Miracle-dz/UniFAR-FTUs huggingface dataset:
K-means clustering for efficient registration of multi-view point sets

This paper cast the multi-view registration into a clustering problem, which can be solved by the proposed approach based on the K-means clustering. For the clustering, all the centroids are uniformly sampled from the initially aligned point sets involved in the multi-view registration. Then, two standard K-means steps are utilized to assign all points to one special cluster and update each cluster centroid. Subsequently, the shape comprised by all cluster centroids can be used to sequentially estimate the rigid transformation for each point set. To obtain accurate results, the K-means steps and transformation estimation should be alternately and iteratively applied to all point sets. Finally, the proposed approach was tested on some public data sets and compared with the-state-of-art algorithms. Experimental results illustrate its good efficiency for the registration of multi-view point sets.
Truecluster: scalable statistical clustering with model selection

Data based classification is fundamental to most branches of science. Despite of progress in statistical computing and predictive modelling, cluster analysis until today lacks model selection robustness and scalability to large datasets. We consider the important problem of deciding about the optimal number of clusters given an arbitrary definition of space and clusteriness. We show how to construct a Cluster Information Criterion that allows objective model selection. Differing from other approaches, our truecluster method does not require specific assumptions about underlying distributions, distance definitions or cluster models. Truecluster puts arbitrary clustering algorithms into a generic unified (sampling based) statistical framework. It is scalable to big datasets and provides robust cluster assignments and casewise diagnostics. Truecluster will make clustering more objective, allows for automation and will save time and costs. www.truecluster. com Copyright (C) Dr. Jens Oehlschlägel 2005, all rights reserved. We thank Thomas Augustin and Stefan Pilz for their helpful comments on the draft of this paper.
Motion-Based Design of Passive Damping Devices to Mitigate Wind-Induced Vibrations in Stay Cables

Wind action can induce large amplitude vibrations in the stay cables of bridges. To reduce the vibration level of these structural elements, different types of passive damping devices are usually installed. In this paper, a motion-based design method is proposed and implemented in order to achieve the optimum design of different passive damping devices for stay cables under wind action. According to this method, the design problem is transformed into an optimization problem. Thus, its main aim is to minimize the different terms of a multi-objective function, considering as design variables the characteristic parameters of each considered passive damping device. The multi-objective function is defined in terms of the scaled characteristic parameters, one single-function for each parameter, and an additional function that checks the compliance of the considered design criterion. Genetic algorithms are considered as a global optimization method. Three passive damping devices have been studied herein: viscous, elastomeric and friction dampers. As a benchmark structure, the Alamillo bridge (Seville, Spain), is considered in order to validate the performance of the proposed method. Finally, the parameters of the damping devices designed according to this proposal are successfully compared with the results provided by a conventional design method.
The Development and Validation of a First Generation X-Ray Scatter Computed Tomography Algorithm for the Reconstruction of Electron Density Breast Images Using Monte Carlo Simulation

ii LIST OF ABREVIATIONS . vii TABLE OF CONTENTS. xi LIST OF FIGURES . xiv LIST OF TABLES. xx 1. Rationale . 1 2. Introduction. 7 2.1. Breast imaging . 7 2.2. X-ray mammography. 9 2.3. Breast CT . 15 2.4. Scatter imaging . 22 3. Physics background . 28 3.1. Photon interactions. 28 3.1. 1. Materials mixtures . 30 3.2. Physics of scattering . 34 3.2.1. Incoherent scatter . 35 3.2.2. Coherent scatter . 37 3.3. Computed Tomography . 38 3.4. Computer simulations: Analytical and Monte Car lo . 41 3.4. 1. Analytical simulations . 41 3.4. 2. Monte Carlo simulations. 44
A physical scattering model of repeat-pass InSAR correlation for vegetation

A physical scattering model of repeat-pass InSAR correlation over forested areas is derived by accounting for the changes in the dielectric properties and positions of the scatterers in the scene between overpasses. This derivation is based on the discrete representation of a sparse random medium (such as forest canopy) along with the solution to the Foldy–Lax multiple scattering equations. In addition to taking into account the random motion of scatterers, which has been investigated in previous work, the derived repeat-pass InSAR correlation model in this paper includes the effects of moisture-induced dielectric fluctuations. This is accomplished by incorporating a separate correlation profile that takes into account these fluctuations into the framework. Once constructed, the mathematical formulation of this scattering model is cast into a modified version of the Random Volume over Ground model such that it can separately take into account dielectric fluctuations in the ground and volume components. This model is then validated using a modified version of ESA’s PolSARproSim simulation to show that similar results can be obtained from both the simulation and the theoretical model.
Directly selecting differentially expressed genes for single-cell clustering analyses

In single-cell RNA sequencing (scRNA-seq) studies, cell-types and their associated marker genes are often identified by clustering and differential expression gene (DEG) analysis. scRNA-seq data contain many genes not relevant to cell-types and gene selection procedures are needed for more accurate clustering. An ideal gene selection procedure should select all DEGs between cell-types for best cell-type identification. However, because cell-types are unknown, gene selection and DEG analysis are performed separately using different methods. Genes are selected using surrogate criteria not directly related with clustering, which often miss important genes or select unimportant genes. Clustering accuracy could be seriously influenced because of the inferior gene selection. DEGs are often detected by comparing different clusters, leading to many false DEGs due to the selection bias problem. In this paper, we present Festem, a unified method for gene selection and DEG analysis in scRNA-seq studies. Festem investigates gene’s clustering information based on the observation that marginal distributions of DEGs are mixtures of their different cell-type-conditional distributions, and can directly select the clustering-informative DEGs and avoid the selection bias problem. Extensive simulation and real data analyses show that Festem achieves high precision and recall for DEG detection, and enables more accurate clustering and cell-type identification. Applications to several scRNA-seq datasets demonstrate that Festem can identify cell-types that are often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify CD8+ T cell-types and find that their marker genes are novel prognostic biomarkers.
The factorization method for a partially coated cavity in inverse scattering

We consider the interior inverse scattering problem of recovering the shape of an impenetrable partially coated cavity. The scattered fields incited by point source waves are measured on a closed curve inside the cavity. We prove the validity of the factorization method for reconstructing the shape of the cavity. However, we are not able to apply the basic theorem introduced by Kirsch and Grinberg to treat the key operator directly, and some auxiliary operators have to be considered. In this paper, we provide theoretical validation of the factorization method to the problem, and some numerical results are presented to show the viability of our method.
Numerical study of turbulent flow in a rectangular T-junction

In this paper, we report on a numerical study of the interaction and merging of a turbulent crossflow with an incoming turbulent jet in a T-junction with rectangular cross section. Our study is based on wall-resolved and experimentally validated large eddy simulations. The bulk Reynolds number of the crossflow is 15 000. Further, we consider cases with two different momentum ratios, namely, MR = 2 and MR = 0.5. In the presentation of the results, we elaborate on the main features of the flow, namely, the shear layers that emanate from the corners of the entry of the jet, the large recirculation bubble downstream the incoming jet, and the mixing process beyond the reattachment point. For validation purposes, we compare our simulations with existing experimental data. This comparison shows a good agreement between our numerical predictions and the measurements. First- and second-order statistics of the flow are also presented and analyzed in detail. Our simulations reveal two features of the flow that have .
Sex, gender diversity, and brain structure in children ages 9 to 11 years old

There remains little consensus about the relationship between sex and brain structure, particularly in childhood. Moreover, few pediatric neuroimaging studies have analyzed both sex and gender as variables of interest - many of which included small sample sizes and relied on binary definitions of gender. The current study examined gender diversity with a continuous felt-gender score and categorized sex based on X and Y allele frequency in a large sample of children ages 9-11 years-old (N=7693). Then, a statistical model-building approach was employed to determine whether gender diversity and sex independently or jointly relate to brain morphology, including subcortical volume, cortical thickness, gyrification, and white matter microstructure. The model with sex, but not gender diversity, was the best-fitting model in 75% of gray matter regions and 79% of white matter regions examined. The addition of gender to the sex model explained significantly more variance than sex alone with regard to bilateral cerebellum volume, left precentral cortical thickness, as well as gyrification in the right superior frontal gyrus, right parahippocampal gyrus, and several regions in the left parietal lobe. For mean diffusivity in the left uncinate fasciculus, the model with sex, gender, and their interaction captured the most variance. Nonetheless, the magnitude of variance accounted for by sex was small in all cases and felt-gender score was not a significant predictor on its own for any white or gray matter regions examined. Overall, these findings demonstrate that at ages 9-11 years-old, sex accounts for a small proportion of variance in brain structure, while gender diversity is not directly associated with neurostructural diversity. Highlights Sex-related variance in regional human brain structure is widespread at ages 9-11 Together, sex and gender diversity accounted for more variance in only a few regions Felt-gender diversity itself was not significantly related to any outcome at ages 9-11 Effect sizes for sex and felt-gender estimates were small
Evaluation of shear piezoelectric coefficient d15 of piezoelectric ceramics by using piezoelectric cantilever beam in dynamic resonance

ABSTRACT A novel method for characterization on the shear piezoelectric coefficient of piezoelectric ceramics was proposed. A shear-mode piezoelectric cantilever with coplanar electrode structure was fabricated via arranging two PZT-51 elements poled opposite in series connection, and a principle model based on Timoshenko beam theory was proposed to establish the relationship between peak-to-peak output voltage and frequency. The measured values are in good agreement with the results simulated by finite element method (FEM) with maximum relative error less than 4.5%. It is reliable and convenient for the proposed method to characterize the shear piezoelectric coefficient of piezoelectric ceramics.
Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes

The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.
Ghost Imaging for Online Angiography

Ghost imaging (GI) has characteristics that make it promising for applications in life sciences and other fields, such as its high sensitivity and strong anti-interference compared with traditional imaging. This paper presents a new approach for online angiography using GI. Two signals are correlation-calculated to detect the object image: one of them is the random light field generated by a computer, and the other enters the optical fiber path after being transmitted to the detected object via a modulator. A new approach for the real-time imaging of intravascular flow, vascular wall structures, and components of atherosclerotic plaque is proposed, which has the advantages of a high sensitivity and anti-interference.
An investigation into the performance of real-time GPS+GLONASS Precise Point Positioning (PPP) in New Zealand

Abstract This paper presents an investigation into the performance of real-time Global Navigation Satellite Systems (GNSS) Precise Point Positioning (PPP) in New Zealand. The motivation of the research is to evaluate the feasibility of using PPP technique and a satellite based augmentation system such as the Japanese Quasi-Zenith Satellite System (QZSS) to deliver a real-time precise positioning solution in support of a nation-wide high accuracy GNSS positioning coverage in New Zealand. Two IGS real-time correction streams are evaluated alongside with the PPP correction messages transmitted by the QZSS satellite known as MDC1. MDC1 corrections stream is generated by Japan Aerospace Exploration Agency (JAXA) using the Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) software and are currently transmitted in test mode by the QZSS satellite. The IGS real-time streams are the CLK9B real-time corrections stream generated by the French Centre National D’études Spatiales (CNES) using the PPP-Wizard software, and the CLK81 real-time corrections stream produced by GMV using their MagicGNSS software. GNSS data is collected from six New Zealand CORS stations operated by Land Information New Zealand (LINZ) over a one-week period in 2015. GPS and GLONASS measurements are processed in a real-time PPP mode using the satellite orbit and clock corrections from the real-time streams. The results show that positioning accuracies of 6 cm in horizontal component and 15 cm in vertical component can be achieved in real-time PPP. The real-time GPS+GLONASS PPP solution required 30 minutes to converge to within 10 cm horizontal positioning accuracy.
Reliable protein-protein docking with AlphaFold, Rosetta, and replica-exchange

Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases. 1 In this work, we combine AlphaFold as a structural template generator with a physics-based replica exchange docking algorithm. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol 2 to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 66% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (19% success rate), AlphaRED demonstrates a success rate of 51%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.
An adaptive biomolecular condensation response is conserved across environmentally divergent species

Cells must sense and respond to sudden maladaptive environmental changes—stresses—to survive and thrive. Across eukaryotes, stresses such as heat shock trigger conserved responses: growth arrest, a specific transcriptional response, and biomolecular condensation of protein and mRNA into structures known as stress granules under severe stress. The composition, formation mechanism, adaptive significance, and even evolutionary conservation of these condensed structures remain enigmatic. Here we provide an unprecedented view into stress-triggered condensation, its evolutionary conservation and tuning, and its integration into other well-studied aspects of the stress response. Using three morphologically near-identical budding yeast species adapted to different thermal environments and diverged by up to 100 million years, we show that proteome-scale biomolecular condensation is tuned to species-specific thermal niches, closely tracking corresponding growth and transcriptional responses. In each species, poly(A)-binding protein—a core marker of stress granules—condenses in isolation at species-specific temperatures, with conserved molecular features and conformational changes modulating condensation. From the ecological to the molecular scale, our results reveal previously unappreciated levels of evolutionary selection in the eukaryotic stress response, while establishing a rich, tractable system for further inquiry.
General aspects of sound localization through internally coupled ears

In internally coupled ears (ICE), eardrum vibrations are coupled through an air-filled interaural cavity. This dissertation presents a mathematical analysis of ICE’s role in generating sound localization cues in the form of time and amplitude differences between the ears. In particular, it analyzes the eardrum‘s role in segregating hearing into low- and high-frequency regimes. Its results also lead to a method of estimating the eardrum's material properties through in vivo measurements. The role played by ICE under water, incl. high-frequency hearing, is investigated as well.
Hypertexture

We model phenomena intermediate between shape and texture by using space-filling applicative functions to modulate density. The model is essentially an extension of procedural solid texture synthesis, but evaluated throughout a volumetric region instead of only at surfaces.We have been able to obtain visually realistic representations of such shape+texture (hypertexture) phenomena as hair, fur, fire, glass, fluid flow and erosion effects. We show how this is done, first by describing a set of base level functions to provide basic texture and control capability, then by combining these to synthesize various phenomena.Hypertexture exists within an intermediate region between object and not-object. We introduce a notion of generalized boolean shape operators to combine shapes having such a region.Rendering is accomplished by ray marching from the eye point through the volume to accumulate opacity along each ray. We have implemented our hypertexture rendering algorithms on a traditional serial computer, a distributed network of computers and a coarse-grain MIMD computer. Extensions to the rendering technique incorporating refraction and reflection effects are discussed.
Using MODIS weekly evapotranspiration to monitor drought

The Stratospheric Observatory For Infrared Astronomy (SOFIA) is a 2.5-m telescope mounted inside of a Boeing 747SP. Planning and executing astronomical observations from an aircraft moving at 500 miles per hour has its own unique challenges and advantages. Scheduling and optimizing an entire year of science observations is a balancing act with target availability, instrument availability, and operational constraints. A SOFIA flight is well choreographed, and successfully executing observations on SOFIA requires many systems and people to work together- from the telescope assembly compensating for the continual vibration and movement of the plane in order to accurately point the telescope, the expertise of the telescope operators to prepare the telescope for use by the instrument operators, aircraft operations ensuring that the aircraft is ready for flight, and the mission systems control computers keeping track of all the data. In this paper we will discuss what it takes to plan a SOFIA flight, and what we do once we’re in the air. We will share a typical science flight, as well as more challenging and unique observations that require SOFIA being in the right place at the right time.
Apitherapie in der Onkologie

Ingredients of honey and propolis (bee glue) have proven antiproliferative and immunostimulant activities. Honey may well be used in cases of poorly healing wounds andulceratedtumours.Currently thehighestevidencefor a clinical use is the prophylaxis and treatmentof radiation inducted mucositis especially forhead andneckcancers.Becauseofthepossibilityof infectionswithclostridiamedicinal grade honey is recommended
Die homöopathische Krebsbehandlung (Teil 3) Übersichtsarbeit auf der Basis des Seminars "Die Krebsbehandlung in der Homöopathie" vom Juni 1997 in Bad Imnau mit Dario Spinedi

Manifest Cancer is normally preceded by a pre-cancerous stage. This should be diagnosed and treated early. The signs of the precancerous stage and the most important Cancer favoring factors are presented. In many cases the precancerous Symp¬ toms vanish as soon as the tumor develops. During the course of healing they can recur. The "re-sup-pression" of a Symptom, which has been treated improperly before, can have serious consequences. Finally we show, that a homeopathic treat-ment which lasted many years, has proven to be an especially effective Cancer prevention.