Miracle-dz/UniFAR-FTUs huggingface dataset:
Live Cell Imaging Methods to Quantify Signaling Molecule Dynamics

using high-resolution bio-imaging methods and biophysical techniques can reveal information about the real-time activities of cell signaling networks. Modeling the temporal changes that can be observed with fluorescence microscopy and their relationship to protein function along complex signaling pathways can help to shed light on the underlying mechanisms that regulate cell signaling. Here, we describe the use of quantum dots (QDs) and Visible Fluorescent Protein (VFP) labels to track and model the dynamics of membrane receptors and cytoplasmic signaling proteins.
Thermochemical characterization of Ca4La6(SiO4)6(OH)2 a synthetic La- and OH-analogous of britholite: implication for monazite and LREE apatites stability

Thermochemical characterization of Ca4La6(SiO4)6(OH)2 a synthetic La- and OH-analogous of britholite: implication for monazite and LREE apatites stability In this contribution, monazite (LREEPO4) solubility is addressed in a chemical system involving REE-bearing hydroxylapatite, (Ca, LREE)10(PO4,SiO4)6(OH)2. For this purpose, a synthetic (La)- and (OH)-analogous of britholite, Ca4La6(SiO4)6(OH)2, was synthesised and its thermodynamic properties were measured. Formation enthalpy of -14,618.4±31.0 kJ·mol-1 was obtained by high-temperature drop-solution calorimetry using a Tian-calvet twin calorimeter (Bochum, Germany) at 975 K using lead borate as solvent. Heat capacities (Cp) were measured in the 143-323 K and 341-623 K ranges with an automated Perkin-Elmer DSC 7. For calculations of solubility diagrams at 298 K, the GEMS program was used because it takes into account solid solutions. In conditions representative of those expected in nuclear waste disposal, calculations show that La-monazite is stable from pH = 4 to 9 with a minimum of solubility at pH = 7. La-bearing hydroxylapatite precipitates at pH > 7 with a nearly constant composition of 99% hydroxylapatite and 1% La-britholite. Each mineral buffers solution at extremely low lanthanum concentrations (log{La} = 10-10-10-15 mol·kg-1 for pH = 4 to 13). In terms of chemical durability, both La-monazite and La-rich apatite present low solubility, a requisite property for nuclear-waste forms.
One-nucleon transfers to resonance states

I.J. Thompson1, J.E. Escher1, G. Arbanas2, Ch. Elster3, V. Eremenko3,4, L. Hlophe3, and F.M. Nunes5, TORUS Collaboration 1 Lawrence Livermore National Laboratory L-414, Livermore, CA 94551, USA, 2 Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA, 3 Ohio University, Athens, OH 45701, USA, 4 M.V. Lomonosov Moscow State University, Moscow, 119991, Russia 5 Michigan State University, East Lansing, MI 48824, USA
N E ] 1 0 M ar 2 01 9 Automatically Evolving CNN Architectures Based on Blocks Yanan Sun and

The performance of Convolutional Neural Networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extended expertise in both CNNs and the investigated problem is required, which is not necessarily held by every user interested in CNNs or the problem domain. In this paper, we propose to automatically evolve CNN architectures by using a genetic algorithm based on ResNet blocks and DenseNet blocks. The proposed algorithm is completely automatic in designing CNN architectures, particularly, neither pre-processing before it starts nor post-processing on the designed CNN is needed. Furthermore, the proposed algorithm does not require users with domain knowledge on CNNs, the investigated problem or even genetic algorithms. The proposed algorithm is evaluated on CIFAR10 and CIFAR100 against 18 state-of-the-art peer competitors. Experimental results show that it outperforms state-of-the-art CNNs hand-crafted and CNNs designed by automatic peer competitors in terms of the classification accuracy, and achieves the competitive classification accuracy against semi-automatic peer competitors. In addition, the proposed algorithm consumes much less time than most peer competitors in finding the best CNN architectures.
Hybrid Dilated Convolution Guided Feature Filtering and Enhancement Strategy for Hyperspectral Image Classification

With the increasing maturity of optics and photonics, hyperspectral technology has also greatly advanced. Hyperspectral images composed of hundreds of adjacent bands and containing useful information can be easily obtained. However, unlike ordinary remote sensing images, each sample in hyperspectral remote sensing images has high-dimensional features and contains rich spatial and spectral information, which greatly increases the difficulty of feature selection and mining, increases the computational complexity, and limits the recognition accuracy of the model. Therefore, in this letter, a novel hybrid dilated-convolution-guided feature filtering and enhancement strategy (HDCFE-Net) model is proposed to classify hyperspectral images. Dilated convolution can reduce the spatial feature loss without reducing the receptive field and can obtain distant features. It can also be combined with the traditional convolution without losing its original information. We propose a feature filtering and enhancement strategy that eliminates redundant features and reduces computational complexity. The core concept is to set a threshold feature value, like the rounding method, to filter and enhance features. Experiments on three well-known hyperspectral datasets—Indian Pines (IPs), Pavia University (PU), and Salinas—show that in less than 1% (IPs: 5%) of the training samples, the overall accuracy (OA) of our method is 77%, 89%, and 91%, respectively, which is superior to several well-known methods. The experiments demonstrated the effectiveness and superiority of HDCFE-Net.
An integrated global observing system for sea surface temperature using satellites and in situ data: research to operations.

Abstract This paper describes the optimal design and its research-to-operation transition of an integrated global observing system of satellites and in situ observations. The integrated observing system is used for climate assessment using sea surface temperature (SST). Satellite observations provide superior samplings while in situ observations provide the ground truth. Observing System Simulation Experiments (OSSEs) were used to objectively design an efficient in situ system to reduce satellite biases to a required accuracy. The system design was peer reviewed and was then transitioned into operations as a U.S. contribution to the international Global Climate Observing System (GCOS). A system performance measure was also formulated and operationally tracked under the Government Performance Results Act (GPRA). Additional OSSEs assisted the planning, programming, budgeting, and execution system at the National Oceanic and Atmospheric Administration (NOAA) to maximize design efficiency. This process of res.
Tropospheric Emission Spectrometer ( TES ) Spectrum Reconstruction Analysis Summary

— The purpose of this article is to summarize the attempts made to carry out spectrum reconstruction for interferograms obtained from the Tropospheric Emission Spectrometer (TES) on-board the Aura satellite, after the laser used to trigger the sampling unit malfunctioned in the spring of 2016. In particular, we highlight the drawbacks that made reconstruction problematic and the approaches attempted to overcome them. These drawbacks include ill-conditioning of the reconstruction problem incurred with nonuniformly spaced interferogram samples, discrepancies in the reported and true spatial locations of the samples, and low resolution of the spatial location data. Algorithms used to overcome these pitfalls, involving regularized reconstruction methods in the case of unknown/imperfect sample locations, are presented, along with reconstruction results showing the difficulties associated with the spectrum reconstruction problem.
The Seasonal and Long Term Changes in Mesospheric Water Vapor

This study explores the feasibility of identifying long term changes in mesospheric water vapor as a result of increasing level of methane in the atmosphere and the solar cycle variation of Lyman α. The study is based on recent measurements of water vapor in the mesosphere and the solar Lyman α flux from the UARS (Upper Atmosphere Research Satellite) HALOE (Halogen Occultation Experiment) and the SOLSTICE (Solar Stellar Iradiance Comparison Experiment) instruments during the declining phase of the solar cycle 22. The solar activity during this period decreased from a near maximum to a near minimum level. The analysis of these data sets, in conjunction with the NASA/GSFC two dimensional chemistry and transport model suggests that on a seasonal time scale, the temporal changes in mesospheric water vapor are largely controlled by the vertical advection associated with the meridional circulation. On the time scale of a solar cycle, H2O may vary by about 30–40 % near the mesopause height (∼80 km) to about 1–2% in the lower mesosphere (60–65 km) caused by the solar cycle modulation of Lyman α. In comparison, the secular increase in H2O related to methane increase in the atmosphere is about 0.4% /year at all heights in the mesosphere.
Effect of Mineral and Bio-fertilizer Application on Growth and Yield of Wheat Triticum aestivum L.

Field experiment was conducted during 20122013 in silt loam soil at experimental station of Crops Department, Abu-Ghraib, State Board of Agricultural Researches in Baghdad, Iraq to study the effect of bio-fertilizers and NP levels on some agronomic traits and yield components of wheat by three bacterial genera (Azotobacter sp., Azospirillum sp. and Pseudomonas sp.) isolated from rhizosphere soil and Zea mays roots to study their effects singly or in combination with NP levels on growth and yield of wheat Triticum aestivum L. cultivar IPA 99. The experimental design was a completely randomized block design with three replications for each treatment . The results showed that the combination of biofertilizer (A. chroococcum, A. brasilense and P. fluorescens) and 100% NP significantly affected on some agronomic traits and yield components, plant height (99.2 cm), flag leaf area (80.56 cm), flag leaf chlorophyll content (55.05 SPAD), number of tillers.plant(28.4), biomass fresh and dry weight (266.89, 85.89 gm) and all these results affected positively on biological yield (20.967 ton.h), spike length (13.2 cm), number of spikes.m (548.30), average weight of 1000 grains (33.02 gm) and grain yield (6.403 ton.h). Laboratory experiment results revealed that there was no antagonism between the isolated bacteria (A. chroococcum, A. brasilense and P. fluorescens) therefore they were used as bio-fertilizer.
Adiabatic flux insertion and growing of Laughlin states of cavity Rydberg polaritons

Recently, the creation of a strong magnetic field in a photonic cavity system has been demonstrated [N. Schine et al., Nature (London) 534, 671 (2016). ]. Using this setup, we propose a scheme to adiabatically transfer flux quanta simultaneously to all cavity photons. The flux transfer is achieved using external light fields with orbital angular momentum and a near-resonant dense atomic medium as a mediator. Furthermore, by coupling the cavity fields to a Rydberg state, strong photon-photon interactions can be realized and fractional quantum Hall states can be prepared. To this end, a growing protocol is discussed consisting of a sequence of flux insertion and subsequent single-photon insertion. Specifically, we discuss the growing of the $\ensuremath{\nu}=1/2$ bosonic Laughlin state. First we adiabatically insert two photonic flux quanta, creating a two-quasihole excitation, and second we refill the hole with a single photon using the strong photon-photon interactions.
Analysis of Cyclic Fluctuations of Charge Motion and Mixture Formation in a DISI Engine in Stratified Operation

The processes of an internal combustion engine are subject to cyclic fluctuations, which have direct consequence on the operational and emission behavior of the engine. Direct injection gasoline engines have fluctuations that are induced and superimposed by the flow and the injection. In stratified operation they can cause serious operating problems, such as misfiring. Currently, the state of knowledge on the formation and causes of cyclic fluctuations is rather limited, which can be attributed to the complex nature of flow instabilities.
Copy-paste network Network input Residuals + Blending Result

In this work, we propose a novel system for smart copy-paste, enabling the synthesis of high-quality results given a masked source image content and a target image context as input. Our system naturally resolves both shading and geometric inconsistencies between source and target image, resulting in a merged result image that features the content from the pasted source image, seamlessly pasted into the target context. Our framework is based on a novel training image transformation procedure that allows to train a deep convolutional neural network end-to-end to automatically learn a representation that is suitable for copy-pasting. Our training procedure works with any image dataset without additional information such as labels, and we demonstrate the effectiveness of our system on two popular datasets, high-resolution face images and the more complex Cityscapes dataset. Our technique outperforms the current state of the art on face images, and we show promising results on the Cityscapes dataset, demonstrating that our system generalizes to much higher resolution than the training data.
Effect of loading rate on the properties of lightly stabilized granular materials by using flexural beam testing

An experimental study has been undertaken to investigate the effect of loading rate on the flexural properties of lightly stabilized granular materials. The flexural beam samples were prepared from a typical granular material stabilized lightly with 1.5% cement-flyash and moisture at optimum moisture content (OMC). Then the samples were cured for 7 days and tested by monotonic third-point load testing at different loading rates ranging from 0.01 to 1.2 mm/min. However, an improved testing setup was used to measure the mid-depth vertical deflection internally for characterizing the lightly stabilized materials more reliably. The experimental results show that the loading rate has a significant influence on the modulus of rupture and flexural modulus of lightly stabilized material. Details of the newly improved experimental arrangement and rate sensitivity on the flexural properties of lightly stabilized materials are presented in this paper. RÉSUMÉ Une étude expérimentale a été entrepris pour étudier l'effet du taux de charge sur les propriétés de flexion de la légère stabilisation des matériaux granulaires. Les échantillons ont été préparés à la flexion du faisceau à partir d'un matériau typique granulaire stabilisé légèrement avec du ciment-cendres volantes de 1,5% et d'humidité à teneur en humidité optimale. Ensuite, les échantillons ont été mûris pendant 7 jours et testés par les tests de charge monotone troisième point au taux de charge différents, allant de 0,01 à 1,2 mm / min. Toutefois, une installation d'essai améliorée a été utilisée pour mesurer la flèche à mi-profondeur verticale interne pour caractériser les matériaux légèrement stabilisée de manière plus fiable. Les résultats expérimentaux montrent que le taux de chargement a une influence significative sur la résistance à la flexion et module de flexion du matériau légèrement stabilisée. Détails de l'accord récemment améliorée et la sensibilité aux taux d'expérimentation sur les propriétés de flexion des matériaux légèrement stabilisée sont présentés dans le présent document.
Topological insulators with arbitrarily tunable entanglement

We elucidate how Chern and topological insulators fulfill an area law for the entanglement entropy. By explicit construction of a family of lattice Hamiltonians, we are able to demonstrate that the area law contribution can be tuned to an arbitrarily small value but is topologically protected from vanishing exactly. We prove this by introducingnovelmethodstoboundentanglemententropiesfromcorrelationsusingperturbationbounds,drawing intuition from ideas of quantum information theory. This rigorous approach is complemented by an intuitive understanding in terms of entanglement edge states. These insights have a number of important consequences: The area law has no universal component, no matter how small, and the entanglement scaling cannot be used as a faithful diagnostic of topological insulators. This holds for all Renyi entropies which uniquely determine the entanglement spectrum, which is hence also nonuniversal. The existence of arbitrarily weakly entangled topological insulators furthermore opens up possibilities of devising correlated topological phases in which the entanglement entropy is small and which are thereby numerically tractable, specifically in tensor network approaches.
Mode-matching for Optical Antennas-Supplementary Material

Thorsten Feichtner Helmholtz-Zentrum Berlin für Materialien und Energie GmbH, Institut Nanoarchitekturen für die Energieumwandlung, Hahn-Meitner-Platz 1, 14109 Berlin, Germany Max Planck Institute for the Science of Light Photonic Nanostructures, Günther-Scharowsky-Strasse 1, Bau 26, 91058 Erlangen, Germany and Nano-Optics & Biophotonics Group, Department of Experimental Physics 5, Röntgen Research Center for Complex Material Research (RCCM), Physics Institute, University of Würzburg, Am Hubland, D-97074 Würzburg, Germany
Hard coal project valuation based on real options approach: multiplicative vs. arithmetic stochastic process

Precise valuation of the economic efficiency of risky investment projects in the mineral sector has a direct impact on the range of future investments. Since the mid-90s, a number of enterprises have also been giving increased attention to the valuation of managerial flexibility that cannot normally be estimated with classical discounted cash flow (DCF) analysis. This has been the result of a development in the real options analysis (ROA) and the simplification of its algorithms, most of which have been achieved through: ♦ incorporating lattice models,♦ introducing a single uncertain project parameter (gross present value, PV) as an underlying instrument,♦ assuming that the underlying asset follows the multiplicative stochastic process,♦ introducing the ‘marketed asset disclaimer’ (MAD) assumption.
September Arctic sea ice minimum prediction – a skillful new statistical approach

Abstract. Sea ice in both polar regions is an important indicator of the expression of global climate change and its polar amplification. Consequently, broad interest exists on sea ice coverage, variability and long-term change. However, its predictability is complex and it depends strongly on different atmospheric and oceanic parameters. In order to provide insights into the potential development of a monthly/seasonal signal of sea ice evolution, we applied a robust statistical model based on different oceanic and atmospheric parameters to calculate an estimate of the September sea ice extent (SSIE) on a monthly timescale. Although previous statistical attempts of monthly/seasonal SSIE forecasts show a relatively reduced skill, when the trend is removed, we show here that the September sea ice extent has a high predictive skill, up to 4 months ahead, based on previous months' oceanic and atmospheric conditions. Our statistical model skillfully captures the interannual variability of the SSIE and could provide a valuable tool for identifying relevant regions and oceanic and atmospheric parameters that are important for the sea ice development in the Arctic and for detecting sensitive/critical regions in global coupled climate models with a focus on sea ice formation.
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients

In recent years deep artificial neural networks (DNNs) have been successfully employed in numerical simulations for a multitude of computational problems including, for example, object and face recognition, natural language processing, fraud detection, computational advertisement, and numerical approximations of partial differential equations (PDEs). These numerical simulations indicate that DNNs seem to possess the fundamental flexibility to overcome the curse of dimensionality in the sense that the number of real parameters used to describe the DNN grows at most polynomially in both the reciprocal of the prescribed approximation accuracy $ \varepsilon > 0 $ and the dimension $ d \in \mathbb{N}$ of the function which the DNN aims to approximate in such computational problems. There is also a large number of rigorous mathematical approximation results for artificial neural networks in the scientific literature but there are only a few special situations where results in the literature can rigorously justify the success of DNNs in high-dimensional function approximation. The key contribution of this paper is to reveal that DNNs do overcome the curse of dimensionality in the numerical approximation of Kolmogorov PDEs with constant diffusion and nonlinear drift coefficients. We prove that the number of parameters used to describe the employed DNN grows at most polynomially in both the PDE dimension $ d \in \mathbb{N}$ and the reciprocal of the prescribed approximation accuracy $ \varepsilon > 0 $. A crucial ingredient in our proof is the fact that the artificial neural network used to approximate the solution of the PDE is indeed a deep artificial neural network with a large number of hidden layers.
CMOS low-noise switched charge sensitive preamplifier for CdTe and CdZnTe X-ray detectors

CdTe and CdZnTe X-ray detector arrays for imaging and spectroscopy provide low capacitance current sources with low leakage currents. The optimal shaping time for low-noise operation is relatively high in CMOS analog channels that provide the readout for these detectors. The shaper is centered at lower frequencies, and thus the 1/f noise from the electronics is the main noise source that limits the resolution of the channel. The optimal dimensions of the input stage MOSFET are determined by this noise. In this paper a design criterion for the optimization of the resolution and the power consumption in a 1/f noise dominated readout is introduced. A readout based on CMOS switched charge sensitive preamplifier without feedback resistor has been designed and fabricated in the CMOS 2-/spl mu/ low-noise analog process provided by MOSIS. This design provides high sensitivity and the possibility to integrate a large number of channels with low power consumption. Measurements of the performance of a first prototype chip are presented.
Functional analysis of a novel antagonistic antibody against the short epitope of the a 1 A-adrenergic receptor

Functional analysis of a novel antagonistic antibody against the short epitope of the a1A-adrenergic receptor
First Excited 0 + States in Deformed Nuclei

The nature of vibrations or excitations built on a deformed nuclear shape remains an open question in nuclear structure. The Gd nucleus is one of the most extensively studied nuclei with the (n, γ), (n, e−), (α, 2n) reactions yielding a spectrum of six excited 0 states. This work reports on the measurements of level lifetimes in the Gd nucleus following neutron capture using the GRID (Gamma Ray Induced Doppler broadening) technique at the Institut Laue-Langevin (ILL) in Grenoble, France. In total, twelve level lifetimes have been measured, including the three K = 0 states at 1049, 1154, and 1715 keV. The K = 0 state at 1049 is more strongly connected to the ground state levels than the K = 0 state at 1154 keV by factors of 5 and the K = 0 state at 1715 keV is connected to the K = 2 band by an order of magnitude stronger B(E2) values than to the ground state levels. PACS codes: 21.10. -k, 21.10. Tg, 21.10. Re, 27.70. +q