Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Deep Learning Algorithm for the License Plate Detection and Recognition in Vehicle-to-Vehicle Communications

With the rapid development of Internet of Things (IoT) in the field of transportation, the vehicle-to-vehicle (V2V) communication not only becomes available on a large scale, but also will be an indispensable part of the future transportation. License plates are the identification of vehicles, so the license plate detection and recognition in the V2V communication scenario is very important. However, the existing license plate detection and recognition methods are suffering from a low accuracy rate issue. To solve this issue, we propose a hybrid deep learning algorithm as the license plate detection and recognition model by fusing YOLOV3 and CRNN. The proposed model enables the network itself to better utilize the different fine-grained features in the high and low layers to carry out multi-scale detection and recognition. In this model, we utilize the fast and accurate performance of YOLOV3, and the excellent detection ability of CRNN. As a result, this proposed model reaps the benefit of both. Finally, we test this proposed model in difficult scenarios and low-quality license plate images caused by weather, and results show this proposed license plate detection and recognition model can achieve a higher mean average precision, better comprehensive performance, and excellent robustness.
Mapping the Surface of the Magnetar 1E 1048.1-5937 in Outburst and Quiescence Through Phase Resolved X-ray Spectroscopy

We model the pulse profiles and the phase resolved spectra of the anomalous X-ray pulsar 1E 1048.1-5937 obtained with XMM-Newton to map its surface temperature distribution during an active and a quiescent epoch. We develop and apply a model that takes into account the relevant physical and geometrical effects on the neutron star surface, magnetosphere, and spacetime. Using this model, we determine the observables at infinity as a function of pulse phase for different numbers and sizes of hot spots on the surface. We show that the pulse profiles extracted from both observations can be modeled with a single hot spot and an antipodal cool component. The size of the hot spot changes from $\approx 80^{\circ}$ in 2007, 3 months after the onset of a dramatic flux increase, to $\approx 30^{\circ}$ during the quiescent observation in 2011, when the pulsed fraction returned to the pre-outburst $\approx$ 65\% level. For the 2007 observation, we also find that a model consisting of a single 0.4 keV hot spot with a magnetic field strength of $1.8 \times 10^{14}$ G accounts for the spectra obtained at three different pulse phases but under predicts the flux at the pulse minimum, where the contribution to the emission from the cooler component is non-negligible. The inferred temperature of the spot stays approximately constant between different pulse phases, in agreement with a uniform temperature, single hot spot model. These results suggest that the emitting area grows significantly during outbursts but returns to its persistent and significantly smaller size within a few year timescale.
A New Dust Budget In The Large Magellanic Cloud

The origin of dust in a galaxy is poorly understood. Recently, the surveys of the Large Magellanic Cloud (LMC) provide astrophysical laboratories for the dust studies. By a method of population synthesis, we investigate the contributions of dust produced by asymptotic giant branch (AGB) stars, common envelope (CE) ejecta and type II supernovae (SNe II) to the total dust budget in the LMC. Based on our models, the dust production rates (DPRs) of AGB stars in the LMC are between about $2.5\times10^{-5}$ and $4.0\times10^{-6}M_\odot{\rm yr^{-1}}$. The uncertainty mainly results from different models for the dust yields of AGB stars. The DPRs of CE ejecta are about $6.3\times10^{-6}$(The initial binary fraction is 50\%). These results are within the large scatter of several observational estimates. AGB stars mainly produce carbon grains, which is consistent with the observations. Most of dust grains manufactured by CE ejecta are silicate and iron grains. The contributions of SNe II are very uncertain. Compared with SNe II without reverse shock, the DPRs of AGB stars and CE ejecta are negligible. However, if only 2 \% of dust grains produced by SNe II can survive after reverse shock, the contributions of SNe II are very small. The total dust masses produced by AGB stars in the LMC are between $2.8\times10^4$ and $3.2\times10^5M_\odot$, and those produced by CE ejecta are about $6.3\times10^4$. They are much lower than the values estimated by observations. Therefore, there should be other dust sources in the LMC.
Photometric-Stereo-Based Defect Detection System for Metal Parts

Automated inspection technology based on computer vision is now widely used in the manufacturing industry with high speed and accuracy. However, metal parts always appear in high gloss or shadow on the surface, resulting in the overexposure of the captured images. It is necessary to adjust the light direction and view to keep defects out of overexposure and shadow areas. However, it is too tedious to adjust the position of the light direction and view the variety of parts’ geometries. To address this problem, we design a photometric-stereo-based defect detection system (PSBDDS), which combines the photometric stereo with defect detection to eliminate the interference of highlights and shadows. Based on the PSBDDS, we introduce a photometric-stereo-based defect detection framework, which takes images captured in multiple directional lights as input and obtains the normal map through the photometric stereo model. Then, the detection model uses the normal map as input to locate and classify defects. Existing learning-based photometric stereo methods and defect detection methods have achieved good performance in their respective fields. However, photometric stereo datasets and defect detection datasets are not sufficient for training and testing photometric-stereo-based defect detection methods, thus we create a photometric stereo defect detection (PSDD) dataset using our PSBDDS to eliminate gaps between learning-based photometric stereo and defect detection methods. Furthermore, experimental results prove the effectiveness of the proposed PSBBD and PSDD dataset.
The impact of baryonic processes on the two-point correlation functions of galaxies, subhaloes and matter

The observed clustering of galaxies and the cross-correlation of galaxies and mass provide important constraints on both cosmology and models of galaxy formation. Even though the dissipation and feedback processes associated with galaxy formation are thought to affect the distribution of matter, essentially all models used to predict clustering data are based on collisionless simulations. Here, we use large hydrodynamical simulations to investigate how galaxy formation affects the autocorrelation functions of galaxies and subhaloes, as well as their cross-correlation with matter. We show that the changes due to the inclusion of baryons are not limited to small scales and are even present in samples selected by subhalo mass. Samples selected by subhalo mass cluster ~10% more strongly in a baryonic run on scales r > 1Mpc/h, and this difference increases for smaller separations. While the inclusion of baryons boosts the clustering at fixed subhalo mass on all scales, the sign of the effect on the cross-correlation of subhaloes with matter can vary with radius. We show that the large-scale effects are due to the change in subhalo mass caused by the strong feedback associated with galaxy formation and may therefore not affect samples selected by number density. However, on scales r < r_vir significant differences remain after accounting for the change in subhalo mass. We conclude that predictions for galaxy-galaxy and galaxy-mass clustering from models based on collisionless simulations will have errors greater than 10% on sub-Mpc scales, unless the simulation results are modified to correctly account for the effects of baryons on the distributions of mass and satellites.
Evolution of Massive Galaxy Structural Properties and Sizes via Star Formation In the GOODS NICMOS Survey

We present a study of the resolved star-forming properties of a sample of distant massive M_*>10^11M_solar galaxies in the GOODS NICMOS Survey (GNS). We derive dust corrected UV star formation rates (SFRs) as a function of radius for 45 massive galaxies within the redshift range 1.5<z<3 in order to measure the spatial location of ongoing star formation. We find that the star formation rates present in different regions of a galaxy reflect the already existent stellar mass density, i.e. high density regions have higher star formation rates than lower density regions, on average. This observed star formation is extrapolated in several ways to the present day, and we measure the amount of new stellar mass that is created in individual portions of each galaxy to determine how the stellar mass added via star formation changes the observed stellar mass profile, the Sersic index (n) and effective radius (R_e) over time. We find that these massive galaxies fall into three broad classifications of star formation distribution. These different star formation distributions increase the effective radii over time, which are on average a factor of ~16pm5% larger, with little change in n (average Delta n=-0.9pm0.9) after evolution. We also implement a range of simple stellar migration models into the simulated evolutionary path of these galaxies in order to gauge its effect on the properties of our sample. This yields a larger increase in the evolved R_e than the pure static star formation model, with a maximum average increase of Delta R_e~54pm19%, but with little change in n, Delta n ~-1.1pm1. 3. These results are not in agreement with the observed change in the R_e and n between z~2.5 and 0 obtained via various observational studies. We conclude that star formation and stellar migration alone cannot account for the observed change in structural parameters for this galaxy population (abridged).
Phase lags of quasi-periodic oscillations across source states in the low-mass X-ray binary 4U 1636–53

While there are many dynamical mechanisms and models that try to explain the origin and phenomenology of the quasi-periodic oscillations (QPOs) seen in the X-ray light curves of low-mass X-ray binaries, few of them address how the radiative processes occurring in these extreme environments give rise to the rich set of variability features actually observed in these light curves. A step towards this end comes from the study of the energy and frequency dependence of the phase lags of these QPOs. Here we used a methodology that allowed us to study, for the first time, the dependence of the phase lags of all QPOs in the range of 1-1300 Hz detected in the low-mass X-ray binary 4U 1636-53 upon energy and frequency as the source changes its states as it moves through the colour-colour diagram. Our results suggest that within the context of models of up-scattering Comptonization, the phase lags dependences upon frequency and energy can be used to extract size scales and physical conditions of the medium that produces the lags.
How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics

Multivariate analyses play an important role in high energy physics. Such analyses often involve performing an unbinned maximum likelihood fit of a probability density function (p.d.f.) to the data. This paper explores a variety of unbinned methods for determining the goodness of fit of the p.d. f. to the data. The application and performance of each method is discussed in the context of a real-life high energy physics analysis (a Dalitz-plot analysis). Several of the methods presented in this paper can also be used for the non-parametric determination of whether two samples originate from the same parent p.d. f. This can be used, e.g., to determine the quality of a detector Monte Carlo simulation without the need for a parametric expression of the efficiency.
HOW BAD OR GOOD ARE THE EXTERNAL FORWARD SHOCK AFTERGLOW MODELS OF GAMMA-RAY BURSTS?

The external forward shock models have been the standard paradigm to interpret the broadband afterglow data of gamma-ray bursts (GRBs). One prediction of the models is that some afterglow temporal breaks at different energy bands should be achromatic; that is, the break times should be the same in different frequencies. Multiwavelength observations in the Swift era have revealed chromatic afterglow behaviors at least in some GRBs, casting doubts on the external forward shock origin of GRB afterglows. In this paper, using a large sample of GRBs with both X-ray and optical afterglow data, we perform a systematic study to address the question: how bad or good are the external forward shock models? Our sample includes 85 GRBs up to 2014 March with well-monitored X-ray and optical light curves. Based on how well the data abide by the external forward shock models, we categorize them into five grades and three samples. The first two grades (Grade I and II) include 45 of 85 GRBs. They show evidence of, or are consistent with having, an achromatic break. The temporal and spectral behaviors in each afterglow segment are consistent with the predictions (the “closure relations”) of the forward shock models. These GRBs are included in the Gold sample. The next two grades (Grade III and IV) include 37 of 85 GRBs. They are also consistent with having an achromatic break, even though one or more afterglow segments do not comply with the closure relations. These GRBs are included in the Silver sample. Finally, Grade V (3/85) shows direct evidence of chromatic behaviors, suggesting that the external shock models are inconsistent with the data. These are included in the Bad sample. We further perform statistical analyses of various observational properties (temporal index α, spectral index β, break time tb) and model parameters (energy injection index q, electron spectral index p, jet opening angle θ j ? > , radiative efficiency ηγ, and so on) of the GRBs in the Gold sample, and derive constraints on the magnetization parameter ϵB in the forward shock. Overall, we conclude that the simplest external forward shock models can account for the multiwavelength afterglow data of at least half of the GRBs. When more advanced modeling (e.g., long-lasting reverse shock, structured jets, arbitrary circumburst medium density profile) is invoked, up to >90% of the afterglows may be interpreted within the framework of the external shock models.
MODEL-DEPENDENT ESTIMATE ON THE CONNECTION BETWEEN FAST RADIO BURSTS AND ULTRA HIGH ENERGY COSMIC RAYS

The existence of fast radio bursts (FRBs), a new type of extragalatic transient, has recently been established, and quite a few models have been proposed. In this work, we discuss the possible connection between the FRB sources and ultra high energy (>1018 eV) cosmic rays. We show that in the blitzar model and the model of merging binary neutron stars, which includes the huge energy release of each FRB central engine together with the rather high rate of FRBs, the accelerated EeV cosmic rays may contribute significantly to the observed ones. In other FRB models, including, for example, the merger of double white dwarfs and the energetic magnetar radio flares, no significant EeV cosmic ray is expected. We also suggest that the mergers of double neutron stars, even if they are irrelevant to FRBs, may play a nonignorable role in producing EeV cosmic ray protons if supramassive neutron stars are formed in a sufficient fraction of mergers and the merger rate is ≳ 103 yr−1 Gpc−3. Such a possibility will be unambiguously tested in the era of gravitational wave astronomy.
Observational constraints on transverse gravity: A generalization of unimodular gravity

We explore the hypothesis that the set of symmetries enjoyed by the theory that describes gravity is not the full group of diffeomorphisms (Diff(M)), as in General Relativity, but a maximal subgroup of it (TransverseDiff(M)), with its elements having a jacobian equal to unity; at the infinitesimal level, the parameter describing the coordinate change x? x? + ? (x) is transverse, i.e., ? = 0. Incidentally, this is the smaller symmetry one needs to propagate consistently a graviton, which is a great theoretical motivation for considering these theories. Also, the determinant of the metric, g, behaves as a transverse scalar, so that these theories can be seen as a generalization of the better-known unimodular gravity. We present our results on the observational constraints on transverse gravity, in close relation with the claim of equivalence with general scalar-tensor theory. We also comment on the structure of the divergences of the quantum theory to the one-loop order.
Kuperberg invariants for balanced sutured 3-manifolds

Abstract We construct quantum invariants of balanced sutured 3-manifolds with a ${\text {Spin}^c}$ structure out of an involutive (possibly nonunimodular) Hopf superalgebra H. If H is the Borel subalgebra of ${U_q(\mathfrak {gl}(1|1))}$ , we show that our invariant is computed via Fox calculus, and it is a normalization of Reidemeister torsion. The invariant is defined via a modification of a construction of Kuperberg, where we use the ${\text {Spin}^c}$ structure to take care of the nonunimodularity of H or $H^{*}$ .
Herschel far-infrared observations of the Carina Nebula Complex. - III: Detailed cloud structure and feedback effects

The Carina Nebula complex (CNC) represents one of the most massive star-forming regions in our Galaxy and shows strong feedback from the high massive stars. We use our Herschel FIR observations to study the properties of the clouds over the entire area of the CNC. The good angular resolution of the Herschel maps corresponds to physical scales of 0.1 - 0.4 pc, and allows us to analyze the small-scale structures of the clouds. The full extent of the CNC was mapped with PACS and SPIRE from 70 to 500 micron. We determine temperatures and column densities at each point in this maps by modeling the observed FIR SEDs. We also derive a map showing the strength of the UV field. We investigate the relation between the cloud properties and the spatial distribution of the high-mass stars, and compute total cloud masses for different density thresholds. Our Herschel maps resolve, for the first time, the small-scale structure of the dense clouds. Several particularly interesting regions, including the prominent pillars south of eta Car, are analyzed in detail. We compare the cloud masses derived from the Herschel data to previous mass estimates based on sub-mm and molecular line data. Our maps also reveal a peculiar "wave"-like pattern in the northern part of the Carina Nebula. Finally, we characterize two prominent cloud complexes at the periphery of our Herschel maps, which are probably molecular clouds in the Galactic background. We find that the density and temperature structure of the clouds in most parts of the CNC is dominated by the strong feedback from the numerous massive stars, rather than random turbulence. Comparing the cloud mass and the star formation rate derived for the CNC to other Galactic star forming regions suggests that the CNC is forming stars in an particularly efficient way. We suggest this to be a consequence of triggered star formation by radiative cloud compression.
The growth index of matter perturbations and modified gravity

We place tight constraints on the growth index by using the recent growth history results of 2dFGRS, SDSS-LRG, VIMOS-VLT deep Survey (VVDS) and WiggleZ datasets. In particular, we investigate several parametrizations of the growth index (z), by comparing their cosmological evolution using observational growth rate data at different redshifts. Utilizing a standard likelihood analysis we find that the use of the combined growth data provided by the 2dFGRS, SDSS-LRG, VVDS and WiggleZ galaxy surveys, puts the most stringent constraints on the value of the growth index. As an example, assuming a constant growth index we obtain that = 0.602±0.055for the con
The Circuit Complexity of Inference

Belief propagation is one of the foundations of probabilistic and causal reasoning. In this paper, we study the circuit complexity of some of the various tasks it performs. Specifically, in the broadcast tree model (which has important applications to phylogenetic reconstruction and close connections to community detection), we show the following: (1) No $\mathbf{AC}^0$ circuit can guess the label of the root with positive advantage over random guessing, independent of the depth for any non-trivial choice of parameters. (2) There is a $\mathbf{TC}^0$ circuit that competes with the Bayes optimal predictor in some range of parameters above the Kesten-Stigum bound. (3) There is a $16$ label broadcast tree model in which it is possible to accurately guess the label of the root, but beating random guessing is $\mathbf{NC}^1$-hard. Our work yields a simple and natural generative model where large depth really is necessary for performing various types of inference, that have intriguing parallels with phase transitions from statistical physics.
Five-Dimensional Black Hole Capture Cross-Sections

Department of Physics, Simon Fraser University8888 University Drive, Burnaby, BC Canada V5A 1S6(Dated: March 6, 2008)We study scattering and capture of particles by a rotating black hole in the ﬁve-dimensionalspacetime described by the Myers-Perry metric. The equations of geodesic motion are integrable,and allow us to calculate capture conditions for a free particle sent towards a black hole from inﬁnity. We introduce a three-dimensional impact parameter describing asymptotic initial conditions in thescattering problem for a given initial velocity. The capture surface in impact parameter spaceis a sphere for a non-rotating black hole, and is deformed for a rotating black hole. We obtainasymptotic expressions that describe such deformations for small rotational parameters, and usenumerical calculations to investigate the arbitrary rotation case, which allows us to visualize thecapture surface as extremal rotation is approached.
Effects of Mothers’ Mindset and Perceived Child Competence on Their Learning Involvement

Abstract Parent’s beliefs about intelligence can influence children’s learning through parenting practices. It is unclear how parents’ perceptions of the child’s ability may affect these processes. This experimental study explored the joint effects of mothers’ growth mindset and perceptions of child competence on their learning involvement. Children (N = 121, 52% female, ages 9–15) completed a set of problem-solving tasks, and mothers were told that their children had either performed well or poorly; or received no feedback. In a subsequent problem-solving task, mothers’ growth mindset positively predicted supportive behaviors, and mothers’ low competence beliefs positively predicted unsupportive and controlling behaviors. No interactions were found. Findings suggest parental growth mindset may help foster positive parenting practices, regardless of their children’s academic competence.