Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating High Fidelity Surface Meshes of Neocortical Neurons using Skin Modifiers

We present the results of exploring the capabilities of skinning modiﬁers to generate high ﬁdelity polygonal surface meshes of neurons from their morphological skeletons that are segmented from optical microscopy slides. Our algorithm is implemented in Blender as an add-on relying on its standard Python API. The implementation is also integrated into an open source domain speciﬁc framework, NeuroMorphoVis, that is used to visualize and analyze neuronal morphologies available from the neurosci-entiﬁc community. Our technique is applied to create meshes for a set of neurons with 55 different morphologies reconstructed from the neocortex of a 14-days-old rat. The generated meshes are used to visualize full compartmental simulations of neocortical activity for analysis purposes and also to create high quality scientiﬁc illustrations of in silico neuronal circuits for media production with physically-based path tracers.
Preserving reciprocal consistency in distributed graph databases

Our earlier work identifies reciprocal consistency as an important property that must be preserved in distributed graph databases. It also demonstrates that a failure to do so seriously undermines the integrity of the database itself in the long term. Reciprocal consistency can be maintained as a part of enforcing any known isolation guarantee and such an enforcement is also known to lead to reduction in performance. Therefore, in practice, distributed graph databases are often built atop BASE databases with no isolation guarantees, benefiting from good performance but leaving them susceptible to corruption due to violations of reciprocal consistency. This paper designs and presents a lightweight, locking-free protocol and then evaluates the protocol's abilities to preserve reciprocal consistency and also offer good throughput. Our evaluations establish that the protocol can offer both integrity guarantees and sound performance when the value of its parameter is chosen appropriately.
An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions.
A Pathway to the Automated Global Assessment of Water Level in Reservoirs with Synthetic Aperture Radar (SAR)

Global measurements of reservoir water levels are crucial for understanding Earth’s hydrological dynamics, especially in the context of global industrialization and climate change. Although radar altimetry has been used to measure the water level of some reservoirs with high accuracy, it is not yet feasible unless the water body is sufficiently large or directly located at the satellite’s nadir. This study proposes a gauging method applicable to a wide range of reservoirs using Sentinel–1 Synthetic Aperture Radar data and a digital elevation model (DEM). The method is straightforward to implement and involves estimating the mean slope–corrected elevation of points along the reservoir shoreline. We test the model on six case studies and show that the estimated water levels are accurate to around 10% error on average of independently verified values. This study represents a substantial step toward the global gauging of lakes and reservoirs of all sizes and in any location where a DEM is available.
Hash-based Hierarchical Caching for Interactive Previews in Global Illumination Rendering

Modern Monte-Carlo-based rendering systems still suffer from the computational complexity involved in the generation of noise-free images, making it challenging to synthesize interactive previews. We present a framework suited for rendering such previews of static scenes using a caching technique that builds upon a linkless octree. Our approach allows for memory-efﬁcient storage and constant-time lookup to cache diffuse illumination at multiple hitpoints along the traced paths. Non-diffuse surfaces are dealt with in a hybrid way in order to reconstruct view-dependent illumination while maintaining interactive frame rates. By evaluating the visual ﬁdelity against ground truth sequences and by benchmarking, we show that our approach compares well to low-noise path traced results, but with a greatly reduced computational complexity allowing for interactive frame rates. This way, our caching technique provides a useful tool for global illumination previews and multi-view rendering.
Setting the foundation for helminths systems biology

Next generation sequencing technologies and bioinformatics have provided cost-effective methods to obtain and analyze large amounts of genome and transcriptome data from many different organisms. This information has to be integrated in order to have a system-level understanding of any organism. Parasites, in particular helminths, present new challenges not only for systems biology but for many other disciplines in science because of their diversity and evolution. Helminths are adapted to a diverse set of niches and very little is known about how they interact and manipulate their hosts or how they sense and interpret the environment. In this review, different sequencing technologies are described and it is also discussed how to avoid possible biases in each of them. The technological advances in different ’omics approaches and bioinformatics allow us to study helminths in more detail in order to contribute to current knowledge of parasitology and underpin future explorations.
Estimation of Forest Growing Stock Volume with UAV Laser Scanning Data: Can It Be Done without Field Data?

Laser scanning data from unmanned aerial vehicles (UAV-LS) offer new opportunities to estimate forest growing stock volume ( V ) exclusively based on the UAV-LS data. We propose a method to measure tree attributes and using these measurements to estimate V without the use of field data for calibration. The method consists of five steps: i) Using UAV-LS data, tree crowns are automatically identified and segmented wall-to-wall. ii) From all detected tree crowns, a sample is taken where diameter at breast height (DBH) can be recorded reliably as determined by visual assessment in the UAV-LS data. iii) Another sample of crowns is taken where tree species were identifiable from UAV image data. iv) DBH and tree species models are fit using the samples and applied to all detected tree crowns. v) Single tree volumes are predicted with existing allometric models using predicted species and DBH, and height directly obtained from UAV-LS. The method was applied to a Riegl-VUX data set with an average density of 1130 points m−2 and 3 cm orthomosaic acquired over an 8.8 ha managed boreal forest. The volumes of the identified trees were aggregated to estimate plot-, stand-, and forest-level volumes which were validated using 58 independently measured field plots. The root-mean-square deviance ( R M S D % ) decreased when increasing the spatial scale from the plot (32.2%) to stand (27.1%) and forest level (3.5%). The accuracy of the UAV-LS estimates varied given forest structure and was highest in open pine stands and lowest in dense birch or spruce stands. On the forest level, the estimates based on UAV-LS data were well within the 95% confidence interval of the intense field survey estimate, and both estimates had a similar precision. While the results are encouraging for further use of UAV-LS in the context of fully airborne forest inventories, future studies should confirm our findings in a variety of forest types and conditions.
Evaluation of Water Vapor Radiative Effects Using GPS Data Series over Southwestern Europe

Water vapor radiative effects (WVRE) at surface in the long-wave (LW) and short-wave (SW) spectral ranges under cloud and aerosol free conditions are analyzed for seven stations in Spain over the 2007–2015 period. WVRE is calculated as the difference between the net flux obtained by two radiative transfer simulations; one with water vapor from Global Positioning System (GPS) measurements and the other one without any water vapor (dry atmosphere). The WVRE in the LW ranges from 107.9 Wm 2 to 296.7 Wm − 2 , while in the SW it goes from − 64.9 Wm − 2 to − 6.0 Wm − 2 . The results show a clear seasonal cycle, which allows the classification of stations in three sub-regions. In general, for total (SW + LW) and LW WVRE, winter (DJF) and spring (MAM) values are lower than summer (JJA) and autumn (SON). However, in the case of SW WVRE, the weaker values are in winter and autumn, and the stronger ones in summer and spring. Positive trends for LW (and total) WVRE may partially explain the well-known increase of surface air temperatures in the study region. Additionally, negative trends for SW WVRE are especially remarkable, since they represent about a quarter of the contribution of aerosols to the strong brightening effect (increase of the SW radiation flux at surface associated with a reduction of the cloud cover and aerosol load) observed since the 2000s in the Iberian Peninsula, but with opposite sign, so it is suggested that water vapor could be partially masking the full magnitude of this brightening.
Remote Sensing Image Denoising via Low-Rank Tensor Approximation and Robust Noise Modeling

Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/or underestimating the complexity of realistic noise. In this paper, we propose a new remote sensing image denoising method by integrating intrinsic image characterization and robust noise modeling. Specifically, we use low-Tucker-rank tensor approximation to capture the global multi-factor correlation within the underlying image, and adopt a non-identical and non-independent distributed mixture of Gaussians (non-i.i.d. MoG) assumption to encode the statistical configurations of the embedded noise. Then, we incorporate the proposed image and noise priors into a full Bayesian generative model and design an efficient variational Bayesian algorithm to infer all involved variables by closed-form equations. Moreover, adaptive strategies for the selection of hyperparameters are further developed to make our algorithm free from burdensome hyperparameter-tuning. Extensive experiments on both simulated and real multispectral/hyperspectral images demonstrate the superiority of the proposed method over the compared state-of-the-art ones.
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.
Orion: time estimated causally consistent key-value store

This paper presents a causally consistent key-value store Orion, which uses a novel protocol for Read Only Transactions (ROT). Unlike most of the existing protocols, Orion uses only one round of communication in the best case, and not more than two rounds in the worst case. We provide a theoretical bound on its communication complexity and qualitatively compare it with recent ROT protocols. We also quantitatively compare Orion with state-of-the-art protocol CausalSpartanX and illustrate that Orion achieves up to 1.7x higher throughput and generates 10x fewer messages on widely-used YCSB workload.
Fish Bulletin 152. Food Habits of Albacore, Bluefin Tuna, and Bonito In California Waters

The authors investigated food habits of albacore, Thunnus alalunga, bluefin tuna, Thunnus thynnus, and bonito, Sarda chiliensis, in the eastern North Pacific Ocean during 1968 and 1969. While most stomach samples came from fish caught commercially off southern California and Baja California, some came from fish taken in central California, Oregon, and Washington waters. Standard procedures included enumeration of food items, volumetric analysis, and measure of frequency of occurrence. The authors identified the majority of forage organisms to the specific level through usual taxonomic methods for whole animals. Identification of partially digested animals was accomplished through the use of otoliths for fish, beaks for cephalopods, and the exoskeleton for invertebrates. A pictorial guide to beaks of certain eastern Pacific cephalopods was prepared and proved helpful in identifying stomach contents. This guide is presented in this publication. The study indicates the prominent forage for bluefin tuna, bonito, and albacore in California waters is the northern anchovy, Engraulis mordax.
Medical Animations: A Survey and a Research Agenda

Animation is a potentially powerful instrument to convey complex information with movements, smooth transitions between different states that employ the strong human capabilities to perceive and interpret motion. Animation is a natural choice to display time-dependent data where the dynamic nature of the data is mapped to a kind of video (temporal animation). Clipping planes may be smoothly translated and object transparency adapted to control visibility and further support emphasis of spatial relations, e.g. around a tumor. Animation, however, may also be employed for static data, e.g. to move a camera along a predeﬁned path to convey complex anatomical structures. Virtual endoscopy, where the virtual camera is moved inside an air-ﬁlled or ﬂuid-ﬁlled structure is a prominent example for these non-temporal animations. Animations, however, are complex visualizations that may depict a larger number of changes in a short period of time. Thus, they need to be assessed in their capability to actually convey information. In this paper, we give a survey of temporal and non-temporal animated visualizations focussed on medical applications and discuss the research potential that arises. To be employed more widely, cognitive limitations, e.g. change blindness, need to be considered. The reduction of complexity in temporal animations is an essential topic to enable the detection and interpretation of changes. Emphasis techniques may guide the user’s attention and improve the perception of essential features. Finally, interaction beyond the typical video recorder functionality is considered. Although our focus is medicine, the discussion of a research agenda is partially based on cartography, where animation is widely used.
Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services that underpin human well-being. Given the importance of FD, it is critical to monitor its variations in an explicit manner across space and time, a highly demanding task that cannot be resolved solely by field data. Today, high hopes are placed on satellite-based observations to complement field plot data. The promise is that multiscale monitoring of plant FD, ecosystem functioning, and their services is now possible at global scales in near real-time. However, non-trivial scale challenges remain to be overcome before plant ecology can capitalize on the latest advances in Earth Observation (EO). Here, we articulate the existing scale challenges in linking field and satellite data and further elaborated in detail how to address these challenges via the latest innovations in optical and radar sensor technologies and image analysis algorithms. Addressing these challenges not only requires novel remote sensing theories and algorithms but also urges more effective communication between remote sensing scientists and field ecologists to foster mutual understanding of the existing challenges. Only through a collaborative approach can we achieve the global plant functional diversity monitoring goal.
Assisting Serious Games Level Design with an Augmented Reality Application and Workflow

With the rise in popularity of serious games there is an increasing demand for virtual environments based on real-world locations. Emergency evacuation or fire safety training are prime examples of serious games that would benefit from accurate location depiction together with any application involving personal space. However, creating digital indoor models of real-world spaces is a difficult task and the results obtained by applying current techniques are often not suitable for use in real-time virtual environments. To address this problem, we have developed an application called LevelEd AR that makes indoor modelling accessible by utilizing consumer grade technology in the form of Apple’s ARKit and a smartphone. We compared our system to that of a tape measure and a system based on an infra-red depth sensor and application. We evaluated the accuracy and efficiency of each system over four different measuring tasks of increasing complexity. Our results suggest that our application is more accurate than the depth sensor system and as accurate and more time efficient as the tape measure over several tasks. Participants also showed a preference to our LevelEd AR application over the depth sensor system regarding usability. Finally, we carried out a preliminary case study that demonstrates how LevelEd AR can be successfully used as part of current industry workflows for serious games level design.
The Iris dataset revisited. A partial ordering study

The well-known Iris data set has been studied applying partial ordering methodology. Previous studies, e.g., applying supervision learning such as neural networks (NN) and support-vector machines (SVM) perfectly distinguish between the three Iris subgroups, i.e., Iris Setosa, Iris Versicolour and Iris Virginica, respectively, in contrast to, e.g., K-means clustering that only separates the full Iris data set in two clusters. In the present study applying partial ordering methodology further discloses the difference between the different classification methods. The partial ordering results appears to be in perfect agreement with the results of the K-means clustering, which means that the clear separation in the three Iris subsets applying NN and SVM is neither recognized by clustering nor by partial ordering methodology.
Deep Learning based Approach for Detecting Diseases in Endoscopy

In this paper, we discuss our submissions for the Endoscopic Disease Detection Challenge (EDD2020) [1], which had two sub-challenges. The ﬁrst task involved a bounding box based multi-class detection of diseases, namely Polyp, Barrett’s Esophagus (BE), Cancer, Suspicious and High-Grade Dysplasia (HGD). The second task involved creating semantic masks of the images for the aforementioned class of diseases. For the disease detection task we submitted the predictions of a Faster R-CNN with a ResNeXt-101 backbone and achieved a dscore of 0.1335 ± 0.0936. For the semantic segmentation task, we employed a U-NET with a ResNeXt-50 backbone that achieved an sscore of 0.5031.
Characterization of Microbial Community Structure Associated with Pollution in Xiaoqing River Sediment

To understand the impacts of anthropogenic activities on structure and composition of microbial communities and evaluate how microbial communities respond environmental gradients at river sediments, the composition of microbial communities in sediments from Xiaoqing River (in spring, summer and autumn) were assessed using DGGE and real-time PCR approaches. Meanwhile, 16S rRNA clone library construction of three sites was constructed to represent the composition and structure of microbial communities in the three distinct site-groups. The gene copy number was ranged from 107-108 cells/g that was most influenced by sample sediment density and medium grain size. Through analysis of DGGE gel profile, there were no distinct variation on richness, evenness and Shannon-Weaver index with all samples, which ranged 3.69-5.21, 1.73-2.30, and 0.56-0.76 respectively. The clustering result on the DGGE patterns showed that the microbial diversity of all samples were more similar than 40%, while the distinction was formed with three groups at a level of 46% similarity. Redundancy analysis revealed that the distribution of microbial composition seemed to be determined by the variables of nitrite, medium grain size and total carbon content. The clone library of three sites revealed that the Proteobacteria was the dominant phylum, which consistent with DGGE bands sequencing result. In addition, members of Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes and Verrucomicrobia were recorded in all three libraries. The distribution of functional populations as denitrifier and anammox bacteria will be the focus of future research.