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Reinforcement learning (RL) faces challenges in trajectory planning for urban automated driving due to the poor convergence of RL and the difficulty in designing reward functions. The convergence problem is alleviated by combining RL with supervised learning. However, most existing approaches only reason one step ahead and lack the capability to plan for multiple future steps. Besides, although inverse reinforcement learning holds promise for solving the reward function design issue, existing methods for automated driving impose a linear structure assumption on reward functions, making them difficult to apply to urban automated driving. In light of these challenges, this paper proposes a novel RL-based trajectory planning method that integrates RL with imitation learning to enable multi-step planning. Furthermore, a transformer-based Bayesian reward function is developed, providing effective reward signals for RL in urban scenarios. Moreover, a hybrid-driven trajectory planning framework is proposed to enhance safety and interpretability. The proposed methods were validated on the large-scale real-world urban automated driving nuPlan dataset. The results demonstrated the significant superiority of the proposed methods over the baselines in terms of the closed-loop metrics. The code is available at https://github.com/Zigned/nuplan_zigned., Comment: 19 pages, 9 figures

We report two binary systems, LAMOST J035540+381550 (hereafter J035540) and LAMOST J035916+400732 (hereafter J035916), identified through the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution survey (LRS). Each of these two systems contains an M-type star orbiting with a invisible compact object candidate. Follow-up spectroscopic observations of Palomar 200-inch telescope (P200) enhance radial velocity measurements. We use radial velocities from LAMOST and P200, as well as light curves from Zwicky Transient Facility (ZTF) to constrain orbital parameters. The masses of the visible M-type stars are estimated by fitting the MIST isochrones and SEDs. The mass functions for the unseen companions are: $0.22\pm 0.01 M_{\odot}$ for J035540 and $0.16\pm 0.01 M_{\odot}$ for J035916. With the orbital and stellar parameters derived above and assuming the orbital inclination is 90 degree (edge-on), we find that the minimum masses of the invisible companions exceeds that of the visible stars. The single-lined feature and the dynamical evidence suggest the presence of compact objects. J035540's ZTF light curve, modeled with PHOEBE, yields a compact object mass of $0.70^{+0.12}_{-0.05} M_{\odot}$. For J035916, ellipsoidal modulation analysis constrains the light curve amplitude, yielding a compact object mass range of $0.57-0.90 M_{\odot}$. The mass estimates indicate that both are likely white dwarfs. These findings underscore the efficiency of optical time-domain surveys and dynamical methods in identifying faint, massive white dwarfs, along with other compact objects in binaries., Comment: 12 pages, 9 figures, accepted for publication in MNRAS

Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content, eliminating any distortions or synthetic details. While traditional diffusion-based SR techniques have demonstrated remarkable abilities to enhance image detail, they are prone to artifact introduction during iterative procedures. Such artifacts, ranging from trivial noise to unauthentic textures, deviate from the true structure of the source image, thus challenging the integrity of the super-resolution process. In this work, we propose Self-Adaptive Reality-Guided Diffusion (SARGD), a training-free method that delves into the latent space to effectively identify and mitigate the propagation of artifacts. Our SARGD begins by using an artifact detector to identify implausible pixels, creating a binary mask that highlights artifacts. Following this, the Reality Guidance Refinement (RGR) process refines artifacts by integrating this mask with realistic latent representations, improving alignment with the original image. Nonetheless, initial realistic-latent representations from lower-quality images result in over-smoothing in the final output. To address this, we introduce a Self-Adaptive Guidance (SAG) mechanism. It dynamically computes a reality score, enhancing the sharpness of the realistic latent. These alternating mechanisms collectively achieve artifact-free super-resolution. Extensive experiments demonstrate the superiority of our method, delivering detailed artifact-free high-resolution images while reducing sampling steps by 2X. We release our code at https://github.com/ProAirVerse/Self-Adaptive-Guidance-Diffusion.git.

Recently, the methods based on implicit neural representations have shown excellent capabilities for arbitrary-scale super-resolution (ASSR). Although these methods represent the features of an image by generating latent codes, these latent codes are difficult to adapt for different magnification factors of super-resolution, which seriously affects their performance. Addressing this, we design Multi-Scale Implicit Transformer (MSIT), consisting of an Multi-scale Neural Operator (MSNO) and Multi-Scale Self-Attention (MSSA). Among them, MSNO obtains multi-scale latent codes through feature enhancement, multi-scale characteristics extraction, and multi-scale characteristics merging. MSSA further enhances the multi-scale characteristics of latent codes, resulting in better performance. Furthermore, to improve the performance of network, we propose the Re-Interaction Module (RIM) combined with the cumulative training strategy to improve the diversity of learned information for the network. We have systematically introduced multi-scale characteristics for the first time in ASSR, extensive experiments are performed to validate the effectiveness of MSIT, and our method achieves state-of-the-art performance in arbitrary super-resolution tasks., Comment: Super-resolution, Arbitrary-Scale Super-Resolution, Multi-Scale, Transformer

Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spiking activation are two fundamental functions of a photonic spiking neural network (PSNN). However, they are separately implemented with different photonic materials and devices, hindering the large-scale integration of PSNN. Here, we propose, fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spiking activation based on a distributed feedback (DFB) laser with a saturable absorber (DFB-SA). A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation. Furthermore, a four-channel DFB-SA array is fabricated for realizing matrix convolution of a spiking convolutional neural network, achieving a recognition accuracy of 87% for the MNIST dataset. The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.

Recently, Transformer-based methods have achieved impressive results in single image super-resolution (SISR). However, the lack of locality mechanism and high complexity limit their application in the field of super-resolution (SR). To solve these problems, we propose a new method, Efficient Mixed Transformer (EMT) in this study. Specifically, we propose the Mixed Transformer Block (MTB), consisting of multiple consecutive transformer layers, in some of which the Pixel Mixer (PM) is used to replace the Self-Attention (SA). PM can enhance the local knowledge aggregation with pixel shifting operations. At the same time, no additional complexity is introduced as PM has no parameters and floating-point operations. Moreover, we employ striped window for SA (SWSA) to gain an efficient global dependency modelling by utilizing image anisotropy. Experimental results show that EMT outperforms the existing methods on benchmark dataset and achieved state-of-the-art performance. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLab., Comment: Super-resolution, Long-range attention, Transformer, Locality

Transformers have achieved remarkable results in single-image super-resolution (SR). However, the challenge of balancing model performance and complexity has hindered their application in lightweight SR (LSR). To tackle this challenge, we propose an efficient striped window transformer (ESWT). We revisit the normalization layer in the transformer and design a concise and efficient transformer structure to build the ESWT. Furthermore, we introduce a striped window mechanism to model long-term dependencies more efficiently. To fully exploit the potential of the ESWT, we propose a novel flexible window training strategy that can improve the performance of the ESWT without additional cost. Extensive experiments show that ESWT outperforms state-of-the-art LSR transformers, and achieves a better trade-off between model performance and complexity. The ESWT requires fewer parameters, incurs faster inference, smaller FLOPs, and less memory consumption, making it a promising solution for LSR., Comment: SOTA lightweight super-resolution transformer. 8 pages, 9 figures and 6 tables. The Code is available at https://github.com/Fried-Rice-Lab/FriedRiceLab

Recent studies have revealed the global deposition on Earth of radioactive elements (e.g., $^{60}$Fe) resulting from the metal-enriched ejecta of nearby (within $\sim 100$ pc) supernova explosions. The majority of neutron stars in our Solar neighborhood remain to be discovered. Here we report the discovery of the nearest ($127.7 \pm 0.3$ pc) neutron star candidate in the single-lined spectroscopic binary LAMOST J235456.76+335625.7 (hereafter J2354). Utilizing the multi-epoch spectra and high-cadence periodic light curves, we measure the mass of the visible star ($M_{\rm vis}=0.70\pm 0.05\ M_{\odot}$) and determine the mass function of the invisible object $f(M)=0.525 \pm 0.004\ M_{\odot}$, i.e., the mass of the unseen compact object is $M_{\rm inv} \geq 1.26 \pm 0.03\ M_{\odot}$. The excess UV emission due to a hot supramassive white dwarf is absent. Hence, it is likely that J2354 harbors a neutron star. J2354 is X-ray dim (the $0.1$--$2.4$ keV luminosity $<10^{30}\ {\rm erg\ s^{-1}}$) since it is not detected in the ROSAT all-sky surveys in X-ray. One-hour exceptionally sensitive radio follow-up observations with FAST, the largest single-dish radio telescope, failed to reveal any radio pulsating signals (the potential pulse power at $1.4$ GHz is $<6.8\times 10^{23}\ {\rm erg\ s^{-1}}$). Hence, the neutron star candidate in J2354 can only be discovered via our time-resolved observations. The alternative scenario involving a nearby supramassive cold white dwarf cannot be fully excluded. Our discovery demonstrates a promising way to unveil the missing population of backyard inactive neutron stars or supramassive cold white dwarfs in binaries by exploring the optical time domain, thereby facilitating understanding of the supernovae explosion and metal-enrichment history in our Solar neighborhood., Comment: 35 pages, 8 figures, to be submitted

Searching for compact objects (black holes, neutron stars, or white dwarfs) in the Milky Way is essential for understanding the stellar evolution history, the physics of compact objects, and the structure of our Galaxy. Compact objects in binaries with a luminous stellar companion are perfect targets for optical observations. Candidate compact objects can be achieved by monitoring the radial velocities of the companion star. However, most of the spectroscopic telescopes usually obtain stellar spectra at a relatively low efficiency, which makes a sky survey for millions of stars practically impossible. The efficiency of a large-scale spectroscopic survey, the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST), presents a specific opportunity to search for compact object candidates, i.e., simply from the spectroscopic observations. Late-type K/M stars are the most abundant populations in our Galaxy. Owing to the relatively large Keplerian velocities in the close binaries with a K/M-dwarf companion, a hidden compact object could be discovered and followed-up more easily. In this study, compact object candidates with K/M-dwarf companions are investigated with the LAMOST low-resolution stellar spectra. Based on the LAMOST Data Release 5, we obtained a sample of $56$ binaries, each containing a K/M-dwarf with a large radial velocity variation $\Delta V_{\rm R} > 150~{\rm km~s}^{-1}$. Complemented with the photometric information from the Transiting Exoplanet Survey Satellite, we derived a sample of $35$ compact object candidates, among which, the orbital periods of $16$ sources were revealed by the light curves. Considering two sources as examples, we confirmed that a compact object existed in the two systems by fitting the radial velocity curve. This study demonstrates the principle and the power of searching for compact objects through LAMOST., Comment: 20 pages, 7 figures, 4 tables, Science China Physics, Mechanics & Astronomy, Vol.65 No.2:229711

We report a single-lined white dwarf-main sequence binary system, LAMOST J172900.17+652952.8, which is discovered by LAMOST's medium resolution time-domain surveys. The radial velocity semi-amplitude and orbital period of the optical visible star are measured by using the Palomar 200-inch telescope follow-up observations and the light curves from TESS. Thus the mass function of the invisible candidate white dwarf is derived, $f(M_{\rm{2}}) = 0.120\,\pm\,0.003\,M_{\odot}$. The mass of the visible star is measured based on the spectral energy distribution fitting, $M_{\mathrm{1}}$ = $0.81^{+0.07}_{-0.06}\,M_{\odot}$. Hence, the mass of its invisible companion is $M_{\rm{2}}\,\gtrsim\,0.63\,M_{\odot}$. The companion ought to be a compact object rather than a main-sequence star owing to the mass ratio $q = M_{\rm{2}} / M_{\rm 1} \gtrsim 0.78$ and the single-lined spectra. The compact object is likely to be a white dwarf except for small inclination angle, $i\,\lesssim\,40^{\circ}$. By using the GALEX NUV flux, the effective temperature of the white dwarf candidate is constrained as $T_{\rm eff}^{\rm WD}\,\lesssim\,12000-13500$ K. It is difficult to detect white dwarfs which are outshone by their bright companions via single-epoch optical spectroscopic surveys. Therefore, the optical time-domain surveys can play an important role in unveiling invisible white dwarfs and other compact objects in binaries., Comment: 15 pages, 6 figures, ApJ, 936, 33

We present LAMOST~J041920.07+072545.4 (hereafter J0419), a close binary consisting of a bloated extremely low mass pre-white dwarf (pre-ELM WD) and a compact object with an orbital period of 0.607189~days. The large-amplitude ellipsoidal variations and the evident Balmer and He~I emission lines suggest a filled Roche lobe and ongoing mass transfer. No outburst events were detected in the 15 years of monitoring of J0419, indicating a very low mass transfer rate. The temperature of the pre-ELM, $T_\mathrm{eff} = 5793_{-133}^{+124}\,\rm K$, is cooler than the known ELMs, but hotter than most CV donors. Combining the mean density within the Roche lobe and the radius constrained from our SED fitting, we obtain the mass of the pre-ELM, $M_1 = 0.176\pm 0.014\,M_\odot$. The joint fitting of light and radial velocity curves yields an inclination angle of $i = 66.5_{-1.7}^{+1.4}$ degrees, corresponding to the compact object mass of $M_2 = 1.09\pm 0.05\,M_\odot$. The very bloated pre-ELM has a smaller surface gravity ($\log g = 3.9\pm 0.01$, $R_1 = 0.78 \pm 0.02\,R_\odot$) than the known ELMs or pre-ELMs. The temperature and the luminosity ($L_\mathrm{bol} = 0.62_{-0.10}^{+0.11}\,L_\odot$) of J0419 are close to the main sequence, which makes the selection of such systems through the HR diagram inefficient. Based on the evolutionary model, the relatively long period and small $\log g$ indicate that J0419 could be close to the "bifurcation period" in the orbit evolution, which makes J0419 to be a unique source to connect ELM/pre-ELM WD systems, wide binaries and cataclysmic variables., Comment: 25 pages, 13 figures, ApJ, 933, 193

Optical time-domain surveys can unveil and characterize exciting but less-explored non-accreting and/or non-beaming neutron stars (NS) in binaries. Here we report the discovery of such a NS candidate using the LAMOST spectroscopic survey. The candidate, designated LAMOST J112306.9+400736 (hereafter J1123), is in a single-lined spectroscopic binary containing an optically visible M star. The star's large radial velocity variation and ellipsoidal variations indicate a relatively massive unseen companion. Utilizing follow-up spectroscopy from the Palomar 200-inch telescope and high-precision photometry from TESS, we measure a companion mass of $1.24_{-0.03}^{+0.03}~M_{\odot}$. Main-sequence stars with this mass are ruled out, leaving a NS or a massive white dwarf (WD). Although a massive WD cannot be ruled out, the lack of UV excess radiation from the companion supports the NS hypothesis. Deep radio observations with FAST yielded no detections of either pulsed or persistent emission. J1123 is not detected in numerous X-ray and gamma-ray surveys. These non-detections suggest that the NS candidate is not presently accreting and pulsing. Our work exemplifies the capability of discovering compact objects in non-accreting close binaries by synergizing the optical time-domain spectroscopy and high-cadence photometry., Comment: 53 pages, 15 figures, publication in Nature Astronomy

We search for compact objects in binaries based on Gaia DR3. A sample of ten targets is derived under the conditions: radial velocity variable, low temperature ($T_{\rm eff} < 6000$ K), high mass function ($f(M_2) > 1 M_\odot$), and ellipsoidal-like light curves. Two targets have LAMOST spectroscopic observations, one of which is a double-lined spectroscopic binary. The observational data of seven targets are not self-consistent, since their photometric periods are even shorter than the theoretical minimum orbital periods calculated by the stellar parameters from Gaia DR3. After excluding these seven inconsistent targets and another target contaminated by a near-bright star, the remaining two targets may contain compact objects worth follow-up observations. This work may serve as an example to demonstrate the feasibility of searching for compact objects in the massive Gaia data., Comment: 15 pages, 8 figures, accepted for publication in ApJ

Single-line spectroscopic binaries recently contribute to the stellar-mass black hole discovery, independently of the X-ray transient method. We report the identification of a single-line binary system LTD064402+245919, with an orbital period of 14.50 days. The observed component is a subgiant with a mass of 2.77$\pm$0.68M$_{\odot}$, radius 15.5$\pm$2.5R$_{\odot}$, effective temperature $T_{\rm eff}$ 4500$\pm$200K, and surface gravity log\emph{g} 2.5$\pm$0.25dex. The discovery makes use of the LAMOST time-domain (LAMOST-TD) and ZTF survey. Our general-purpose software pipeline applies the Lomb-Scargle periodogram to determine the orbital period and uses machine-learning to classify the variable type from the folded light curves. We apply a combined model to estimate the orbital parameters from both the light and radial velocity curves, taking constraints on the primary star mass, mass function, and detection limit of secondary luminosity into consideration. We obtain a radial velocity semi-amplitude of 44.6$\pm$1.5 km s$^{-1}$, mass ratio of 0.73$\pm$0.07, and an undetected component mass of 2.02$\pm$0.49M$_{\odot}$ when the type of the undetected component is not set. We conclude that the inclination is not well constrained, and that the secondary mass is larger than 1M$_{\odot}$ when the undetected component is modelled as a compact object. According to our investigations using an MCMC simulation, increasing the spectra SNR by a factor of 3 would enable the secondary light to be distinguished (if present). The algorithm and software in this work are able to serve as general-purpose tools for the identification of compact objects quiescent in X-rays., Comment: Accepted by ApJ