanced dense motion and repair ability was proposed in this paper. Firstly, when generating optical flow, we propose an attention mechanism that optimizes the feature representation of the image in both channel and spatial domains through maximum pooling. This enables better distortion of the source image into the feature domain of the driving image. Secondly, we further propose a multi-scale occlusion restoration module that generates a multi-resolution occlusion map by upsampling the low-resolution occlusion map. Following this, the generator redraws the occluded part of the reconstruction result across multiple scales through the multi-resolution occlusion map to achieve more accurate and vivid visual effects. In addition, the proposed model can be trained effectively in an unsupervised manner. We evaluated the proposed model on three benchmark datasets. The experimental results showed that multiple evaluation indicators were improved by our proposed method, and the visual effect of the animated videos obviously outperformed the FOMM. On the Voxceleb1 dataset, the pixel error, average keypoints distance and average Euclidean distance by our proposed method were reduced by 6.5%, 5.1% and 0.7%, respectively. On the TaiChiHD dataset, the pixel error, average keypoints distance and missing keypoints rate measured by our proposed method were reduced by 4.9%, 13.5% and 25.8%, respectively.

A Wide-Area Deep Ocean Floor Mapping System: Design and Sea Tests

Mapping the seafloor in the deep ocean is currently performed using sonar systems on surface vessels (low-resolution maps) or undersea vessels (high-resolution maps). Surface-based mapping can cover a much wider search area and is not burdened by the complex logistics required for deploying undersea vessels. However, practical size constraints for a towbody or hull-mounted sonar array result in limits in beamforming and imaging resolution. For cost-effective high-resolution mapping of the deep ocean floor from the surface, a mobile wide-aperture sparse array with subarrays distributed across multiple autonomous surface vessels (ASVs) has been designed. Such a system could enable a surface-based sensor to cover a wide area while achieving high-resolution bathymetry, with resolution cells on the order of 1 m2 at a 6 km depth. For coherent 3D imaging, such a system must dynamically track the precise relative position of each boat’s sonar subarray through ocean-induced motions, estimate water column and bottom reflection properties, and mitigate interference from the array sidelobes. Sea testing of this core sparse acoustic array technology has been conducted, and planning is underway for relative navigation testing with ASVs capable of hosting an acoustic subarray.

Structural and DNA end resection study of the bacterial NurA-HerA complex

BackgroundThe nuclease NurA and the ATPase/translocase HerA play a vital role in repair of double-strand breaks (DSB) during the homologous recombination in archaea. A NurA-HerA complex is known to mediate DSB DNA end resection, leading to formation of a free 3′ end used to search for the homologous sequence. Despite the structures of individual archaeal types of NurA and HerA having been reported, there is limited information regarding the molecular mechanisms underlying this process. Some bacteria also possess homologs of NurA and HerA; however, the bacterial type of this complex, as well as the detailed mechanisms underlying the joining of NurA-HerA in DSB DNA end resection, remains unclear.ResultsWe report for the first time the crystal structures of Deinococcus radiodurans HerA (drHerA) in the nucleotide-free and ADP-binding modes. A D. radiodurans NurA-HerA complex structure was constructed according to a low-resolution cryo-electron microscopy map. We performed site-directed mutagenesis to map the drNurA-HerA interaction sites, suggesting that their interaction is mainly mediated by ionic links, in contrast to previously characterized archaeal NurA-HerA interactions. The key residues responsible for the DNA translocation activity, DNA unwinding activity, and catalytic activities of the drNurA-HerA complex were identified. A HerA/FtsK-specific translocation-related motif (TR motif) that guarantees the processivity of double-stranded DNA (dsDNA) translocation was identified. Moreover, a mechanism for the translocation-regulated resection of the 5′ tail of broken dsDNA and the corresponding generation of a recombinogenic 3′ single-stranded DNA tail by the drNurA-HerA complex was elucidated.ConclusionsOur work provides new insights into the mechanism underlying bacterial NurA-HerA-mediated DSB DNA end resection, and the way this complex digests the 5′ tail of a DNA duplex and provides long 3′ free end for strand invasion in the bacterial homologous recombination process.

Progressive Transformer Machine for Natural Character Reenactment

Character reenactment aims to control a target person’s full-head movement by a driving monocular sequence that is made up of the driving character video. Current algorithms utilize convolution neural networks in generative adversarial networks, which extract historical and geometric information to iteratively generate video frames. However, convolution neural networks can merely capture local information with limited receptive fields and ignore global dependencies that play a crucial role in face synthesis, leading to generating unnatural video frames. In this work, we design a progressive transformer module that introduces multi-head self-attention with convolution refinement to simultaneously capture global-local dependencies. Specifically, we utilize the non-lapping window-based multi-head self-attention mechanism with hierarchical architecture to obtain the larger receptive fields at low-resolution feature map and thus extract global information. To better model local dependencies, we introduce the convolution operation to further refine the attentional weight in the multi-head self-attention mechanism. Finally, we use several stacked progressive transformer modules with the down-sampling operation to encode information of appearance information of previously generated frames and parameterized 3D face information of the current frame. Similarly, we use several stacked progressive transformer modules with the up-sampling operation to iteratively generate video frames. In this way, it can capture global-local information to facilitate generating video frames that are globally natural while preserving sharp outlines and rich detail information. Extensive experiments on several standard benchmarks suggest that the proposed method outperforms current leading algorithms.

Structural basis of signal transduction within PAS regulated Ser/Thr kinase.

To control integral cellular processes, nature has developed molecular switches where the sensory domain senses external cues and controls the function of relevant effector domains. Here, we focus on the superfamily of PAS (Per-ARNT-Sim) sensory domains and their roles in regulating Ser/Thr Kinases (STKs). PAS domains achieve regulatory control by internally binding ligands/cofactors (e.g., O2 binding to heme in FixL; flavins within the Light-Oxygen-Voltage (LOV)-type PAS domains). While aspects of PAS sensing are understood, the signaling mechanism from sensory to effector domain and Protein-Protein Interactions (PPIs) within full-length proteins or complexes remain elusive. To address these, we investigated plant phototropin, Avena sativa Phototropin1 (AsPhot1). AsPhot1 has two LOV domains followed by the STK domain, where the LOV2 regulates the kinase function to maintain essential plant physiology via blue light sensing. We used biochemical assays in combination with HDX-MS to study the signal transduction from LOV2 to the STK domain. Our data shows substantial conformation changes in the dark vs. light state upon photoactivation, highlighting key regions might be necessary for LOV-regulated kinase functions. We also observed the activation of kinase involves destabilizing the linker region between LOV2-STK, which interacts with the n-lobe of the kinase near the ATP binding site. We speculated that upon illumination, rearrangement of these linkers might hold the key to the STK substrate association and downstream functions. Additionally, using a low-resolution cryo-EM map, combined with local HDX-MS data and computational resources (e.g., AlphaFold), we built a model for the LOV2-STK complex. Our model highlights critical PPI sites within AsPhot1 and provides additional insights into understanding the generality of how protein kinases interact with their associated domain via conserved residues and subdomains. These findings may help develop novel optogenetic tools and therapeutics for mammalian PAS regulated kinase.

A Fast Spatial–Spectral NMF for Hyperspectral Unmixing

This letter proposes a fast yet efficient method to solve the hyperspectral unmixing problem in the challenging unsupervised context, i.e., when the endmember spectral signatures are unknown. First, a coarse approximation of the hyperspectral image is computed by spatially averaging neighboring pixels, which significantly reduces the amount of pixels to be handled. This reduced set of hyperspectral pixels is unmixed to derive coarse solutions of the unmixing problem, i.e., coarse estimates of the endmember signatures and the corresponding low-resolution abundance maps. Then, the plain resolution abundance maps are estimated from the corresponding hyperspectral image based on the coarse endmember signatures. A sparsity promoting prior exploiting the low resolution map complements the conventional data fitting term to promote spatial smoothness while mitigating the loss of details in the edge areas. Finally, a least square optimization problem is solved to obtain the actual endmember signatures from the hyperspectral image and the abundance maps of plain resolution estimated in the previous step. Numerical experiments show that the proposed method is fast and performs well compared to state-of-the-art approaches from the literature.

Surface Water Extent Mapping in Denmark: Comparing Airborne Thermal Imagery and Satellite Earth Observation

Mapping and prediction of inundated areas are increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observe flooding patterns for training, calibration, validation, and benchmarking. At the regional to continental scales, satellite earth observation (EO) is the established method for surface water extent (SWE) mapping, and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared with low-resolution SWE maps derived from satellite earth observation and with potential flooded areas derived from the high-resolution digital elevation model. We conclude that UASs have significant potential for SWE mapping at intermediate scales (up to a few square kilometers), can bridge the scale gap between ground observations and satellite EO, and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Research on Camouflaged Human Target Detection Based on Deep Learning.

In the task of camouflaged human target detection, the target is highly integrated with the complex environment background, which is difficult to identify and leads to false detection and missed detection. A detection algorithm MC-YOLOv5s is proposed for the characteristics of camouflaged targets. The algorithm takes YOLOv5s as the basic framework. First, a multispectral channel attention module is embedded in the backbone feature extraction network, which enhances the network's ability to extract camouflaged target features, weakens the attention to the surrounding background, and effectively improves the algorithm's antibackground interference. Second, the original upsampling operation is replaced by a lightweight general upsampling operator to achieve effective fusion of high-resolution low-level feature maps and low-resolution high-level feature maps. Finally, the K-means++ clustering method is used to optimize the anchor boxes of the dataset target, and the sizes of the generated priori boxes are allocated to each detection layer, which increases the matching degree between the priori boxes and the actual target boxes, and further improves the detection accuracy of the algorithm. The training and verification were carried out on the military camouflaged personnel dataset (MCPD), the precision (P), recall (R), and mean average precision (mAP) of the MC-YOLOv5s algorithm reached 97.4%, 86.1%, and 94%, respectively. Compared to the original YOLOv5s model, mean average precision (mAP) is increased by 3.7 percentage points. The improved algorithm has better detection effect, is more sensitive to camouflaged targets, and achieves accurate positioning and identification of camouflaged human targets. If the proposed MC-YOLOv5s is applied to personnel search and rescue in complex battlefields and natural disaster environments, it can greatly improve personnel search and rescue efficiency and life survival rate, and reduce the consumption of human and material resources in rescue.

Structural basis for redox dependent retention of ERAP1 by ERp44 in endoplasmic reticulum

ERp44, a PDI family chaperone with RDEL signal sequence, sequesters proteins from cis-golgi to ER. Deletion of ERp44 in mice releases intracellular ERAP1 to plasma, and increases angiotensin II processing and blood pressure. Previous experiments in transfected cells had implicated ERp44 C29 in the thioredoxin-like a-domain and ERAP1 C498 in the domain-II “exon-10” loop as important in the retention mechanism. However, other key domain-domain interactions between the two proteins and mechanism of release of ERAP1 from ERp44 is yet to be studied. Using purified recombinant ERp44 expressed in E. coli and ERAP1 expressed in insect cells, we confirmed that the C29-C498 intermolecular disulfide formed spontaneously upon mixing ERp44 and ERAP1 resulting in a monodisperse 1:1 complex. Previously ERp44 interaction with other targets has been shown to be pH dependent, but we found that ERp44 interaction with ERAP1 was not highly pH sensitive in the range 5.7 – 7.7, suggesting a possible novel mode of interaction. The interaction was specific for ERp44, as PDI and ERp57, other oxidoreductases, did not form a complex with ERAP1. We are using cryo-electron microscopy to get an insight into the structural basis for interaction of ERAP1 and ERp44. Preliminary data collected on Talos (200kV) yielded a low-resolution 3D model map of ERAP1-ERp44 at 8.5Ǻ that resembles ERAP1 in the open conformation, with substantial density extending from the vicinity of the exon 10 loop. A model for the complex fits well into this density, positioned with ERp44 C29 adjacent to ERAP1 C498. We will examine the ERAP1-ERp4 structure to see if we can identify the structural basis for the preferential reactivity of ERp44 C29 over C63, and for ERAP1 C498 over C486, and to understand the mechanism of release of ERAP1 from the complex through a disulfide exchange reaction.

Fast-HBNet: Hybrid Branch Network for Fast Lane Detection

As one of the fundamental visual tasks in the unmanned driving area, lane detection attracts increasing attention. In practical applications, lane points are very difficult to localize because they usually appear to be sparse and incomplete due to the influence of illumination and environment. Conventional lane detection methods rely on coarse features and carefully designed postprocessing to detect the lane lines. However, these methods are usually slow, and the stability and generalization ability are unsatisfactory. In this paper, we propose a novel lane detection network – Fast-HBNet(Fast-Hybrid Branch Network), which exploits both global semantic information and spatial contexts. To enlarge receptive fields and encode more detailed information, the compound transformation is employed and the proposed hybrid branch network extracts four diverse feature maps with different receptive fields and spatial contexts. Besides, we design a Hierarchical Feature Learning (HFL) module to learn lane features from the scale, channel, and spatial levels to enhance the generalization ability of our detector. These features are further selectively coalesced to generate unified lane feature maps with large receptive fields and rich detailed information. In other words, our network can encode the global semantic information from the high-resolution feature maps and the fine-grained details in the low-resolution feature maps. Experimental results conducted on the TuSimple (2017) and CULane [Pan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2018)] datasets demonstrate that the proposed Fast-HBNet outperforms numerous state-of-the-art lane detectors in both speed and accuracy. Particularly, Fast-HBNet achieves an accuracy of 96.88% on the TuSimple dataset at a speed of 76 FPS.

Balanced Spatial Feature Distillation and Pyramid Attention Network for Lightweight Image Super-resolution

Recently, the attention mechanism became the key issue for image super-resolution (SR) because it has the ability to extract different features from the image according to the used attention type. Despite the great success of attention-based methods, the conflict among features of different attention types can affect the SR performance. In this paper, we propose an efficient single image SR model called balanced spatial feature distillation and pyramid attention (BSPAN). The idea of BSPAN is based on the trade-off among the extracted features of different attention types. Also, we propose a balanced spatial feature distillation block (BSFDB) as the backbone of BSPAN so that the network can effectively advantage from the different attention features. Two different attention types, namely spatial attention residual feature distillation (SARFD) and classical attention (CA) are considered in the BSFDB to achieve a leveling between them based on the contents of the low-resolution feature map using the balancing attention block. The BSFDB block is designed to improve the SR performance and has lightweight parameters and low computation complexity. Moreover, to further improve the SR performance, the pyramid attention is introduced in the middle of the BSPAN network for extracting long-range features at a variety of locations and scales. Evaluation based on five benchmark datasets, we concluded that the balancing between features of a variety of attention types can effectively improve the SR performance. So, the proposed BSPAN model achieves significant enhancements in comparison with the state-of-the-art and superior visual quality and reconstruction accuracy.

Deep feature pyramid network for EEG emotion recognition

In this work, the Feature Pyramid Network (FPN) is proposed for improving the performance of electroencephalography (EEG) emotion recognition. Differential Entropy (DE) is extracted from each EEG channel as the basic feature. Then the feature matrix was constructed through biharmonic spline interpolation to obtain the correlation information between EEG channels. Next, the proposed FPN is applied to fuse multiscale EEG spatial information for obtaining deeper semantic features. FPN integrates the semantic information of low-level and high-level feature maps through up-sampling and lateral connections. It can reduce the semantic gap between feature maps and increase the receptive field of low-resolution feature maps. Finally, a linear Support Vector Machine (SVM) is utilized for emotion recognition. The experiment results of the proposed method have a state-of-the-art performance of valence (94.29%) and arousal (96.97%) by 70–30 cross-validation on the Database for Emotion Analysis using Physiological Signals (DEAP). And the accuracy of 97.12% was obtained on the SJTU Emotion EEG Dataset (SEED). Moreover, we carried out the LOSO CV experiment on DEAP. The accuracy of valence and arousal reached 80.38% and 82.33%, respectively.

Driver Distraction Detection Using Octave-Like Convolutional Neural Network

This study proposes a lightweight convolutional neural network with an octave-like convolution mixed block, called OLCMNet, for detecting driver distraction under a limited computational budget. The OLCM block uses point-wise convolution (PC) to expand feature maps into two sets of branches. In the low-frequency branches, we perform average pooling, depth-wise convolution (DC), and upsampling to obtain a low-resolution low-frequency feature map, reducing spatial redundancy and connection density. In the high-frequency branches, the expanded feature map with the original resolution is fed to the DC operator, gaining an apposite receptive field to capture fine details. The feature concatenation of the low-frequency and high-frequency branches is encoded sequentially by a squeeze-and-excitation (SE) module and PC operator, realizing feature global information fusion. Introducing another SE module at the last stage, the OLCMNet facilitates further sensitive information exchange between layers. In addition, with an augmented reality head-up display (ARHUD) platform, we create a Lilong Distracted Driving Behavior (LDDB) Dataset through a series of on-road experiments. Such a dataset contains 14808 videos collected from an infrared camera, covering six driving behaviors of 2468 participants. We manually annotate these videos at five frames per second, obtaining a total of 267378 images. Compared with the existing methods, the embedded hardware platform experiments indicate that OLCMNet hits acceptable trade-offs, namely, 89.53% accuracy for StateFarm Dataset and 95.98% accuracy LDDB Dataset when the latency is 32.8 ± 4.6ms.

CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions

Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge the receptive field, but this increases the parameters. Most CNN models use pooling layers to extract important features, but the pooling operations cause information loss. Transposed convolution can increase the spatial size of the feature maps to recover the lost low-resolution information. In this study, we used two branches with different dilated rates to obtain different size features. The dilated convolution can capture richer information, and the outputs from the two channels are concatenated together as input for the next block. The small size feature maps of the top blocks are transposed to increase the spatial size of the feature maps to recover low-resolution prediction maps. We evaluated the model on three image classification benchmark datasets (CIFAR-10, SVHN, and FMNIST) with four state-of-the-art models, namely, VGG16, VGG19, ResNeXt, and DenseNet. The experimental results show that CDTNet achieved lower loss, higher accuracy, and faster convergence speed in the training and test stages. The average test accuracy of CDTNet increased by 54.81% at most on SVHN with VGG19 and by 1.28% at least on FMNIST with VGG16, which proves that CDTNet has better performance and strong generalization abilities, as well as fewer parameters.

A Pilot Study in Archaeological Metal Detector Geophysical Survey

Abstract Metal detection (MD) has traditionally been viewed as a limited geophysical survey method for the identification of metal objects below the surface. However, this pilot study examines techniques utilizing the “ground balance” function, common to most modern metal detectors, to identify subsurface magnetic anomalies. The results of surveys have yielded inconclusive results on the use of metal detectors for feature identification. However, the results of this study suggest a high potential for more efficient and more productive archaeological reconnaissance surveys. The ground balance function, when combined with systematic sampling and geographic information systems interpolation methods, yields low-resolution subsoil magnetic susceptibility maps. Compared to other geophysical methods, such as gradiometry or electrical resistivity, the depth range of MD is limited. However, this technique, in upland contexts with shallow subsoils or sites with high potential for recent ground disturbing activities, can reveal subtle changes in the subsurface that traditional MD techniques would miss. Further studies are recommended to explore the many situations in which a metal detector can provide an informative alternative, though not a replacement, for other geophysical survey methods. This pilot study was funded by the National Geographic Society’s Early Career Grant program.

Real-Time Optical Mapping of Contracting Cardiac Tissues With GPU-Accelerated Numerical Motion Tracking.

Optical mapping of action potentials or calcium transients in contracting cardiac tissues are challenging because of the severe sensitivity of the measurements to motion. The measurements rely on the accurate numerical tracking and analysis of fluorescence changes emitted by the tissue as it moves, and inaccurate or no tracking can produce motion artifacts and lead to imprecise measurements that can prohibit the analysis of the data. Recently, it was demonstrated that numerical motion-tracking and -stabilization can effectively inhibit motion artifacts, allowing highly detailed simultaneous measurements of electrophysiological phenomena and tissue mechanics. However, the field of electromechanical optical mapping is still young and under development. To date, the technique is only used by a few laboratories, the processing of the video data is time-consuming and performed offline post-acquisition as it is associated with a considerable demand for computing power. In addition, a systematic review of numerical motion tracking algorithms applicable to optical mapping data is lacking. To address these issues, we evaluated 5 open-source numerical motion-tracking algorithms implemented on a graphics processing unit (GPU) and compared their performance when tracking and compensating motion and measuring optical traces in voltage- or calcium-sensitive optical mapping videos of contracting cardiac tissues. Using GPU-accelerated numerical motion tracking, the processing times necessary to analyze optical mapping videos become substantially reduced. We demonstrate that it is possible to track and stabilize motion and create motion-compensated optical maps in real-time with low-resolution (128 x 128 pixels) and high resolution (800 x 800 pixels) optical mapping videos acquired at 500 and 40 fps, respectively. We evaluated the tracking accuracies and motion-stabilization capabilities of the GPU-based algorithms on synthetic optical mapping videos, determined their sensitivity to fluorescence signals and noise, and demonstrate the efficacy of the Farnebäck algorithm with recordings of contracting human cardiac cell cultures and beating hearts from 3 different species (mouse, rabbit, pig) imaged with 4 different high-speed cameras. GPU-accelerated processing provides a substantial increase in processing speed, which could open the path for more widespread use of numerical motion tracking and stabilization algorithms during routine optical mapping studies.