Traffic forecasting plays a key role in Intelligent Transportation Systems, and significant strides have been made in this field. However, most existing methods can only predict up to four hours in the future, which doesn't quite meet real-world demands. we identify that the prediction horizon is limited to a few hours mainly due to the separation of temporal and spatial factors, which results in high complexity. Drawing inspiration from Albert Einstein's relativity theory, which suggests space and time are unified and inseparable, we introduce Extralonger, which unifies temporal and spatial factors. Extralonger notably extends the prediction horizon to a week on real-world benchmarks, demonstrating superior efficiency in the training time, inference time, and memory usage. It sets new standards in long-term and extra-long-term scenarios. The code is available at https://github.com/PlanckChang/Extralonger.

Aggregated relative search frequencies offer a unique composite signal reflecting people's habits, concerns, interests, intents, and general information needs, which are not found in other readily available datasets. Temporal search trends have been successfully used in time series modeling across a variety of domains such as infectious diseases, unemployment rates, and retail sales. However, most existing applications require curating specialized datasets of individual keywords, queries, or query clusters, and the search data need to be temporally aligned with the outcome variable of interest. We propose a novel approach for generating an aggregated and anonymized representation of search interest as foundation features at the community level for geospatial modeling. We benchmark these features using spatial datasets across multiple domains. In zip codes with a population greater than 3000 that cover over 95% of the contiguous US population, our models for predicting missing values in a 20% set of holdout counties achieve an average $R^2$ score of 0.74 across 21 health variables, and 0.80 across 6 demographic and environmental variables. Our results demonstrate that these search features can be used for spatial predictions without strict temporal alignment, and that the resulting models outperform spatial interpolation and state of the art methods using satellite imagery features.

Robot manipulation of 3D Deformable Objects is essential for many activities and applications in the real world, such as household [1, 2] and healthcare [3], and is still an open challenge despite extensive studies. Recently, data-driven solutions have shown impressive and promising results in 3D deformable object manipulation by learning-based approaches [4, 5], where sufficient data is essential to improve model training or policy learning. To obtain training data, some previous works collected synthetic data from simulators [6]. Still, there is an unavoidable gap between the real world and the simulator since the existing simulators cannot accurately simulate all real-world physical characteristics (e.g., friction, impact, and stiffness) [7]. To mitigate the gap, some researchers [8, 9, 10, 11] collect Real-World Data (RWD); for example, [8, 9] collects RGB-D images and point clouds, [10] collects 3D mesh models, [11] uses a professional system with 106 cameras to obtain the 3D reconstructions of deformed mesh.

Recent text-to-image personalization methods have shown great promise in teaching a diffusion model user-specified concepts given a few images for reusing the acquired concepts in a novel context. With massive efforts being dedicated to personalized generation, a promising extension is personalized editing, namely to edit an image using personalized concepts, which can provide a more precise guidance signal than traditional textual guidance. To address this, a straightforward solution is to incorporate a personalized diffusion model with a text-driven editing framework. However, such a solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods. Specifically, we enhance the source image conditioned editability of a personalized diffusion model via a novel Editability Driven Score Distillation (EDSD) objective. Moreover, we identify a mode trapping issue with EDSD, and propose a mode shifting regularization with spatial feature guided sampling to avoid such an issue. We further employ two key modifications to the Delta Denoising Score framework that enable high-fidelity local editing with personalized concepts. Extensive experiments validate that DreamSteerer can significantly improve the editability of several T2I personalization baselines while being computationally efficient.

Extracting valuable insights from complex datasets is a ubiquitous challenge in modern data analysis and machine learning. Topological Data Analysis(TDA) [ELZ02, ZC04] has recently gained attention as a powerful method for addressing this challenge by utilizing tools from algebraic topology. Topological data analysis is particularly advantageous due to its robustness against noise and its ability to capture global, higherdimensional topological features, which traditional geometric and graph-based methods often miss [EH22]. In topological data analysis, data is first transformed into a series of combinatorial structures called a filtration of simplicial complexes. A simplicial complex consists of simplices (i.e., points, lines, triangles, tetrahedra, and their higher-dimensional analogs) that are connected or "glued" together.

During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturing and predicting the spatial-temporal anatomic changes from a sequence of patient-specific image series presents a considerable challenge. Thus, the development of a computational framework becomes highly desirable for a multitude of practical applications. In this context, we propose a strategy of Spatial-Temporal Neural Representation learning with Prior embedding (ST-NeRP) for patient-specific imaging study. Our strategy involves leveraging an Implicit Neural Representation (INR) network to encode the image at the reference time point into a prior embedding. Subsequently, a spatial-temporally continuous deformation function is learned through another INR network. This network is trained using the whole patient-specific image sequence, enabling the prediction of deformation fields at various target time points. The efficacy of the ST-NeRP model is demonstrated through its application to diverse sequential image series, including 4D CT and longitudinal CT datasets within thoracic and abdominal imaging. The proposed ST-NeRP model exhibits substantial potential in enabling the monitoring of anatomical changes within a patient throughout the therapeutic journey.

Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answering, and spatial reasoning. However, existing methods for mining and reasoning from GeoKGs, such as popular knowledge graph embedding (KGE) techniques, lack geographic awareness. This study aims to enhance general-purpose KGE by developing new strategies and integrating geometric features of spatial relations, including topology, direction, and distance, to infuse the embedding process with geographic intuition. The new model is tested on downstream link prediction tasks, and the results show that the inclusion of geometric features, particularly topology and direction, improves prediction accuracy for both geoentities and spatial relations. Our research offers new perspectives for integrating spatial concepts and principles into the GeoKG mining process, providing customized GeoAI solutions for geospatial challenges.

The assumptions of stationarity and isotropy often stated over spatial processes have not aged well during the last two decades, partly explained by the combination of computational developments and the increasing availability of high-resolution spatial data. While a plethora of approaches have been developed to relax these assumptions, it is often a costly tradeoff between flexibility and a diversity of computational challenges. In this paper, we present a class of covariance functions that relies on fixed, observable spatial information that provides a convenient tradeoff while offering an extra layer of numerical and visual representation of the flexible spatial dependencies. This model allows for separate parametric structures for different sources of nonstationarity, such as marginal standard deviation, geometric anisotropy, and smoothness. It simplifies to a Mat\'ern covariance function in its basic form and is adaptable for large datasets, enhancing flexibility and computational efficiency. We analyze the capabilities of the presented model through simulation studies and an application to Swiss precipitation data.

Spatial expressions in situated communication can be ambiguous, as their meanings vary depending on the frames of reference (FoR) adopted by speakers and listeners. While spatial language understanding and reasoning by vision-language models (VLMs) have gained increasing attention, potential ambiguities in these models are still under-explored. To address this issue, we present the COnsistent Multilingual Frame Of Reference Test (COMFORT), an evaluation protocol to systematically assess the spatial reasoning capabilities of VLMs. We evaluate nine state-of-the-art VLMs using COMFORT. Despite showing some alignment with English conventions in resolving ambiguities, our experiments reveal significant shortcomings of VLMs: notably, the models (1) exhibit poor robustness and consistency, (2) lack the flexibility to accommodate multiple FoRs, and (3) fail to adhere to languagespecific or culture-specific conventions in cross-lingual tests, as English tends to dominate other languages. With a growing effort to align vision-language models with human cognitive intuitions, we call for more attention to the ambiguous nature and cross-cultural diversity of spatial reasoning. The recent success of large language models has sparked breakthroughs in multi-modalities, leading to the development of many vision-language models (VLMs; Chen et al., 2023b; OpenAI, 2024; Reid et al., 2024, inter alia). With some benchmarks developed to evaluate the downstream performance of these models (Liu et al., 2023c; Yue et al., 2024), there has been growing excitement around evaluations and analyses inspired by human cognitive capabilities such as referential grounding (Ma et al., 2023a), compositional reasoning (Ma et al., 2023c), visual illusions (Zhang et al., 2023; Guan et al., 2024), and theory of mind (Jin et al., 2024). One direction among them that captures significant attention is spatial language understanding and reasoning, leading to several benchmarks (Kamath et al., 2023; Liu et al., 2023a) and enhanced models (Chen et al., 2024a; Cheng et al., 2024). Indeed, spatial cognition is a crucial part of human cognitive capability, developed since infancy and continuing through the elementary school years (Tommasi & Laeng, 2012; Vasilyeva & Lourenco, 2012). Language is closely intertwined with spatial cognition, with each contributing to the acquisition of the other (Hayward & Tarr, 1995; Regier & Carlson, 2001; Pyers et al., 2010; Pruden et al., 2011; Gentner et al., 2013). While spatial language and non-linguistic spatial representations in memory are closely correlated and share foundational properties, they are, to some extent, divergent-- spatial conventions are not consistently preserved across different languages or tasks, and humans demonstrate flexibility in using multiple coordinate systems for both non-linguistic reasoning and linguistic expressions (Munnich et al., 2001; Shusterman & Li, 2016).

In Continual Learning (CL) contexts, concept drift typically refers to the analysis of changes in data distribution. A drift in the input data can have negative consequences on a learning predictor and the system's stability. The majority of concept drift methods emphasize the analysis of statistical changes in non-stationary data over time. In this context, we consider another perspective, where the concept drift also integrates substantial changes in the topological characteristics of the data stream. In this article, we introduce a novel framework for monitoring changes in multi-dimensional data streams. We explore variations in the topological structures of the data, presenting another angle on the standard concept drift. Our developed approach is based on persistent entropy and topology-preserving projections in a continual learning scenario. The framework operates in both unsupervised and supervised environments. To show the utility of the proposed framework, we analyze the model across three scenarios using data streams generated with MNIST samples. The obtained results reveal the potential of applying topological data analysis for shift detection and encourage further research in this area.