With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consumin
g process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and developed APIs.
This paper introduces a novel motion planning algorithm, incrementally stochastic and accelerated gradient information mixed optimization (iSAGO), for robotic manipulators in a narrow workspace. Primarily, we propose the overall scheme of iSAGO integ
rating the accelerated and stochastic gradient information for efficient descent in the penalty method. In the stochastic part, we generate the adaptive stochastic moment via the random selection of collision checkboxes, interval time-series, and penalty factor based on Adam to solve the body-obstacle stuck case. Due to the slow convergence of STOMA, we integrate the accelerated gradient and stimulate the descent rate in a Lipschitz constant reestimation framework. Moreover, we introduce the Bayesian tree inference (BTI) method, transforming the whole trajectory optimization (SAGO) into an incremental sub-trajectory optimization (iSAGO) to improve the computational efficiency and success rate. Finally, we demonstrate the key coefficient tuning, benchmark iSAGO against other planners (CHOMP, GPMP2, TrajOpt, STOMP, and RRT-Connect), and implement iSAGO on AUBO-i5 in a storage shelf. The result shows the highest success rate and moderate solving efficiency of iSAGO.
A large gap exists between fully-supervised object detection and weakly-supervised object detection. To narrow this gap, some methods consider knowledge transfer from additional fully-supervised dataset. But these methods do not fully exploit discrim
inative category information in the fully-supervised dataset, thus causing low mAP. To solve this issue, we propose a novel category transfer framework for weakly supervised object detection. The intuition is to fully leverage both visually-discriminative and semantically-correlated category information in the fully-supervised dataset to enhance the object-classification ability of a weakly-supervised detector. To handle overlapping category transfer, we propose a double-supervision mean teacher to gather common category information and bridge the domain gap between two datasets. To handle non-overlapping category transfer, we propose a semantic graph convolutional network to promote the aggregation of semantic features between correlated categories. Experiments are conducted with Pascal VOC 2007 as the target weakly-supervised dataset and COCO as the source fully-supervised dataset. Our category transfer framework achieves 63.5% mAP and 80.3% CorLoc with 5 overlapping categories between two datasets, which outperforms the state-of-the-art methods. Codes are avaliable at https://github.com/MediaBrain-SJTU/CaT.
While graphene shows a characteristic conical dispersion with a vanishing density of states (DOS) near the Fermi energy E$_F$, it has been suggested that under extremely-high doping ($sim$ 1/4), the extended flat band can be shifted to near E$_F$, re
sulting in a diverging DOS with strong many-body interactions and electronic instabilities. Although such highly-doped graphene has attracted tremendous research interests, so far the experimental demonstration of doping-induced flat band as well as its associated intriguing phenomena remains rather limited. Here, we report the observation of an extended flat band around the M point in a Li-intercalated graphene, in which the Li ions not only dope graphene with a high electron concentration, but also induce a Kekule order which breaks the chiral symmetry. At such high electron doping, pronounced electron-phonon and electron-electron interactions are clearly identified by the notable kinks in the band dispersion and a strong reduction of the band width. Moreover, by following the evolution of the band structure upon Li intercalation, we find that the flat band and the Kekule order, with the characteristic flat band near M and folded Dirac cones near $Gamma$ respectively, emerge simultaneously, which indicates that they are strongly coupled. Our work identifies Li-intercalated graphene as a fertile platform for investigating the unique physics of the extended flat band, strong many-body interactions as well as the Kekule order.
Speech evaluation is an essential component in computer-assisted language learning (CALL). While speech evaluation on English has been popular, automatic speech scoring on low resource languages remains challenging. Work in this area has focused on m
onolingual specific designs and handcrafted features stemming from resource-rich languages like English. Such approaches are often difficult to generalize to other languages, especially if we also want to consider suprasegmental qualities such as rhythm. In this work, we examine three different languages that possess distinct rhythm patterns: English (stress-timed), Malay (syllable-timed), and Tamil (mora-timed). We exploit robust feature representations inspired by music processing and vector representation learning. Empirical validations show consistent gains for all three languages when predicting pronunciation, rhythm and intonation performance.
Long-term gamma-ray variability of a non-blazar Active Galactic Nucleus (AGN) PKS 0521-36 is investigated by using Fermi-LAT pass 8 data covering from 2008 August to 2021 March. The results show that the histogram of the gamma-ray fluxes follows a lo
g-normal distribution. Interestingly, in the analysis of about 5.8-year (from MJD 56317 to 58447) LAT data between two outbursts (occurring during 2012 October and 2019 May respectively), a quasi-periodic oscillation (QPO) with a period of about 1.1 years (about 5 sigma of significance) is found in the Lomb-Scargle Periodogram (LSP), the Weighted Wavelet Z-transform (WWZ) and the REDFIT results. This quasi-periodic signal also appears in the results of Gaussian process modeling the light curve. Therefore, the robustness of the QPO is examined by four different methods. This is the first gamma-ray QPO found in a mildly beamed jet. Our results imply that the gamma-ray outbursts play an important role in the formation of the gamma-ray QPO.
The low-energy excitations of graphene are relativistic massless Dirac fermions with opposite chiralities at valleys K and K. Breaking the chiral symmetry could lead to gap opening in analogy to dynamical mass generation in particle physics. Here we
report direct experimental evidences of chiral symmetry breaking (CSB) from both microscopic and spectroscopic measurements in a Li-intercalated graphene. The CSB is evidenced by gap opening at the Dirac point, Kekule-O type modulation, and chirality mixing near the gap edge. Our work opens up opportunities for investigating CSB related physics in a Kekule-ordered graphene.
Electron-phonon interaction and related self-energy are fundamental to both the equilibrium properties and non-equilibrium relaxation dynamics of solids. Although electron-phonon interaction has been suggested by various time-resolved measurements to
be important for the relaxation dynamics of graphene, the lack of energy- and momentum-resolved self-energy dynamics prohibits direct identification of the role of specific phonon modes in the relaxation dynamics. Here by performing time- and angle-resolved photoemission spectroscopy measurements on a Kekule-ordered graphene with folded Dirac cones at the $Gamma$ point, we have succeeded in resolving the self-energy effect induced by coupling of electrons to two phonons at $Omega_1$ = 177 meV and $Omega_2$ = 54 meV and revealing its dynamical change in the time domain. Moreover, these strongly coupled phonons define energy thresholds, which separate the hierarchical relaxation dynamics from ultrafast, fast to slow, thereby providing direct experimental evidence for the dominant role of mode-specific phonons in the relaxation dynamics
Sequence-to-sequence models provide a viable new approach to generative summarization, allowing models that are no longer limited to simply selecting and recombining sentences from the original text. However, these models have three drawbacks: their
grasp of the details of the original text is often inaccurate, and the text generated by such models often has repetitions, while it is difficult to handle words that are beyond the word list. In this paper, we propose a new architecture that combines reinforcement learning and adversarial generative networks to enhance the sequence-to-sequence attention model. First, we use a hybrid pointer-generator network that copies words directly from the source text, contributing to accurate reproduction of information without sacrificing the ability of generators to generate new words. Second, we use both intra-temporal and intra-decoder attention to penalize summarized content and thus discourage repetition. We apply our model to our own proposed COVID-19 paper title summarization task and achieve close approximations to the current model on ROUEG, while bringing better readability.
In order to further exploit the potential of joint multi-antenna radar-communication (RadCom) system, we propose two transmission techniques respectively based on separated and shared antenna deployments. Both techniques are designed to maximize the
weighted sum rate (WSR) and the probing power at targets location under average power constraints at the antennas such that the system can simultaneously communicate with downlink users and detect the target within the same frequency band. Based on a Weighted Minimized Mean Square Errors (WMMSE) method, the separated deployment transmission is designed via semidefinite programming (SDP) while the shared deployment problem is solved by majorization-minimization (MM) algorithm. Numerical results show that the shared deployment outperforms the separated deployment in radar beamforming. The tradeoffs between WSR and probing power at target are compared among both proposed transmissions and two practically simpler dual-function implementations i.e., time division and frequency division. Results show that although the separated deployment enables spectrum sharing, it experiences a performance loss compared with frequency division, while the shared deployment outperforms both and surpasses time division in certain conditions.
