arXiv subjects

Nan Li

Publications and source records attributed to Nan Li.

At least 307 records · Page 17Linked to original sources

arXiv2019

Existence of nonconstant CR-holomorphic functions of polynomial growth in Sasakian Manifolds

In this paper, we show that there exists a nonconstant CR holomorphic function of polynomial growth in a complete noncompact Sasakian manifold of nonnegative pseudohermitian bisectional curvature with the CR maximal volume growth property. This is the very first step toward the CR analogue of Yau uniformization conjecture which states that any complete noncompact Sasakian manifold of positive pseudohermitian bisectional curvature is CR biholomorphic to the standard Heisenberg group.

math.DG↗

arXiv2019

Tiny Buffer TCP for Data Center Networks

A low and stable buffer occupancy is critical to achieve high throughput, low packet drop rate, low latency, and low jitter for data center networks. It also allows switch chips to support higher port density, larger lookup tables, or richer functions. Tiny Buffer TCP creatively uses the common RED-based ECN with two novel congestion-window adjustment schemes to significantly reduce the required buffer size. Aiming to eliminate the residual packets in the bottleneck queue, Queue Canceling Decrease amortizes the ideal window reduction to the same number of flows so as to minimize the impact to active flows. In order to keep the buffer occupancy low and stable, Reduced Additive Increase recovers the flow window at a slower pace than normal. We implemented TBTCP in Linux kernel and conducted ns2-based simulations and real network-based tests. Our results show that compared to DCTCP, TBTCP reduces the switch buffer requirement by more than 80%, increases the bandwidth utilization by up to 15%, improves the FCT performance by up to 39%, and achieve a 71% better RTT fairness index.

cs.NI↗

arXiv2019

Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.

astro-ph.IM↗

arXiv2019

A Reference Governor for Nonlinear Systems with Disturbance Inputs Based on Logarithmic Norms and Quadratic Programming

This note describes a reference governor design for a continuous-time nonlinear system with an additive disturbance. The design is based on predicting the response of the nonlinear system by the response of a linear model with a set-bounded prediction error, where a state-and-input dependent bound on the prediction error is explicitly characterized using logarithmic norms. The online optimization is reduced to a convex quadratic program with linear inequality constraints. Two numerical examples are reported.

eess.SY↗

arXiv2019

Detection-averse optimal and receding-horizon control for Markov decision processes

In this paper, we consider a Markov decision process (MDP), where the ego agent has a nominal objective to pursue while needs to hide its state from detection by an adversary. After formulating the problem, we first propose a value iteration (VI) approach to solve it. To overcome the "curse of dimensionality" and thus gain scalability to larger-sized problems, we then propose a receding-horizon optimization (RHO) approach to obtain approximate solutions. We use examples to illustrate and compare the VI and RHO approaches, and to show the potential of our problem formulation for practical applications.

eess.SY↗

arXiv2019

CosmoDC2: A Synthetic Sky Catalog for Dark Energy Science with LSST

This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried out by the LSST Dark Energy Science Collaboration (LSST DESC). The catalog is based on a trillion-particle, 4.225 Gpc^3 box cosmological N-body simulation, the `Outer Rim' run. It covers 440 deg^2 of sky area to a redshift of z=3 and is complete to a magnitude depth of 28 in the r-band. Each galaxy is characterized by a multitude of properties including stellar mass, morphology, spectral energy distributions, broadband filter magnitudes, host halo information and weak lensing shear. The size and complexity of cosmoDC2 requires an efficient catalog generation methodology; our approach is based on a new hybrid technique that combines data-driven empirical approaches with semi-analytic galaxy modeling. A wide range of observation-based validation tests has been implemented to ensure that cosmoDC2 enables the science goals of the planned LSST DESC DC2 analyses. This paper also represents the official release of the cosmoDC2 data set, including an efficient reader that facilitates interaction with the data.

astro-ph.CO↗

arXiv2019

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as how network performance depends on the inclusion of lens galaxy light, the addition of colour information and varying signal-to-noise. Our CNN was trained and tested with strong galaxy-galaxy lens images simulated to match the imaging characteristics of the Large Synoptic Survey Telescope (LSST) and Euclid. For images including lens galaxy light, the CNN can recover the lens model parameters with an acceptable accuracy, although a 34 per cent average improvement in accuracy is obtained when lens light is removed. However, the inclusion of colour information can largely compensate for the drop in accuracy resulting from the presence of lens light. While our findings show similar accuracies for single epoch Euclid VIS and LSST r-band datasets, we find a 24 per cent increase in accuracy by adding g- and i-band images to the LSST r-band without lens light and a 20 per cent increase with lens light. The best network performance is obtained when it is trained and tested on images where lens light exactly follows the mass, but when orientation and ellipticity of the light is allowed to differ from those of the mass, the network performs most consistently when trained with a moderate amount of scatter in the difference between the mass and light profiles.

astro-ph.IM↗

arXiv2019

Prospects of detecting dark matter through cosmic-ray antihelium with the antiproton constraints

Cosmic-ray (CR) antihelium is an important observable for dark matter (DM) indirect searches due to extremely low secondary backgrounds towards low energies. In a given DM model, the predicted CR antihelium flux is expected to be strongly correlated with that of CR antiprotons. In this work, we use the AMS-02 $\bar p/p$ data to constrain the DM annihilation cross section, and the ALICE data on the $^3\overline{\text{He}}$ and $\overline{\text{T}}$ productions to determine the parameters in the coalescence model for anti-nucleus formation. The hadronic cross sections are estimated using Monte-Carlo event generators including $\texttt{EPOS-LHC}$ and $\texttt{DPMJET}$. Based on these constraints, we make predictions for the maximal antihelium flux for typical DM annihilation final states, and perform a detailed analysis on the uncertainties due to the DM density profiles and CR propagation models. We find that the results are highly insensitive to both of them, but still significantly depends on the hadronization models in event generators. The prospects of detecting antihelium for the AMS-02 experiment is discussed. We show that with very optimistic assumptions, CR $^3\overline{\text{He}}$ is within the reach of the AMS-02 experiment. The $^3\overline{\text{He}}$ events which can be detected by AMS-02 are likely to have kinetic energy $T \gtrsim 30$~GeV, which is consistent with the preliminary AMS-02 search results. The events which can be observed by AMS-02 are likely to arise dominantly from secondary backgrounds rather than DM interactions.

hep-ph↗

arXiv2019

The importance of secondary halos for strong lensing in massive galaxy clusters across redshift

Cosmological cluster-scale strong gravitational lensing probes the mass distribution of the dense cores of massive dark matter halos and the structures along the line of sight from background sources to the observer. It is frequently assumed that the primary lens mass dominates the lensing, with the contribution of secondary masses along the line of sight being neglected. Secondary mass structures may, however, affect both the detectability of strong lensing in a given survey and modify the properties of the lensing that is detected. In this paper, we utilize a large cosmological N-body simulation and a multiple lens plane (and many source planes) ray-tracing technique to quantify the influence of line of sight halos on the detectability of cluster-scale strong lensing in a cluster sample with a mass limit that encompasses current cluster catalogs from the South Pole Telescope. We extract both primary and secondary halos from the "Outer Rim" simulation and consider two strong lensing realizations: one with only the primary halos included, and the other contains all secondary halos down to a mass limit. In both cases, we use the same source information extracted from the Hubble Ultra Deep Field, and create realistic lensed images consistent with moderately deep ground-based imaging. The results demonstrate that down to the mass limit considered the total number of lenses is boosted by about 13-21% when considering the complete multi-halo lightcone. The increment in strong lens counts peaks at lens redshifts of 0.6 approximately with no significant effect at z<0.3. The strongest trends are observed relative to the primary halo mass, with no significant impact in the most massive quintile of the halo sample, but increasingly boosting the observed lens counts toward small primary halo masses, with an enhancement greater than 50% in the least massive quintile of the halo masses considered.

astro-ph.CO↗

arXiv2019

Game-Theoretic Modeling of Multi-Vehicle Interactions at Uncontrolled Intersections

Motivated by the need to develop simulation tools for verification and validation of autonomous driving systems operating in traffic consisting of both autonomous and human-driven vehicles, we propose a framework for modeling vehicle interactions at uncontrolled intersections. The proposed interaction modeling approach is based on game theory with multiple concurrent leader-follower pairs, and accounts for common traffic rules. We parameterize the intersection layouts and geometries to model uncontrolled intersections with various configurations, and apply the proposed approach to model the interactive behavior of vehicles at these intersections. Based on simulation results in various traffic scenarios, we show that the model exhibits reasonable behavior expected in traffic, including the capability of reproducing scenarios extracted from real-world traffic data and reasonable performance in resolving traffic conflicts. The model is further validated based on the level-of-service traffic quality rating system and demonstrates manageable computational complexity compared to traditional multi-player game-theoretic models.

eess.SY↗

arXiv2019

The Strong Gravitational Lens Finding Challenge

Large scale imaging surveys will increase the number of galaxy-scale strong lensing candidates by maybe three orders of magnitudes beyond the number known today. Finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method. To achieve these objectives automated methods must be developed. Because gravitational lenses are rare objects reducing false positives will be particularly important. We present a description and results of an open gravitational lens finding challenge. Participants were asked to classify 100,000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets. A variety of methods were used including visual inspection, arc and ring finders, support vector machines (SVM) and convolutional neural networks (CNN). We find that many of the methods will be easily fast enough to analyse the anticipated data flow. In test data, several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness, size or contrast with the lens galaxy without making a single false-positive identification. This is significantly better than direct inspection by humans was able to do. (abridged)

astro-ph.GA↗

arXiv2019

Multinomial Logit Bandit with Linear Utility Functions

Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon $T$. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret no better than $\tilde{O}\big(\sqrt{NT}\big)$ which is not preferred for large candidate set size $N$. In this paper, we consider the {\it linear utility} MNL choice model whose item utilities are represented as linear functions of $d$-dimension item features, and propose an algorithm, titled {\bf LUMB}, to exploit the underlying structure. It is proven that the proposed algorithm achieves $\tilde{O}\big(dK\sqrt{T}\big)$ regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.

cs.LG↗

arXiv2019

An analytical safe approximation to joint chance-constrained programming with additive Gaussian noises

We propose a safe approximation to joint chance-constrained programming where the constraint functions are additively dependent on a normally-distributed random vector. The approximation is analytical, meaning that it requires neither numerical integrations nor sampling-based probability approximations. Under mild assumptions, the approximation is a standard nonlinear program. We compare this new safe approximation to another analytical safe approximation for joint chance-constrained programming based on Boole's inequality through two examples representing the constrained control of linear Gaussian-Markov models. It is shown that our proposed safe approximation has a lower degree of conservatism compared to the one based on Boole's inequality.

math.OC↗

arXiv2018

Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts

In this paper, we propose a decision making algorithm for autonomous vehicle control at a roundabout intersection. The algorithm is based on a game-theoretic model representing the interactions between the ego vehicle and an opponent vehicle, and adapts to an online estimated driver type of the opponent vehicle. Simulation results are reported.

cs.GT↗

arXiv2018

On the Sormani-Wenger Intrinsic Flat Convergence of Alexandrov Spaces

We study sequences of integral current spaces $(X_j,d_j,T_j)$ such that the integral current structure $T_j$ has weight $1$ and no boundary and, all $(X_j,d_j)$ are closed Alexandrov spaces with curvature uniformly bounded from below and diameter uniformly bounded from above. We prove that for such sequences either their limits collapse or the Gromov-Hausdorff and Sormani-Wenger Intrinsic Flat limits agree. The latter is done showing that the lower $n$ dimensional density of the mass measure at any regular point of the Gromov-Hausdorff limit space is positive by passing to a filling volume estimate. In an appendix we show that the filling volume of the standard $n$ dimensional integral current space coming from an $n$ dimensional sphere of radius $r>0$ in Euclidean space equals $r^n$ times the filling volume of the $n$ dimensional integral current space coming from the $n$ dimensional sphere of radius $1$.

math.MG↗

arXiv2018

The black ring entropy from the Weyl tensor

A black ring is an asymptotically flat vacuum solution of the Einstein equations with an event horizon of topology $S^1\times S^2$. A connection between the black ring entropy and its Weyl tensor $C_{μνλρ}$ is explored by interpreting the Weyl scalar invariant $C_{μνλρ} C^{μνλρ}$ as the entropy density in 5-dimensional space-time. We calculate the proper volume integral of $C_{μνλρ} C^{μνλρ}$ for a neutral black ring and prove that it is proportional to the entropy of a thin black ring. Similar calculations are extended to more general cases: the black string, the black ring with two angular momenta, and the black ring with a cosmological constant. The proportionality still maintains or is valid at least at the leading order.

gr-qc↗

arXiv2018

On the throttling process of the Kerr-Newman-anti-de Sitter black holes in the extended phase space

The throttling process of the Kerr-Newman-anti-de Sitter (KN-AdS) black holes is systematically studied in the extended phase space. In this framework, the cosmological constant is interpreted as a varying thermodynamic pressure, and the black hole mass is identified with enthalpy. The throttling process is essentially an isenthalpic (i.e., constant-mass) process for the KN-AdS black holes. The Joule-Thomson coefficient, inversion temperature, inversion curve, and isenthalpic curve are investigated in order, with both analytical and numerical methods. It is found that there are no maximum inversion temperatures, but only minimum ones that are around one half of the critical temperatures of the KN-AdS black holes. Two characteristic masses are also discussed to show the detailed features in the throttling behaviors of the KN-AdS black holes.

gr-qc↗

arXiv2018

An Attention-Based Approach for Single Image Super Resolution

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.

cs.CV↗