Recently bookmarked papers

with concepts:
  • Based on constraints from Big Bang nucleosynthesis and the cosmic microwave background, the baryon content of the high-redshift Universe can be precisely determined. However, at low redshift, about one-third of the baryons remain unaccounted for, which poses the long-standing missing baryon problem. The missing baryons are believed to reside in large-scale filaments in the form of warm-hot intergalactic medium (WHIM). In this work, we employ a novel stacking approach to explore the hot phases of the WHIM. Specifically, we utilize the 470 ks Chandra LETG data of the luminous quasar, H1821+643, along with previous measurements of UV absorption line systems and spectroscopic redshift measurements of galaxies toward the quasar's sightline. We repeatedly blueshift and stack the X-ray spectrum of the quasar corresponding to the redshifts of the 17 absorption line systems. Thus, we obtain a stacked spectrum with $8.0$ Ms total exposure, which allows us to probe X-ray absorption lines with unparalleled sensitivity. Based on the stacked data, we detect an OVII absorption line that exhibits a Gaussian line profile and is statistically significant at the $3.3 \sigma$ level. Since the redshifts of the UV absorption line systems were known a priori, this is the first definitive detection of an X-ray absorption line originating from the WHIM. The equivalent width of the OVII line is $(4.1\pm1.3) \ \mathrm{m\AA}$, which corresponds to an OVII column density of $(1.4\pm0.4)\times10^{15} \ \mathrm{cm^{-2}}$. We constrain the absorbing gas to have a density of $n_{\rm H} = (1-2)\times10^{-6} \ \rm{cm^{-3}}$ for a single WHIM filament. We derive $\Omega_{\rm b} \rm(O\,VII) = (0.0023 \pm 0.0007) \, \left[ f_{O\,VII} \, {Z/Z_{\odot}} \right]^{-1}$ for the cosmological mass density of OVII, assuming that all 17 systems contribute equally.
    Absorption lineWarm hot intergalactic mediumGalaxyMissing baryonsOVII absorption lineQuasarStatistical significanceForeground galaxyMilky WayEquivalent width...
  • This paper attempts to strengthen the pursued research on social engineering (SE) threat identification, and control, by means of the author's illustrated classification, which includes attack types, determining the degree of possible harm to each types of possible, known attacks, countermeasures by types of threats that leads to loss of personal or corporate confidential information (user id, passwords, closed documentation). Further, this analytical study will become the starting point for deeper, practically oriented and tested research.
    Classification
  • We compute the three-loop scattering amplitude of four gravitons in ${\mathcal N}=8$ supergravity. Our results are analytic formulae for a Laurent expansion of the amplitude in the regulator of dimensional regularisation. The coefficients of this series are closed formulae in terms of well-established harmonic poly-logarithms. Our results display a remarkable degree of simplicity and represent an important stepping stone in the exploration of the structure of scattering amplitudes. In particular, we observe that to this loop order the four graviton amplitude is given by uniform weight $2L$ functions, where $L$ is the loop order.
    GravitonScattering amplitudeSupergravityPermutationPath integralSuper Yang-Mills theoryAnalytic continuationGauge theoryLoop amplitudeLoop momentum...
  • Yes. In a perturbed Friedmann model, the difference of the Hubble constants measured in two rest-frames is independent of the source peculiar velocity and depends only on the relative velocity of the observers, to lowest order in velocity. Therefore this difference should be zero when averaging over sufficient sources, which are at large enough distances to suppress local nonlinear inhomogeneity. We use a linear perturbative analysis to predict the Doppler effects on redshifts and distances. Since the observed redshifts encode the effect of local bulk flow due to nonlinear structure, our linear analysis is able to capture aspects of the nonlinear behaviour. Using the largest available distance compilation from CosmicFlows-3, we find that the data is consistent with simulations based on the concordance model, for sources at $20-150\,$Mpc.
    Lambda-CDM modelMonte Carlo methodHubble flowLuminosity distancePeculiar velocityCosmologyCosmic microwave backgroundHubble constantDoppler effectCosmic rest frame...
  • We study the equilibration of right-handed electrons in the symmetric phase of the Standard Model. Due to the smallness of the electron Yukawa coupling, it happens relatively late in the history of the Universe. We compute the equilibration rate at leading order in the Standard Model couplings, by including gauge interactions, the top Yukawa- and the Higgs self-interaction. The dominant contribution is due to $ 2 \to 2 $ particle scattering, even though the rate of (inverse) Higgs decays is strongly enhanced by multiple soft scattering which is included by Landau-Pomeranchuk-Migdal (LPM) resummation. Our numerical result is substantially larger than approximations presented in previous literature.
    Standard ModelHiggs boson decayHyperchargeYukawa couplingSterile neutrino productionLepton numberWeak hyperchargeStatisticsThermal massResummation...
  • We reemphasise the usefulness of angular correlations in LHC searches for missing transverse energy ($E_T^{\mathrm{miss}}$) signatures that involve jet $(j)$ pairs with large invariant mass. For the case of mono-jet production via gluon-fusion, we develop a realistic analysis strategy that allows to split the dark matter (DM) signal into distinct one jet-like and two jet-like event samples. By performing state-of-the-art Monte Carlo simulations of both the mono-jet signature and the standard model background, it is shown that the dijet azimuthal angle difference $\Delta \phi_{j_1 j_2}$ in $2 j +E_T^{\mathrm{miss}}$ production provides a powerful discriminant in realistic searches. Employing a shape fit to $\Delta \phi_{j_1 j_2}$, we then determine the LHC reach of the mono-jet channel in the context of spin-0 $s$-channel DM simplified models. The constraints obtained by the proposed $\Delta \phi_{j_1 j_2}$ shape fit turn out to be significantly more stringent than those that derive from standard $E_T^{\mathrm{miss}}$ shape analyses.
    Dark matterStandard ModelMono-jetSystematic errorLarge Hadron ColliderHigh-luminosity LHCMissing transverse energyPseudoscalarIntegrated luminosityMonte Carlo method...
  • The discovery of fast radio bursts (FRBs) about a decade ago opened up new possibilities for probing the ionization history of the Intergalactic Medium (IGM). In this paper we study the use of FRBs for tracing the epoch of HeII reionization, using simulations of their dispersion measures. We model dispersion measure contributions from the Milky Way, the IGM (homogeneous and inhomogeneous) and a possible host galaxy as a function of redshift and star formation rate. We estimate the number of FRBs required to distinguish between a model of the Universe in which helium reionization occurred at z = 3 from a model in which it occurred at z = 6 using a 2-sample Kolmogorov-Smirnoff test. We find that if the IGM is homogeneous >1100 FRBs are needed and that an inhomogeneous model in which traversal of the FRB pulse through galaxy halos increases the number of FRBs modestly, to >1600. We also find that to distinguish between a reionization that occurred at z = 3 or z = 3.5 requires ~5700 FRBs in the range 3 < z < 5.
    Fast Radio BurstsDispersion measureIntergalactic mediumHelium reionizationHost galaxyGalaxyMilky WayReionizationLine of sightGalactic halo...
  • The ages of the oldest stellar objects in our galaxy provide an independent test of the current cosmological model as they give a lower limit to the age of the Universe. Recent accurate parallaxes by the Gaia space mission, accurate measurements of the metallicity of stars, via individual elemental abundances, and advances in the modelling of stellar evolution, provide new, higher-precision age estimates of the oldest stellar populations in the galaxy: globular clusters and very-low-metallicity stars. The constraints on the age of the Universe, $t_U$, so obtained are determined from the local Universe and at late time. It is well known that local and early-Universe determinations of another cosmological parameter closely related to the age of the Universe, the Hubble constant $H_0$, show a $\gtrsim 3 \sigma$ tension. In the standard cosmological model, $\Lambda$CDM, $t_U$ and $H_0$ are related by the matter density parameter $\Omega_{m,0}$. We propose to combine local $t_U$ constraints with late-time $\Omega_{m,0}$ estimates in a $\Lambda$CDM framework, to obtain a low-redshift $H_0$ determination that does not rely on early Universe physics. A proof-of-principle of this approach with current data gives $H_0=71\pm2.8$ ($H_0= 69.3 \pm 2.7$) km s$^{-1}$ Mpc$^{-1}$ from globular clusters (very-low-metallicity stars) with excellent prospects for improved constraints in the near future.
    The age of the UniverseCosmological modelCosmic microwave backgroundStellar agesThe early UniverseLambda-CDM modelStarSupernovaMilky WayStellar populations...
  • The phenomenological basis for Modified Newtonian Dynamics (MOND) is the radial-acceleration-relation (RAR) between the observed acceleration, $a=V^2_{rot}(r)/r$, and the acceleration accounted for by the observed baryons (stars and cold gas), $a_{bar}=V_{bar}^2(r)/r$. We show that the RAR arises naturally in the NIHAO sample of 89 high-resolution LCDM cosmological galaxy formation simulations. The overall scatter from NIHAO is just 0.079 dex, consistent with observational constraints. However, we show that the scatter depends on stellar mass. At high masses ($10^9 <M_{star} <10^{11}$ Msun) the simulated scatter is just $\simeq 0.04$ dex, increasing to $\simeq 0.11$ dex at low masses ($10^7 < M_{star} <10^{9}$Msun). Observations show a similar dependence for the intrinsic scatter. At high masses the intrinsic scatter is consistent with the zero scatter assumed by MOND, but at low masses the intrinsic scatter is non-zero, strongly disfavoring MOND. Applying MOND to our simulations yields remarkably good fits to most of the circular velocity profiles. In cases of mild disagreement the stellar mass-to-light ratio and/or "distance" can be tuned to yield acceptable fits, as is often done in observational mass models. In dwarf galaxies with $M_{star}\sim10^6$Msun MOND breaks down, predicting lower accelerations than observed and in our LCDM simulations. The assumptions that MOND is based on (e.g., asymptotically flat rotation curves, zero intrinsic scatter in the RAR), are approximately, but not exactly, true in LCDM. Thus if one wishes to go beyond Newtonian dynamics there is more freedom in the RAR than assumed by MOND.
    NIHAO simulationModified Newtonian DynamicsGalaxyRotation CurveStellar massSpitzer Photometry and Accurate Rotation CurvesCircular velocityIntrinsic scatterDark matterLambda-CDM model...
  • New light pseudoscalars, such as axion-like particles, appear in many well-motivated extensions of the Standard Model and provide an exciting target for present and future experiments. We study the experimental sensitivity for such particles by revising the CHARM exclusion contour, updating bounds from LHCb and presenting prospects for NA62 and SHiP. We first consider a simplified model of a light pseudoscalar $A$ and then propose a model-independent approach applicable to any spin-0 boson light enough to be produced in B-meson decays. As illustration, we provide upper bounds on $\text{BR}(B \to K\,A) \times \text{BR}(A \to \mu^+\mu^-)$ as a function of the boson lifetime and mass for models that satisfy minimal flavour violation. Our results demonstrate the important complementarity between different experiments resulting from their different geometries.
    PseudoscalarLHCb experimentB mesonNA62 experimentSHiP experimentRare B-meson decayStandard ModelDecay widthFixed target experimentsBranching ratio...
  • The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.
    Neural networkReinforcement learningArchitectureArtificial intelligenceDeep Neural NetworksMinimaxTemporal difference learningRankMobilityBayesian...
  • Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing with medical images. For supervised image analysis in medical imaging, having image data along with their corresponding annotated ground-truths is costly as well as time consuming since annotations of the data is done by medical experts manually. In this paper, we propose a new Generative Adversarial Network for Medical Imaging (MI-GAN). The MI-GAN generates synthetic medical images and their segmented masks, which can then be used for the application of supervised analysis of medical images. Particularly, we present MI-GAN for synthesis of retinal images. The proposed method generates precise segmented images better than the existing techniques. The proposed model achieves a dice coefficient of 0.837 on STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance on both the datasets.
    Deep learningConvolutional neural networkOptimizationGround truthDice's coefficientTraining setMachine learningNeural networkArchitectureImage Processing...
  • This major reference work is a one-shot knowledge base on electroporation and the use of pulsed electric fields of high intensity and their use in biology, medicine, biotechnology, and food and environmental technologies. The Handbook offers a widespread and well-structured compilation of 156 chapters ranging from the foundations to applications in industry and hospital. It is edited and written by most prominent researchers in the field. With regular updates and growing in its volume it is suitable for academic readers and researchers regardless of their disciplinary expertise, and will also be accessible to students and serious general readers. The Handbook's 276 authors have established scholarly credentials and come from a wide range of disciplines. This is crucially important in a highly interdisciplinary field of electroporation and the use of pulsed electric fields of high intensity and its applications in different fields from medicine, biology, food proce ssing, agriculture, process engineering, energy and environment. An Editorial Board of distinguished scholars from across the world has selected and reviewed the various chapters to ensure the highest quality of this Handbook. The book was edited by an international team of Section Editors: P. Thomas Vernier, Boris Rubinsky, Juergen Kolb, Damijan Miklavcic, Marie-Pierre Rols, Javier Raso, Richard Heller, Gregor Serša, Dietrich Knorr, and Eugene Vorobiev.
    IntensityKnowledge baseMedical physicsBiophysicsFieldElectric fieldTeamsEnergy...
  • For a massive gauge theory with Higgs mechanism in a physical gauge, the longitudinal polarization of gauge bosons can be naturally identified as mixture of the goldstone component and a remnant gauge component that vanishes at the limit of zero mass, making the goldstone equivalence manifest. In light of this observation, we re-examine the Feynman rules of massive gauge theory by treating gauge fields and their corresponding goldstone fields as single objects, writing them uniformly as 5-component "vector" fields. The gauge group is taken to be $SU(2)_L$ to preserve custodial symmetry. We find the derivation of gauge-goldstone propagators becomes rather trivial by noticing there is a remarkable parallel between massless gauge theory and massive gauge theory in this notation. We also derive the Feynman rules of all vertices, finding the vertex for self-interactions of vector (gauge-goldstone) bosons are especially simplified. We then demonstrate that the new form of the longitudinal polarization vector and the standard form give the same results for all the 3-point on-shell amplitudes. This on-shell matching confirms similar results obtained with on-shell approach for massive scattering amplitudes by Arkani-Hamed et.al.. Finally we calculate some $1\rightarrow 2$ collinear splitting amplitudes by making use of the new Feynman rules and the on-shell match condition.
    Goldstone bosonFeynman rulesGauge theoryPolarization vectorGauge symmetryGauge fieldPropagatorOn-shell amplitudeVector bosonScattering amplitude...
  • We examine the effects that dynamical instability has on shaping the orbital properties of exoplanetary systems. Using N-body simulations of non-EMS (Equal Mutual Separation), multi-planet systems we find that the lower limit of the instability timescale $t$ is determined by the minimal mutual separation $K_{\rm min}$ in units of the mutual Hill radius. Planetary systems showing instability generally include planet pairs with period ratio $<1.33$. Our final period ratio distribution of all adjacent planet pairs shows dip-peak structures near first-order mean motion resonances similar to those observed in the \kepler\ planetary data. Then we compare the probability density function (PDF) of the de-biased \kepler\ period ratios with those in our simulations and find a lack of planet pairs with period ratio $> 2.1$ in the observations---possibly caused either by inward migration before the dissipation of the disk or by planet pairs not forming with period ratios $> 2.1$ with the same frequency they do with smaller period ratios. By comparing the PDF of the period ratio between simulation and observation, we obtain an upper limit of 0.03 on the scale parameter of the Rayleigh distributed eccentricities when the gas disk dissipated. Finally, our results suggest that a viable definition for a `packed' or `compact' planetary system be one that has at least one planet pair with a period ratio less than 1.33. This criterion would imply that 4\% of the \kepler\ systems (or 6\% of the systems with more than two planets) are compact.
    PlanetInstabilityEccentricityInclinationArchitectureTadpoleTerrestrial planetStatisticsSolar systemCritical value...
  • Kepler and Hubble photometry of a total of four transits by the Jupiter-sized Kepler-1625b have recently been interpreted to show evidence of a Neptune-sized exomoon. The profound implications of this first possible exomoon detection and the physical oddity of the proposed moon, that is, its giant radius prompt us to re-examine the data and the Bayesian Information Criterion (BIC) used for detection. We combine the Kepler data with the previously published Hubble light curve. In an alternative approach, we perform a synchronous polynomial detrending and fitting of the Kepler data combined with our own extraction of the Hubble photometry. We generate five million MCMC realizations of the data with both a planet-only model and a planet-moon model and compute the BIC difference (DeltaBIC) between the most likely models, respectively. DeltaBIC values of -44.5 (using previously published Hubble data) and -31.0 (using our own detrending) yield strongly support the exomoon interpretation. Most of our orbital realizations, however, are very different from the best-fit solutions, suggesting that the likelihood function that best describes the data is non-Gaussian. We measure a 73.7min early arrival of Kepler-1625b for its Hubble transit at the 3 sigma level, possibly caused by a 1 day data gap near the first Kepler transit, stellar activity, or unknown systematics. The radial velocity amplitude of a possible unseen hot Jupiter causing Kepler-1625b's transit timing variation could be some 100m/s. Although we find a similar solution to the planet-moon model as previously proposed, careful consideration of its statistical evidence leads us to believe that this is not a secure exomoon detection. Unknown systematic errors in the Kepler/Hubble data make the DeltaBIC an unreliable metric for an exomoon search around Kepler-1625b, allowing for alternative interpretations of the signal.
    PlanetBayesian information criterionLight curveTerrestrial planetAstronomical UnitStarMonte Carlo Markov chainSemimajor axisInclinationLikelihood function...
  • We present results on the stellar density radial profile of the outer regions of NGC6779, a Milky Way globular cluster recently proposed as a candidate member of the Gaia Sausage structure, a merger remnant of a massive dwarf galaxy with the Milky Way. Taking advantage of the Pan-STARRS PS1 public astrometric and photometric catalogue, we built the radial profile for the outermost cluster regions using horizontal branch and main sequence stars, separately, in order to probe for different profile trends because of difference stellar masses. Owing to its relatively close location to the Galactic plane, we have carefully treated the chosen colour-magnitude regions properly correcting them by the amount of interstellar extinction measured along the line-of-side of each star, as well as cleaned them from the variable field star contamination observed across the cluster field. In the region spanning from the tidal to the Jacobi radii the resulting radial profiles show a diffuse extended halo, with an average power law slope of -1. While analysing the relationships between the Galactocentric distance, the half-mass density, the half-light radius, the slope of the radial profile of the outermost regions, the internal dynamical evolutionary stage, among others, we found that NGC6779 shows structural properties similar to those of the remaining Gaia Sausage candidate globular clusters, namely, they are massive clusters (>10^5Mo) in a moderately early dynamical evolutionary stage, with observed extra-tidal structures.
    Globular clusterStarMilky WayMain sequence starHertzsprung-Russell diagramPan-STARRS 1Horizontal branch starPan-STARRSLine of sightMessier 56...
  • This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for reinforcement learning, representing a more difficult class of problems than considered in most prior work. It is a multi-agent problem with multiple players interacting; there is imperfect information due to a partially observed map; it has a large action space involving the selection and control of hundreds of units; it has a large state space that must be observed solely from raw input feature planes; and it has delayed credit assignment requiring long-term strategies over thousands of steps. We describe the observation, action, and reward specification for the StarCraft II domain and provide an open source Python-based interface for communicating with the game engine. In addition to the main game maps, we provide a suite of mini-games focusing on different elements of StarCraft II gameplay. For the main game maps, we also provide an accompanying dataset of game replay data from human expert players. We give initial baseline results for neural networks trained from this data to predict game outcomes and player actions. Finally, we present initial baseline results for canonical deep reinforcement learning agents applied to the StarCraft II domain. On the mini-games, these agents learn to achieve a level of play that is comparable to a novice player. However, when trained on the main game, these agents are unable to make significant progress. Thus, SC2LE offers a new and challenging environment for exploring deep reinforcement learning algorithms and architectures.
    Reinforcement learningArchitectureApplication programming interfaceBlizzardDeep Reinforcement LearningPythonImperfect informationNeural networkRankLong short term memory...
  • We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. We apply our method to seven Atari 2600 games from the Arcade Learning Environment, with no adjustment of the architecture or learning algorithm. We find that it outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
    Reinforcement learningNeural networkArchitectureQ-learningDeep learningDeep Reinforcement LearningDeep Neural NetworksTD-GammonHidden layerConvolutional neural network...
  • We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to find behavior clusters, which are, in turn, used with classification algorithms to learn the outcome predictor. For our numerical experiments, we consider League of Legends, a popular team-based role-playing game developed by Riot Games. We observe the learned clusters to not only corroborate well with game knowledge, but also provide insights surprising to expert players. We also demonstrate that game outcomes can be predicted with fairly high accuracy given team composition-based features.
    K-means++StatisticsExpectation maximizationSupport vector machineK-means clusteringCovarianceMATLABClassificationLogistic regressionHierarchical clustering...
  • It is important to monitor and analyze crowd events for the sake of city safety. In an EDOF (extended depth of field) image with a crowded scene, the distribution of people is highly imbalanced. People far away from the camera look much smaller and often occlude each other heavily, while people close to the camera look larger. In such a case, it is difficult to accurately estimate the number of people by using one technique. In this paper, we propose a Depth Information Guided Crowd Counting (DigCrowd) method to deal with crowded EDOF scenes. DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region. Then Digcrowd maps the far-view region to its crowd density map and uses a detection method to count the people in the near-view region. In addition, we introduce a new crowd dataset that contains 1000 images. Experimental results demonstrate the effectiveness of our DigCrowd method
    CountingConvolutional neural networkNeural networkObject detectionRegressionStatisticsCounting methodArchitectureActivity patternsImage Processing...
  • People detection in single 2D images has improved greatly in recent years. However, comparatively little of this progress has percolated into multi-camera multi-people tracking algorithms, whose performance still degrades severely when scenes become very crowded. In this work, we introduce a new architecture that combines Convolutional Neural Nets and Conditional Random Fields to explicitly model those ambiguities. One of its key ingredients are high-order CRF terms that model potential occlusions and give our approach its robustness even when many people are present. Our model is trained end-to-end and we show that it outperforms several state-of-art algorithms on challenging scenes.
    Convolutional neural networkMean fieldInferenceBackground subtractionDeep learningGenerative modelMaximum Likelihood PrincipleGround truthDiscriminative modelConditional random field...
  • Anomalies (unusual patterns) in time-series data give essential, and often actionable information in critical situations. Examples can be found in such fields as healthcare, intrusion detection, finance, security and flight safety. In this paper we propose new conformalized density- and distance-based anomaly detection algorithms for a one-dimensional time-series data. The algorithms use a combination of a feature extraction method, an approach to assess a score whether a new observation differs significantly from a previously observed data, and a probabilistic interpretation of this score based on the conformal paradigm.
    Anomaly detectionTime SeriesHyperparameterTraining setCalibrationTwitterNumentaFeature extractionSecurityStatistics...
  • Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterize both localization and background-versus-foreground errors. To address localization errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitized training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitized set of training and test annotations.
    RegressionTraining setTrue positionArchitectureClassificationFilter bankConvolutional neural networkHuman annotatorsOrientationNeural network...
  • Modeling the distribution of natural images is a landmark problem in unsupervised learning. This task requires an image model that is at once expressive, tractable and scalable. We present a deep neural network that sequentially predicts the pixels in an image along the two spatial dimensions. Our method models the discrete probability of the raw pixel values and encodes the complete set of dependencies in the image. Architectural novelties include fast two-dimensional recurrent layers and an effective use of residual connections in deep recurrent networks. We achieve log-likelihood scores on natural images that are considerably better than the previous state of the art. Our main results also provide benchmarks on the diverse ImageNet dataset. Samples generated from the model appear crisp, varied and globally coherent.
    Recurrent neural networkArchitectureCompressibilityGenerative modelUnsupervised learningDeep Neural NetworksBinary numberConvolutional neural networkLatent variableScheduling...
  • Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity for language require the ability to consolidate experience into concepts, which act as basic building blocks of understanding and reasoning. We present a framework that defines a concept by an energy function over events in the environment, as well as an attention mask over entities participating in the event. Given few demonstration events, our method uses inference-time optimization procedure to generate events involving similar concepts or identify entities involved in the concept. We evaluate our framework on learning visual, quantitative, relational, temporal concepts from demonstration events in an unsupervised manner. Our approach is able to successfully generate and identify concepts in a few-shot setting and resulting learned concepts can be reused across environments. Example videos of our results are available at sites.google.com/site/energyconceptmodels
    OptimizationInferenceConcept learningInverse Reinforcement LearningMeta learningLangevin dynamicsArchitectureMonte Carlo Markov chainPartition functionAutoencoder...
  • High-redshift QSO spectra show large spatial fluctuations in the Ly-alpha opacity of the intergalactic medium on surprisingly large scales at z>~5.5. We present a radiative transfer simulation of cosmic reionization driven by galaxies that reproduces this large scatter and the rapid evolution of the Ly-alpha opacity distribution at 5<z<6. The simulation also reproduces the low Thomson scattering optical depth reported by the latest CMB measurement and is consistent with the observed short near-zones and strong red damping wings in the highest-redshift QSOs. It also matches the rapid disappearance of observed Ly-alpha emission by galaxies at z>~6. Reionization is complete at z=5.3 in our model, and 50% of the volume of the Universe is ionized at z=7. Agreement with the Ly-alpha forest data in such a late reionization model requires a rapid evolution of the ionizing emissivity of galaxies that peaks at z~6.8. The late end of reionization results in a large scatter in the photoionisation rate and the neutral hydrogen fraction at redshifts as low as z<~5.5 with large residual neutral 'islands' that can produce very long Gunn-Peterson troughs resembling those seen in the data.
    ReionizationOpacityGalaxyNeutral hydrogen gasQuasarIntergalactic mediumLyman-alpha forestMean free pathRadiative transfer simulationsUltraviolet background...
  • Experiments measuring the parameters of active neutrino oscillations can also search for sterile neutrinos in a part of sterile neutrino parameter space. In this paper we analyze the sensitivity of the upcoming experiment DUNE to the active-sterile neutrino mixing for the sterile neutrinos with masses at GeV scale. As it relies on still-undecided design of the Near Detector, we consider several possible configurations. Our most optimistic predictions show that the limit on mixing can be approximately of the same order as the previous estimates made for the LBNE. We present our results as separate plots for sterile neutrino mixing with electron, muon and tau neutrinos. Generally, DUNE has good prospects to probe large region of previously unavailable part of the parameter space before the new projects (like SHiP) join the searches.
    Sterile neutrinoActive neutrinoSterile neutrino decayKaonTau neutrinoDUNE experimentActive-sterile neutrino mixingProton beamMeson decaysMuon...
  • A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have made a theory of learning dynamics elusive. In this work, we show that for wide neural networks the learning dynamics simplify considerably and that, in the infinite width limit, they are governed by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters. Furthermore, mirroring the correspondence between wide Bayesian neural networks and Gaussian processes, gradient-based training of wide neural networks with a squared loss produces test set predictions drawn from a Gaussian process with a particular compositional kernel. While these theoretical results are only exact in the infinite width limit, we nevertheless find excellent empirical agreement between the predictions of the original network and those of the linearized version even for finite practically-sized networks. This agreement is robust across different architectures, optimization methods, and loss functions.
    Neural networkArchitectureGaussian processBayesianEntropyStochastic gradient descentOptimizationActivation functionHidden layerBayesian approach...
  • Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Despite their computational efficiency, the density estimation performance of flow-based generative models significantly falls behind those of state-of-the-art autoregressive models. In this work, we introduce masked convolutional generative flow (MaCow), a simple yet effective architecture of generative flow using masked convolution. By restricting the local connectivity in a small kernel, MaCow enjoys the properties of fast and stable training, and efficient sampling, while achieving significant improvements over Glow for density estimation on standard image benchmarks, considerably narrowing the gap to autoregressive models.
    Generative modelArchitectureInferenceNeural networkLatent variableGenerative Adversarial NetConvolutional neural networkPermutationAblationLatent space...
  • We examine the practice of joint training for neural network ensembles, in which a multi-branch architecture is trained via single loss. This approach has recently gained traction, with claims of greater accuracy per parameter along with increased parallelism. We introduce a family of novel loss functions generalizing multiple previously proposed approaches, with which we study theoretical and empirical properties of joint training. These losses interpolate smoothly between independent and joint training of predictors, demonstrating that joint training has several disadvantages not observed in prior work. However, with appropriate regularization via our proposed loss, the method shows new promise in resource limited scenarios and fault-tolerant systems, e.g., IoT and edge devices. Finally, we discuss how these results may have implications for general multi-branch architectures such as ResNeXt and Inception.
    ArchitectureNeural networkEntropyArithmeticClassificationRegressionInferenceInternet of ThingsRegularizationF1 score...
  • Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) on symmetric positive definite (SPD) manifolds for classification tasks. The joint learning considers the interaction between dimensionality reduction and dictionary learning procedures by connecting them into a unified framework. We exploit a Riemannian optimization framework for solving DL and DR problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms existing state-of-the-arts algorithms when used for image classification tasks.
    ManifoldClassificationOptimizationGraphTangent spaceRiemannian metricImage ProcessingGeodesicData samplingNearest-neighbor site...
  • Visual Grounding (VG) aims to locate the most relevant region in an image, based on a flexible natural language query but not a pre-defined label, thus it can be a more useful technique than object detection in practice. Most state-of-the-art methods in VG operate in a two-stage manner, wherein the first stage an object detector is adopted to generate a set of object proposals from the input image and the second stage is simply formulated as a cross-modal matching problem that finds the best match between the language query and all region proposals. This is rather inefficient because there might be hundreds of proposals produced in the first stage that need to be compared in the second stage, not to mention this strategy performs inaccurately. In this paper, we propose an simple, intuitive and much more elegant one-stage detection based method that joints the region proposal and matching stage as a single detection network. The detection is conditioned on the input query with a stack of novel Relation-to-Attention modules that transform the image-to-query relationship to an relation map, which is used to predict the bounding box directly without proposing large numbers of useless region proposals. During the inference, our approach is about 20x ~ 30x faster than previous methods and, remarkably, it achieves 18% ~ 41% absolute performance improvement on top of the state-of-the-art results on several benchmark datasets. We release our code and all the pre-trained models at https://github.com/openblack/rvg.
    AttentionInferenceFeature vectorObject detectionGround truthEmbeddingNatural languageConvolutional neural networkComputational linguisticsRegression...
  • The high amount of sensors required for autonomous driving poses enormous challenges on the capacity of automotive bus systems. There is a need to understand tradeoffs between bitrate and perception performance. In this paper, we compare the image compression standards JPEG, JPEG2000, and WebP to a modern encoder/decoder image compression approach based on generative adversarial networks (GANs). We evaluate both the pure compression performance using typical metrics such as peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and others, but also the performance of a subsequent perception function, namely a semantic segmentation (characterized by the mean intersection over union (mIoU) measure). Not surprisingly, for all investigated compression methods, a higher bitrate means better results in all investigated quality metrics. Interestingly, however, we show that the semantic segmentation mIoU of the GAN autoencoder in the highly relevant low-bitrate regime (at 0.0625 bit/pixel) is better by 3.9% absolute than JPEG2000, although the latter still is considerably better in terms of PSNR (5.91 dB difference). This effect can greatly be enlarged by training the semantic segmentation model with images originating from the decoder, so that the mIoU using the segmentation model trained by GAN reconstructions exceeds the use of the model trained with original images by almost 20% absolute. We conclude that distributed perception in future autonomous driving will most probably not provide a solution to the automotive bus capacity bottleneck by using standard compression schemes such as JPEG2000, but requires modern coding approaches, with the GAN encoder/decoder method being a promising candidate.
    Generative Adversarial NetSemantic segmentationQuantizationAutoencoderAutonomous drivingLatent spaceInferenceSignal to noise ratioGround truthGenerative model...
  • Contrast is subject to dramatic changes across the visual field, depending on the source of light and scene configurations. Hence, the human visual system has evolved to be more sensitive to contrast than absolute luminance. This feature is equally desired for machine vision: the ability to recognise patterns even when aspects of them are transformed due to variation in local and global contrast. In this work, we thoroughly investigate the impact of image contrast on prominent deep convolutional networks, both during the training and testing phase. The results of conducted experiments testify to an evident deterioration in the accuracy of all state-of-the-art networks at low-contrast images. We demonstrate that "contrast-augmentation" is a sufficient condition to endow a network with invariance to contrast. This practice shows no negative side effects, quite the contrary, it might allow a model to refrain from other illuminance related over-fittings. This ability can also be achieved by a short fine-tuning procedure, which opens new lines of investigation on mechanisms involved in two networks whose weights are over 99.9% correlated, yet astonishingly produce utterly different outcomes. Our further analysis suggests that the optimisation algorithm is an influential factor, however with a significantly lower effect; and while the choice of an architecture manifests a negligible impact on this phenomenon, the first layers appear to be more critical.
    ClassificationArchitectureDeep Neural NetworksMutual informationOverfittingIlluminanceStochastic gradient descentNeural networkEntropyAutonomous driving...
  • The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications.
    Semantic segmentationImage segmentationConvolutional neural networkArchitectureClassificationFeature extractionSaturnian satellitesAutonomous systemInferenceTraining set...
  • Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions. However, saliency maps are often visually noisy. Although several hypotheses were proposed to account for this phenomenon, there are few works that provide rigorous analyses of noisy saliency maps. In this paper, we identify that noise occurs in saliency maps when irrelevant features pass through ReLU activation functions. Then we propose Rectified Gradient, a method that solves this problem through layer-wise thresholding during backpropagation. Experiments with neural networks trained on CIFAR-10 and ImageNet showed effectiveness of our method and its superiority to other attribution methods.
    Deep Neural NetworksBackpropagationConvolutional neural networkActivation functionClassificationPythonNeural networkAdversarial examplesGradient flowArchitecture...
  • In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make a forensic similarity decision on it in the future. To do this, we propose a two part deep-learning system composed of a CNN-based feature extractor and a three-layer neural network, called the similarity network. This system maps pairs of image patches to a score indicating whether they contain the same or different forensic traces. We evaluated system accuracy of determining whether two image patches were 1) captured by the same or different camera model, 2) manipulated by the same or different editing operation, and 3) manipulated by the same or different manipulation parameter, given a particular editing operation. Experiments demonstrate applicability to a variety of forensic traces, and importantly show efficacy on "unknown" forensic traces that were not used to train the system. Experiments also show that the proposed system significantly improves upon prior art, reducing error rates by more than half. Furthermore, we demonstrated the utility of the forensic similarity approach in two practical applications: forgery detection and localization, and database consistency verification.
    Convolutional neural networkSplicingClassificationArchitectureScale factorDeep learningFeature vectorFeature spaceNeural networkEntropy...
  • We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth resolution comparable to pulsed lidar measurements. Existing scanning lidar systems achieve low spatial resolution at large ranges due to mechanically-limited angular sampling rates, restricting scene understanding tasks to close-range clusters with dense sampling. In addition, today's lidar detector technologies, short-pulsed laser sources and scanning mechanics result in high cost, power consumption and large form-factors. We depart from point scanning and propose a learned architecture that recovers high-fidelity dense depth from three temporally gated images, acquired with a flash source and a high-resolution CMOS sensor. The proposed architecture exploits semantic context across gated slices, and is trained on a synthetic discriminator loss without the need of dense depth labels. The method is real-time and essentially turns a gated camera into a low-cost dense flash lidar which we validate on a wide range of outdoor driving captures and in simulations.
    ArchitectureLasersCMOSGround truthTime-of-flightIntensitySunlightMountingPrecisionGenerative Adversarial Net...
  • 3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing). The ground truth for 3D medical images is very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this paper, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), to register 3D medical images. Three technical components ameliorate our unsupervised learning system for 3D end-to-end medical image registration: (1) We cascade the registration subnetworks; (2) We integrate affine registration into our network; and (3) We incorporate an additional invertibility loss into the training process. Experimental results demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-the-art performance in medical image registration.
    Ground truthConvolutional neural networkOptimizationUnsupervised learningMutual informationATLAS detectorNeural networkEntropyRegularizationSupervised learning...
  • Employing one or more additional classifiers to break the self-learning loop in tracing-by-detection has gained considerable attention. Most of such trackers merely utilize the redundancy to address the accumulating label error in the tracking loop, and suffer from high computational complexity as well as tracking challenges that may interrupt all classifiers (e.g. temporal occlusions). We propose the active co-tracking framework, in which the main classifier of the tracker labels samples of the video sequence, and only consults auxiliary classifier when it is uncertain. Based on the source of the uncertainty and the differences of two classifiers (e.g. accuracy, speed, update frequency, etc.), different policies should be taken to exchange the information between two classifiers. Here, we introduce a reinforcement learning approach to find the appropriate policy by considering the state of the tracker in a specific sequence. The proposed method yields promising results in comparison to the best tracking-by-detection approaches.
    Q-learningReinforcement learningAttentionActive learningShort Term MemorySupervised learningPrecisionSVM classifierGround truthDistance sampling...
  • Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g. daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed.
    Deep Neural NetworksArchitectureConvolutional neural networkAutonomous drivingHyperparameterSupervised learningObject detectionSemantic segmentationAutonomous vehiclesSaturnian satellites...
  • Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper we demonstrate a crucial phenomenon: deep learning typically yields unstablemethods for image reconstruction. The instabilities usually occur in several forms: (1) tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction, (2) a small structural change, for example a tumour, may not be captured in the reconstructed image and (3) (a counterintuitive type of instability) more samples may yield poorer performance. Our new stability test with algorithms and easy to use software detects the instability phenomena. The test is aimed at researchers to test their networks for instabilities and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.
    InstabilityMagnetic resonance imagingDeep learningNeural networkArchitectureInverse problemsConvolutional neural networkClassificationGround truthRadon transform...
  • In image quality enhancement processing, it is the most important to predict how humans perceive processed images since human observers are the ultimate receivers of the images. Thus, objective image quality assessment (IQA) methods based on human visual sensitivity from psychophysical experiments have been extensively studied. Thanks to the powerfulness of deep convolutional neural networks (CNN), many CNN based IQA models have been studied. However, previous CNN-based IQA models have not fully utilized the characteristics of human visual systems (HVS) for IQA problems by simply entrusting everything to CNN where the CNN-based models are often trained as a regressor to predict the scores of subjective quality assessment obtained from IQA datasets. In this paper, we propose a novel HVS-inspired deep IQA network, called Deep HVS-IQA Net, where the human psychophysical characteristics such as visual saliency and just noticeable difference (JND) are incorporated at the front-end of the Deep HVS-IQA Net. To our best knowledge, our work is the first HVS-inspired trainable IQA network that considers both the visual saliency and JND characteristics of HVS. Furthermore, we propose a rank loss to train our Deep HVS-IQA Net effectively so that perceptually important features can be extracted for image quality prediction. The rank loss can penalize the Deep HVS-IQA Net when the order of its predicted quality scores is different from that of the ground truth scores. We evaluate the proposed Deep HVS-IQA Net on large IQA datasets where it outperforms all the recent state-of-the-art IQA methods.
    Hypervelocity starConvolutional neural networkRankGround truthNetworksObjective...
  • Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of these networks strongly depends on the training data. An issue with collecting training data is labeling. Labeling by humans is necessary to obtain the ground truth label; however, labeling requires huge costs. Therefore, we propose an automatic labeled data generation pipeline, for which we can change any parameters or data generation environments. Our approach uses a human model named Dhaiba and a background of Miraikan and consequently generated realistic artificial data. We present 500k+ data generated by the proposed pipeline. This paper also describes the specification of the pipeline and data details with evaluations of various approaches.
    Point cloudGround truthDeep Neural NetworksNetworks...
  • This work proposes a new method to accurately complete sparse LiDAR maps guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is indispensable in order to achieve precise depth predictions. A multitude of applications depend on the awareness of their surroundings, and use depth cues to reason and react accordingly. On the one hand, monocular depth prediction methods fail to generate absolute and precise depth maps. On the other hand, stereoscopic approaches are still significantly outperformed by LiDAR based approaches. The goal of the depth completion task is to generate dense depth predictions from sparse and irregular point clouds which are mapped to a 2D plane. We propose a new framework which extracts both global and local information in order to produce proper depth maps. We argue that simple depth completion does not require a deep network. However, we additionally propose a fusion method with RGB guidance from a monocular camera in order to leverage object information and to correct mistakes in the sparse input. This improves the accuracy significantly. Moreover, confidence masks are exploited in order to take into account the uncertainty in the depth predictions from each modality. This fusion method outperforms the state-of-the-art and ranks first on the KITTI depth completion benchmark. Our code with visualizations is available.
    RankPoint cloudGround truthConvolutional neural networkAttentionImage ProcessingAutonomous vehiclesRegressionRoboticsInference...
  • This paper considers the problem of nonlinear dimensionality reduction. Unlike existing methods, such as LLE, ISOMAP, which attempt to unfold the true manifold in the low dimensional space, our algorithm tries to preserve the nonlinear structure of the manifold, and shows how the manifold is folded in the high dimensional space. We call this method Tangent Distance Preserving Mapping (TDPM). TDPM uses tangent distance instead of geodesic distance, and then applies MDS to the tangent distance matrix to map the manifold into a low dimensional space in which we can get its nonlinear structure.
    ManifoldDistance matrixGeodesicAlgorithms...
  • Gatherings of thousands to millions of people occur frequently for an enormous variety of events, and automated counting of these high density crowds is used for safety, management, and measuring significance of these events. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (i$k$NN) maps, even when used directly in existing state-of-the-art network structures. We also provide a new network architecture MUD-i$k$NN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided i$k$NN labeling. This upsampling combined with the i$k$NN maps further outperforms the existing state-of-the-art methods. The full label comparison emphasizes the importance of the labeling scheme, with the i$k$NN labeling being particularly effective. We demonstrate the accuracy of our MUD-i$k$NN network and the i$k$NN labeling scheme on a variety of datasets.
    RegressionCountingConvolutional neural networkNearest-neighbor siteArchitectureGround truthUniversal Conductance FluctuationsDeep Neural NetworksGaussian distributionNetwork Coding...
  • The concept of beauty has been debated by philosophists and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability. In this paper, we present a novel study on mining beauty semantics of facial attributes based on big data, with an attempt to objectively construct descriptions of beauty in a quantitative manner. We first deploy a deep convolutional neural network (CNN) to extract facial attributes, and then investigate correlations between these features and attractiveness on two large-scale datasets labelled with beauty scores. Not only do we discover the secrets of beauty verified by statistical significance tests, our findings also align perfectly with existing psychological studies that, e.g., small nose, high cheekbones, and femininity contribute to attractiveness. We further leverage these high-level representations to original images by a generative adversarial network (GAN). Beauty enhancements after synthesis are visually compelling and statistically convincing verified by a user survey of 10,000 data points.
    Generative Adversarial NetConvolutional neural networkArchitectureClassificationBig dataStatistical significanceStatisticsStatistical estimatorSupport vector machineRandom forest...
  • We present a deep-learning network that detects multiple small objects (hundreds to thousands) in a scene while simultaneously estimating their x,y pixel locations together with a characteristic feature-set (for instance, target orientation and color). All estimations are performed in a single, forward pass which makes implementing the network fast and efficient. In this paper, we describe the architecture of our network --- nicknamed ALIEN --- and detail its performance when applied to vehicle detection.
    ArchitectureOrientationDeep learningInferenceObject detectionFeature extractionNeural networkConvolutional neural networkRegressionIndicator function...