Searcharxiv⌕ Search

arXiv subjects

Xing Gao

Publications and source records attributed to Xing Gao.

At least 109 records · Page 6Linked to original sources
arXiv2023

Towards Knowledge-driven Autonomous Driving

This paper explores the emerging knowledge-driven autonomous driving technologies. Our investigation highlights the limitations of current autonomous driving systems, in particular their sensitivity to data bias, difficulty in handling long-tail scenarios, and lack of interpretability. Conversely, knowledge-driven methods with the abilities of cognition, generalization and life-long learning emerge as a promising way to overcome these challenges. This paper delves into the essence of knowledge-driven autonomous driving and examines its core components: dataset \& benchmark, environment, and driver agent. By leveraging large language models, world models, neural rendering, and other advanced artificial intelligence techniques, these components collectively contribute to a more holistic, adaptive, and intelligent autonomous driving system. The paper systematically organizes and reviews previous research efforts in this area, and provides insights and guidance for future research and practical applications of autonomous driving. We will continually share the latest updates on cutting-edge developments in knowledge-driven autonomous driving along with the relevant valuable open-source resources at: \url{https://github.com/PJLab-ADG/awesome-knowledge-driven-AD}.

cs.RO↗
arXiv2023

Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator

Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in the existing dataset, it is challenging to edit the scene and expand the diversity of traffic flow and scenario. To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario. We complete the modeling task of the rainy weather in the CARLA simulator and establish a pipeline for LiDAR data collection. In particular, we pay special attention to the spray and splash rolled up by the wheels of surrounding vehicles in rain and complete the simulation of this special scenario through the Spray Emitter method we developed. In addition, we examine the influence of different weather conditions on the intensity of the LiDAR echo, develop a prediction network for the intensity of the LiDAR echo, and complete the simulation of 4-feat LiDAR point cloud data. In the experiment, we observe that the model augmented by the synthetic data improves the object detection task's performance in the rainy sequence of the Waymo Open Dataset. Both the code and the dataset will be made publicly available at https://github.com/PJLab-ADG/PCSim#rainypcsim.

cs.RO↗
arXiv2023

SceneDM: Scene-level Multi-agent Trajectory Generation with Consistent Diffusion Models

Realistic scene-level multi-agent motion simulations are crucial for developing and evaluating self-driving algorithms. However, most existing works focus on generating trajectories for a certain single agent type, and typically ignore the consistency of generated trajectories. In this paper, we propose a novel framework based on diffusion models, called SceneDM, to generate joint and consistent future motions of all the agents, including vehicles, bicycles, pedestrians, etc., in a scene. To enhance the consistency of the generated trajectories, we resort to a new Transformer-based network to effectively handle agent-agent interactions in the inverse process of motion diffusion. In consideration of the smoothness of agent trajectories, we further design a simple yet effective consistent diffusion approach, to improve the model in exploiting short-term temporal dependencies. Furthermore, a scene-level scoring function is attached to evaluate the safety and road-adherence of the generated agent's motions and help filter out unrealistic simulations. Finally, SceneDM achieves state-of-the-art results on the Waymo Sim Agents Benchmark. Project webpage is available at https://alperen-hub.github.io/SceneDM.

cs.RO↗
arXiv2023

CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment

Language models trained on large-scale corpus often generate content that is harmful, toxic, or contrary to human preferences, making their alignment with human values a critical concern. Reinforcement learning from human feedback (RLHF) with algorithms like PPO is a prevalent approach for alignment but is often complex, unstable, and resource-intensive. Recently, ranking-based alignment methods have emerged, offering stability and effectiveness by replacing the RL framework with supervised fine-tuning, but they are costly due to the need for annotated data. Considering that existing large language models (LLMs) like ChatGPT are already relatively well-aligned and cost-friendly, researchers have begun to align the language model with human preference from AI feedback. The common practices, which unidirectionally distill the instruction-following responses from LLMs, are constrained by their bottleneck. Thus we introduce CycleAlign to distill alignment capabilities from parameter-invisible LLMs (black-box) to a parameter-visible model (white-box) in an iterative manner. With in-context learning (ICL) as the core of the cycle, the black-box models are able to rank the model-generated responses guided by human-craft instruction and demonstrations about their preferences. During iterative interaction, the white-box models also have a judgment about responses generated by them. Consequently, the agreement ranking could be viewed as a pseudo label to dynamically update the in-context demonstrations and improve the preference ranking ability of black-box models. Through multiple interactions, the CycleAlign framework could align the white-box model with the black-box model effectively in a low-resource way. Empirical results illustrate that the model fine-tuned by CycleAlign remarkably exceeds existing methods, and achieves the state-of-the-art performance in alignment with human value.

cs.CL↗
arXiv2023

Confirmation of a Sub-Saturn-size transiting exoplanet orbiting a G dwarf: TOI-1194 b and a very low mass companion star: TOI-1251 B from TESS

We report the confirmation of a sub-Saturn-size exoplanet, TOI-1194 b with a mass about $0.456_{-0.051}^{+0.055}$ $M_{J}$, and a very low mass companion star with a mass of about $96.5\pm1.5$ $M_J$, TOI-1251 B. Exoplanet candidates provided by the Transiting Exoplanet Survey Satellite (TESS) are suitable for further follow-up observations by ground-based telescopes with small and medium apertures. The analysis is performed based on data from several telescopes worldwide, including telescopes in the Sino-German multiband photometric campaign, which aimed at confirming TESS Objects of Interest (TOIs) using ground-based small-aperture and medium-aperture telescopes, especially for long-period targets. TOI-1194 b is confirmed based on the consistent periodic transits depths from the multiband photometric data. We measure an orbital period of $2.310644\pm0.000001$ d, and radius is $0.767_{-0.041}^{+0.045}$ $R_J$, and amplitude of RV curve is $69.4_{-7.3}^{+7.9}$ m/s. TOI-1251 B is confirmed based on the multiband photometric and high-resolution spectroscopic data, whose orbiting period is $5.963054_{-0.000001}^{+0.000002}$ d, the radius is $0.947_{-0.033}^{+0.035}$ $R_J$, and amplitude of RV curve is $9849_{-40}^{+42}$ m/s.

astro-ph.EP↗
arXiv2023

Discovery of the Closest Ultrastripped Supernova: SN 2021agco in UGC 3855

We present the discovery and studies of the helium-rich, fast-evolving supernova (SN) 2021agco at a distance of $\sim$ 40 Mpc. Its early-time flux is found to rise from half peak to the peak of $-16.06\pm0.42$ mag in the $r$ band within $2.4^{+1.5}_{-1.0}$ days, and the post-peak light curves also decline at a much faster pace relative to normal stripped-envelope SNe of Type Ib/Ic. The early-time spectrum of SN~2021agco ($t \approx 1.0$ days after the peak) is characterized by a featureless blue continuum superimposed with a weak emission line of ionized C III, and the subsequent spectra show prominent He I lines. Both the photometric and spectroscopic evolution shows close resemblances to SN 2019dge, which is believed to have an extremely stripped progenitor. We reproduce the multicolor light curves of SN 2021agco with a model combining shock-cooling emission with \Ni decay. The best-fit results give an ejecta mass of $\approx 0.3$~M$_\odot$ and a synthesized nickel mass of $\approx 2.2\times10^{-2}$~M$_\odot$. The progenitor is estimated to have an envelope radius $R_{\rm env} \approx 80$~R$_\odot$ and a mass $M_{\rm env} \approx 0.10$~M$_\odot$. All these suggest that SN~2021agco can be categorized as an ultrastripped SN~Ib, representing the closest object of this rare subtype. This SN is found to explode in the disk of an Sab-type galaxy with an age of $\sim 10.0$~Gyr and low star-forming activity. Compared to normal SNe Ib/c, the host galaxies of SN 2021agco and other ultrastripped SNe tend to have relatively lower metallicity, which complicates the properties of their progenitor populations.

astro-ph.HE↗
arXiv2023

CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility

With the rapid evolution of large language models (LLMs), there is a growing concern that they may pose risks or have negative social impacts. Therefore, evaluation of human values alignment is becoming increasingly important. Previous work mainly focuses on assessing the performance of LLMs on certain knowledge and reasoning abilities, while neglecting the alignment to human values, especially in a Chinese context. In this paper, we present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs in terms of both safety and responsibility criteria. As a result, we have manually collected adversarial safety prompts across 10 scenarios and induced responsibility prompts from 8 domains by professional experts. To provide a comprehensive values evaluation of Chinese LLMs, we not only conduct human evaluation for reliable comparison, but also construct multi-choice prompts for automatic evaluation. Our findings suggest that while most Chinese LLMs perform well in terms of safety, there is considerable room for improvement in terms of responsibility. Moreover, both the automatic and human evaluation are important for assessing the human values alignment in different aspects. The benchmark and code is available on ModelScope and Github.

cs.CL↗
arXiv2023

DialoGPS: Dialogue Path Sampling in Continuous Semantic Space for Data Augmentation in Multi-Turn Conversations

In open-domain dialogue generation tasks, contexts and responses in most datasets are one-to-one mapped, violating an important many-to-many characteristic: a context leads to various responses, and a response answers multiple contexts. Without such patterns, models poorly generalize and prefer responding safely. Many attempts have been made in either multi-turn settings from a one-to-many perspective or in a many-to-many perspective but limited to single-turn settings. The major challenge to many-to-many augment multi-turn dialogues is that discretely replacing each turn with semantic similarity breaks fragile context coherence. In this paper, we propose DialoGue Path Sampling (DialoGPS) method in continuous semantic space, the first many-to-many augmentation method for multi-turn dialogues. Specifically, we map a dialogue to our extended Brownian Bridge, a special Gaussian process. We sample latent variables to form coherent dialogue paths in the continuous space. A dialogue path corresponds to a new multi-turn dialogue and is used as augmented training data. We show the effect of DialoGPS with both automatic and human evaluation.

cs.CL↗
arXiv2023

ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human

In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format. Different from other open-domain dialogue models that focus on large-scale pre-training and scaling up model size or dialogue corpus, we aim to build a powerful and practical dialogue system for digital human with diverse skills and good multi-task generalization by internet-augmented instruction tuning. To this end, we first conduct large-scale pre-training on both common document corpus and dialogue data with curriculum learning, so as to inject various world knowledge and dialogue abilities into ChatPLUG. Then, we collect a wide range of dialogue tasks spanning diverse features of knowledge, personality, multi-turn memory, and empathy, on which we further instruction tune \modelname via unified natural language instruction templates. External knowledge from an internet search is also used during instruction finetuning for alleviating the problem of knowledge hallucinations. We show that \modelname outperforms state-of-the-art Chinese dialogue systems on both automatic and human evaluation, and demonstrates strong multi-task generalization on a variety of text understanding and generation tasks. In addition, we deploy \modelname to real-world applications such as Smart Speaker and Instant Message applications with fast inference. Our models and code will be made publicly available on ModelScope: https://modelscope.cn/models/damo/ChatPLUG-3.7B and Github: https://github.com/X-PLUG/ChatPLUG .

cs.CL↗
arXiv2023

Multibridge VO2-Based Resistive Switching Devices in a Two-Terminal Configuration

Vanadium dioxide (VO2) exhibits a hysteretic insulator-to-metal transition near room temperature, forming the foundation for various forms of resistive switching devices. Usually, these are realized in the form of two-terminal bridge-like structures. We show here that by incorporating multiple, parallel VO2 bridges in a single two-terminal device, a wider range of possible characteristics can be obtained, including a manifold of addressable resistance states. Different device configurations are studied, in which the number of bridges, the bridge dimensions and the interbridge distances are varied. The switching characteristics of the multibridge devices are influenced by the thermal crosstalk between the bridges. Scanning Thermal Microscopy has been used to image the current distributions at various voltage/current bias conditions. This work presents a route to realize devices exhibiting highly non-linear, multistate current-voltage characteristics, with potential applications in e.g., tunable electronic components and novel, neuromorphic information processing circuitry.

physics.app-ph↗
arXiv2023

Unsupervised Multi-Criteria Adversarial Detection in Deep Image Retrieval

The vulnerability in the algorithm supply chain of deep learning has imposed new challenges to image retrieval systems in the downstream. Among a variety of techniques, deep hashing is gaining popularity. As it inherits the algorithmic backend from deep learning, a handful of attacks are recently proposed to disrupt normal image retrieval. Unfortunately, the defense strategies in softmax classification are not readily available to be applied in the image retrieval domain. In this paper, we propose an efficient and unsupervised scheme to identify unique adversarial behaviors in the hamming space. In particular, we design three criteria from the perspectives of hamming distance, quantization loss and denoising to defend against both untargeted and targeted attacks, which collectively limit the adversarial space. The extensive experiments on four datasets demonstrate 2-23% improvements of detection rates with minimum computational overhead for real-time image queries.

cs.CV↗
arXiv2023

Optical and Near-infrared Observations of the Distant but Bright 'New Year's Burst' GRB 220101A

High-redshift gamma-ray bursts (GRBs) provide a powerful tool to probe the early universe, but still for relatively few do we have good observations of the afterglow. We here report the optical and near-infrared observations of the afterglow of a relatively high-redshift event, GRB\,220101A, triggered on New Year's Day of 2022. With the optical spectra obtained at XL2.16/BFOSC and NOT/ALFOSC, we determine the redshift of the burst at $z= 4.615$. Based on our optical and near-infrared data, combined with the X-ray data, we perform multiband fit with the python package \emph{afterglowpy}. A jet-break at $\sim$ 0.7 day post-burst is found to constrain the opening angle of the jet as $\sim$ 3.4 degree. We also determine circumburst density of $n_0 = 0.15\ {\rm cm}^{-3}$ as well as kinetic energy $E_{\rm K, iso} = 3.52\times 10^{54}$ erg. The optical afterglow is among the most luminous ever detected. We also find a ``mirror'' feature in the lightcurve during the prompt phase of the burst from 80 s to 120 s. The physical origin of such mirror feature is unclear.

astro-ph.HE↗
arXiv2023

Near-field imaging of domain switching in in-operando VO$_{2}$ devices

Experimental insight in the nanoscale dynamics underlying switching in novel memristive devices is limited owing to the scarcity of techniques that can probe the electronic structure of these devices. Scattering scanning near-field optical microscopy is a relatively novel approach to probe the optical response of materials with a spatial resolution well below the diffraction limit. We use this non-invasive tool to demonstrate that it provides detailed information on the origin and memory behaviour of ultra-thin films of vanadium dioxide. Simultaneously recorded $I(V)$ characteristics and near-field maps show that discontinuities in the I(V) characteristics arise from the sudden switching of insulating domains to metallic domains. At the threshold voltage, the domains form a continuous current path. The metallic domains persist once the bias voltage is removed, but narrow monoclinic regions appear around the domain boundaries. The key advantage of our approach is that it provides detailed information on the electronic structure at length scales raging from tens of nanometers up to tens of microns and is easily applied under \textit{in operando} conditions.

cond-mat.str-el↗
arXiv2023

Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks

Due to the complex and changing interactions in dynamic scenarios, motion forecasting is a challenging problem in autonomous driving. Most existing works exploit static road graphs to characterize scenarios and are limited in modeling evolving spatio-temporal dependencies in dynamic scenarios. In this paper, we resort to dynamic heterogeneous graphs to model the scenario. Various scenario components including vehicles (agents) and lanes, multi-type interactions, and their changes over time are jointly encoded. Furthermore, we design a novel heterogeneous graph convolutional recurrent network, aggregating diverse interaction information and capturing their evolution, to learn to exploit intrinsic spatio-temporal dependencies in dynamic graphs and obtain effective representations of dynamic scenarios. Finally, with a motion forecasting decoder, our model predicts realistic and multi-modal future trajectories of agents and outperforms state-of-the-art published works on several motion forecasting benchmarks.

cs.AI↗
arXiv2023

Photometric and Spectroscopic Observations of GRB 190106A: Emission from Reverse and Forward Shocks with Late-time Energy Injection

Early optical observations of gamma-ray bursts can significantly contribute to the study of the central engine and physical processes therein. However, of the thousands observed so far, still only a few have data at optical wavelengths in the first minutes after the onset of the prompt emission. Here we report on GRB\,190106A, whose afterglow was observed in optical bands just 36 s after the {\em Swift}/BAT trigger, i.e., during the prompt emission phase. The early optical afterglow exhibits a bimodal structure followed by a normal decay, with a faster decay after $\sim \rm T_{0}+$1 day. We present optical photometric and spectroscopic observations of GRB\,190106A. We derive the redshift via metal absorption lines from Xinglong 2.16-m/BFOSC spectroscopic observations. From the BFOSC spectrum, we measure $z= 1.861\pm0.002$. The double-peak optical light curve is a significant feature predicted by the reverse-forward external shock model. The shallow decay followed by a normal decay in both the X-ray and optical light curves is well explained with the standard forward-shock model with late-time energy injection. Therefore, GRB\,190106A offers a case study for GRBs emission from both reverse and forward shocks.

astro-ph.HE↗
arXiv2023

The study of eleven contact binaries with mass ratios less than 0.1

Multi-band photometric observations of eleven totally eclipsing contact binaries were carried out. Applying the Wilson-Devinney program, photometric solutions were obtained. There are two W-subtype systems, which are CRTS J133031.1+161202 and CRTS J154254.0+324652, and the rest systems are A-subtype systems. CRTS J154254.0+324652 has the highest fill-out factor with 94.3$\%$, and the lowest object is CRTS J155009.2+493639 with only 18.9$\%$. The mass ratios of the eleven systems are all less than 0.1, which means that they are extremely low mass ratio binary systems. We performed period variation investigation and found that the orbital periods of three systems decrease slowly, which may be caused by the angular momentum loss, and of six systems increase slowly, which indicates that the materials may transfer from the secondary component to the primary component. LAMOST low$-$resolution spectra of four objects were analyzed, and using the spectral subtraction technique, H$α$ emission line was detected, which means that the four objects exhibit chromospheric activity. In order to understand their evolutionary status, the mass-luminosity and mass-radius diagrams were plotted. The two diagrams indicate that the primary component is in the main sequence evolution stage, and the secondary component is above TAMS, indicating that they are over-luminous. To determine whether the eleven systems are in stable state, the ratio of spin angular momentum to orbital angular momentum ($J_{s}/J_{o}$) and the instability parameters were calculated, and we argued that CRTS J234634.7+222824 is on the verge of a merger.

astro-ph.SR↗
arXiv2022

A Poincaré-Birkhoff-Witt theorem for the universal enveloping algebra of a Rota-Baxter Lie algebra

Rota-Baxter associative algebras and Rota-Baxter Lie algebras are both important in mathematics and mathematical physics, with the former a basic structure in quantum field renormalization and the latter a operator form of the classical Yang-Baxter equation. An outstanding problem posed by Gubarev is to determine whether there is a Poincaré-Birkhoff-Witt theorem for the universal enveloping Rota-Baxter associative algebra of a Rota-Baxter Lie algebra. This paper resolves this problem positively, working with operated algebras and applying the method of Gröbner-Shirshov bases.

math.QA↗
arXiv2022

M31N 1926-07c: A Recurrent Nova in M31 with a 2.8 Year Recurrence Time

The M31 recurrent nova M31N 1926-07c has had five recorded eruptions. Well-sampled light curves of the two most recent outbursts, in January of 2020 (M31N 2020-01b) and September 2022 (M31N 2022-09a), are presented showing that the photometric evolution of the two events were quite similar, with peak magnitudes of $R=17.2\pm0.1$ and $R=17.1\pm0.1$, and $t_2$ times of $9.7\pm0.9$ and $8.1\pm0.5$ days for the 2020 and 2022 eruptions, respectively. After considering the dates of the four most recent eruptions (where the cycle count is believed to be known), a mean recurrence interval of $\langle P_\mathrm{rec}\rangle=2.78\pm0.03$ years is found, establishing that M31N 1926-07c has one of the shortest recurrence times known.

astro-ph.SR↗