arXiv subjects

Yao Li

Publications and source records attributed to Yao Li.

At least 163 records · Page 9Linked to original sources

arXiv2022

LAMOST MRS-N Observations of the W80 Region

The spectral observations and analysis for the W80 Region are presented by using the data of Medium-Resolution Spectroscopic Survey of Nebulae (MRS-N) with the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). A total of 2982 high-quality nebular spectra have been obtained in the 20 square degree field of view (FoV) which covers the W80 complex, and the largest sample of spectral data have been established for the first time. The relative intensities, radial velocities (RVs), and Full Widths at Half Maximum (FWHMs) are measured with the high spectral resolution of LAMOST MRS, for H$α$ $λ$ 6563 Å, [\ion{N}{ii}] $λ$$λ$ 6548 Å, 6584 Å\ , and [\ion{S}{ii}] $λ$$λ$ 6716 Å, 6731 Å\ emission lines. In the field of view of whole W80 Region, the strongest line emissions are found to be consistent with the bright nebulae, NGC 7000, IC 5070, and LBN 391, and weak line emissions also truly exist in the Middle Region, where no bright nebulae are detected by the wide-band optical observations. The large-scale spectral observations to the W80 Region reveal the systematic spatial variations of RVs and FWHMs, and several unique structural features. A 'curved feature' to the east of the NGC 7000, and a 'jet feature' to the west of the LBN 391 are detected to be showing with larger radial velocities. A 'wider FWHM region' is identified in the eastern part of the NGC 7000. The variations of [\ion{S}{ii}] / H$α$ ratios display a gradient from southwest to northeast in the NGC 7000 region, and manifest a ring shape around the 'W80 bubble' ionized by an O-type star in the L935. Further spectral and multi-band observations are guaranteed to investigate in detail the structural features.

astro-ph.SR↗

arXiv2022

N-Cloth: Predicting 3D Cloth Deformation with Mesh-Based Networks

We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies. We use graph convolution to transform the cloth and object meshes into a latent space to reduce the non-linearity in the mesh space. Our network can predict the target 3D cloth mesh deformation based on the initial state of the cloth mesh template and the target obstacle mesh. Our approach can handle complex cloth meshes with up to 100K triangles and scenes with various objects corresponding to SMPL humans, non-SMPL humans or rigid bodies. In practice, our approach can be used to generate plausible cloth simulation at 30-45 fps on an NVIDIA GeForce RTX 3090 GPU. We highlight its benefits over prior learning-based methods and physically-based cloth simulators.

cs.GR↗

arXiv2022

The Data Processing of the LAMOST Medium-Resolution Spectral Survey of Galactic Nebulae (LAMOST MRS-N Pipeline)

The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution spectral survey of Galactic Nebulae (MRS-N) has conducted for three years since Sep. 2018 and observed more than 190 thousands nebular spectra and 20 thousands stellar spectra. However, there is not yet a data processing pipeline for nebular data. To significantly improve the accuracy of nebulae classification and their physical parameters, we developed the MRS-N Pipeline. This article presented in detail each data processing step of the MRS-N Pipeline, such as removing cosmic rays, merging single exposure, fitting sky light emission lines, subtracting skylight, wavelength recalibration, measuring nebular parameters, creating catalogs and packing spectra. Finally, a description of the data products, including nebular spectra files and parameter catalogs, is provided.

astro-ph.SR↗

arXiv2022

Improving Pedestrian Priority via Grouping and Virtual Lanes

The shared space design is applied in urban streets to support barrier-free movement and integrate traffic participants (such as pedestrians, cyclists and vehicles) into a common road space. Regardless of the low-speed environment, sharing space with motor vehicles can make vulnerable road users feel uneasy. Yet, walking in groups increases their confidence as well as influence the yielding behavior of drivers. Therefore, we propose an innovative approach to support the crossing of pedestrians via grouping and project the virtual lanes in shared spaces. This paper presents the important components of the crowd steering system, discusses the enablers and gaps in the current approach, and illustrates the proposed idea with concept diagrams.

cs.HC↗

arXiv2022

Forecasting SQL Query Cost at Twitter

With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query with traditional DBMS approaches. Can we estimate the cost of each query more efficiently without any computation in a SQL engine kernel? Can machine learning techniques help to estimate SQL query resource utilization? The answers are yes. We propose a SQL query cost predictor service, which employs machine learning techniques to train models from historical query request logs and rapidly forecasts the CPU and memory resource usages of online queries without any computation in a SQL engine. At Twitter, infrastructure engineers are maintaining a large-scale SQL federation system across on-premises and cloud data centers for serving ad-hoc queries. The proposed service can help to improve query scheduling by relieving the issue of imbalanced online analytical processing (OLAP) workloads in the SQL engine clusters. It can also assist in enabling preemptive scaling. Additionally, the proposed approach uses plain SQL statements for the model training and online prediction, indicating it is both hardware and software-agnostic. The method can be generalized to broader SQL systems and heterogeneous environments. The models can achieve 97.9\% accuracy for CPU usage prediction and 97\% accuracy for memory usage prediction.

cs.DB↗

arXiv2022

Scattering Amplitudes of Kaluza-Klein Strings and Extended Massive Double-Copy

We study the scattering amplitudes of massive Kaluza-Klein (KK) states of open and closed bosonic strings under toroidal compactification. We analyze the structure of vertex operators for the KK strings and derive an extended massive KLT-like relation which connects the $N$-point KK closed-string amplitude to the products of two KK open-string amplitudes at tree level. Taking the low energy field-theory limit of vanishing Regge slope, we derive double-copy construction formula of the $N$-point massive KK graviton amplitude from the sum of proper products of the corresponding KK gauge boson amplitudes. Then, using the string-based massive double-copy formula, we derive the exact tree-level four-point KK gauge boson amplitudes and KK graviton amplitudes, which fully agree with those given by the KK field-theory calculations. With these, we give an explicit prescription on constructing the exact four-point KK graviton amplitudes from the sum of proper products of the corresponding color-ordered KK gauge boson amplitudes. We further analyze the string-based double-copy construction of five-point and six-point scattering amplitudes of massive KK gauge bosons and KK gravitons.

hep-th↗

arXiv2022

$\rm ^{83}Rb$/$\rm ^{83m}Kr$ production and cross-section measurement with 3.4 MeV and 20 MeV proton beams

$\rm ^{83m}Kr$, with a short lifetime, is an ideal calibration source for liquid xenon or liquid argon detectors. The $\rm ^{83m}Kr$ isomer can be generated through the decay of $\rm ^{83} Rb$ isotope which is usually produced by proton beams bombarding natural krypton atoms. In this paper, we report a successful production of $\rm ^{83}Rb/^{83m}Kr$ with a proton beam energy of 3.4 MeV, and the first measurement of the production rate with such low energy proton beams. Another production attempt is performed using the newly available 20 MeV proton beam in China, and the measured production rate is consistent with previous measurements. The produced $\rm ^{83m}Kr$ source has been successfully injected into the PandaX-II liquid xenon detector, yielding enough statistics for detector calibration.

nucl-ex↗

arXiv2022

On the improved conditions for some primal-dual algorithms

The convex minimization of $f(\mathbf{x})+g(\mathbf{x})+h(\mathbf{A}\mathbf{x})$ over $\mathbb{R}^n$ with differentiable $f$ and linear operator $\mathbf{A}: \mathbb{R}^n\rightarrow \mathbb{R}^m$, has been well-studied in the literature. By considering the primal-dual optimality of the problem, many algorithms are proposed from different perspectives such as monotone operator scheme and fixed point theory. In this paper, we start with a base algorithm to reveal the connection between several algorithms such as AFBA, PD3O and Chambolle-Pock. Then, we prove its convergence under a relaxed assumption associated with the linear operator and characterize the general constraint on primal and dual stepsizes. The result improves the upper bound of stepsizes of AFBA and indicates that Chambolle-Pock, as the special case of the base algorithm when $f=0$, can take the stepsize of the dual iteration up to $4/3$ of the previously proven one.

math.OC↗

arXiv2021

A Review of Adversarial Attack and Defense for Classification Methods

Despite the efficiency and scalability of machine learning systems, recent studies have demonstrated that many classification methods, especially deep neural networks (DNNs), are vulnerable to adversarial examples; i.e., examples that are carefully crafted to fool a well-trained classification model while being indistinguishable from natural data to human. This makes it potentially unsafe to apply DNNs or related methods in security-critical areas. Since this issue was first identified by Biggio et al. (2013) and Szegedy et al.(2014), much work has been done in this field, including the development of attack methods to generate adversarial examples and the construction of defense techniques to guard against such examples. This paper aims to introduce this topic and its latest developments to the statistical community, primarily focusing on the generation and guarding of adversarial examples. Computing codes (in python and R) used in the numerical experiments are publicly available for readers to explore the surveyed methods. It is the hope of the authors that this paper will encourage more statisticians to work on this important and exciting field of generating and defending against adversarial examples.

cs.CR↗

arXiv2021

Decentralized Composite Optimization with Compression

Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice. While existing decentralized algorithms with communication compression mostly focus on the problems with only smooth components, we study the decentralized stochastic composite optimization problem with a potentially non-smooth component. A \underline{Prox}imal gradient \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, Prox-LEAD, is proposed with rigorous theoretical analyses in the general stochastic setting and the finite-sum setting. Our theorems indicate that Prox-LEAD works with arbitrary compression precision, and it tremendously reduces the communication cost almost for free. The superiorities of the proposed algorithms are demonstrated through the comparison with state-of-the-art algorithms in terms of convergence complexities and numerical experiments. Our algorithmic framework also generally enlightens the compressed communication on other primal-dual algorithms by reducing the impact of inexact iterations, which might be of independent interest.

cs.LG↗

arXiv2021

ErrorCompensatedX: error compensation for variance reduced algorithms

Communication cost is one major bottleneck for the scalability for distributed learning. One approach to reduce the communication cost is to compress the gradient during communication. However, directly compressing the gradient decelerates the convergence speed, and the resulting algorithm may diverge for biased compression. Recent work addressed this problem for stochastic gradient descent by adding back the compression error from the previous step. This idea was further extended to one class of variance reduced algorithms, where the variance of the stochastic gradient is reduced by taking a moving average over all history gradients. However, our analysis shows that just adding the previous step's compression error, as done in existing work, does not fully compensate the compression error. So, we propose ErrorCompensatedX, which uses the compression error from the previous two steps. We show that ErrorCompensatedX can achieve the same asymptotic convergence rate with the training without compression. Moreover, we provide a unified theoretical analysis framework for this class of variance reduced algorithms, with or without error compensation.

cs.DC↗

arXiv2021

Tilting flat bands in an empty microcavity

Recently microcavities with anisotropic materials are shown to be able to create novel bands with non-zero local Berry curvature. The anisotropic refractive index of the cavity layer is believed to be critical in opening an energy gap at the tilted Dirac points. In this work, we show that an anticrossing between a cavity mode and a Bragg mode can also form within an empty microcavity without any birefringent materials. Flat bands are observed within the energy gap due to the particular refractive index distribution of the sample. The intrinsic TE-TM splitting and XY splitting induce the squeezing of the cavity modes in momentum space, so that the flat bands are spin-dependently tilted. Our results pave the way to investigate the spin orbit coupling of photons in a simple microcavity without anisotropic cavity layers.

physics.optics↗

arXiv2021

Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting

As cameras are increasingly deployed in new application domains such as autonomous driving, performing 3D object detection on monocular images becomes an important task for visual scene understanding. Recent advances on monocular 3D object detection mainly rely on the ``pseudo-LiDAR'' generation, which performs monocular depth estimation and lifts the 2D pixels to pseudo 3D points. However, depth estimation from monocular images, due to its poor accuracy, leads to inevitable position shift of pseudo-LiDAR points within the object. Therefore, the predicted bounding boxes may suffer from inaccurate location and deformed shape. In this paper, we present a novel neighbor-voting method that incorporates neighbor predictions to ameliorate object detection from severely deformed pseudo-LiDAR point clouds. Specifically, each feature point around the object forms their own predictions, and then the ``consensus'' is achieved through voting. In this way, we can effectively combine the neighbors' predictions with local prediction and achieve more accurate 3D detection. To further enlarge the difference between the foreground region of interest (ROI) pseudo-LiDAR points and the background points, we also encode the ROI prediction scores of 2D foreground pixels into the corresponding pseudo-LiDAR points. We conduct extensive experiments on the KITTI benchmark to validate the merits of our proposed method. Our results on the bird's eye view detection outperform the state-of-the-art performance by a large margin, especially for the ``hard'' level detection.

cs.CV↗

arXiv2021

Reasoning about the garden of forking paths

Lazy evaluation is a powerful tool for functional programmers. It enables the concise expression of on-demand computation and a form of compositionality not available under other evaluation strategies. However, the stateful nature of lazy evaluation makes it hard to analyze a program's computational cost, either informally or formally. In this work, we present a novel and simple framework for formally reasoning about lazy computation costs based on a recent model of lazy evaluation: clairvoyant call-by-value. The key feature of our framework is its simplicity, as expressed by our definition of the clairvoyance monad. This monad is both simple to define (around 20 lines of Coq) and simple to reason about. We show that this monad can be effectively used to mechanically reason about the computational cost of lazy functional programs written in Coq.

cs.PL↗

arXiv2021

Insect-Computer Hybrid System for Autonomous Search and Rescue Mission

There is still a long way to go before artificial mini robots are really used for search and rescue missions in disaster-hit areas due to hindrance in power consumption, computation load of the locomotion, and obstacle-avoidance system. Insect-computer hybrid system, which is the fusion of living insect platform and microcontroller, emerges as an alternative solution. This study demonstrates the first-ever insect-computer hybrid system conceived for search and rescue missions, which is capable of autonomous navigation and human presence detection in an unstructured environment. Customized navigation control algorithm utilizing the insect's intrinsic navigation capability achieved exploration and negotiation of complex terrains. On-board high-accuracy human presence detection using infrared camera was achieved with a custom machine learning model. Low power consumption suggests system suitability for hour-long operations and its potential for realization in real-life missions.

cs.RO↗

arXiv2021

Census of Variable Stars toward Serpens Main

We have monitored a 3 deg2 area toward Serpens Main in the Pan-STARRS1 r, i, and z bands from 2016 April to September. Light curves of more than 11,000 stars in each band were obtained, and 143 variables have been identified. Among those, 119 variables are new discoveries, while 24 variables were previously known. We present variability classes and periods of 99 stars. Of these, 81 are located in the upper giant branch, displaying long periods, while the remaining 18 variables are pre-main-sequence objects with short periods. We also identify eight eclipsing binary systems, including the known binary V0623 Ser, and derive their physical parameters. According to a clustering analysis of Gaia DR2 stars in the observed field, there are 10 variable members in Serpens Main, where six members have been classified as young stellar objects in previous studies. Here we provide color-magnitude and color-color diagrams for these variables. The color variability of most variables in the color-magnitude diagrams produce the expected displacements, while the movements of cluster members point in different directions; this behavior may be associated with accretion spots or circumstellar disks.

astro-ph.SR↗

arXiv2021

Stationary distributions of persistent ecological systems

We analyze ecological systems that are influenced by random environmental fluctuations. We first provide general conditions which ensure that the species coexist and the system converges to a unique invariant probability measure (stationary distribution). Since it is usually impossible to characterize this invariant probability measure analytically, we develop a powerful method for numerically approximating invariant probability measures. This allows us to shed light upon how the various parameters of the ecosystem impact the stationary distribution. We analyze different types of environmental fluctuations. At first we study ecosystems modeled by stochastic differential equations. In the second setting we look at piecewise deterministic Markov processes. These are processes where one follows a system of differential equations for a random time, after which the environmental state changes, and one follows a different set of differential equations -- this procedure then gets repeated indefinitely. Finally, we look at stochastic differential equations with switching, which take into account both the white noise fluctuations and the random environmental switches. As applications of our theoretical and numerical analysis, we look at competitive Lotka--Volterra, Beddington-DeAngelis predator-prey, and rock-paper-scissors dynamics. We highlight new biological insights by analyzing the stationary distributions of the ecosystems and by seeing how various types of environmental fluctuations influence the long term fate of populations.

q-bio.PE↗

arXiv2021

Linear Convergent Decentralized Optimization with Compression

Communication compression has become a key strategy to speed up distributed optimization. However, existing decentralized algorithms with compression mainly focus on compressing DGD-type algorithms. They are unsatisfactory in terms of convergence rate, stability, and the capability to handle heterogeneous data. Motivated by primal-dual algorithms, this paper proposes the first \underline{L}in\underline{EA}r convergent \underline{D}ecentralized algorithm with compression, LEAD. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error, and we provide the first consensus error bound in such settings without assuming bounded gradients. Experiments on convex problems validate our theoretical analysis, and empirical study on deep neural nets shows that LEAD is applicable to non-convex problems.

cs.LG↗