Influencing factors of resident satisfaction in smart community services： An empirical study in Chengdu

Smart communities have shown great advantages in China\u27s pandemic control, but also exposed the shortcomings that some smart community services (SCS) are out of touch with residents\u27 needs in the post-pandemic era. Therefore, This study aims to explore those SCSs were needed to promote the sustainable development of smart communities. Based on the expectation disconfirmation theory and the modified ASCI model, this study establishes a smart community service resident satisfaction model and analyzes it with Amos structural equation model. The study results are as follows: (1) SCS outcome, ICT infrastructure, and SCS delivery all have a positive influence on resident satisfaction and their performances decrease in turn. (2) some of the factors that drive resident satisfaction most, such as Smart Property Service and Public Facility, have a lower rating. (3) residents are more concerned about the cost (including financial and emotional costs) than the quality of the SCSs. (4) Most residents\u27 expectations of SCS are irrational and that’s why it does not have a significant impact on satisfaction. (5) Resident Satisfaction is an important factor in enhancing Resident Confidence in SCS and promoting Resident Participation in improving SCS. This enlightens us that improving resident satisfaction is one of the effective ways to promote the sustainable development of Smart Community and continuously enhance the emergency response capabilities of grassroots communities in the post-pandemic era

Uncertainty-aware Unsupervised Multi-Object Tracking

Without manually annotated identities, unsupervised multi-object trackers are inferior to learning reliable feature embeddings. It causes the similarity-based inter-frame association stage also be error-prone, where an uncertainty problem arises. The frame-by-frame accumulated uncertainty prevents trackers from learning the consistent feature embedding against time variation. To avoid this uncertainty problem, recent self-supervised techniques are adopted, whereas they failed to capture temporal relations. The interframe uncertainty still exists. In fact, this paper argues that though the uncertainty problem is inevitable, it is possible to leverage the uncertainty itself to improve the learned consistency in turn. Specifically, an uncertainty-based metric is developed to verify and rectify the risky associations. The resulting accurate pseudo-tracklets boost learning the feature consistency. And accurate tracklets can incorporate temporal information into spatial transformation. This paper proposes a tracklet-guided augmentation strategy to simulate tracklets' motion, which adopts a hierarchical uncertainty-based sampling mechanism for hard sample mining. The ultimate unsupervised MOT framework, namely U2MOT, is proven effective on MOT-Challenges and VisDrone-MOT benchmark. U2MOT achieves a SOTA performance among the published supervised and unsupervised trackers.Comment: Accepted by International Conference on Computer Vision (ICCV) 202

PCPL: Predicate-Correlation Perception Learning for Unbiased Scene Graph Generation

Today, scene graph generation(SGG) task is largely limited in realistic scenarios, mainly due to the extremely long-tailed bias of predicate annotation distribution. Thus, tackling the class imbalance trouble of SGG is critical and challenging. In this paper, we first discover that when predicate labels have strong correlation with each other, prevalent re-balancing strategies(e.g., re-sampling and re-weighting) will give rise to either over-fitting the tail data(e.g., bench sitting on sidewalk rather than on), or still suffering the adverse effect from the original uneven distribution(e.g., aggregating varied parked on/standing on/sitting on into on). We argue the principal reason is that re-balancing strategies are sensitive to the frequencies of predicates yet blind to their relatedness, which may play a more important role to promote the learning of predicate features. Therefore, we propose a novel Predicate-Correlation Perception Learning(PCPL for short) scheme to adaptively seek out appropriate loss weights by directly perceiving and utilizing the correlation among predicate classes. Moreover, our PCPL framework is further equipped with a graph encoder module to better extract context features. Extensive experiments on the benchmark VG150 dataset show that the proposed PCPL performs markedly better on tail classes while well-preserving the performance on head ones, which significantly outperforms previous state-of-the-art methods.Comment: To be appeared on ACMMM 202

Coherent perfect absorber and laser induced by directional emissions in the non-Hermitian photonic crystals

In this study, we propose the application of non-Hermitian photonic crystals (PCs) with anisotropic emissions. Unlike a ring of exceptional points (EPs) in isotropic non-Hermitian PCs, the EPs of anisotropic non-Hermitian PCs appear as lines symmetrical about the $\Gamma$ point. The non-Hermitian Hamiltonian indicates that the formation of EPs is related to the non-Hermitian strength. The real spectrum appears in the $\Gamma$Y direction and has been validated as the complex conjugate medium (CCM) by effective medium theory (EMT). But for the $\Gamma$X direction, EMT indicates that the effective refractive index has a large imaginary part, which forms an evanescent wave inside the PCs. Thence, coherent perfect absorber (CPA) and laser effects can be achieved in the directional emission of the $\Gamma$Y. The outgoing wave in the $\Gamma$X direction is weak, which can significantly reduce the losses and electromagnetic interference caused by the leakage waves. Furthermore, the non-Hermitian PCs enable many fascinating applications such as signal amplification, collimation, and angle sensors.Comment: 11 pages, 11 figure

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Previewer For Multi-Scale Object Detector

Most multi-scale detectors face a challenge of small-size false positives due to the inadequacy of low-level features, which have small receptive field sizes and weak semantic capabilities. This paper demonstrates independent predictions from different feature layers on the same region is beneficial for reducing false positives. We propose a novel light-weight previewer block, which previews the objectness probability for the potential regression region of each prior box, using the stronger features with larger receptive fields and more contextual information for better predictions. This previewer block is generic and can be easily implemented in multi-scale detectors, such as SSD, RFBNet and MS-CNN. Extensive experiments are conducted on PASCAL VOC and KITTI pedestrian benchmark to show the superiority of the proposed method

Mechanism of pressure management by injecting nitrogen in casing annulus of deepwater wells

The influence factors of annular pressure buildup are analyzed, and the temperature and pressure characteristics of annulus trap medium are simulated for deepwater wells. The method of pressure management by injecting nitrogen in casing annulus is proposed based the experiment result. Limited by the well structure and subsea system, long casing annulus exists between technical casing and production casing and it is filled with water-based, synthetic based or oil-based drilling fluid. In the process of oil/gas test and production, the temperature of the trapped fluid rises significantly under the influence of the well-bore fluid and the trap pressure buildup appears because of liquid heat expansion. Experiments show that isothermal compressibility coefficient and thermal expansion coefficient are the key influencing factors for annulus pressure buildup. Trap pressure is very sensitive to the type of trapped medium (liquid, gas). Injecting 5%−20% volume fraction of nitrogen into the annulus can effectively control the annulus pressure build-up, and avoid casing collapse. Field practice shows that the method, convenient and highly reliable, can ensure the borehole safety during testing and production of deepwater oil and gas. Key words: deepwater well, annular pressure buildup, annular temperature, nitrogen foam, pressure managemen