Search Results

Intelligent Transportation Systems (ITS) are essential for modern urban development, with urban flow prediction being a key component. Accurate flow prediction optimizes routes and resource allocation, benefiting residents, businesses, and the ...

• Mutual reinforcement with multi-scale regional information for urban flow prediction.
• Proposed multi-scale region classification with manual annotations for spatial feature extraction.
• Adaptive environment fusion and real-time ...

The complexity and unpredictability of disaster areas pose significant challenges to post-disaster rescue operations. UAVs, with their flexibility and rapid response capability, are able to perform various tasks and provide critical field data. ...

Repetitive positive and negative sequential patterns (PNSPs) recognize the repetitive characteristics of positive (occurring) and negative (nonoccurring) sequential patterns, thereby providing more comprehensive information than traditional ...

In this paper, we investigate the use of diffusion models which are pre-trained on large-scale image-caption pairs for open-vocabulary 3D semantic understanding. We propose a novel method, namely Diff2Scene, which leverages frozen representations ...$\mathrm{}$

Knowledge Base Question Generation (KBQG) aims to generate natural language questions according to a subgraph and the target answer(s). Existing methods consume significant computational resources for pre-training or fine-tuning multiple models, ...

In the field of computer-aided robotics, trajectory scheduling is crucial for multi-robot systems operating in complex, dynamic, and different kinds of environments. This paper introduces a novel trajectory optimization algorithm that integrates the Long ...

We introduce VideoPrism, a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. We pretrain VideoPrism on a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with ...

Knowledge Graphs (KG) is a type of knowledge base organized in the framework of semantic network, which has directed graph structures. As a basic task performed with KGs, the aim of Knowledge Graph Question Answering (KGQA) is to retrieval from a KG the ...

Omics data is characterized by high-dimensional small samples and contains a great number of gene features unrelated to diseases. Pan-cancer exhibits heterogeneity and can be subdivided into different subtypes. Identifying disease subtypes is pivotal for ...

As a new form of data management, the global discrete grid can describe and exchange geographic information in a standardized way on a global scale, which can be used for efficient storage and application of large-scale global spatial data, and it ...

Negative sequential patterns (NSP) focus on non-occurring events and play a role that cannot be replaced by positive sequential patterns (PSP). Considering the repetitive occurrence of sequential patterns in a sequence, repetitive NSP (RNSP) ...

We are concerned with a worst-case scenario in model generalization, in the sense that a model aims to perform well on many unseen domains while there is only one single domain available for training. We propose Meta-Learning based Adversarial Domain ...

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in ...

Incomplete multi-view clustering has attracted much attention due to its ability to handle partial multi-view data. Recently, similarity-based methods have been developed to explore the complete relationship among incomplete multiview data. Although ...

There are hundreds of millions of natural proteins in nature, and these natural proteins can play different roles due to their special structure. The first field of AI for Science is the prediction and design of protein structure, and simulating the ...

Urban flow prediction is critical for urban planning, management, and safety. However, owing to the inherent instability of urban flows, prediction accuracy requires the fusion of multi-view influencing factors. Current prediction methods are ...

• Multi-view fusion based on contrastive learning is used for urban flow prediction.
• Effective sampling methods are proposed for spatio-temporal contrastive learning.
• Appropriate multi-view extraction and fusion prediction networks ...

Based on the research of traditional wireless sensor network localization algorithms, the limitations when relying only on the wireless sensor networks (WSN) for localization in three-dimensional (3D) environments are analyzed. By fusing data from WSN ...

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training ...

Display Omitted

• A model is proposed to solve the stability problem of molecular graph generation.
• The penalty mechanism designed for the discriminator can resolve model collapse.
• A batch normalized parallel layer is designed for the generator.

Abstaining models have been widely used in safety-critical fields to avoid uncertain classification and reduce misclassification costs. Previous abstaining models have two main disadvantages. First, the costs of classification and rejection need ...

To address the issues of restricted computing resources and significant latency experienced by mobile devices in mobile edge computing (MEC), we introduce an approach based on an enhanced genetic algorithm. This method optimizes task offloading and ...