150,665 research outputs found
Substructure and Boundary Modeling for Continuous Action Recognition
This paper introduces a probabilistic graphical model for continuous action
recognition with two novel components: substructure transition model and
discriminative boundary model. The first component encodes the sparse and
global temporal transition prior between action primitives in state-space model
to handle the large spatial-temporal variations within an action class. The
second component enforces the action duration constraint in a discriminative
way to locate the transition boundaries between actions more accurately. The
two components are integrated into a unified graphical structure to enable
effective training and inference. Our comprehensive experimental results on
both public and in-house datasets show that, with the capability to incorporate
additional information that had not been explicitly or efficiently modeled by
previous methods, our proposed algorithm achieved significantly improved
performance for continuous action recognition.Comment: Detailed version of the CVPR 2012 paper. 15 pages, 6 figure
Towards spatio‐temporal data modeling of geo‐tagged shipping information
Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science
"Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.Spatio-temporal data models deal with capturing information characterized by both spatial and temporal semantics. In this paper we review current approaches for spatio-temporal data modelling and present out initial results for selecting the most relevant approach: Object-Oriented modeling for means of modeling geo-tagged shipping information. The shipping information is provided by the well-known LLLOYD’s lists dataset. We have introduced the case study and dataset characteristics used in the research project and presented our data model in Unified Modeling Language (UML). The model focuses on spatio-temporal events where characteristics are categorized as thematic, spatial and temporal attributes. The paper follows up with discussion on the selected approach and results, and finally ends with presenting the future outlook
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction
Online Social Networks (OSNs) evolve through two pervasive behaviors: follow
and unfollow, which respectively signify relationship creation and relationship
dissolution. Researches on social network evolution mainly focus on the follow
behavior, while the unfollow behavior has largely been ignored. Mining unfollow
behavior is challenging because user's decision on unfollow is not only
affected by the simple combination of user's attributes like informativeness
and reciprocity, but also affected by the complex interaction among them.
Meanwhile, prior datasets seldom contain sufficient records for inferring such
complex interaction. To address these issues, we first construct a large-scale
real-world Weibo dataset, which records detailed post content and relationship
dynamics of 1.8 million Chinese users. Next, we define user's attributes as two
categories: spatial attributes (e.g., social role of user) and temporal
attributes (e.g., post content of user). Leveraging the constructed dataset, we
systematically study how the interaction effects between user's spatial and
temporal attributes contribute to the unfollow behavior. Afterwards, we propose
a novel unified model with heterogeneous information (UMHI) for unfollow
prediction. Specifically, our UMHI model: 1) captures user's spatial attributes
through social network structure; 2) infers user's temporal attributes through
user-posted content and unfollow history; and 3) models the interaction between
spatial and temporal attributes by the nonlinear MLP layers. Comprehensive
evaluations on the constructed dataset demonstrate that the proposed UMHI model
outperforms baseline methods by 16.44% on average in terms of precision. In
addition, factor analyses verify that both spatial attributes and temporal
attributes are essential for mining unfollow behavior.Comment: 8 pages, 7 figures, Accepted by AAAI 202
Conceptual Modeling for Federated Geographical Information Systems over the Web
A conceptual model provides the best support for federating heterogeneous datastores into a unified framework and elaborating a global consistent description of all available data. It therefore plays a central role for exchange of information within the coming information society. This paper introduces a conceptual model for applications using spatio-temporal data. We discuss in particular the features which support spatial and temporal modeling. An example of conceptual design is given using a pedology application. Advantages of our approach for database design are assessed through comparison with traditional geographical information systems modeling techniques
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Spatio-temporal graph learning is a fundamental problem in the Web of Things
era, which enables a plethora of Web applications such as smart cities, human
mobility and climate analysis. Existing approaches tackle different learning
tasks independently, tailoring their models to unique task characteristics.
These methods, however, fall short of modeling intrinsic uncertainties in the
spatio-temporal data. Meanwhile, their specialized designs limit their
universality as general spatio-temporal learning solutions. In this paper, we
propose to model the learning tasks in a unified perspective, viewing them as
predictions based on conditional information with shared spatio-temporal
patterns. Based on this proposal, we introduce Unified Spatio-Temporal
Diffusion Models (USTD) to address the tasks uniformly within the
uncertainty-aware diffusion framework. USTD is holistically designed,
comprising a shared spatio-temporal encoder and attention-based denoising
networks that are task-specific. The shared encoder, optimized by a
pre-training strategy, effectively captures conditional spatio-temporal
patterns. The denoising networks, utilizing both cross- and self-attention,
integrate conditional dependencies and generate predictions. Opting for
forecasting and kriging as downstream tasks, we design Gated Attention (SGA)
and Temporal Gated Attention (TGA) for each task, with different emphases on
the spatial and temporal dimensions, respectively. By combining the advantages
of deterministic encoders and probabilistic diffusion models, USTD achieves
state-of-the-art performances compared to deterministic and probabilistic
baselines in both tasks, while also providing valuable uncertainty estimates
Toward a further understanding of object feature binding: a cognitive neuroscience perspective.
The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major
competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory.
The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on
binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature
analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more
difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in
object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information
processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be
able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued
that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem
Integrating Temporal Fluctuations in Crop Growth with Stacked Bidirectional LSTM and 3D CNN Fusion for Enhanced Crop Yield Prediction
Optimizing farming methods and guaranteeing a steady supply of food depend critically on accurate predictions of crop yields. The dynamic temporal changes that occur during crop growth are generally ignored by conventional crop growth models, resulting in less precise projections. Using a stacked bidirectional Long Short-Term Memory (LSTM) structure and a 3D Convolutional Neural Network (CNN) fusion, we offer a novel neural network model that accounts for temporal oscillations in the crop growth process. The 3D CNN efficiently recovers spatial and temporal features from the crop development data, while the bidirectional LSTM cells capture the sequential dependencies and allow the model to learn from both past and future temporal information. Our model's prediction accuracy is improved by combining the LSTM and 3D CNN layers at the top, which better captures temporal and spatial patterns. We also provide a novel label-related loss function that is optimized for agricultural yield forecasting. Because of the relevance of temporal oscillations in crop development and the dynamic character of crop growth, a new loss function has been developed. This loss function encourages our model to learn and take advantage of the temporal trends, which improves our ability to estimate crop yield. We perform comprehensive experiments on real-world crop growth datasets to verify the efficacy of our suggested approach. The outcomes prove that our unified strategy performs far better than both baseline crop growth prediction algorithms and cutting-edge applications of deep learning. Improved crop yield prediction accuracy is achieved with the integration of temporal variations via the merging of bidirectional LSTM and 3D CNN and a unique loss function. This study helps move the science of estimating crop yields forward, which is important for informing agricultural policy and ensuring a steady supply of food
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