Miracle-dz/UniFAR-FTUs huggingface dataset:
MultiSegVA: Using Visual Analytics to Segment Biologging Time Series on Multiple Scales

Segmenting biologging time series of animals on multiple temporal scales is an essential step that requires complex techniques with careful parameterization and possibly cross-domain expertise. Yet, there is a lack of visual-interactive tools that strongly support such multi-scale segmentation. To close this gap, we present our MultiSegVA platform for interactively defining segmentation techniques and parameters on multiple temporal scales. MultiSegVA primarily contributes tailored, visual-interactive means and visual analytics paradigms for segmenting unlabeled time series on multiple scales. Further, to flexibly compose the multi-scale segmentation, the platform contributes a new visual query language that links a variety of segmentation techniques. To illustrate our approach, we present a domain-oriented set of segmentation techniques derived in collaboration with movement ecologists. We demonstrate the applicability and usefulness of MultiSegVA in two real-world use cases from movement ecology, related to behavior analysis after environment-aware segmentation, and after progressive clustering. Expert feedback from movement ecologists shows the effectiveness of tailored visual-interactive means and visual analytics paradigms at segmenting multi-scale data, enabling them to perform semantically meaningful analyses. A third use case demonstrates that MultiSegVA is generalizable to other domains.
Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments

The ability to recognize previously mapped locations is an essential feature for autonomous systems. Unstructured planetary-like environments pose a major challenge to these systems due to the similarity of the terrain. As a result, the ambiguity of the visual appearance makes state-of-the-art visual place recognition approaches less effective than in urban or man-made environments. This paper presents a method to solve the loop closure problem using only spatial information. The key idea is to use a novel continuous and probabilistic representations of terrain elevation maps. Given 3D point clouds of the environment, the proposed approach exploits Gaussian Process (GP) regression with linear operators to generate continuous gradient maps of the terrain elevation information. Traditional image registration techniques are then used to search for potential matches. Loop closures are verified by leveraging both the spatial characteristic of the elevation maps (SE (2) registration) and the probabilistic nature of the GP representation. A submap-based localization and mapping framework is used to demonstrate the validity of the proposed approach. The performance of this pipeline is evaluated and benchmarked using real data from a rover that is equipped with a stereo camera and navigates in challenging, unstructured planetary-like environments in Morocco and on Mt. Etna.
Vertex operator superalgebra/sigma model correspondences: The four-torus case

We propose a correspondence between vertex operator superalgebras and families of sigma models in which the two structures are related by symmetry properties and a certain reflection procedure. The existence of such a correspondence is motivated by previous work on ${\cal N}=(4,4)$ supersymmetric non-linear sigma models on K3 surfaces, and on a vertex operator superalgebra with Conway group symmetry. Here we present an example of the correspondence for ${\cal N}=(4,4)$ supersymmetric non-linear sigma models on four-tori, and compare it to the K3 case.
A survey on Blockchain-based applications for reforming data protection, privacy and security

The modern society, economy and industry have been changed remarkably by many cutting-edge technologies over the last years, and many more are in development and early implementation that will in turn led even wider spread of adoptions and greater alteration. Blockchain technology along with other rising ones is expected to transform virtually every aspect of global business and individuals' lifestyle in some areas. It has been spreading with multi-sector applications from financial services to healthcare, supply chain, and cybersecurity emerging every passing day. Simultaneously, in the digital world, data protection and privacy are the most enormous issues which customers, companies and policymakers also take seriously into consideration due to the recent increase of security breaches and surveillance in reported incidents. In this case, blockchain has the capability and potential to revolutionize trust, security and privacy of individual data in the online world. Hence, the purpose of this paper is to study the actual cases of Blockchain applied in the reformation of privacy and security field by discussing its impacts as well as the opportunities and challenges.
Novel Functions of ErbB4 and NRG-1 in Development and Cancer

Cells communicate, or signal, with each other constantly to ensure proper functioning of tissues and organs. Cell signaling is often performed by interplay of receptors and ligands that bind these receptors. ErbB receptors (epidermal growth factor receptors, EGFR, HER) bind extracellular growth factors and transduce these signals inside of cells. ErbB dysfunction promotes carcinogenesis, and also results in numerous defects during normal development. This study focused on the functions of one member of the ErbB receptor family, ErbB4, and growth factor, neuregulin-1 (NRG-1), that can bind and activate ErbB4. This study aimed to find novel functions of ErbB4 and NRG-1. Hypoxia, or deficiency of oxygen, is common in cancer and ischemic conditions. One of the key findings of the work was the identification and characterization of a cross-talk between ErbB4 and Hypoxiainducible factor 1α (HIF-1α), the central mediator of hypoxia signaling. ErbB4 activation by NRG-1 was found to increase HIF-1α activity. Interestingly, this regulation occurred in reciprocal manner as HIF-1α was also able to increase protein levels of NRG-1 and ErbB4. Moreover, expression of NRG-1 and ErbB4 was associated with HIF activity in vivo in human clinical samples and in mice. Reduction of functional ErbB4 in developing zebrafish embryos resulted in defects in development of the skeletal muscles. To study ErbB4 functions in pathological situation in humans, clinical samples of serous ovarian carcinoma were analyzed using tissue microarrays and real-time RT-PCR. A specific isoform of ErbB4, CYT1, was associated with poor survival in serous ovarian cancer and increased anchorage independent growth of ovarian cancer cells in vitro. These observations demonstrate that ErbB4 and NRG-1 are essential regulators of cellular response to hypoxia, of development, and of ovarian carcinogenesis.
Distinctive 3D local deep descriptors

We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignment. Point cloud patches are extracted, canonicalised with respect to their estimated local reference frame and encoded into rotation-invariant compact descriptors by a PointNet-based deep neural network. DIPs can effectively generalise across different sensor modalities because they are learnt end-to-end from locally and randomly sampled points. Because DIPs encode only local geometric information, they are robust to clutter, occlusions and missing regions. We evaluate and compare DIPs against alternative hand-crafted and deep descriptors on several indoor and outdoor datasets consiting of point clouds reconstructed using different sensors. Results show that DIPs (i) achieve comparable results to the state-of-the-art on RGB-D indoor scenes (3DMatch dataset), (ii) outperform state-of-the-art by a large margin on laser-scanner outdoor scenes (ETH dataset), and (iii) generalise to indoor scenes reconstructed with the Visual-SLAM system of Android ARCore. Source code: https://github.com/fabiopoiesi/dip.
Localized Topological Simplification of Scalar Data

This paper describes a localized algorithm for the topological simplification of scalar data, an essential pre-processing step of topological data analysis (TDA). Given a scalar field <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-lukasczyk-3030353-eqinline-1-small.tif"/></alternatives></inline-formula> and a selection of extrema to preserve, the proposed localized topological simplification (LTS) derives a function g that is close to <inline-formula><tex-math notation="LaTeX">$f$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-lukasczyk-3030353-eqinline-2-small.tif"/></alternatives></inline-formula> and only exhibits the selected set of extrema. Specifically, sub- and superlevel set components associated with undesired extrema are first locally flattened and then correctly embedded into the global scalar field, such that these regions are guaranteed-from a combinatorial perspective-to no longer contain any undesired extrema. In contrast to previous global approaches, LTS only and independently processes regions of the domain that actually need to be simplified, which already results in a noticeable speedup. Moreover, due to the localized nature of the algorithm, LTS can utilize shared-memory parallelism to simplify regions simultaneously with a high parallel efficiency (70%). Hence, LTS significantly improves interactivity for the exploration of simplification parameters and their effect on subsequent topological analysis. For such exploration tasks, LTS brings the overall execution time of a plethora of TDA pipelines from minutes down to seconds, with an average observed speedup over state-of-the-art techniques of up to <inline-formula><tex-math notation="LaTeX">$\times 36$</tex-math><alternatives><graphic position="float" orientation="portrait" xlink:href="27tvcg02-lukasczyk-3030353-eqinline-3-small.tif"/></alternatives></inline-formula>. Furthermore, in the special case where preserved extrema are selected based on topological persistence, an adapted version of LTS partially computes the persistence diagram and simultaneously simplifies features below a predefined persistence threshold. The effectiveness of LTS, its parallel efficiency, and its resulting benefits for TDA are demonstrated on several simulated and acquired datasets from different application domains, including physics, chemistry, and biomedical imaging.
End-to-End Hyperspectral-Depth Imaging with Learned Diffractive Optics

To extend the capabilities of spectral imaging, hyperspectral and depth imaging have been combined to capture the higher-dimensional visual information. However, the form factor of the combined imaging systems increases, limiting the applicability of this new technology. In this work, we propose a monocular imaging system for simultaneously capturing hyperspectral-depth (HS-D) scene information with an optimized diffractive optical element (DOE). In the training phase, this DOE is optimized jointly with a convolutional neural network to estimate HS-D data from a snapshot input. To study natural image statistics of this high-dimensional visual data and to enable such a machine learning-based DOE training procedure, we record two HS-D datasets. One is used for end-to-end optimization in deep optical HS-D imaging, and the other is used for enhancing reconstruction performance with a real-DOE prototype. The optimized DOE is fabricated with a grayscale lithography process and inserted into a portable HS-D camera prototype, which is shown to robustly capture HS-D information. In extensive evaluations, we demonstrate that our deep optical imaging system achieves state-of-the-art results for HS-D imaging and that the optimized DOE outperforms alternative optical designs.
Consequences of reduced bird densities for seed dispersal

v Preface . vi Chapter 1: Introduction . 1 Seed dispersal . 2 Effects of bird declines on dispersal . 5 Seed dispersal in New Zealand . 6 Thesis outline . 7 Chapter 2: Bird density effects on diet . 9 Abstract . 10 Introduction . 11 Methods . 13 Study sites . 13 Bird counts . 15 Fruit abundance and feeding observations . 15 Fruit pulp nutrient analysis . 16 Fruit removal rates . 16 Data analysis . 18 Results . 19 Bird counts . 19 Fruit abundance and feeding observations . 25 Fruit pulp nutrient analysis . 28 Fruit removal rates . 28 Discussion . 35 Bird counts . 35 Feeding observations . 36 Fruit removal rates . 37 Effect of bird declines on fruit removal . 37 Appendix 2.1 . 40 Chapter 3: Quantifying seed dispersal by birds and possums . 41 Abstract . 42 Introduction . 43 Methods . 45 Study site . 45 Seed dispersal quantity . 46 Seed dispersal quality . 50 Results . 51 Seed dispersal quantity . 51
Design and Simulation of a Hybrid Architecture for Edge Computing in 5G and Beyond

Edge Computing in 5G and Beyond is a promising solution for ultra-low latency applications (e.g., Autonomous Vehicle, Augmented Reality, and Remote Surgery), which have an extraordinarily low tolerance for delay and require fast data processing for a very high volume of data. The requirements of delay-sensitive applications (e.g., Low latency, proximity, and Location/Context-awareness) cannot be satisfied by Cloud Computing due to the high latency between User Equipment and Cloud. Nevertheless, Edge Computing in 5G and beyond can promise ultra-high-speed data processing thanks to the placement of computation capabilities closer to endpoint devices, where 5G encourages the speed rate that is 10 times faster than 4G LTE-Advanced. This paper deeply investigates Edge Computing in 5G and characterizes it based on the requirements of ultra-low latency applications. As a contribution, we propose a hybrid architecture that takes advantage of novel and sustainable technologies (e.g., D2D communication, Massive MIMO, SDN, and NFV) and has major features such as scalability, reliability, and ultra-low latency support. The proposed architecture is evaluated based on agent-based simulations demonstrating that our proposal can satisfy requirements and has the ability to respond to high volume demands with low latency.
Variational MIxture of Normalizing Flows

In the past few years, deep generative models, such as generative adversarial networks \autocite{GAN}, variational autoencoders \autocite{vaepaper}, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those early methods, they model the target distributions \emph{implicitly}, in the sense that the probability density functions induced by them are not explicitly accessible. This fact renders those methods unfit for tasks that require, for example, scoring new instances of data with the learned distributions. Normalizing flows have overcome this limitation by leveraging the change-of-variables formula for probability density functions, and by using transformations designed to have tractable and cheaply computable Jacobians. Although flexible, this framework lacked (until recently \autocites{semisuplearning_nflows, RAD}) a way to introduce discrete structure (such as the one found in mixtures) in the models it allows to construct, in an unsupervised scenario. The present work overcomes this by using normalizing flows as components in a mixture model and devising an end-to-end training procedure for such a model. This procedure is based on variational inference, and uses a variational posterior parameterized by a neural network. As will become clear, this model naturally lends itself to (multimodal) density estimation, semi-supervised learning, and clustering. The proposed model is illustrated on two synthetic datasets, as well as on a real-world dataset. Keywords: Deep generative models, normalizing flows, variational inference, probabilistic modelling, mixture models.
Prenatal diagnosis of a diploid‐tetraploid fetus

Dear Sir, The finding of a diploid-tetraploid mosaicism (2n-4n) in cultured amniotic cells is generally considered a culture artifact and the fetus is chromosomally normal (1). The tetraploid cells arise from the amnios and not from the fetus. Nonetheless, on special occasions, its presence indicates a fetus with multiple congenital defects, chromosomically abnormal. We report a case of diploid-tetraploid mosaicism found in amniocytes which was confirmed in fetal fibroblasts. The propositus was the product of the second pregnancy of a young, healthy, non consanguineous couple with an early spontaneous abortion. She was referred for genetic counseling at 20.5 weeks of gestation after ultrasound findings of oligohydramnios and severe hydrops. Inmunological and infectious causes were excluded. Level of alpha-feto protein in maternal serum was normal. Cytogenetic results from amniotic culture revealed a diploidtetraploid mosaicism in the three flasks with 69% of the cells 4 n. Intrauterine death occurred at 28th week of gestation. Physical examination showed: ocular hypertelorism, low malformed ears, high palate, micrognatia and ascitis. Weight was 2.100 g, length 39 cm and head circumference 26.5 cm; internally the fetus had a hypoplastic left lung and hydrotorax. The mixoploidy was confirmed in skin fibroblasts with 21% of 4n cells. Parental karyotypes were normal. In order to determinate the origin of the tetraploidy DNA fingerprinting was performed from chorionic villus, fetal muscle and peripheral blood samples from both parents. Half of the chorionic villus sample bands were in common with the father’s bands and the other half with the mother’s bands. Two mechanisms have been proposed to explain the origin of tetraploidies. In 95% of the cases there is a failure of cytokinesis after the first mitotic division of the zygote, resulting in a mosaic pattern (2). The other mechanism proposed is trispermy, in the remaining 5%. For this case we suggest the former one in the light of the fingerprinting findings. As a conclusion we emphasize the need to confirm the mosaicism in fetal tissues when a diploid-tetraploid mosaicism is
LaDDer: Latent Data Distribution Modelling with a Generative Prior

In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i.e. its topology and structural properties. We propose LaDDer to achieve accurate modelling of the latent data distribution in a variational autoencoder framework and to facilitate better representation learning. The central idea of LaDDer is a meta-embedding concept, which uses multiple VAE models to learn an embedding of the embeddings, forming a ladder of encodings. We use a non-parametric mixture as the hyper prior for the innermost VAE and learn all the parameters in a unified variational framework. From extensive experiments, we show that our LaDDer model is able to accurately estimate complex latent distribution and results in improvement in the representation quality. We also propose a novel latent space interpolation method that utilises the derived data distribution.
A hybrid WENO method with modified ghost fluid method for compressible two-medium flow problems

In this paper, we develop a simplified hybrid weighted essentially non-oscillatory (WENO) method combined with the modified ghost fluid method (MGFM) [28] to simulate the compressible two-medium flow problems. The MGFM can turn the two-medium flow problems into two single-medium cases by defining the ghost fluids status in terms of the predicted the interface status, which makes the material interface "invisible". For the single medium flow case, we adapt between the linear upwind scheme and the WENO scheme automatically by identifying the regions of the extreme points for the reconstruction polynomial as same as the hybrid WENO scheme [50]. Instead of calculating their exact locations, we only need to know the regions of the extreme points based on the zero point existence theorem, which is simpler for implementation and saves computation time. Meanwhile, it still keeps the robustness and has high efficiency. Extensive numerical results for both one and two dimensional two-medium flow problems are performed to demonstrate the good performances of the proposed method.
Simultaneous determination of 4 compositions in Epimedium koreanum Nakai by UPLC

Objective: To develop an UPLC method for the simultaneous determination of neochlorogenic acid,chlorogenic acid,cryptochlorogenic acid and hyperin in Epimedium koreanum Nakai.Methods: Chromatography was performed on a Acquity BEH C18 column(2.1mm×50mm,1.7μm) with the mobile phase of methanol and water containing 0.1% acetic acid;Flow rate was 0.4 mL/min and column temperature was 45℃;The detection wavelength was set at 260nm and 325nm.Results: The correlation coefficient between concentration and chromatographic peak area of neochlorogenic acid,chlorogenic acid,cryptochlorogenic acid and hyperinwas over 0.9996 in the range of 0.576-57.600,1.806-180.600,1.056-105.600 and 1.530-153. 000μg/mL,respectively.The average recoveries of the four compounds were 96.54%,103.60%,98.61% and 102.10%,respectively.Conclusion: The method is convenient and feasible for determination of the content of neochlorogenic acid,chlorogenic acid,cryptochlorogenic acid and hyperin in Epimedium koreanum Nakai.
Rota-Bater paried comodule and Rota-Bater paired Hopf module

In this paper, we introduce the conception of Rota-Baxter paired comodules, which is dual to Rota-Baxter paired modules in [14]. We mainly discuss some properties of Rota-Baxter paired comodules, especially we give the characterization of generic Rota-Baxter paired comodules, which has important application for the construction of Rota-Baxter comodules. Moreover, we construct Rota-Baxter paired comodules on Hopf algebras, weak Hopf algebras, weak Hopf modules, dimodules, relative Hopf modules and Rota-Baxter paired comodules. And then we finally introduce the conception of Rota-Baxter paired Hopf modules by combining Rota-Baxter paired module with Rota-Baxter paired comodule, and give the structure theorem of generic Rota-Baxter paired Hopf modules.
Mass Sensitivity or Gravimetric Satellites

A point mass on the surface of the Earth gives the highest frequency content for orbiting gravimetric measurements, with the maximum frequency for gradiometers or satellite-to-satellite tracking determined by orbital altitude. Frequency-domain expressions are found for orbiting gravimetric measurements of a point-like source on the surface of the Earth. The response of orbiting gradiometers such as GOCE and satelliteto-satellite tracking missions such as GRACE-FO are compared. The optimal signal-to-noise ratio as a function of noise in the measurement apparatus is computed, and from that the minimum detectable mass is inferred. The point mass magnitude that gives signal-to-noise ratio = 3 is for GOCE M3 = 200 Gton and for the laser ranging interferometer measurement on GRACE-FO M3 = 0.5 Gton. For the laser ranging interferometer measurement, the optimal filter for detecting point-like masses has a passband of 1 to 20 mHz, differing from the 0.3 to 20 mHz admittance filter of Ghobadi-Far et al. (2018), which is not specialized for detecting point-like masses. M3 for future GRACE-like missions with different orbital parameters and improved instrument sensitivity is explored, and the optimum spacecraft separation is found. c © 2020 COSPAR. Published by Elsevier Ltd All rights reserved.