189 research outputs found
Representation Learning for Scale-free Networks
Network embedding aims to learn the low-dimensional representations of
vertexes in a network, while structure and inherent properties of the network
is preserved. Existing network embedding works primarily focus on preserving
the microscopic structure, such as the first- and second-order proximity of
vertexes, while the macroscopic scale-free property is largely ignored.
Scale-free property depicts the fact that vertex degrees follow a heavy-tailed
distribution (i.e., only a few vertexes have high degrees) and is a critical
property of real-world networks, such as social networks. In this paper, we
study the problem of learning representations for scale-free networks. We first
theoretically analyze the difficulty of embedding and reconstructing a
scale-free network in the Euclidean space, by converting our problem to the
sphere packing problem. Then, we propose the "degree penalty" principle for
designing scale-free property preserving network embedding algorithm: punishing
the proximity between high-degree vertexes. We introduce two implementations of
our principle by utilizing the spectral techniques and a skip-gram model
respectively. Extensive experiments on six datasets show that our algorithms
are able to not only reconstruct heavy-tailed distributed degree distribution,
but also outperform state-of-the-art embedding models in various network mining
tasks, such as vertex classification and link prediction.Comment: 8 figures; accepted by AAAI 201
Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai
Unprecedented human mobility has driven the rapid urbanization around the
world. In China, the fraction of population dwelling in cities increased from
17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses
challenges for policymakers and important questions for researchers. To
investigate the process of migrant integration, we employ a one-month complete
dataset of telecommunication metadata in Shanghai with 54 million users and 698
million call logs. We find systematic differences between locals and migrants
in their mobile communication networks and geographical locations. For
instance, migrants have more diverse contacts and move around the city with a
larger radius than locals after they settle down. By distinguishing new
migrants (who recently moved to Shanghai) from settled migrants (who have been
in Shanghai for a while), we demonstrate the integration process of new
migrants in their first three weeks. Moreover, we formulate classification
problems to predict whether a person is a migrant. Our classifier is able to
achieve an F1-score of 0.82 when distinguishing settled migrants from locals,
but it remains challenging to identify new migrants because of class imbalance.
This classification setup holds promise for identifying new migrants who will
successfully integrate into locals (new migrants that misclassified as locals).Comment: A modified version. The paper was accepted by AAAI 201
The Global Convergence of a New Mixed Conjugate Gradient Method for Unconstrained Optimization
We propose and generalize a new nonlinear conjugate gradient method for unconstrained optimization. The global convergence is proved with the Wolfe line
search. Numerical experiments are reported which support the theoretical analyses and show the presented methods outperforming CGDESCENT method
ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video Object Segmentation
The Associating Objects with Transformers (AOT) framework has exhibited
exceptional performance in a wide range of complex scenarios for video object
segmentation. In this study, we introduce MSDeAOT, a variant of the AOT series
that incorporates transformers at multiple feature scales. Leveraging the
hierarchical Gated Propagation Module (GPM), MSDeAOT efficiently propagates
object masks from previous frames to the current frame using a feature scale
with a stride of 16. Additionally, we employ GPM in a more refined feature
scale with a stride of 8, leading to improved accuracy in detecting and
tracking small objects. Through the implementation of test-time augmentations
and model ensemble techniques, we achieve the top-ranking position in the
EPIC-KITCHEN VISOR Semi-supervised Video Object Segmentation Challenge.Comment: Top 1 solution for EPIC-KITCHEN Challenge 2023: Semi-Supervised Video
Object Segmentatio
ZJU ReLER Submission for EPIC-KITCHEN Challenge 2023: TREK-150 Single Object Tracking
The Associating Objects with Transformers (AOT) framework has exhibited
exceptional performance in a wide range of complex scenarios for video object
tracking and segmentation. In this study, we convert the bounding boxes to
masks in reference frames with the help of the Segment Anything Model (SAM) and
Alpha-Refine, and then propagate the masks to the current frame, transforming
the task from Video Object Tracking (VOT) to video object segmentation (VOS).
Furthermore, we introduce MSDeAOT, a variant of the AOT series that
incorporates transformers at multiple feature scales. MSDeAOT efficiently
propagates object masks from previous frames to the current frame using two
feature scales of 16 and 8. As a testament to the effectiveness of our design,
we achieved the 1st place in the EPIC-KITCHENS TREK-150 Object Tracking
Challenge.Comment: Top 1 solution for EPIC-KITCHEN Challenge 2023: TREK-150 Single
Object Tracking. arXiv admin note: text overlap with arXiv:2307.0201
Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets
Time series modeling has attracted extensive research efforts; however,
achieving both reliable efficiency and interpretability from a unified model
still remains a challenging problem. Among the literature, shapelets offer
interpretable and explanatory insights in the classification tasks, while most
existing works ignore the differing representative power at different time
slices, as well as (more importantly) the evolution pattern of shapelets. In
this paper, we propose to extract time-aware shapelets by designing a two-level
timing factor. Moreover, we define and construct the shapelet evolution graph,
which captures how shapelets evolve over time and can be incorporated into the
time series embeddings by graph embedding algorithms. To validate whether the
representations obtained in this way can be applied effectively in various
scenarios, we conduct experiments based on three public time series datasets,
and two real-world datasets from different domains. Experimental results
clearly show the improvements achieved by our approach compared with 17
state-of-the-art baselines.Comment: An extended version with 11 pages including appendix; Accepted by
AAAI'202
Explore Synergistic Interaction Across Frames for Interactive Video Object Segmentation
Interactive Video Object Segmentation (iVOS) is a challenging task that
requires real-time human-computer interaction. To improve the user experience,
it is important to consider the user's input habits, segmentation quality,
running time and memory consumption.However, existing methods compromise user
experience with single input mode and slow running speed. Specifically, these
methods only allow the user to interact with one single frame, which limits the
expression of the user's intent.To overcome these limitations and better align
with people's usage habits, we propose a framework that can accept multiple
frames simultaneously and explore synergistic interaction across frames (SIAF).
Concretely, we designed the Across-Frame Interaction Module that enables users
to annotate different objects freely on multiple frames. The AFI module will
migrate scribble information among multiple interactive frames and generate
multi-frame masks. Additionally, we employ the id-queried mechanism to process
multiple objects in batches. Furthermore, for a more efficient propagation and
lightweight model, we design a truncated re-propagation strategy to replace the
previous multi-round fusion module, which employs an across-round memory that
stores important interaction information. Our SwinB-SIAF achieves new
state-of-the-art performance on DAVIS 2017 (89.6%, J&F@60). Moreover, our
R50-SIAF is more than 3 faster than the state-of-the-art competitor under
challenging multi-object scenarios
A Distributed Solution for Efficient K Shortest Paths Computation over Dynamic Road Networks
The problem of identifying the k-shortest paths KSPs for short in a dynamic
road network is essential to many location-based services. Road networks are
dynamic in the sense that the weights of the edges in the corresponding graph
constantly change over time, representing evolving traffic conditions. Very
often such services have to process numerous KSP queries over large road
networks at the same time, thus there is a pressing need to identify
distributed solutions for this problem. However, most existing approaches are
designed to identify KSPs on a static graph in a sequential manner, restricting
their scalability and applicability in a distributed setting. We therefore
propose KSP-DG, a distributed algorithm for identifying k-shortest paths in a
dynamic graph. It is based on partitioning the entire graph into smaller
subgraphs, and reduces the problem of determining KSPs into the computation of
partial KSPs in relevant subgraphs, which can execute in parallel on a cluster
of servers. A distributed two-level index called DTLP is developed to
facilitate the efficient identification of relevant subgraphs. A salient
feature of DTLP is that it indexes a set of virtual paths that are insensitive
to varying traffic conditions in an efficient and compact fashion, leading to
very low maintenance cost in dynamic road networks. This is the first treatment
of the problem of processing KSP queries over dynamic road networks. Extensive
experiments conducted on real road networks confirm the superiority of our
proposal over baseline methods.Comment: A shorter version of this technical report has been accepted for
publication as a regular paper in TKDE. arXiv admin note: substantial text
overlap with arXiv:2004.0258
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