Miracle-dz/UniFAR-FTUs huggingface dataset:
High-field and low-field magnetoresistances of CoFe nanoparticles elaborated by organometallic chemistry

We report on magnetotransport measurements on CoFe nanoparticles surrounded by an insulating organic layer. Samples were obtained by evaporating a solution of nanoparticles on a patterned substrate. Typical behavior of Coulomb blockade in array of nanoparticles is observed. High- and low-field magnetoresistances have been evidenced. Below 10K, a large high-field magnetoresistance is measured, reaching up to 500%. Its amplitude decreases strongly with increasing voltage. At 1.6K, this high-field magnetoresistance vanishes and an inverse low-field tunneling magnetoresistance is observed.
Development and Analysis of Sparse Spasmodic Sampling Techniques

The Compressive Sensing (CS) has wide range of applications in various domains. The sampling of sparse signal, which is periodic or aperiodic in nature, is still an out of focus topic. This paper proposes novel Sparse Spasmodic Sampling (SSS) techniques for different sparse signal in original domain. The SSS techniques are proposed to overcome the drawback of the existing CS sampling techniques, which can sample any sparse signal efficiently and also find location of non-zero components in signals. First, Sparse Spasmodic Sampling model-1 (SSS-1) which samples random points and also include non-zero components is proposed. Another sampling technique, Sparse Spasmodic Sampling model-2 (SSS-2) has the same working principle as model-1 with some advancements in design. It samples equi-distance points unlike SSS-1. It is demonstrated that, using any sampling technique, the signal is able to reconstruct with a reconstruction algorithm with a smaller number of measurements. Simulation results are provided to demonstrate the effectiveness of the proposed sampling techniques.
A Coherent Physics Picture of Topological Insulators at Single-Particle Level

The study of topological property of band insulators is an interesting branch of condensed matter physics. Two types of topologically nontrivial insulators have been extensively studied. The first type is characterized by a nonzero TKNN invariant or Chern number[1] which is directly related to the quantization of Hall conductance in the integer quantum Hall effect. Haledane propose a model with this type of band structure even in the absence of a macroscopic magnetic field[2]. We refer to such materials "Chern insulator". The second type called "Z2 topological insulators" is proposed recently[3, 4]. Quantum spin Hall effect has been predicted and observed in such systems. [5, 6]. Despite the recent intensively study there are still some fundamental problems that aren't quite clear about Z2 insulators even at the single-particle level. For example, it's claimed that Z2 insulators will return to its origin state after two cycles, thus coupling to the reservoirs is important for the Z2 insulators to continuously pump spin[7]. Theoretical and experimental results show quantum spin Hall effect is an edge state transport property of the materials and coupling to the reservoir seems not play an important role. So the Z2 picture is not satisfactory in explaining these phenomena. We study the relationship of the ground states of Z2 insulators and that of Chern insulator. Combined with the results of recent researches on polarization of Chern insulators[8] and topology of edge states[9] we propose a coherent physics picture of topological insulators.
On the Nature of the X-Ray Emission from the Ultraluminous X-Ray Source, M33 X-8: New Constraints from NuSTAR and XMM-Newton

We present nearly simultaneous NuSTAR and XMM-Newton observations of the nearby (832 kpc) ultraluminous X-ray source (ULX) M33 X-8. M33 X-8 has a 0.3–10 keV luminosity of LX ≈ 1.4 × 1039 erg s−1, near the boundary of the “ultraluminous” classification, making it an important source for understanding the link between typical Galactic X-ray binaries and ULXs. Past studies have shown that the 0.3–10 keV spectrum of X-8 can be characterized using an advection-dominated accretion disk model. We find that when fitting to our NuSTAR and XMM-Newton observations, an additional high-energy (≳10 keV) Comptonization component is required, which allows us to rule out single advection-dominated disk and classical sub-Eddington models. With our new constraints, we analyze XMM-Newton data taken over the last 17 yr to show that small (≈30%) variations in the 0.3–10 keV flux of M33 X-8 result in spectral changes similar to those observed for other ULXs. The two most likely phenomenological scenarios suggested by the data are degenerate in terms of constraining the nature of the accreting compact object (i.e., black hole versus neutron star). We further present a search for pulsations using our suite of data; however, no clear pulsations are detected. Future observations designed to observe M33 X-8 at different flux levels across the full 0.3–30 keV range would significantly improve our constraints on the nature of this important source.
Lung Segmentation Pipeline for CT Images

Segmentation is one of the fundamental tasks in biomedical image processing. Adequate image segmentation and computer-aided diagnosis systems are excellent allies for healthcare professionals. There are multiple methods for image segmentation using image processing techniques that are still being used and developed. These have advantages over machine learning models and deliver reliable and fast results as training data for their operation do not limit them. This work proposes a 3-step semi-automatic pipeline for lung computed tomography image segmentation. It starts with preprocessing, in which the input image is enhanced; then, the image is segmented using the region growing technique, and finally, the segmentation mask is enhanced by applying a hole-filling process. The experimental results of the pipeline provided a Dice Coefficient of 0.9633 and an Intersection over Union of 0.9341 on average.
Synthesis of Carbon nanofibers (CNFs) by PECVD using Ni catalyst printed by spark ablation

The precise control of patterned arrays of carbon nanofibers (CNFs) is an issue of interest in a wide range of applications. In the present work, we report the synthesis of CNFs grown by plasma-enhanced CVD (PECVD) from a Ni catalyst patterned via aerosol printing of nanoparticles (NPs) by a spark ablation technique. The printing speeds were adjusted to vary the catalyst density and characterize the CNFs distribution in the pre-patterned lines. Depending on the printing speed, different CNFs distributions are obtained, ranging from dense vertically-aligned CNFs (VACNFs) to sparse individual CNFs.
Optimized cell geometry for buffer-gas-cooled molecular-beam sources

We have designed, constructed, and commissioned a cryogenic helium buffer-gas source for producing a cryogenially-cooled molecular beam and evaluated the effect of different cell geometries on the intensity of the produced molecular beam, using ammonia as a test molecule. Planar and conical entrance and exit geometries are tested. We observe a three fold enhancement in the NH$_3$ signal for a cell with planar-entrance and conical-exit geometry, compared to that for a typically used `box'-like geometry with planar entrance and exit. These observations are rationalized by flow-field simulations for the different buffer-gas cell geometries. The full thermalization of molecules with the helium buffer-gas is confirmed through rotationally-resolved REMPI spectra yielding a rotational temperature of 5 K.
Moffatt-drift-driven large-scale dynamo due to ${\it\alpha}$ fluctuations with non-zero correlation times

We present a theory of large-scale dynamo action in a turbulent flow that has stochastic, zero-mean fluctuations of the ${\it\alpha}$ parameter. Particularly interesting is the possibility of the growth of the mean magnetic field due to Moffatt drift, which is expected to be finite in a statistically anisotropic turbulence. We extend the Kraichnan–Moffatt model to explore effects of finite memory of ${\it\alpha}$ fluctuations, in a spirit similar to that of Sridhar & Singh (Mon. Not. R. Astron. Soc., vol. 445, 2014, pp. 3770–3787). Using the first-order smoothing approximation, we derive a linear integro-differential equation governing the dynamics of the large-scale magnetic field, which is non-perturbative in the ${\it\alpha}$ -correlation time ${\it\tau}_{{\it\alpha}}$ . We recover earlier results in the exactly solvable white-noise limit where the Moffatt drift does not contribute to the dynamo growth/decay. To study finite-memory effects, we reduce the integro-differential equation to a partial differential equation by assuming that ${\it\tau}_{{\it\alpha}}$ be small but non-zero and the large-scale magnetic field is slowly varying. We derive the dispersion relation and provide an explicit expression for the growth rate as a function of four independent parameters. When ${\it\tau}_{{\it\alpha}}\neq 0$ , we find that: (i) in the absence of the Moffatt drift, but with finite Kraichnan diffusivity, only strong ${\it\alpha}$ fluctuations can enable a mean-field dynamo (this is qualitatively similar to the white-noise case); (ii) in the general case when also the Moffatt drift is non-zero, both weak and strong ${\it\alpha}$ fluctuations can lead to a large-scale dynamo; and (iii) there always exists a wavenumber ( $k$ ) cutoff at some large $k$ beyond which the growth rate turns negative, irrespective of weak or strong ${\it\alpha}$ fluctuations. Thus we show that a finite Moffatt drift can always facilitate large-scale dynamo action if sufficiently strong, even in the case of weak ${\it\alpha}$ fluctuations, and the maximum growth occurs at intermediate wavenumbers.
Two time solution to quantum measurement paradoxes

It is hypothesized that the Langevin time of stochastic quantum quantization is a physical time over which quantum fields at all values of space and coordinate time fluctuate. The average over paths becomes a time average as opposed to an ensemble average. It is further hypothesized that the Langevin time also paces the motion of particles through coordinate time and is equal to the coordinate time of the present hypersurface in the frame of the Hubble expansion. Despite having a preferred frame, special relativity continues to hold in this formulation as a dynamical symmetry due to the presumed Lorentz invariance of interactions. The measurement process becomes an integral part of the theory and is realized as a process of spontaneous symmetry breaking. The continuously fluctuating history of fields, characteristic of having two times, and the switch from ensemble averages to time averages allows for logical and straightforward explanations of many quantum measurement paradoxes. The fluctuating history also evades hidden-variable prohibitions allowing an essentially classical system to underlie quantum mechanics. These changes to the stochastic quantization paradigm makes this stochastic classical system differ somewhat from standard quantum mechanics, so, in principle, distinguishable from it.
Cognitive Intelligence of a Cloud-Based Internet of Things in Precision Agriculture Applications

Improvement in Precision Agriculture is the field that is a very crucial requirement for any country on this earth. Using Wireless Sensor Network, we can receive and control the agriculture parameters. Arduino microcontroller is used to collect and process the received data and sent it to the cloud for getting current information of land to the individual farmer for taking corrective action for improvement of the yield of crops. Cloud-Based Internet of Thing technologies in Precision Agriculture will be the solution to recover the human being's requirements mostly related to the quality and quantity of foods. This technology will be very useful to a farmer to monitor and control their land any time and anywhere through their smartphone. So the requirements of any country and individual farmers should satisfy with less effort and less cost.
Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics, endurance and device degradation. These negatively impact the computation accuracy of CIM. It is therefore essential to deal with these non-idealities and fabrication imperfections in order to harness the full potential of CIM. This paper discusses the non-ideality challenges and provides potential solutions. Furthermore, the paper outlines the potential future directions for CIM architectures.
PARTICLE ACCELERATION IN THE EXPANDING BLAST WAVE OF η CARINA'S GREAT ERUPTION OF 1843

Non-thermal hard X-ray and high-energy (HE; 1 MeV ⩽ E ⩽ 100 GeV) γ-ray emission in the direction of η Carina has been recently detected using the INTEGRAL, AGILE, and Fermi satellites. So far this emission has been interpreted in the framework of particle acceleration in the colliding wind region between the two massive stars. However, the existence of a very fast moving blast wave which originates in the historical 1843 “Great Eruption” provides an alternative particle acceleration site in this system. Here, we explore an alternate scenario and find that inverse Compton emission from electrons accelerated in the blast wave can naturally explain both the flux and spectral shape of the measured hard X-ray and HE γ-ray emission. This scenario is further supported by the lack of significant variability in the INTEGRAL and Fermi measured fluxes.
Embedding (R+R^2)-Inflation into Supergravity

We find the natural embedding of the (R+R^2)-inflationary model into the recently constructed N=1 F(\cal R)-supergravity. It gives a simple and viable realization of chaotic inflation in supergravity. The only requirement for a slow-roll inflation is the existence of the (\cal R)^3-term with an anomalously large coefficient in Taylor expansion of the F(\cal R) function, where \cal R is the covariantly-chiral scalar supercurvature superfield.
Data-Driven Human-Like Path Planning for Autonomous Driving Based on Imitation Learning

In autonomous driving systems, path planning algorithms play a crucial role in ensuring vehicle safety, driving efficiency, and passenger comfort. The traditional path planning algorithms cannot meet the real-time and passenger comfort requirements of autonomous driving, an alternative is imitation learning, a data-driven approach that mimics expert agents' driving behavior and leverages recent advances in supervised learning. We present a learning-based planner that aims to robustly drive a vehicle by mimicking human drivers' driving behavior. First, we leverage a mid-to-mid approach that allows us to reduce sample complexity. Furthermore, we introduce task losses that penalize undesirable behaviors, which is done by introducing a differentiable rasterizer that directly converts the waypoints output by the network into images. Most importantly, we add the state information of ego vehicle at the historical time to the original LSTM network input which improves the completion rate of scene tasks and the comfort of passengers. Finally, we experiment on the nuPlan dataset to verify the effectiveness of our method.
OUTFLOW AND HOT DUST EMISSION IN HIGH-REDSHIFT QUASARS

Correlations of hot dust emission with outflow properties are investigated, based on a large z ∼ 2 non-broad absorption line quasar sample built from the Wide-field Infrared Survey and the Sloan Digital Sky Survey data releases. We use the near-infrared slope and the infrared to UV luminosity ratio to indicate the hot dust emission relative to the emission from the accretion disk. In our luminous quasars, these hot dust emission indicators are almost independent of the fundamental parameters, such as luminosity, Eddington ratio and black hole mass, but moderately dependent on the blueshift and asymmetry index (BAI) and FWHM of C iv lines. Interestingly, the latter two correlations dramatically strengthen with increasing Eddington ratio. We suggest that, in high Eddington ratio quasars, C iv regions are dominated by outflows so the BAI and FWHM (C iv) can reliably reflect the general properties and velocity of outflows, respectively. In low Eddington ratio quasars, on the other hand, C iv lines are primarily emitted by virialized gas so the BAI and FWHM (C iv) become less sensitive to outflows. Therefore, the correlations for the highest Eddington ratio quasars are more likely to represent the true dependence of hot dust emission on outflows and the correlations for the entire sample are significantly diluted by the low Eddington ratio quasars. Our results show that an outflow with a large BAI or velocity can double the hot dust emission on average. We suggest that outflows either contain hot dust in themselves or interact with the dusty interstellar medium or torus.
The X-Shooter Lens Survey - II. Sample presentation and spatially-resolved kinematics

We present the X-Shooter Lens Survey (XLENS) data. The main goal of XLENS is to disentangle the stellar and dark matter content of massive early-type galaxies (ETGs), through combined strong gravitational lensing, dynamics and spectroscopic stellar population studies. The sample consists of 11 lens galaxies covering the redshift range from 0.1 to 0.45 and having stellar velocity dispersions between 250 and 380 km s(-1). All galaxies have multiband, high-quality HST imaging. We have obtained long-slit spectra of the lens galaxies with X-Shooter on the VLT. We are able to disentangle the dark and luminous mass components by combining lensing and extended kinematics data sets, and we are also able to precisely constrain stellar mass-to-light ratios and infer the value of the low-mass cut-off of the initial mass functions (IMF), by adding spectroscopic stellar population information. Our goal is to correlate these IMF parameters with ETG masses and investigate the relation between baryonic and non-baryonic matter during the mass assembly and structure formation processes. In this paper, we provide an overview of the survey, highlighting its scientific motivations, main goals and techniques. We present the current sample, briefly describing the data reduction and analysis process, and we present the first results on spatially-resolved kinematics.
Cosmic shear calibration with forward modeling

Weak Gravitational Lensing is a powerful probe of the dark sector of the Universe. One of the main challenges for this technique is the treatment of systematics in the measurement of cosmic shear from galaxy shapes. In an earlier work, [1] have proposed the Monte Carlo Control Loops (MCCL) to overcome these effects using a forward modeling approach. We focus here on one of the control loops in this method, the task of which is the calibration of the shear measurement. For this purpose, we first consider the requirements on the shear systematics for a given survey and propagate them to different systematics terms. We use two one-point statistics to calibrate the shear measurement and six further one-point statistics as diagnostics. We also propagate the systematics levels that we estimate from the one-point functions to the two-point functions for the different systematic error sources. This allows us to assess the consistency between the systematics levels measured in different ways. To test the method, we construct synthetic sky surveys with an area of 1,700°2. With some simplifying assumptions, we are able to meet the requirements on the shear calibration for this survey configuration. Furthermore, we account for the total residual shear systematics in terms of the contributing sources. We discuss how this framework can be applied within analyses of current and future weak lensing surveys.
TESTING MAGNETIC FIELD MODELS FOR THE CLASS 0 PROTOSTAR L1527

For the Class 0 protostar L1527 we compare 131 polarization vectors from SCUPOL/JCMT, SHARP/CSO, and TADPOL/CARMA observations with the corresponding model polarization vectors of four ideal-MHD, nonturbulent, cloud core collapse models. These four models differ by their initial magnetic fields before collapse; two initially have aligned fields (strong and weak) and two initially have orthogonal fields (strong and weak) with respect to the rotation axis of the L1527 core. Only the initial weak orthogonal field model produces the observed circumstellar disk within L1527. This is a characteristic of nearly all ideal-MHD, nonturbulent, core collapse models. In this paper we test whether this weak orthogonal model also has the best agreement between its magnetic field structure and that inferred from the polarimetry observations of L1527. We found that this is not the case; based on the polarimetry observations, the most favored model of the four is the weak aligned model. However, this model does not produce a circumstellar disk, so our result implies that a nonturbulent, ideal-MHD global collapse model probably does not represent the core collapse that has occurred in L1527. Our study also illustrates the importance of using polarization vectors covering a large area of a cloud core to determine the initial magnetic field orientation before collapse; the inner core magnetic field structure can be highly altered by a collapse, and so measurements from this region alone can give unreliable estimates of the initial field configuration before collapse.
A CANDELS–3D-HST SYNERGY: RESOLVED STAR FORMATION PATTERNS AT 0.7 < z < 1.5

We analyze the resolved stellar populations of 473 massive star-forming galaxies at 0.7 < z < 1.5, with multi-wavelength broadband imaging from CANDELS and Hα surface brightness profiles at the same kiloparsec resolution from 3D-HST. Together, this unique data set sheds light on how the assembled stellar mass is distributed within galaxies, and where new stars are being formed. We find the Hα morphologies to resemble more closely those observed in the ACS I band than in the WFC3 H band, especially for the larger systems. We next derive a novel prescription for Hα dust corrections, which accounts for extra extinction toward H ii regions. The prescription leads to consistent star formation rate (SFR) estimates and reproduces the observed relation between the Hα/UV luminosity ratio and visual extinction, on both a pixel-by-pixel and a galaxy-integrated level. We find the surface density of star formation to correlate with the surface density of assembled stellar mass for spatially resolved regions within galaxies, akin to the so-called “main sequence of star formation” established on a galaxy-integrated level. Deviations from this relation toward lower equivalent widths are found in the inner regions of galaxies. Clumps and spiral features, on the other hand, are associated with enhanced Hα equivalent widths, bluer colors, and higher specific SFRs compared to the underlying disk. Their Hα/UV luminosity ratio is lower than that of the underlying disk, suggesting that the ACS clump selection preferentially picks up those regions of elevated star formation activity that are the least obscured by dust. Our analysis emphasizes that monochromatic studies of galaxy structure can be severely limited by mass-to-light ratio variations due to dust and spatially inhomogeneous star formation histories.