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Long-duration gamma-ray bursts (GRBs) associated with supernovae (SNe) are believed to originate from massive star core-collapse events, whereas short-duration GRBs that are related to compact star mergers are expected to be accompanied by kilonovae. GRB 211227A, which lasted about 84 s, had an initial short/hard spike followed by a series of soft gamma-ray extended emission at redshift $z=$0.228. We performed follow-up observations of the optical emission using BOOTES, LCOGT, and the Lijiang 2.4m telescope, but we detected no associated supernova signature, even down to very stringent limits at such a low redshift. We observed the host galaxy within a large error-circle and roughly estimate the physical offset of GRB 211227A as $20.47\pm14.47$ kpc from the galaxy center. These properties are similar to those of GRB 060614, and suggest that the progenitor of GRB 211227A is not favored to be associated with the death of massive stars. Hence, we propose that GRB 211227A originates from a compact star merger. Calculating pseudo-kilonova emission for this case by adopting the typical parameters, we find that any associated pseudo-kilonova is too faint to be detected. If this is the case, it explains naturally the characteristics of the prompt emission, the lack of SN and kilonova emission, and the large physical offset from the galaxy center., Comment: 12 pages, 7 figures, and 1 Table. Matched with the published verison

Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of the image. When estimating prototypes, in a metric-based setting, it is thus important to determine which regions are relevant for which labels, but the limited amount of training data makes this highly challenging. As a solution, in this paper we propose to use word embeddings as a form of prior knowledge about the meaning of the labels. In particular, visual prototypes are obtained by aggregating the local feature maps of the support images, using an attention mechanism that relies on the label embeddings. As an important advantage, our model can infer prototypes for unseen labels without the need for fine-tuning any model parameters, which demonstrates its strong generalization abilities. Experiments on COCO and PASCAL VOC furthermore show that our model substantially improves the current state-of-the-art., Comment: Accepted by AAAI2022

Assuming that the shallow-decaying phase in the early X-ray lightcurves of gamma-ray bursts (GRBs) is attributed to the dipole radiations (DRs) of a newborn magnetar, we present a comparative analysis for the magnetars born in death of massive stars and merger of compact binaries with long and short GRB (lGRB and sGRB) data observed with the {\em Swift} mission. We show that the typical braking index ($n$) of the magnetars is $\sim 3$ in the sGRB sample, and it is $\sim 4$ for the magnetars in the lGRB sample. Selecting a sub-sample of the magnetars whose spin-down is dominated by DRs ($n\lesssim 3$) and adopting a universal radiation efficiency of $0.3$, we find that the typical magnetic field strength ($B_p$) is $10^{16}$ G {\em vs.} $10^{15}$ G and the typical initial period ($P_0$) is $\sim 20$ ms {\em vs.} $2$ ms for the magnetars in the sGRBs {\em vs.} lGRBs. They follow the same relation between $P_0$ and the isotropic GRB energy as $ P_0\propto E_{\rm jet}^{-0.4}$. We also extend our comparison analysis to superluminous supernovae (SLSNe) and stable pulsars. Our results show that a magnetar born in merger of compact stars tends to have a stronger $B_p$ and a longer $P_0$ by about one order of magnitude than that born in collapse of massive stars. Its spin-down is dominated by the magnetic DRs as old pulsars, being due to its strong magnetic field strength, whereas the early spin-down of magnetars born in massive star collapse is governed by both the DRs and gravitational wave (GW) emission. A magnetar with a faster rotation speed should power a more energetic jet, being independent of its formation approach., Comment: 10 pages, 2 tables, 7 figures. Published in MNRAS

Group activity recognition is a crucial yet challenging problem, whose core lies in fully exploring spatial-temporal interactions among individuals and generating reasonable group representations. However, previous methods either model spatial and temporal information separately, or directly aggregate individual features to form group features. To address these issues, we propose a novel group activity recognition network termed GroupFormer. It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer. Specifically, our GroupFormer has three appealing advantages: (1) A tailor-modified Transformer, Clustered Spatial-Temporal Transformer, is proposed to enhance the individual representation and group representation. (2) It models the spatial and temporal dependencies integrally and utilizes decoders to build the bridge between the spatial and temporal information. (3) A clustered attention mechanism is utilized to dynamically divide individuals into multiple clusters for better learning activity-aware semantic representations. Moreover, experimental results show that the proposed framework outperforms state-of-the-art methods on the Volleyball dataset and Collective Activity dataset. Code is available at https://github.com/xueyee/GroupFormer., Comment: Accepted at ICCV2021

Gas cooling and accretion in haloes delivers mass and angular momentum onto galaxies. In this work, we investigate the accuracy of the modelling of this important process in several different semi-analytic (SA) galaxy formation models (GALFORM, L-GALAXIES and MORGANA) through comparisons with a hydrodynamical simulation performed with the moving-mesh code AREPO. Both SA models and the simulation were run without any feedback or metal enrichment, in order to focus on the cooling and accretion process. All of the SA models considered here assume that gas cools from a spherical halo. We found that the assumption that the gas conserves its angular momentum when moving from the virial radius, $r_{\rm vir}$, to the central region of the halo, $r\sim 0.1 r_{\rm vir}$, is approximately consistent with the results from our simulation, in which gas typically retains $70-80\%$ of its angular momentum during this process. We also found that, compared to the simulation, the MORGANA model tends to overestimate the mean specific angular momentum of cooled-down gas, the L-GALAXIES model also tends to overestimate this in low-redshift massive haloes, while the two older GALFORM models tend to underestimate the angular momentum. In general, the predictions of the new GALFORM cooling model developed by Hou et al. agree the best with the simulation., Comment: Accepted for publication on MNRAS

As a crucial task of autonomous driving, 3D object detection has made great progress in recent years. However, monocular 3D object detection remains a challenging problem due to the unsatisfactory performance in depth estimation. Most existing monocular methods typically directly regress the scene depth while ignoring important relationships between the depth and various geometric elements (e.g. bounding box sizes, 3D object dimensions, and object poses). In this paper, we propose to learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection. Specifically, a principled geometry formula with projective modeling of 2D and 3D depth predictions in the monocular 3D object detection network is devised. We further implement and embed the proposed formula to enable geometry-aware deep representation learning, allowing effective 2D and 3D interactions for boosting the depth estimation. Moreover, we provide a strong baseline through addressing substantial misalignment between 2D annotation and projected boxes to ensure robust learning with the proposed geometric formula. Experiments on the KITTI dataset show that our method remarkably improves the detection performance of the state-of-the-art monocular-based method without extra data by 2.80% on the moderate test setting. The model and code will be released at https://github.com/YinminZhang/MonoGeo., Comment: 16 pages, 11 figures

Observationally, there is a small fraction of Gamma-ray bursts (GRBs) with prompt emission observed by Fermi/GBM that are composed of two pulses. Occasionally, the distance to a GRB may be lensed when a high mass astrophysical object resides in the path between the GRB source and observer. In this paper, we describe GRB 200716C, which has a two-pulse emission and duration of a few seconds. We present a Bayesian analysis identifying gravitational lensing in both temporal and spectral properties, and calculate the time delay ($\Delta t\sim 1.92$ s) and magnification ($\gamma\sim 1.5$) between those two pulses based on the temporal fits. One can roughly estimate the lens mass to be about $2.4\times 10^{5}~M_{\odot}$ in the rest frame. We also calculate the false alarm probability for this detection to be about 0.07\% with trial factors, and a present-day number density of about $808 \rm~Mpc^{-3}$ with an energy density $\Omega\sim 1.4\times 10^{-3}$. If the first pulse of this GRB near the trigger time is indeed gravitationally echoed by a second pulse, GRB 200716C may be a short GRB candidate with extended emission., Comment: 22 pages, 2 tables, 7 figures, ApJL in press, added two references

Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos. To model spatial-temporal dependencies of human mobility, we propose a multi-focus Gaussian neighborhood attention (GNA), which can effectively exploit long-range correspondences while maintaining the spatial topological structure of the input videos. In particular, our GNA can also capture the scale variation of human heads well using the equipped multi-focus mechanism. Based on the multi-focus GNA, we develop a unified neural network called GNANet to accurately locate head centers in video clips by fully aggregating spatial-temporal information via a scene modeling module and a context cross-attention module. Moreover, to facilitate future researches in this field, we introduce a large-scale crowd video benchmark named VSCrowd, which consists of 60K+ frames captured in various surveillance scenarios and 2M+ head annotations. Finally, we conduct extensive experiments on three datasets including our SenseCrowd, and the experiment results show that the proposed method is capable to achieve state-of-the-art performance for both video crowd localization and counting.

One possible progenitor of short gamma-ray bursts (GRBs) is thought to be from a double neutron star (NS) merger, and the remnant of such a merger may be a supramassive NS, which is supported by rigid rotation and through its survival of hundreds of seconds before collapsing into a black hole (BH). If this is the case, an optical/infrared transient (namely merger-nova) is generated from the ejected materials and it is powered by radioactive decay from $r$-process, spin-down energy from a supramassive NS, as well as the magnetic wind from a newborn BH. In this paper, we systematically search for the signature of a supramassive NS central engine by analyzing the X-ray emission of short GRBs with internal plateau observed by {\em Swift}, and we find that five candidates of short GRBs have such feature with redshift measurement. Then, we calculate the possible merger-nova emission from those candidates for given the typical model parameters by considering the above three energy sources, and compare its brightness with the sensitivity of some optical telescopes. We find that the merger-nova emission of GRB 060801 in K-, r-, and U-bands with variations of $M_{\rm ej}$ ($10^{-4}-10^{-2} M_{\odot}$), $\kappa$ ($0.1-10 ~\rm cm^{2}~g^{-1}$), and $\beta$ ($0.1-0.3$) is very difficult to detect using the Vera C. Rubin, Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), the Zwicky Transient Facility, and the Roman Space Telescope (Roman), except for the case of large ejecta mass $M_{\rm ej}=10^{-2} M_{\odot}$. However, we are very hopeful to detect the merger-nova emission of GRBs 090515, 100625A, and 101219A using more sensitive instruments, such as Vera C. Rubin, Pan-STARRS, and Roman. Moreover, the merger-nova emission of GRB 160821B is bright enough to detect in our calculations, and it is also consistent with current real observations of merger-nova emission., Comment: 21 pages, 1 table, 6 figures. ApJ in press, two more references added, matched the published version

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers. After analyzing the importance of battery aging and passenger load effects on an optimal energy management strategy, this study introduces the passenger load prediction into the hybrid-electric city buses energy management problem, which is not well studied in the existing literature. The average model, Decision Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, a dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage leveraging cloud techniques. Then, rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to the vehicle onboard controller to handle prediction errors and uncertainties. The proposed cloud-based Dynamic Programming and rule extraction framework with the passenger load prediction show 4% and 11% lower bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% of the dynamic programming with the true passenger load information.

Reinforcement Learning (RL) is widely utilized in the field of robotics, and as such, it is gradually being implemented in the Hybrid Electric Vehicle (HEV) supervisory control. Even though RL exhibits excellent performance in terms of fuel consumption minimization in simulation, the large learning iteration number needs a long learning time, making it hardly applicable in real-world vehicles. In addition, the fuel consumption of initial learning phases is much worse than baseline controls. This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods. Different from previous studies, which initiated Q-learning with zero or random Q values, this study initiates the Q-learning with different supervisory controls (i.e., Equivalent Consumption Minimization Strategy control and heuristic control), and detailed analysis is given. The results show that the proposed warm start Q-learning requires 68.8% fewer iterations than cold start Q-learning. The trained Q-learning is validated in two different driving cycles, and the results show 10-16% MPG improvement when compared to Equivalent Consumption Minimization Strategy control. Furthermore, real-time feasibility is analyzed, and the guidance of vehicle implementation is provided. The results of this study can be used to facilitate the deployment of RL in vehicle supervisory control applications.

The merger of binary neutron stars (NS-NS) as the progenitor of short Gamma-ray bursts (GRBs) has been confirmed by the discovery of the association of the gravitational wave (GW) event GW170817 with GRB 170817A. However, the merger product of binary NS remains an open question. An X-ray plateau followed by a steep decay ("internal plateau") has been found in some short GRBs, implying that a supra-massive magnetar operates as the merger remnant and then collapses into a newborn black hole (BH) at the end of the plateau. X-ray bump or second-plateau following the "internal plateau" is considered as the expected signature from the fallback accretion onto this newborn BH through Blandford-Znajek mechanism (BZ). At the same time, a nearly isotropic wind driven by Blandford-Paynemechanism (BP) from the newborn BH's disk can produce a bright kilonova. Therefore, the bright kilonova observation for a short GRB with "internal plateau" (and followed by X-ray bump or second-plateau) provides further evidence for this scenario. In this paper, we find that GRB 160821B is a candidate of such a case, and the kilonova emission of GRB 160821B is possibly powered by the BP wind from a newborn BH. Future GW detection of GRB 160821B-like events may provide further support to this scenario, enable us to investigate the properties of the magnetar and the newborn BH, and constrain the equation of state of neutron stars., Comment: 6 pages, 3 figures. Accepted for publication in ApJ

GRB 200219A is a short gamma-ray burst (GRB) with an extended emission (EE) lasting $\sim 90$s. By analyzing data observed with the {\em Swift}/BAT and {\em Fermi}/GBM, we find that a cutoff power-law model can adequately fit the spectra of the initial short pulse with $\rm E_{p}=1387^{+232}_{-134}$ keV. More interestingly, together with the EE component and early X-ray data, it exhibits plateau emission smoothly connected with a $\sim t^{-1}$ segment and followed by an extremely steep decay. The short GRB composed of those three segments is unique in the {\em Swift} era and is very difficult to explain with the standard internal/external shock model of a black hole central engine, but could be consistent with the prediction of a magnetar central engine from the merger of an NS binary. We suggest that the plateau emission followed by a $\sim t^{-1}$ decay phase is powered by the spin-down of a millisecond magnetar, which loses its rotation energy via GW quadrupole radiation. Then, the abrupt drop decay is caused by the magnetar collapsing into a black hole before switching to EM-dominated emission. This is the first short GRB for which the X-ray emission has such an intriguing feature powered by a magnetar via GW-dominated radiation. If this is the case, one can estimate the physical parameters of a magnetar, the GW signal powered by a magnetar and the merger-nova emission are also discussed., Comment: 21 pages, 2 Tables, 7 figures, ApJL in press, two more references added, matched the published version

Fault diagnosis is extremely important to the safe operation of Lithium-ion batteries. To avoid severe safety issues (e.g., thermal runaway), initial faults should be timely detected and resolved. In this paper, we consider parallel-connected battery cells with only one voltage and one current sensor. The lack of independent current sensors makes it difficult to detect individual cell degradation. To this end, based on the high-frequency response of the battery, a simplified fault detection-oriented model is derived and validated by a physics-informed battery model. The resistance of the battery string, which is significantly influenced by the faulty cell, is estimated and used as the health indicator. The statistical resistance distribution of battery strings is first analyzed considering the distribution of fresh and aged cells. A fault diagnosis algorithm is proposed and the thresholds (i.e., 2 standard deviation interval) are obtained through statistical analysis. Monte Carlo simulation results show that the proposed fault diagnosis algorithm can balance false alarms and missed detections well. In addition, it is verified that the proposed algorithm is robust to the uniform parameter changes of individual battery cells., Comment: 2020 American Control Conference, accepted

A novel correction algorithm is proposed for multi-class classification problems with corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction. The correction procedure can be coupled with any approximators, such as logistic regression, neural networks of various architectures, etc. When training dataset is sufficiently large, we prove that the corrected models deliver correct classification results as if there is no corruption in the training data. For datasets of finite size, the corrected models produce significantly better recovery results, compared to the models without the correction algorithm. All of the theoretical findings in the paper are verified by our numerical examples.

Connectionist Temporal Classification (CTC) and attention mechanism are two main approaches used in recent scene text recognition works. Compared with attention-based methods, CTC decoder has a much shorter inference time, yet a lower accuracy. To design an efficient and effective model, we propose the guided training of CTC (GTC), where CTC model learns a better alignment and feature representations from a more powerful attentional guidance. With the benefit of guided training, CTC model achieves robust and accurate prediction for both regular and irregular scene text while maintaining a fast inference speed. Moreover, to further leverage the potential of CTC decoder, a graph convolutional network (GCN) is proposed to learn the local correlations of extracted features. Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods., Comment: Accepted by AAAI 2020

The observed internal plateau of X-ray emission in some short GRBs suggests the formation of a remnant supra-massive magnetar following a double neutron star (NS) merger. In this paper, we assume that the rotational energy is lost mainly via gravitational wave (GW) radiation instead of magnetic dipole (MD) radiation, and present further constraints on the NS nuclear equation of state (EoS) via mass quadrupole deformation and r-mode fluid oscillations of the magnetar. We present two short GRBs with measured redshifts, 101219A and 160821B, whose X-ray light curves exhibit an internal plateau. This suggests that a supra-massive NS may survive as the central engine. By considering twelve NS EoSs, within the mass quadrupole deformation scenario we find that the GM1, DD2, and DDME2 models give an $M_{\rm p}$ band falling within the 2$\sigma$ region of the proto-magnetar mass distribution for $\varepsilon=0.01$. This is consistent with the constraints from the MD radiation dominated model of rotational energy loss. However, for an r-mode fluid oscillation model with $\alpha=0.1$ the data suggest that the NS EOS is close to the Shen and APR models, which is obviously different from the MD radiation dominated and mass quadrupole deformation cases., Comment: 7 pages, 1 table, and 4 figures. ApJ in press, matched the published version

Short GRBs with extended emission (EE) that are composed initially of a short-hard spike and followed by a long-lasting EE, are thought to be classified as a subsection of short GRBs. The narrow energy band available during the {\em Swift} era combined with a lack of spectral information prevented discovery of the intrinsic properties of those events. In this paper, we performed a systematic search of short GRBs with EE by using all available {\em Fermi}/GBM data. The search identified 26 GBM-detected short GRBs with EE that are similar to GRB 060614 observed by {\em Swift}/BAT. We focus on investigating the spectral and temporal properties for both the hard spike and the EE components of all 26 GRBs, and explore differences and possible correlations between them. We find that while the peak energy ($E_{\rm p}$) of the hard spikes is a little bit harder than that of the EE, but their fluences are comparable. The harder $E_{\rm p}$ seems to correspond to a larger fluence and peak flux with a large scatter for both the hard spike and EE components. Moreover, the $E_{\rm p}$ of both the hard spikes and EE are compared to other short GRBs. Finally, we also compare the properties of GRB 170817A with those short GRBs with EE and find no significant statistical differences between them. We find that GRB 170817A has the lowest $E_{\rm p}$, likely because it was off-axis., Comment: 12 pages, 6 figures, and 1 table. MNRAS in press, matched the published version