Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple MATLAB/Simulink simulation for PV modules based on one-diode model

This paper proposes a simple and practical simulation model for solar modules using MATLAB/Simulink. The model is based on a single-diode mathematical model of a solar cell and is capable of accurate modeling of I-V and P-V characteristics of a solar module. Model parameters are obtained from manufacturer's datasheets and series and shunt resistances are calculated using a simple method based on open-circuit voltage, short-circuit current and irradiance values. The model is interfaced with SimPowerSystems toolbox. This feature makes the model capable of being used with power electronic devices and elements for advance analyses. The model is validated using measured I-V characteristics of a commercially available crystalline silicon solar module and the effects of environmental conditions (temperature and irradiance) as well as the effects of cell parameters like series and shunt resistances on module characteristics are investigated. The proposed model is capable of being used by solar energy researchers, system analysts and designers as a simple and helpful tool for advance analysis requirements.
Multi-Target Detection With an Arbitrary Spacing Distribution

Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we consider the multi-target detection model, where multiple copies of a target signal occur at unknown locations in a long measurement, further corrupted by additive Gaussian noise. At low noise levels, one can easily detect the signal occurrences and estimate the signal by averaging. However, in the presence of high noise, which is the focus of this paper, detection is impossible. Here, we propose two approaches—autocorrelation analysis and an approximate expectation maximization algorithm—to reconstruct the signal without the need to detect signal occurrences in the measurement. In particular, our methods apply to an arbitrary spacing distribution of signal occurrences. We demonstrate reconstructions with synthetic data and empirically show that the sample complexity of both methods scales as SNR${}^{-3}$ in the low SNR regime.
Mesoscale Organization and Dynamics in Binary Ionic Liquid Mixtures.

The impact of mesoscale organization on dynamics and ion transport in binary ionic liquid mixtures is investigated by broadband dielectric spectroscopy, dynamic-mechanical spectroscopy, x-ray scattering, and molecular dynamics simulations. The mixtures are found to form distinct liquids with macroscopic properties that significantly deviate from weighted contributions of the neat components. For instance, it is shown that the mesoscale morphologies in ionic liquids can be tuned by mixing to enhance the static dielectric permittivity of the resulting liquid by as high as 100% relative to the neat ionic liquid components. This enhancement is attributed to the intricate role of interfacial dynamics associated with the changes in the mesoscopic aggregate morphologies in these systems. These results demonstrate the potential to design the physicochemical properties of ionic liquids through control of solvophobic aggregation.
Does Data Augmentation Lead to Positive Margin?

Data augmentation (DA) is commonly used during model training, as it significantly improves test error and model robustness. DA artificially expands the training set by applying random noise, rotations, crops, or even adversarial perturbations to the input data. Although DA is widely used, its capacity to provably improve robustness is not fully understood. In this work, we analyze the robustness that DA begets by quantifying the margin that DA enforces on empirical risk minimizers. We first focus on linear separators, and then a class of nonlinear models whose labeling is constant within small convex hulls of data points. We present lower bounds on the number of augmented data points required for non-zero margin, and show that commonly used DA techniques may only introduce significant margin after adding exponentially many points to the data set.
Factors characterizing access and latency to first pharmacological treatment in Italian patients with schizophrenia, mood, and anxiety spectrum disorders

Latency to first pharmacological treatment [duration of untreated illness (DUI)] in psychiatric disorders can be measured in years, with differences across diagnostic areas and relevant consequences in terms of socio-occupational functioning and outcome. Within the psychopathological onset of a specific disorder, many factors influence access and latency to first pharmacotherapy and the present study aimed to investigate such factors, through an ad-hoc developed questionnaire, in a sample of 538 patients with diagnoses of schizophrenia-spectrum disorder (SZ), mood disorder (MD), and anxiety disorder (AD). Patients with SZs showed earlier ages at onset, first diagnosis and treatment, as well as shorter DUI compared with other patients (43.17 months vs. 58.64 and 80.43 months in MD and AD; F=3.813, P=0.02). Patients with MD and AD reported more frequently onset-related stressful events, benzodiazepines as first treatment, and autonomous help seeking compared with patients with SZs. In terms of first therapist, psychiatrist referral accounted for 43.6% of the cases, progressively decreasing from SZ to MD and AD (57.6, 41.8, and 38.3%, respectively). The opposite phenomenon was observed for nonpsychiatrist clinician referrals, whereas psychologist referrals remained constant. The present findings confirm the presence of a relevant DUI in a large sample of Italian patients with different psychiatric disorders (5 years, on average), pointing out specific differences, in terms of treatment access and latency, between psychotic and affective patients. Such aspects are relevant for detection of at-risk patients and implement early intervention programs.
X-Ray Properties of Radio-selected Dual Active Galactic Nuclei

Merger simulations predict that tidally induced gas inflows can trigger kiloparsec-scale dual active galactic nuclei (dAGN) in heavily obscured environments. Previously, with the Very Large Array, we have confirmed four dAGN with redshifts between 0.04 < z < 0.22 and projected separations between 4.3 and 9.2 kpc in the Sloan Digital Sky Survey Stripe 82 field. Here, we present Chandra X-ray observations that spatially resolve these dAGN and compare their multiwavelength properties to those of single AGN from the literature. We detect X-ray emission from six of the individual merger components and obtain upper limits for the remaining two. Combined with previous radio and optical observations, we find that our dAGN have properties similar to nearby low-luminosity AGN, and they agree with the black hole fundamental plane relation well. There are three AGN-dominated X-ray sources, whose X-ray hardness-ratio derived column densities show that two are unobscured and one is obscured. The low obscured fraction suggests these dAGN are no more obscured than single AGN, in contrast to the predictions from simulations. These three sources show an apparent X-ray deficit compared to their mid-infrared continuum and optical [O iii] line luminosities, suggesting higher levels of obscuration, in tension with the hardness-ratio derived column densities. Enhanced mid-infrared and [O iii] luminosities from star formation may explain this deficit. There is ambiguity in the level of obscuration for the remaining five components because their hardness ratios may be affected by nonnuclear X-ray emissions, or are undetected altogether. They require further observations to be fully characterized.
Using large-scale structure data and a halo model to constrain generalized dark matter

Constraints on the properties of the cosmological dark matter have previously been obtained in a model-independent fashion using the generalized dark matter (GDM) framework. Here we extend that work in several directions: We consider the inclusion of WiggleZ matter power spectrum data (MPS), and show that this improves the constraints on the two perturbative GDM parameters, $c^2_\mathrm{ s}$ and $c^2_\text{vis}$, by a factor of 3, for a conservative choice of wavenumber range. A less conservative choice can yield an improvement of up to an order of magnitude compared to previous constraints. In order to examine the robustness of this result we develop a GDM halo model (HM) to explore how non-linear structure formation could proceed in this framework, since currently GDM has only been defined perturbatively and only linear theory has been used when generating constraints. We then examine how the HM affects the constraints obtained from the MPS data. The less-conservative wavenumber range shows a significant difference between linear and non-linear modelling, with the latter favouring GDM parameters inconsistent with ΛCDM, underlining the importance of careful non-linear modelling when using this data. We also use this HM to establish the robustness of previously obtained constraints, particularly those that involve weak gravitational lensing of the cosmic microwave background. Additionally, we show how the inclusion of neutrino mass as a free parameter affects previous constraints on the GDM parameters.
Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution

In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360° range images in real time.
Diverse Quantization Phenomena in Layered Materials

The diverse quantization phenomena in 2D condensed-matter systems, being due to a uniform perpendicular magnetic field and the geometry-created lattice symmetries, are the focuses of this book. They cover the diversified magneto-electronic properties, the various magneto-optical selection rules, the unusual quantum Hall conductivities, and the single- and many-particle magneto-Coulomb excitations. The rich and unique behaviors are clearly revealed in few-layer graphene systems with the distinct stacking configurations, the stacking-modulated structures, and the silicon-doped lattices, bilayer silicene/germanene systems with the bottom-top and bottom-bottom buckling structures, monolayer and bilayer phosphorene systems, and quantum topological insulators. The generalized tight-binding model, the static and dynamic Kubo formulas, and the random-phase approximation, are developed/modified to thoroughly explore the fundamental properties and propose the concise physical pictures. The different high-resolution experimental measurements are discussed in detail, and they are consistent with the theoretical predictions.
Predictive inference with the jackknife+

This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, we prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically. Such guarantees are not possible for the original jackknife and we demonstrate examples where the coverage rate may actually vanish. Our theoretical and empirical analysis reveals that the jackknife and the jackknife+ intervals achieve nearly exact coverage and have similar lengths whenever the fitting algorithm obeys some form of stability. Further, we extend the jackknife+ to K-fold cross validation and similarly establish rigorous coverage properties. Our methods are related to cross-conformal prediction proposed by Vovk [2015] and we discuss connections.
Polarization studies of rotating radio transients

We study the polarization properties of 22 known rotating radio transients (RRATs) with the 64-m Parkes radio telescope and present the Faraday rotation measures (RMs) for the 17 with linearly polarized flux exceeding the off-pulse noise by 3σ. Each RM was estimated using a brute-force search over trial RMs that spanned the maximum measurable range $\pm 1.18 \times 10^5 \, \mathrm{rad \, m^2}$ (in steps of 1 $\mathrm{rad \, m^2}$), followed by an iterative refinement algorithm. The measured RRAT RMs are in the range |RM| 1–950 rad m−2 with an average linear polarization fraction of 40 per cent. Individual single pulses are observed to be up to 100 per cent linearly polarized. The RMs of the RRATs and the corresponding inferred average magnetic fields (parallel to the line of sight and weighted by the free electron density) are observed to be consistent with the Galactic plane pulsar population. Faraday rotation analyses are typically performed on accumulated pulsar data, for which hundreds to thousands of pulses have been integrated, rather than on individual pulses. Therefore, we verified the iterative refinement algorithm by performing Monte Carlo simulations of artificial single pulses over a wide range of S/N and RM. At and above an S/N of 17 in linearly polarized flux, the iterative refinement recovers the simulated RM value 100 per cent of the time with a typical mean uncertainty of 5 rad m−2. The method described and validated here has also been successfully used to determine reliable RMs of several fast radio bursts (FRBs) discovered at Parkes.
MAC‐assisted topology control for ad hoc wireless networks

We consider ad hoc wireless networks and the topology control problem defined as minimizing the amount of power needed to maintain connectivity. The issue boils down to selecting the optimum transmission power level at each node based on the position information of reachable nodes. Local decisions regarding the transmission power level induce a subgraph of the maximum powered graph Gmax in which edges represent direct reachability at maximum power. We propose a new algorithm for constructing minimum‐energy path‐preserving subgraphs of Gmax, i.e. ones minimizing the energy consumption between node pairs. Our algorithm involves a modification to the medium access control (MAC) layer. Its superiority over previous solutions, up to 60% improvement in sparse networks, demonstrates once again that strict protocol layering in wireless networks tends to be detrimental to performance. Copyright © 2005 John Wiley & Sons, Ltd.
Black hole – Galaxy correlations in simba

We examine the co-evolution of galaxies and supermassive black holes in the simba cosmological hydrodynamic simulation. simba grows black holes via gravitational torque-limited accretion from cold gas and Bondi accretion from hot gas, while feedback from black holes is modelled in radiative and jet modes depending on the Eddington ratio (fEdd). simba shows generally good agreement with local studies of black hole properties, such as the black hole mass–stellar velocity dispersion (MBH–σ) relation, the black hole accretion rate versus star formation rate (BHAR–SFR), and the black hole mass function. MBH–σ evolves such that galaxies at a given MBH have higher σ at higher redshift, consistent with no evolution in MBH–M⋆. For $M_{\rm BH}\lesssim 10^8\, {\rm M}_{\odot }$, fEdd is anticorrelated with MBH since the BHAR is approximately independent of MBH, while at higher masses fEdd–MBH flattens and has a larger scatter. BHAR versus SFR is invariant with redshift, but fEdd drops steadily with time at a given MBH, such that all but the most massive black holes are accreting in a radiatively efficient mode at $z\gtrsim 2$. The black hole mass function amplitude decreases with redshift and is locally dominated by quiescent galaxies for MBH > 108 M⊙, but for $z\gtrsim 1$ star-forming galaxies dominate at all MBH. The z = 0 fEdd distribution is roughly lognormal with a peak at $f_{\rm Edd}\lesssim 0.01$ as observed, shifting to higher fEdd at higher redshifts. Finally, we study the dependence of black hole properties with H i content and find that the correlation between gas content and SFR is modulated by black hole properties, such that higher SFR galaxies at a given gas content have smaller black holes with higher fEdd.
Galactic Stellar Populations from Photometric Metallicity Distribution Functions

Based on Sloan Digital Sky Survey photometric data, Gu developed a new Monte-Carlo-based method for estimating the stellar metallicity distribution functions (MDFs). This method enables a more reliable determination of MDFs compared with the conventional polynomial-based methods. In this work, MDF determined from the method are well fit by a three-Gaussian model, with peaks at [Fe/H] = −0.68, −1.38, and −1.90, associated with the thick disk, the inner halo, and the outer halo, respectively. The vertical metallicity gradient within 1 < Z < 5 kpc is around R = 8.25 kpc. But the mean radial gradient is almost negligible. The density profile of the thick disk is fitted with a modified double exponential law decaying to a constant at far distance. The scale height and scale length thus estimated are H ≈ 1.13 kpc and L ≈ 3.63 kpc, which are consistent with the results determined from star-count methods in previous studies. The halos are described with a two-axial power-law ellipsoid, and the axis ratios of both the inner halo and the outer halo, inferred from stellar number density in the R–Z plane, are qih ≈ 0.49 and qoh ≈ 0.61, respectively. It also manifests that the outer halo is more spherical than the inner halo. Moreover, the halo power-law indices estimated are nih ≈ 3.4 and noh ≈ 3.1, indicating that the stellar number density of the inner halo changes more steeply than that of outer halo.
Preclinical Characterization of 5-Amino-4-Oxo-[6-11C]Hexanoic Acid as an Imaging Probe to Estimate Protoporphyrin IX Accumulation Induced by Exogenous Aminolevulinic Acid

Preoperative noninvasive imaging to estimate the quantity and spatial distribution of protoporphyrin IX (PpIX) accumulation in tumors induced by 5-aminolevulinic acid (ALA) administration is expected to improve the efficacy of ALA-based fluorescence-guided resection and photo- and sonodynamic therapies. PpIX synthesis from exogenous ALA has been reported to be regulated by ALA influx or ALA dehydratase (ALAD) activity, which catalyzes the first step of the synthesis. In this study, we characterized the properties of a 11C-labeled ALA analog, 5-amino-4-oxo-[6-11C]hexanoic acid (11C-MALA), as a PET tracer to estimate PpIX accumulation. Methods: In vitro uptake of 11C-MALA and 3H-ALA was determined in 5 tumor cell lines after 10-min incubation with each tracer at 37°C. The expression levels of ALAD were determined by Western blot analysis. In vivo distribution and dynamic PET studies were conducted in tumor-bearing mice. In vitro and in vivo accumulation of ALA-induced PpIX was determined by measuring fluorescence in extracts of cells or tumors. Results: In vitro uptake of 11C-MALA in 5 tumor cell lines was correlated with ALAD expression levels and PpIX accumulation. In vivo biodistribution and dynamic PET studies showed that 11C-MALA was rapidly incorporated into tumors, and the tumor-to-muscle ratio of 11C-MALA at 1 min after injection was significantly correlated with that of 3H-ALA. 11C-MALA in tumors was continuously decreased thereafter, and the elimination rate of 11C-MALA from AsPC-1 tumors with the highest ALAD expression level was slower than from other tumors with lower expression levels. These results suggest that the influx and intracellular retention of 11C-MALA reflect ALA influx and ALAD expression levels, respectively. Tumor accumulation of 11C-MALA at 60 min after injection was strongly correlated with PpIX accumulation in tumor tissues. Conclusion: 11C-MALA PET has the potential to noninvasively estimate the quantitative and spatial accumulation of exogenous ALA-induced PpIX.
Endoscopy artifact detection (EAD 2019) challenge dataset

Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs. Precise detection of specific artifacts like pixel saturations, motion blur, specular reflections, bubbles and debris is essential for high-quality frame restoration and is crucial for realizing reliable computer-assisted tools for improved patient care. At present most videos in endoscopy are currently not analyzed due to the abundant presence of multi-class artifacts in video frames. Through the endoscopic artifact detection (EAD 2019) challenge, we address this key bottleneck problem by solving the accurate identification and localization of endoscopic frame artifacts to enable further key quantitative analysis of unusable video frames such as mosaicking and 3D reconstruction which is crucial for delivering improved patient care. This paper summarizes the challenge tasks and describes the dataset and evaluation criteria established in the EAD 2019 challenge.
Slowing down of cosmic growth of supermassive black holes: theoretical prediction of the Eddington ratio distribution

We show the Eddington ratio distributions of supermassive black holes at a wide redshift range (0 < z < 8) obtained with a semi-analytic model of galaxy formation. The distribution is broadly consistent with observational estimates at low redshift. We find that the growth rate of black holes at higher redshift is more likely to exceed the Eddington limit because the typical gas fraction of the host galaxies is higher at higher redshift. We also find that the super- Eddington growth is more common for less massive supermassive black holes, supporting an idea that supermassive black holes have been formed via super-Eddington accretion. These results indicate the "slowing down" of cosmic growth of supermassive black holes: the growth of supermassive black holes with a higher Eddington ratio peaks at higher redshift. We also show the effect of the sample selection on the shape of the Eddington ratio distribution functions and find that shallower observations will miss active galactic nuclei with not only the smaller but also higher Eddington ratios.