Qiao Wang

Micro-scale street-level economic assessment is critical for precise spatial resource allocation during urban renewal. Conventional approaches, often limited by semantic surface-level sensing, frequently misinterpret high nominal commercial density as robust vitality, thereby masking underlying structural decay and market volatility. To address this diagnostic gap, this study introduces a novel visual-semantic and spatiotemporal framework—operationalized as the Street Economic Vitality Index (SEVI)—that integrates multi-source big data to synthesize commercial quality and spatial externalities. Empirical analysis in central Nanjing demonstrates that street vibrancy is driven by a time-dependent synergy between hierarchical brand clustering and the radiative field effects of commercial anchors. Our findings reveal a ``density-competition'' paradox, wherein high-density zones often exhibit significant economic volatility. Furthermore, by decoupling the quasi-causal drivers of street-level dynamics, we identify that commercial quality and physical attrition exert distinct, time-varying influences on urban mobility, with storefront closures acting as a critical spatial deterrent during the night economy. Beyond diagnostic capabilities, this study establishes a multi-dimensional competitive scoring framework to translate continuous metrics into explicit regulatory categories. This typological synthesis successfully maps the street network into diversified, target-oriented pathways—ranging from tactical streetscape humanization (Tactical Humanization) to intensive asset restructuring (Strategic Rezoning)—ultimately serving as a robust decision-support tool for precision spatial governance and resilient urban regeneration.

System consolidation transforms temporary hippocampal representation of memory into long-term storage in cortex. The underlying neural substrate, however, remains enigmatic. Here, we tracked the spatiotemporal evolution of hippocampus (HPC)-cortex local field potentials and single-neuron spikes in behaving animals during fear memory formation. During learning, HPC fast gamma exhibited a progressive phase shift relative to PFC theta oscillations, with gamma power aligning to progressively later phases of the PFC theta cycle. Strikingly, a related phase-shifted coupling pattern re-emerged during subsequent consolidation in association with hippocampal sharp-wave ripples and transient PFC spindle events during NREM sleep. Across this process, interregional interactions evolved from HPC-driven cortical gamma coherence at recent stages to PFC-mediated cortical low-frequency coherence at remote stages. Using closed-loop optogenetic perturbations, we demonstrated a stepwise causal chain of coupling events underlying remote memory formation. Our study revealed HPC-PFC coupling phase shift as a feasible substrate mediating recent-to-remote transformation of memory.

We consider the problem of inferring graph topology from smooth graph signals in a novel but practical scenario where data are located in multiple clients and prohibited from leaving local clients due to factors such as privacy concerns. The main difficulty in this task is how to exploit the potentially heterogeneous data of all clients under data silos. To this end, we first propose an auto-weighted multiple graph learning model to jointly learn a personalized graph for each local client and a single consensus graph for all clients. The personalized graphs match local data distributions, thereby mitigating data heterogeneity, while the consensus graph captures the global information. Moreover, the model can automatically assign appropriate contribution weights to local graphs based on their similarity to the consensus graph. We next devise a tailored algorithm to solve the induced problem, where all raw data are processed locally without leaving clients. Theoretically, we establish a provable estimation error bound and convergence analysis for the proposed model and algorithm. Finally, extensive experiments on synthetic and real data are carried out, and the results illustrate that our approach can learn graphs effectively in the target scenario.

Urban modeling is essential for city planning, scene synthesis, and gaming. Existing image-based methods generate diverse layouts but often lack geometric continuity and scalability, while graph-based methods capture structural relations yet overlook parcel semantics. We present a controllable framework for large-scale 3D vector urban layout generation, conditioned on both geometry and semantics. By fusing geometric and semantic attributes, introducing edge weights, and embedding building height in the graph, our method extends 2D layouts to realistic 3D structures. It also enables users to directly control the output by modifying semantic attributes. Experiments show that it produces valid, large-scale urban models, offering an effective tool for data-driven planning and design.

We consider the problem of learning graphs from smooth signals in an online fashion. A major challenge of this task is that the underlying graph may be time-varying. Existing works typically assume fixed model parameters to learn dynamic graphs, which may suffer the from model mismatch problem and obtain suboptimal results. To this end, we propose an online graph learning approach that handles model mismatch problem by combining multiple expert algorithms via an expert-tracking algorithm. Theoretically, we prove that our algorithm can obtain sublinear dynamic regret, a common metric for evaluating online algorithms. Experimental results of synthetic and real data exhibit the superiority of our method.

The traditional paradigm for studying urban morphology involves the interpretation of Nolli maps, using methods such as morphometrics and visual neural networks. Previous studies on urban morphology discovery have always been based on raster analysis and have been limited to the central city area. Raster analysis can lead to fragmented forms, and focusing only on the central city area ignores many representative urban forms in the suburbs and towns. In this study, a vast and complex dataset was applied to the urban morphology discovery based on the administrative community or village boundary, and a new image deformation pipeline was proposed to enhance the morphological characteristics of building groups. This allows visual neural networks to focus on extracting the morphological characteristics of building groups. Additionally, the research on urban morphology often uses unsupervised learning, which means that the learning process is difficult to control. Therefore, we refined the NT-Xent loss so that it can integrate morphological indicators. This improvement allows the visual neural network to “recognize” the similarity of samples during optimization. By defining the similarity, we can guide the network to bring samples closer or move them farther apart based on certain morphological indicators. Three Chinese cities were used for our testing. Representative urban types were identified, particularly some types located at the urban fringe. The data analysis demonstrated the effectiveness of our image deformation pipeline and loss function, and the sociological analysis illustrated the unique urban functions of these urban types.

The musical signal produced by plucked instruments often exhibits non-stationarity due to variations in the pitch and amplitude, making pitch estimation a challenge. In this paper, we assess different transcription processes and algorithms applied to signals captured by optical sensors mounted on a pipa—a traditional Chinese plucked instrument—played using a range of techniques. The captured signal demonstrates a distinctive arched feature during plucking. This facilitates onset detection to avoid the impact of the spurious energy peaks within vibration areas that arise from pitch-shift playing techniques. Subsequently, we developed a novel time–frequency feature, known as continuous time-period mapping (CTPM), which contains pitch curves. The proposed process can also be applied to playing techniques that mix pitch shifts and tremolo. When evaluated on four renowned pipa music pieces of varying difficulty levels, our fully time-domain-based onset detectors outperformed four short-time methods, particularly during tremolo. Our zero-crossing-based pitch estimator achieved a performance comparable to short-time methods with a far better computational efficiency, demonstrating its suitability for use in a lightweight algorithm in future work.