Search

Superluminous supernovae (SLSNe) are a new class of transients with luminosities $\sim10 -100$ times larger than the usual core-collapse supernovae (SNe). Their origin is still unclear and one widely discussed scenario involves a millisecond magnetar central engine. The GeV-TeV emission of SLSNe has been predicted in the literature but has not been convincingly detected yet. Here we report the search for the $\gamma$-ray emission in the direction of SN 2017egm, one of the closest SLSNe detected so far, with the 15-year {\it Fermi}-LAT Pass 8 data. There is a transient $\gamma$-ray source appearing about 2 months after this event and lasting a few months. Both the peak time and the luminosity of the GeV emission are consistent with the magnetar model prediction, suggesting that such a GeV transient is the high-energy counterpart of SN 2017egm and the central engine of this SLSNe is a young magnetar.

This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, the challenge images were collected with a mobile phone and the speed of algorithms was also measured alongside the quality of their output. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. There were 2 nominations: quality and efficiency. Top 5 solutions in terms of output quality were sorted by evaluation time (see Fig. 1). The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://nightimaging.org., Comment: 10 pages, 10 figures

Thanks to the successful performance of the James Webb Space Telescope, our understanding of the epoch of reionization of the Universe has been advanced. The ultraviolet luminosity functions (UV LFs) of galaxies span a wide range of redshift, not only revealing the connection between galaxies and dark matter (DM) halos but also providing the information during reionization. In this work, we develop a model connecting galaxy counts and apparent magnitude based on UV LFs, which incorporates redshift-dependent star formation efficiency (SFE) and corrections for dust attenuation. By synthesizing some observations across the redshift range $4\le z \le 10$ from various galaxy surveys, we discern the evolving SFE with increasing redshift and DM halo mass through model fitting. Subsequent analyses indicate that the Thomson scattering optical depth was $\tau_{\rm e} = 0.052^{+0.003}_{-0.002}$ and the epoch of reionization started (ended) at $z=20.58^{+6.25}_{-6.75}$ ($z=5.38^{+0.65}_{-0.70}$) which is insensitive to the choice of the truncated magnitude of the UV LFs. Incorporating additional dataset and some reasonable constraints, the amplitude of matter perturbation is found to be $\sigma_8=0.79\pm0.05$, which is consistent with the standard $\Lambda$CDM model. Future galaxy surveys and the dynamical simulations of galaxy evolution will break the degeneracy between SFE and cosmological parameters, improving the accuracy and the precision of the UV LF model further., Comment: 15 pages, 9 figures and 3 tables

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2024/., Comment: CVPR 2024 Mobile Intelligent Photography and Imaging (MIPI) Workshop--Nighttime Flare Removal Challenge Report. Website: https://mipi-challenge.org/MIPI2024/

Domain decomposition provides an effective way to tackle the dilemma of physics-informed neural networks (PINN) which struggle to accurately and efficiently solve partial differential equations (PDEs) in the whole domain, but the lack of efficient tools for dealing with the interfaces between two adjacent sub-domains heavily hinders the training effects, even leads to the discontinuity of the learned solutions. In this paper, we propose a symmetry group based domain decomposition strategy to enhance the PINN for solving the forward and inverse problems of the PDEs possessing a Lie symmetry group. Specifically, for the forward problem, we first deploy the symmetry group to generate the dividing-lines having known solution information which can be adjusted flexibly and are used to divide the whole training domain into a finite number of non-overlapping sub-domains, then utilize the PINN and the symmetry-enhanced PINN methods to learn the solutions in each sub-domain and finally stitch them to the overall solution of PDEs. For the inverse problem, we first utilize the symmetry group acting on the data of the initial and boundary conditions to generate labeled data in the interior domain of PDEs and then find the undetermined parameters as well as the solution by only training the neural networks in a sub-domain. Consequently, the proposed method can predict high-accuracy solutions of PDEs which are failed by the vanilla PINN in the whole domain and the extended physics-informed neural network in the same sub-domains. Numerical results of the Korteweg-de Vries equation with a translation symmetry and the nonlinear viscous fluid equation with a scaling symmetry show that the accuracies of the learned solutions are improved largely.

Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary objective of RAAMove is to facilitate move analysis and automatic move identification. This paper provides a thorough discussion of the corpus construction process, including the scheme, data collection, annotation guidelines, and annotation procedures. The corpus is constructed through two stages: initially, expert annotators manually annotate high-quality data; subsequently, based on the human-annotated data, a BERT-based model is employed for automatic annotation with the help of experts' modification. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances. We also conduct preliminary move identification experiments using the BERT-based model to verify the effectiveness of the proposed corpus and model. The annotated corpus is available for academic research purposes and can serve as essential resources for move analysis, English language teaching and writing, as well as move/discourse-related tasks in Natural Language Processing (NLP)., Comment: Accepted by LREC-COLING 2024

We present MM-AU, a novel dataset for Multi-Modal Accident video Understanding. MM-AU contains 11,727 in-the-wild ego-view accident videos, each with temporally aligned text descriptions. We annotate over 2.23 million object boxes and 58,650 pairs of video-based accident reasons, covering 58 accident categories. MM-AU supports various accident understanding tasks, particularly multimodal video diffusion to understand accident cause-effect chains for safe driving. With MM-AU, we present an Abductive accident Video understanding framework for Safe Driving perception (AdVersa-SD). AdVersa-SD performs video diffusion via an Object-Centric Video Diffusion (OAVD) method which is driven by an abductive CLIP model. This model involves a contrastive interaction loss to learn the pair co-occurrence of normal, near-accident, accident frames with the corresponding text descriptions, such as accident reasons, prevention advice, and accident categories. OAVD enforces the causal region learning while fixing the content of the original frame background in video generation, to find the dominant cause-effect chain for certain accidents. Extensive experiments verify the abductive ability of AdVersa-SD and the superiority of OAVD against the state-of-the-art diffusion models. Additionally, we provide careful benchmark evaluations for object detection and accident reason answering since AdVersa-SD relies on precise object and accident reason information., Comment: Accepted by CVPR2024. This is not the camera-ready version. The Project page: http://www.lotvsmmau.net

Counterfactual explanations (CE) are the de facto method of providing insight and interpretability in black-box decision-making models by identifying alternative input instances that lead to different outcomes. This paper extends the concept of CE to a distributional context, broadening the scope from individual data points to entire input and output distributions, named distributional counterfactual explanation (DCE). In DCE, we take the stakeholder's perspective and shift focus to analyzing the distributional properties of the factual and counterfactual, drawing parallels to the classical approach of assessing individual instances and their resulting decisions. We leverage optimal transport (OT) to frame a chance-constrained optimization problem, aiming to derive a counterfactual distribution that closely aligns with its factual counterpart, substantiated by statistical confidence. Our proposed optimization method, Discount, strategically balances this confidence in both the input and output distributions. This algorithm is accompanied by an analysis of its convergence rate. The efficacy of our proposed method is substantiated through a series of quantitative and qualitative experiments, highlighting its potential to provide deep insights into decision-making models.

With the enhancement of sensitivity of Gravitational Wave (GW) detectors and capabilities of large survey facilities, such as Vera Rubin Observatory Legacy Survey of Space and Time (LSST) and 2.5-m Wide Field Survey Telescope (WFST), we now have the potential to detect an increasing number of distant kilonova (KN). However, distinguishing KN from the plethora of detected transients in ongoing and future follow-up surveys presents a significant challenge. In this study, our objective is to establish an efficient classification mechanism tailored for the follow-up survey conducted by WFST, with a specific focus on identifying KN associated with GW. We employ a novel temporal convolutional neural network architecture, trained using simulated multi-band photometry lasting for 3 days by WFST, accompanied by contextual information, i.e. luminosity distance information by GW. By comparison of the choices of contextual information, we can reach 95\% precision, and 94\% recall for our best model. It also performs good validation on photometry data on AT2017gfo and AT2019npv. Furthermore, we investigate the ability of the model to distinguish KN in a GW follow-up survey. We conclude that there is over 80\% probability that we can capture true KN in selected 20 candidates among $\sim 250$ detected astrophysical transients that have passed real-bogus filter and cross-matching., Comment: 19 pages, 9 figures, 2 tables. Accepted for publication in Universe

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multi-timescale control and communications (MTCC) based on Deep Reinforcement Learning (DRL). In this paper (Part I), we first decompose the problem into a communication-aware DRL-based PC sub-problem and a control-aware DRL-based RRA sub-problem. Then, we focus on the PC sub-problem assuming an RRA policy is given, and propose the MTCC-PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov Decision Process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC-PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of C-V2X communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC-PC with those of the baseline DRL algorithms.

In Part I of this two-part paper (Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control), we decomposed the multi-timescale control and communications (MTCC) problem in Cellular Vehicle-to-Everything (C-V2X) system into a communication-aware Deep Reinforcement Learning (DRL)-based platoon control (PC) sub-problem and a control-aware DRL-based radio resource allocation (RRA) sub-problem. We focused on the PC sub-problem and proposed the MTCC-PC algorithm to learn an optimal PC policy given an RRA policy. In this paper (Part II), we first focus on the RRA sub-problem in MTCC assuming a PC policy is given, and propose the MTCC-RRA algorithm to learn the RRA policy. Specifically, we incorporate the PC advantage function in the RRA reward function, which quantifies the amount of PC performance degradation caused by observation delay. Moreover, we augment the state space of RRA with PC action history for a more well-informed RRA policy. In addition, we utilize reward shaping and reward backpropagation prioritized experience replay (RBPER) techniques to efficiently tackle the multi-agent and sparse reward problems, respectively. Finally, a sample- and computational-efficient training approach is proposed to jointly learn the PC and RRA policies in an iterative process. In order to verify the effectiveness of the proposed MTCC algorithm, we performed experiments using real driving data for the leading vehicle, where the performance of MTCC is compared with those of the baseline DRL algorithms.

Wireless mesh networks play a critical role in enabling key networking scenarios in beyond-5G (B5G) and 6G networks, including integrated access and backhaul (IAB), multi-hop sidelinks, and V2X. However, it still poses a challenge to deliver scalable per-node throughput via mesh networking, which significantly limits the potential of large-scale deployment of wireless mesh networks. Existing research has achieved $O(1)$ per-node throughput in a dense network, but how to achieve scalability remains an unresolved issue for an extended wireless network where the network size increases with a constant node density. This issue prevents a wireless mesh network from large-scale deployment. To this end, this paper aims to develop a theoretical approach to achieving scalable per-node throughput in wireless mesh networks. First, the key factors that limit the per-node throughput of wireless mesh networks are analyzed, through which two major ones are identified, i.e., link sharing and interference. Next, a multi-tier hierarchical architecture is proposed to overcome the link-sharing issue. The inter-tier interference under this architecture is then mitigated by utilizing orthogonal frequency allocation between adjacent tiers, while the intra-tier interference is reduced by considering two specific transmission schemes, one is MIMO spatial multiplexing with time-division, the other is MIMO beamforming. Theoretical analysis shows that the multi-tier mesh networking architecture can achieve a per-node throughput of $\Theta(1)$ in both schemes, as long as certain conditions on network parameters including bandwidth, antenna numbers, and node numbers of each tier are satisfied. A case study on a realistic deployment of 10,000 nodes is then carried out, which demonstrates that a scalable throughput of $\Theta(1)$ is achievable with a reasonable assumption on bandwidth and antenna numbers., Comment: ~12pages, 4 figures, submitted to IEEE TIT, part of this work has been published in IEEE MASS 2022

Real-world image de-weathering aims at removing various undesirable weather-related artifacts. Owing to the impossibility of capturing image pairs concurrently, existing real-world de-weathering datasets often exhibit inconsistent illumination, position, and textures between the ground-truth images and the input degraded images, resulting in imperfect supervision. Such non-ideal supervision negatively affects the training process of learning-based de-weathering methods. In this work, we attempt to address the problem with a unified solution for various inconsistencies. Specifically, inspired by information bottleneck theory, we first develop a Consistent Label Constructor (CLC) to generate a pseudo-label as consistent as possible with the input degraded image while removing most weather-related degradations. In particular, multiple adjacent frames of the current input are also fed into CLC to enhance the pseudo-label. Then we combine the original imperfect labels and pseudo-labels to jointly supervise the de-weathering model by the proposed Information Allocation Strategy (IAS). During testing, only the de-weathering model is used for inference. Experiments on two real-world de-weathering datasets show that our method helps existing de-weathering models achieve better performance. Codes are available at https://github.com/1180300419/imperfect-deweathering., Comment: AAAI2024

Merging multi-exposure images is a common approach for obtaining high dynamic range (HDR) images, with the primary challenge being the avoidance of ghosting artifacts in dynamic scenes. Recent methods have proposed using deep neural networks for deghosting. However, the methods typically rely on sufficient data with HDR ground-truths, which are difficult and costly to collect. In this work, to eliminate the need for labeled data, we propose SelfHDR, a self-supervised HDR reconstruction method that only requires dynamic multi-exposure images during training. Specifically, SelfHDR learns a reconstruction network under the supervision of two complementary components, which can be constructed from multi-exposure images and focus on HDR color as well as structure, respectively. The color component is estimated from aligned multi-exposure images, while the structure one is generated through a structure-focused network that is supervised by the color component and an input reference (\eg, medium-exposure) image. During testing, the learned reconstruction network is directly deployed to predict an HDR image. Experiments on real-world images demonstrate our SelfHDR achieves superior results against the state-of-the-art self-supervised methods, and comparable performance to supervised ones. Codes are available at https://github.com/cszhilu1998/SelfHDR, Comment: ICLR 2024

A sudden roadblock on highways due to many reasons such as road maintenance, accidents, and car repair is a common situation we encounter almost daily. Autonomous Vehicles (AVs) equipped with sensors that can acquire vehicle dynamics such as speed, acceleration, and location can make intelligent decisions to change lanes before reaching a roadblock. A number of literature studies have examined car-following models and lane-changing models. However, only a few studies proposed an integrated car-following and lane-changing model, which has the potential to model practical driving maneuvers. Hence, in this paper, we present an integrated car-following and lane-changing decision-control system based on Deep Reinforcement Learning (DRL) to address this issue. Specifically, we consider a scenario where sudden construction work will be carried out along a highway. We model the scenario as a Markov Decision Process (MDP) and employ the well-known DQN algorithm to train the RL agent to make the appropriate decision accordingly (i.e., either stay in the same lane or change lanes). To overcome the delay and computational requirement of DRL algorithms, we adopt an MEC-assisted architecture where the RL agents are trained on MEC servers. We utilize the highly reputable SUMO simulator and OPENAI GYM to evaluate the performance of the proposed model under two policies; {\epsilon}-greedy policy and Boltzmann policy. The results unequivocally demonstrate that the DQN agent trained using the {\epsilon}-greedy policy significantly outperforms the one trained with the Boltzmann policy.

For improving image composition and aesthetic quality, most existing methods modulate the captured images by striking out redundant content near the image borders. However, such image cropping methods are limited in the range of image views. Some methods have been suggested to extrapolate the images and predict cropping boxes from the extrapolated image. Nonetheless, the synthesized extrapolated regions may be included in the cropped image, making the image composition result not real and potentially with degraded image quality. In this paper, we circumvent this issue by presenting a joint framework for both unbounded recommendation of camera view and image composition (i.e., UNIC). In this way, the cropped image is a sub-image of the image acquired by the predicted camera view, and thus can be guaranteed to be real and consistent in image quality. Specifically, our framework takes the current camera preview frame as input and provides a recommendation for view adjustment, which contains operations unlimited by the image borders, such as zooming in or out and camera movement. To improve the prediction accuracy of view adjustment prediction, we further extend the field of view by feature extrapolation. After one or several times of view adjustments, our method converges and results in both a camera view and a bounding box showing the image composition recommendation. Extensive experiments are conducted on the datasets constructed upon existing image cropping datasets, showing the effectiveness of our UNIC in unbounded recommendation of camera view and image composition. The source code, dataset, and pretrained models is available at https://github.com/liuxiaoyu1104/UNIC.

Mergers of binary neutron stars are multimessenger sources of gravitational waves that have an optical luminous counterpart, commonly referred to as 'kilonova'. Inspired by the detection of GW170817, intensive searches have been conducted during the LIGO/Virgo O3 run. However, despite these efforts, no verified kilonova was detected. In this work, we present a parameter constraint method based on non-detection of optical searching considering both GW skymap, limited sky coverage, cadence, limiting magnitudes and the probability of astrophysical origin. We use our method to place constraints on EoS of neutron star based on follow-up during O3 run and obtain $M_{\rm TOV} = 2.170^{+0.120}_{-0.108}\ M_{\odot}$ at 90\% confidence level with the combination of other observations. And we also take outlook for WFST targeting kilonova throughout the LIGO/Virgo O4 run. With more events handled, we will obtain more stringent constraints on EoS and kilonova populations., Comment: The paper contains some errors in Section 3 about data used in this work and acknowledgement description that should be corrected now

Recent statistics have demonstrated that missing items have become the main cause of loss for retailers in inventory management. To quickly identify missing tags, traditional protocols adopt Aloha-based strategies which take a long time, especially when the number of tags is large. Among them, few works considered the effect of unexpected unknown tags on the missing tag identification process. With the presence of unknown tags, some missing tags may be falsely identified as present. Thus, the system's reliability is hardly guaranteed. In this work, we propose an efficient early-breaking estimation and tree-splitting-based missing tag identification (ETMTI) protocol for large-scale RFID systems. In ETMTI, a new early-breaking estimation and deactivation method is developed to effectively estimate the number of unknown tags and deactivate them within a short time. Next, a new tree-splitting-based missing tag identification method is proposed to quickly identify missing tags with a B-ary splitting tree. Besides, a bit-tracking response strategy is designed to further reduce the time cost. The optimal parameters, time cost, and false negative rate of ETMTI are analyzed theoretically. Simulation results are presented to demonstrate that the proposed ETMTI protocol takes a smaller time and has a lower false negative rate than the best-performing benchmarks.

Consider a multiuser multiple-input multiple-output (MU-MIMO) downlink system in which the base station (BS) sends multiple data streams to multi-antenna users via symbol-level precoding (SLP), where the optimization of receive combining matrix becomes crucial, unlike in the single-antenna user scenario. We begin by introducing a joint optimization problem on the symbol-level transmit precoder and receive combiner. The problem is solved using the alternating optimization (AO) method, and the optimal solution structures for transmit precoding and receive combining matrices are derived by using Lagrangian and Karush-Kuhn-Tucker (KKT) conditions, based on which, the original problem is transformed into an equivalent quadratic programming problem, enabling more efficient solutions. To address the challenge that the above joint design is difficult to implement, we propose a more practical scheme where the receive combining optimization is replaced by the interference rejection combiner (IRC), which is however difficult to directly use because of the rank-one transmit precoding matrix. Therefore, we introduce a new regularized IRC (RIRC) receiver to circumvent the above issue. Numerical results demonstrate that the practical SLP-RIRC method enjoys only a slight communication performance loss compared to the joint transmit precoding and receive combining design, both offering substantial performance gains over the conventional BD-based approaches., Comment: 13 pages, 10 figures

Early results from the James Webb Space Telescope (JWST) observations have hinted at two traces beyond the standard cosmological framework. One is the extraordinarily high stellar masses and their density at $z=7.5\sim9.1$, another is the unexpected abundance of ultraviolet (UV) bright galaxies at $z\ge10$. Nevertheless, both pieces of evidence are not statistically robust yet. In this work, we construct rest-frame UV luminosity functions (LFs) based on a general formation model for these high-redshift galaxy candidates, since UV LFs always carry the information of stellar formation efficiency (SFE), initial mass function (IMF), dust attenuation, and other crucial elements for galaxy evolution. By updating the massive galaxies candidates with spectroscopic observations and exploring the parameter space of SFE, we are able to reasonably explain the cumulative stellar mass density within the redshift range of $7.5\sim9.1$, with only one galaxy exhibiting unusual characteristics. We also reveal a potential nonmonotonic trend of SFE with the increasing redshift. At higher redshift ($z\sim13$), bright UV LFs can be well fitted with non-dust attenuation or top-heavy IMF for Population III stars. The Population III star scenario can also naturally account for the possible dip of the peak SFE evolution curve at $z\sim9$., Comment: 10 pages, 7 figures, and 2 tables. Published in ApJL

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance.

Recently, pulsar timing array (PTA) experiments have provided compelling evidence for the existence of the nanohertz stochastic gravitational wave background (SGWB). In this work, we demonstrated that cosmic string loops generated from cosmic global strings offer a viable explanation for the observed nanohertz SGWB data, requiring a cosmic string tension parameter of $\log(G\mu) \sim -12$ and a loop number density of $\log N \sim 4$. Additionally, we revisited the impact of cosmic string loops on the abundance of massive galaxies at high redshifts. However, our analysis revealed challenges in identifying a consistent parameter space that can concurrently explain both the SGWB data and observations from the James Webb Space Telescope. This indicates the necessity for either extending the existing model employed in this research or acknowledging distinct physical origins for these two phenomena., Comment: 7 pages, 4 figures, accepted for publication in Sci. China-Phys. Mech. Astron. (SCPMA)

Modern consumer cameras usually employ the rolling shutter (RS) mechanism, where images are captured by scanning scenes row-by-row, yielding RS distortions for dynamic scenes. To correct RS distortions, existing methods adopt a fully supervised learning manner, where high framerate global shutter (GS) images should be collected as ground-truth supervision. In this paper, we propose a Self-supervised learning framework for Dual reversed RS distortions Correction (SelfDRSC), where a DRSC network can be learned to generate a high framerate GS video only based on dual RS images with reversed distortions. In particular, a bidirectional distortion warping module is proposed for reconstructing dual reversed RS images, and then a self-supervised loss can be deployed to train DRSC network by enhancing the cycle consistency between input and reconstructed dual reversed RS images. Besides start and end RS scanning time, GS images at arbitrary intermediate scanning time can also be supervised in SelfDRSC, thus enabling the learned DRSC network to generate a high framerate GS video. Moreover, a simple yet effective self-distillation strategy is introduced in self-supervised loss for mitigating boundary artifacts in generated GS images. On synthetic dataset, SelfDRSC achieves better or comparable quantitative metrics in comparison to state-of-the-art methods trained in the full supervision manner. On real-world RS cases, our SelfDRSC can produce high framerate GS videos with finer correction textures and better temporary consistency. The source code and trained models are made publicly available at https://github.com/shangwei5/SelfDRSC. We also provide an implementation in HUAWEI Mindspore at https://github.com/Hunter-Will/SelfDRSC-mindspore., Comment: Accepted by ICCV 2023, available at https://github.com/shangwei5/SelfDRSC

Studies have proposed that there is evidence for cosmological coupling of black holes (BHs) with an index of $k\approx 3$; hence, BHs serve as the astrophysical source of dark energy. However, the data sample is limited for the redshifts of $\leq 2.5$. In recent years, the James Webb Space Telescope (JWST) has detected many high-redshift active galactic nuclei (AGNs) and quasars. Among the JWST NIRSpec-/NIRCam-resolved AGNs, three are determined to be in early-type host galaxies with a redshift of $z\sim 4.5--7$. However, their $M_{\star}$ and $M_{\rm BH}$ are in tension with the predicted cosmological coupling of black holes with $k = 3$ at a confidence level of $\sim 2\sigma$, which challenges the hypothesis that BHs serve as the origin of dark energy. Future work on high-redshift AGNs using the JWST will further assess such a hypothesis by identifying more early-type host galaxies in the higher mass range., Comment: 9 pages, 3 figures, 2 tables; Comments are welcome!

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of previous days, where the policy can generate real-time decisions based on observations of past renewable and load data of previous hours collected by connected sensors. The goal is to reduce operating cost on the premise of ensuring supply-demand balance. In specific, a novel finite-horizon partial observable Markov decision process (POMDP) model is conceived considering the spinning reserve. In order to overcome the challenge of discrete-continuous hybrid action space due to the binary DG switching decision and continuous energy dispatch (ED) decision, a DRL algorithm, namely the hybrid action finite-horizon RDPG (HAFH-RDPG), is proposed. HAFH-RDPG seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and recurrent deterministic policy gradient (RDPG), based on a finite-horizon dynamic programming (DP) framework. Extensive experiments are performed with real-world data in an IoT-driven MG to evaluate the capability of the proposed algorithm in handling the uncertainty due to inter-hour and inter-day power fluctuation and to compare its performance with those of the benchmark algorithms.

Significant progress has been made in self-supervised image denoising (SSID) in the recent few years. However, most methods focus on dealing with spatially independent noise, and they have little practicality on real-world sRGB images with spatially correlated noise. Although pixel-shuffle downsampling has been suggested for breaking the noise correlation, it breaks the original information of images, which limits the denoising performance. In this paper, we propose a novel perspective to solve this problem, i.e., seeking for spatially adaptive supervision for real-world sRGB image denoising. Specifically, we take into account the respective characteristics of flat and textured regions in noisy images, and construct supervisions for them separately. For flat areas, the supervision can be safely derived from non-adjacent pixels, which are much far from the current pixel for excluding the influence of the noise-correlated ones. And we extend the blind-spot network to a blind-neighborhood network (BNN) for providing supervision on flat areas. For textured regions, the supervision has to be closely related to the content of adjacent pixels. And we present a locally aware network (LAN) to meet the requirement, while LAN itself is selectively supervised with the output of BNN. Combining these two supervisions, a denoising network (e.g., U-Net) can be well-trained. Extensive experiments show that our method performs favorably against state-of-the-art SSID methods on real-world sRGB photographs. The code is available at https://github.com/nagejacob/SpatiallyAdaptiveSSID., Comment: CVPR 2023 Camera Ready

We study angular momentum radiation from electrically-biased chiral single molecular junctions using the nonequilibrium Green's function method. Using single helical chains as examples, we make connections between the ability of a chiral molecule to emit photons with angular momentum to the geometrical factors of the molecule. We point out that the mechanism studied here does not involve the magnetic moment. Rather, it relies on inelastic transitions between scattering states originated from two electrodes with different chiral properties and chemical potentials. The required time-reversal symmetry breaking is provided by nonequilibrium electron transport. Our work sheds light on the relationship between geometrical and optoelectrical chiral properties at single molecular limit., Comment: 10 pages, 8 figures

A group of massive galaxies at redshifts of $z\gtrsim 7$ have been recently detected by the James Webb Space Telescope (JWST), which were unexpected to form so early within the framework of standard Big Bang cosmology. In this work, we propose that this puzzle can be explained by the presence of some primordial black holes (PBHs) with a mass of $\sim 1000 M_\odot$. These PBHs, clothed in dark matter halo and undergoing super-Eddington accretion, serve as seeds for the early galaxy formation with masses of $\sim 10^{8}-10^{10}~M_\odot$ at high redshift, thus accounting for the JWST observations. Using a hierarchical Bayesian inference framework to constrain the PBH mass distribution models, we find that the Lognormal model with $M_{\rm c}\sim 750M_\odot$ is preferred over other hypotheses. These rapidly growing BHs are expected to emit strong radiation and may appear as high-redshift compact objects, similar to those recently discovered by JWST. Although we focuse on PBHs in this work, the bound on the initial mass of the seed black holes remains robust even if they were formed through astrophysical channels., Comment: Accepted by Science China Physics, Mechanics & Astronomy

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraints

Expected to be of the highest survey power telescope in the northern hemisphere, the Wide Field Survey Telescope (WFST) will begin its routine observations of the northern sky since 2023. WFST will produce a lot of scientific data to support the researches of time-domain astronomy, asteroids and the solar system, galaxy formation and cosmology and so on. We estimated that the 5 $\sigma$ limiting magnitudes of WFST with 30 second exposure are $u=22.31$ mag, $g=23.42$ mag, $r=22.95$ mag, $i=22.43$ mag, $z=21.50$ mag, $w=23.61$ mag. The above values are calculated for the conditions of $airmass=1.2$, seeing = 0.75 arcsec, precipitable water vapour (PWV) = 2.5 mm and Moon-object separation = $45^{\circ}$ at the darkest New Moon night of the Lenghu site (V=22.30 mag, Moon phase $\theta=0^{\circ}$). The limiting magnitudes in different Moon phase conditions are also calculated. The calculations are based on the empirical transmittance data of WFST optics, the vendor provided CCD quantum efficiency, the atmospherical model transmittance and spectrum of the site. In the absence of measurement data such as sky transmittance and spectrum, we use model data., Comment: Figure 5 (a,b,c) has been updated in this latest version. 12 pages, 5 figures, published in RAA (Research in Astronomy and Astrophysics)

Multivariate time series forecasting with hierarchical structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also reconciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay's data center) and the preliminary results demonstrate efficacy of our proposed method.

Traffic accident prediction in driving videos aims to provide an early warning of the accident occurrence, and supports the decision making of safe driving systems. Previous works usually concentrate on the spatial-temporal correlation of object-level context, while they do not fit the inherent long-tailed data distribution well and are vulnerable to severe environmental change. In this work, we propose a Cognitive Accident Prediction (CAP) method that explicitly leverages human-inspired cognition of text description on the visual observation and the driver attention to facilitate model training. In particular, the text description provides a dense semantic description guidance for the primary context of the traffic scene, while the driver attention provides a traction to focus on the critical region closely correlating with safe driving. CAP is formulated by an attentive text-to-vision shift fusion module, an attentive scene context transfer module, and the driver attention guided accident prediction module. We leverage the attention mechanism in these modules to explore the core semantic cues for accident prediction. In order to train CAP, we extend an existing self-collected DADA-2000 dataset (with annotated driver attention for each frame) with further factual text descriptions for the visual observations before the accidents. Besides, we construct a new large-scale benchmark consisting of 11,727 in-the-wild accident videos with over 2.19 million frames (named as CAP-DATA) together with labeled fact-effect-reason-introspection description and temporal accident frame label. Based on extensive experiments, the superiority of CAP is validated compared with state-of-the-art approaches. The code, CAP-DATA, and all results will be released in \url{https://github.com/JWFanggit/LOTVS-CAP}.

The temporal behavior of the very dim optical afterglow of GRB 080503 is at odds with the regular forward shock afterglow model and a sole kilonova component responsible for optical emission has been speculated in some literature. Here we analyze the optical afterglow data available in archive and construct time-resolved spectra. The significant detection by Keck-I in {\it G/R} bands at $t\sim 3$ day, which has not been reported before, as well as the simultaneous Gemini-North {\it r} band measurement, are in favor of a power-law spectrum that is well consistent with the optical to X-ray spectrum measured at $t\sim 4.5$ day. However, for $t\leq 2$ day, the spectra are thermal-like and a straightforward interpretation is a kilonova emission from a neutron star merger, making it, possibly, the first detection of a very early kilonova signal at $t\sim 0.05$ day. A non-thermal nature of optical emission at late times ($t\sim 2$ day), anyhow, can not be ruled out because of the large uncertainty of the {\it g}-band data. We also propose to classify the neutron star merger induced optical transients, according to the temporal behaviors of the kilonova and the non-thermal afterglow emission, into four types. GRB 080503 would then represent the first observation of a sub-group of neutron star merger driven optical transients (i.e., Type IV) consisting of an early blue kilonova and an adjacent non-thermal afterglow radiation., Comment: Accepted to ApJ. 15 pages, 4 figures, 5 tables

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel's computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1,280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed.

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper., Comment: arXiv admin note: substantial text overlap with arXiv:2211.03885; text overlap with arXiv:2105.07809, arXiv:2211.04470, arXiv:2211.05256, arXiv:2211.05910

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based image signal processing (ISP) pipeline replacing the standard mobile ISPs that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale Fujifilm UltraISP dataset consisting of thousands of paired photos captured with a normal mobile camera sensor and a professional 102MP medium-format FujiFilm GFX100 camera. The runtime of the resulting models was evaluated on the Snapdragon's 8 Gen 1 GPU that provides excellent acceleration results for the majority of common deep learning ops. The proposed solutions are compatible with all recent mobile GPUs, being able to process Full HD photos in less than 20-50 milliseconds while achieving high fidelity results. A detailed description of all models developed in this challenge is provided in this paper.

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper., Comment: arXiv admin note: substantial text overlap with arXiv:2105.08630, arXiv:2211.03885; text overlap with arXiv:2105.08819, arXiv:2105.08826, arXiv:2105.08629, arXiv:2105.07809, arXiv:2105.07825