Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing

The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV ≈ 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture . . . ) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers’ methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer’s involves distortions and loss of information with an estimated residual error of approximately 12 px.
Single Sketch Image based 3D Car Shape Reconstruction with Deep Learning and Lazy Learning

Efficient car shape design is a challenging problem in both the automotive industry and the computer animation/games industry. In this paper, we present a system to reconstruct the 3D car shape from a single 2D sketch image. To learn the correlation between 2D sketches and 3D cars, we propose a Variational Autoencoder deep neural network that takes a 2D sketch and generates a set of multi-view depth and mask images, which form a more effective representation comparing to 3D meshes, and can be effectively fused to generate a 3D car shape. Since global models like deep learning have limited capacity to reconstruct fine-detail features, we propose a local lazy learning approach that constructs a small subspace based on a few relevant car samples in the database. Due to the small size of such a subspace, fine details can be represented effectively with a small number of parameters. With a low-cost optimization process, a high-quality car shape with detailed features is created. Experimental results show that the system performs consistently to create highly realistic cars of substantially different shape and topology.
Patient Motion Compensation for Photogrammetric Registration

Photogrammetry has evolved as a non-invasive alternative for various medical applications, including coregistration of the patient at the time of a surgical operation with pre-surgically acquired data as well as surgical instruments. In this case body surface position regularly has to be determined in a global co-ordinate system with high accuracy. In this paper, we treat this task for multi-view monocular imagery acquiring both body surface as well as e.g. reference markers. To fulfill the high accuracy requirements the patient is not supposed to move while images are taken. An approach towards relaxing this demanding situation is to measure small movements of the patient, e.g. with help of an electromagnetic device, and to compensate for the measured motion prior to body surface triangulation. We present two approaches for motion compensation: disparity shift compensation, and moving cameras compensation both capable of achieving patient registration qualitatively equivalent to motion-free registration.
Pitching Classification and Habit Detection by V-Net

: In this paper, we propose a method that is classified pitching motions using deep learning and detected the habits of pitching. In image classification, there is a method called Grad-CAM to visualize the location related to classification. However, it is difficult to apply the Grad-CAM to conventional video classification methods using 3D-Convolution. To solve this problem, we propose a video classification method based on V-Net. By reconstructing input video, it is possible to visualize the frame and location related to classification result based on Grad-CAM. In addition, we improved the classification accuracy in comparison with conventional methods using 3D-Convolution and reconstruction. From experimental results, we confirmed the effectiveness of our method.
Hand Gesture Recognition based on Near-infrared Sensing Wristband

Wrist-worn gesture sensing systems can be used as a seamless interface for AR/VR interactions and control of various devices. In this paper, we present a low-cost gesture sensing system that utilizes near Infrared Emitters (600 - 1100 nm) and Photo-Receivers encompassing the wrist to infer hand gestures. The proposed system consists of a wristband comprising Infrared emitters and receivers, data acquisition hardware, data post-processing software, and gesture classification algorithms. During the data acquisition process, 24 near Infrared Emitters are sequentially switched on around the wrist, and twelve Photo-diodes measure the light reflected, refracted, and scattered by the tissues inside the wrist. The acquired data corresponding to different gestures are labeled and input into a machine learning algorithm for gesture classification. To demonstrated the accuracy and speed of the proposed system, real-time gesture sensing user studies were conducted. As a result of this comparison, we obtained an average accuracy of 98.06% with standard deviation of 1.82%. In addition, we evaluated that the system can perform six-eight gestures per second in real time using a desktop computer operating with Core i7-7800X CPU at 3.5GHz and 32 GB RAM.
Dynamic Detectors of Oriented Spatial Contrast from Isotropic Fixational Eye Movements

Good vision proficiency and a complex set of eye movements are frequently coexisting. Even during fixation, our eyes keep moving in microscopic and erratic fashion, thus avoiding stationary scenes from fading perceptually by preventing retinal adaptation. We artificially replicate the functionalities of biological vision by exploiting this active strategy with an event-based camera. The resulting neuromorphic active system redistributes the low temporal frequency power of a static image into a range the sensor can detect and encode in the timing of events. A spectral analysis of its output attested both whitening and amplification effects already postulated in biology depending on whether or not the stimulus’ contrast matched the 1/k falloff typical of natural images. Further evaluations revealed that the isotropic statistics of fixational eye movements is crucial for equalizing the response of the system to all possible stimulus orientations. Finally, the design of a biologically-realistic spiking neural network allowed the detection of stimulus’ local orientation by anisotropic spatial summation of synchronous activity with both ON/OFF polarities.
Mirror Symmetry Detection in Digital Images

: This article proposes an approach to the recognition of symmetrical objects in digital images, based on a quantitative asymmetry measure construction of such objects. The object asymmetry measure is determined through the Fourier descriptor of a discrete object boundary points sequence. A method has been developed for calculating the asymmetry measure and determining the most likely symmetry axis based on minimizing the asymmetry measure. The proposed solution using the Fourier descriptor has a quadratic complexity in the number of the object boundary points. A practical assessment of the efﬁciency and effectiveness of the algorithm is obtained by computational experiments with silhouettes of aircraft in remote sensing images.
Image Time Series Classification based on a Planar Spatio-temporal Data Representation

: Image time series such as MRI functional sequences or Satellite Image Time Series (SITS) provide valuable information for the automatic analysis of complex patterns through time. A major issue when analyzing such data is to consider at the same time their temporal and spatial dimensions. In this article we present a novel data representation that makes image times series compatible with classical deep learning model, such as Convolutional Neural Networks (CNN). The proposed approach is based on a novel planar representation of image time series that converts 2 D + t data as 2 D images without loosing too much spatial or temporal information. Doing so, CNN can learn at the same time the parameters of 2 D ﬁlters involving temporal and spatial knowledge. Preliminary results in the remote sensing domain highlight the ability of our approach to discriminate complex agricultural land-cover classes from a SITS.
Optical Flow Estimation using a Correlation Image Sensor based on FlowNet-based Neural Network

: Optical ﬂow estimation is one of a challenging task in computer vision ﬁelds. In this paper, we aim to combine correlation image that enables single frame optical ﬂow estimation with deep neural networks. Correlation image sensor captures temporal correlation between incident light intensity and reference signals, that can record intensity variation caused by object motion effectively. We developed FlowNetS-based neural networks for correlation image input. Our experimental results demonstrate proposed neural networks has succeeded in estimating the optical ﬂow.
Teaching on the Intersection of Visualization and Digital Humanities

: Visualization as a means to generate hypotheses and to communicate insights on digitized cultural heritage data sets has become more and more important in the recent years. While many digital humanities researchers transform their data in order to be processed with ready-to-use tools, others engage in interdisciplinary collaborations with visualization scholars aiming to design novel solutions and interactive visual interfaces as occurring data features are more carefully mapped to visual attributes. Many of those collaborations suffer from loss of time at the beginning of the project as scholars with diverse research backgrounds require to understand each others’ mindsets, ways of thinking and research interests. This paper reports on three years of teaching visualization design for digital humanities projects. It provides an overview of theoretical course contents, practical training, collaboration setups—involving computer science, digital humanities as well as humanities students who experienced typical collaboration obstacles—and remarkable project results.
3D Plant Growth Prediction via Image-to-Image Translation

: This paper presents a method to predict three-dimensional (3D) plant growth with RGB-D images. Based on neural network based image translation and time-series prediction, we construct a system that gives the predicted result of RGB-D images from several past RGB-D images. Since both RGB and depth images are incorporated into our system, the plant growth can be represented in 3D space. In the evaluation, the performance of our proposed network is investigated by focusing on clarifying the importance of each module in the network. We have veriﬁed how the prediction accuracy changes depending on the internal structure of the our network.
DataShiftExplorer: Visualizing and Comparing Change in Multidimensional Data for Supervised Learning

In supervised learning, to ensure the model's validity, it is essential to identify dataset shifts, i.e., when the data distribution changes from the one the model encountered at the time of training. To detect such changes, a comparative analysis of the multidimensional data distributions of the training data and new, unseen datasets is required. In this paper, we span the design space of visualizations for multidimensional comparative data analytics. Based on this design space, we present DataShiftExplorer, a technique tailored to identify and analyze the change in multidimensional data distributions. Throughout examples, we show how DataShiftExplorer facilitates the identification and analysis of data changes, supporting supervised learning.
Fabrication of Micro Spiral Phase Plates in Fused Silica using F2-Laser Microstructuring

The results of our investigations on direct laser fabrication of micro spiral phase plates (SPPs) in fused silica using the fluorine laser microstructuring technique will be presented. The process, which based on the mask projection technique, enables the generation of SPPs with different topological charges, handedness, modulation depths and level numbers. For this, a special double mask rotation system was developed, which allows the fabrication of micro SPPs with an individual configuration with regard to the mentioned parameters. Moreover, the phase of a spherical lens can be added to the helical phase of the SPP using a special mask geometry. Furthermore, we build up a measurement system for a first optical characterization of the fabricated
Hypoglycaemic effect of catalpol in a mouse model of high-fat diet-induced pre-diabetes.

Type 2 diabetes mellitus is a major health problem and a societal burden. Individuals with pre-diabetes are at increased risk of type 2 diabetes mellitus. Catalpol, an iridoid glycoside, has been reported to exert a hypoglycaemic effect in db/db mice, but its effect on the progression of pre-diabetes is unclear. In this study, we established a mouse model of pre-diabetes and examined the hypoglycaemic effect, and the mechanism of any such effect, of catalpol. Catalpol (200 mg/kg/day) had no effect on glucose tolerance or the serum lipid level in a mouse model of impaired glucose tolerance (IGT)-stage pre-diabetes. However, catalpol (200 mg/kg/day) increased insulin sensitivity and decreased the fasting glucose level in a mouse model of impaired fasting glucose/impaired glucose tolerance-stage pre-diabetes. Moreover, catalpol increased the mitochondrial membrane potential (1.52-fold) and ATP content (1.87-fold) in skeletal muscle and improved skeletal muscle function. These effects were mediated by activation of the IRS-1/GLUT4 signalling pathway in skeletal muscle. Our findings will facilitate the development of a novel approach to suppressing the progression of diabetes at an early stage. Novelty bullets • Catalpol prevents the progression of pre-diabetes in a mouse model of pre-diabetes. • Catalpol improves insulin sensitivity in skeletal muscle. • The effects of catalpol are mediated by activation of the IRS-1/GLUT4 signalling pathway.
Transgenic Breeding Approaches for Improving Abiotic Stress Tolerance: Recent Progress and Future Perspectives

The recent rapid climate changes and increasing global population have led to an increased incidence of abiotic stress and decreased crop productivity. Environmental stresses, such as temperature, drought, nutrient deficiency, salinity, and heavy metal stresses, are major challenges for agriculture, and they lead to a significant reduction in crop growth and productivity. Abiotic stress is a very complex phenomenon, involving a variety of physiological and biochemical changes in plant cells. Plants exposed to abiotic stress exhibit enhanced levels of ROS (reactive oxygen species), which are highly reactive and toxic and affect the biosynthesis of chlorophyll, photosynthetic capacity, and carbohydrate, protein, lipid, and antioxidant enzyme activities. Transgenic breeding offers a suitable alternative to conventional breeding to achieve plant genetic improvements. Over the last two decades, genetic engineering/transgenic breeding techniques demonstrated remarkable developments in manipulations of the genes for the induction of desired characteristics into transgenic plants. Transgenic approaches provide us with access to identify the candidate genes, miRNAs, and transcription factors (TFs) that are involved in specific plant processes, thus enabling an integrated knowledge of the molecular and physiological mechanisms influencing the plant tolerance and productivity. The accuracy and precision of this phenomenon assures great success in the future of plant improvements. Hence, transgenic breeding has proven to be a promising tool for abiotic stress improvement in crops. This review focuses on the potential and successful applications, recent progress, and future perspectives of transgenic breeding for improving abiotic stress tolerance and productivity in plants.
Steering the Clinical Translation of Delivery Systems for Drugs and Health Products

Besides the feasibility for industrial scale-up, accelerating the translation from bench to bedside of new technological strategies for controlled delivery and targeting of drugs and other actives relevant for health management, such as medical devices and nutraceuticals, would benefit from an even earlier evaluation in pre-clinical models and clinical settings. At the same time, translational medicine also performs in the opposite direction, incorporating clinical needs and observations into scientific hypotheses and innovative technological proposals. With these aims, the sessions proposed for the 2019 CRS Italy Chapter Workshop will introduce the experience of Italian and worldwide researchers on how to foster the actual work in controlled release and drug delivery towards a reliable pre-clinical and clinical assessment.
Tsinghua facial expression database – A database of facial expressions in Chinese young and older women and men: Development and validation

Perception of facial identity and emotional expressions is fundamental to social interactions. Recently, interest in age associated changes in the processing of faces has grown rapidly. Due to the lack of older faces stimuli, most previous age-comparative studies only used young faces stimuli, which might cause own-age advantage. None of the existing Eastern face stimuli databases contain face images of different age groups (e.g. older adult faces). In this study, a database that comprises images of 110 Chinese young and older adults displaying eight facial emotional expressions (Neutral, Happiness, Anger, Disgust, Surprise, Fear, Content, and Sadness) was constructed. To validate this database, each image was rated on the basis of perceived facial expressions, perceived emotional intensity, and perceived age by two different age groups. Results have shown an overall 79.08% correct identification rate in the validation. Access to the freely available database can be requested by emailing the corresponding authors.
Origin of the natural variation in the storage of dietary carotenoids in freshwater amphipod crustaceans

Carotenoids are diverse lipophilic natural pigments which are stored in variable amounts by animals. Given the multiple biological functions of carotenoids, such variation may have strong implications in evolutionary biology. Crustaceans such as Gammarus amphipods store large amounts of these pigments and inter-population variation occurs. While differences in parasite selective pressure have been proposed to explain this variation, the contribution of other factors such as genetic differences in the gammarid ability to assimilate and/or store pigments, and the environmental availability of carotenoids cannot be dismissed. This study investigates the relative contributions of the gammarid genotype and of the environmental availability of carotenoids in the natural variability in carotenoid storage. It further explores the link of this natural variability in carotenoid storage with major crustacean immune parameters. We addressed these aspects using the cryptic diversity in the amphipod crustacean Gammarus fossarum and a diet supplementation protocol in the laboratory. Our results suggest that natural variation in G. fossarum storage of dietary carotenoids results from both the availability of the pigments in the environment and the genetically-based ability of the gammarids to assimilate and/or store them, which is associated to levels of stimulation of cellular immune defences. While our results may support the hypothesis that carotenoids storage in this crustacean may evolve in response to parasitic pressure, a better understanding of the specific roles of this large pigment storage in the crustacean physiology is needed.
Integrated querying and version control of context-specific biological networks

Abstract Motivation Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. Availability and Implementation VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion Contact tyler.cowman@case.edu
Association of Hypertension With Stroke Recurrence Depends on Ischemic Stroke Subtype

Background and Purpose—The association between hypertension (HTN) and stroke recurrence is unclear, but may be influenced by different subtypes of stroke. This study aims to explore whether HTN contributes to the recurrence of certain subtypes of ischemic stroke (IS). Methods—Data from the China National Stroke Registry was examined and 1-year follow-up data for stroke recurrence was analyzed. Trial of Org 10172 in Acute Stroke Treatment criteria was used to classify the subtypes of all IS. HTN was defined when resting blood pressure was ≥140/90 mm Hg on repeated measurements during hospitalization or a patient had been on antihypertensive medication. Recurrent stroke was defined as a new neurological deficit compatible to IS or intracerebral hemorrhage. The association between HTN and stroke recurrence in patients with different IS subtypes was analyzed by using univariable and multivariable logistic regression models. Results—Of 11 560 patients with IS, 8409 (72.7%) had HTN and 2050 (17.7%) experienced .