174 research outputs found
Multi-Modal Machine Learning for Assessing Gaming Skills in Online Streaming: A Case Study with CS:GO
Online streaming is an emerging market that address much attention. Assessing
gaming skills from videos is an important task for streaming service providers
to discover talented gamers. Service providers require the information to offer
customized recommendation and service promotion to their customers. Meanwhile,
this is also an important multi-modal machine learning tasks since online
streaming combines vision, audio and text modalities. In this study we begin by
identifying flaws in the dataset and proceed to clean it manually. Then we
propose several variants of latest end-to-end models to learn joint
representation of multiple modalities. Through our extensive experimentation,
we demonstrate the efficacy of our proposals. Moreover, we identify that our
proposed models is prone to identifying users instead of learning meaningful
representations. We purpose future work to address the issue in the end
Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments
Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process
Graphical Representations and Worm Algorithms for the O() Spin Model
We present a family of graphical representations for the O() spin model,
where represents the spin dimension, and corresponds to the
Ising, XY and Heisenberg models, respectively. With an integer parameter , each configuration is the coupling of copies of subgraphs
consisting of directed flows and copies of subgraphs constructed by
undirected loops, which we call the XY and Ising subgraphs, respectively. On
each lattice site, the XY subgraphs satisfy the Kirchhoff flow-conservation law
and the Ising subgraphs obey the Eulerian bond condition. Then, we formulate
worm-type algorithms and simulate the O() model on the simple-cubic lattice
for from 2 to 6 at all possible . It is observed that the worm
algorithm has much higher efficiency than the Metropolis method, and, for a
given , the efficiency is an increasing function of . Beside Monte
Carlo simulations, we expect that these graphical representations would provide
a convenient basis for the study of the O() spin model by other
state-of-the-art methods like the tensor network renormalization.Comment: 10 pages, 6 figure
Consistency Regularization for Generalizable Source-free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a well-trained source
model to an unlabelled target domain without accessing the source dataset,
making it applicable in a variety of real-world scenarios. Existing SFDA
methods ONLY assess their adapted models on the target training set, neglecting
the data from unseen but identically distributed testing sets. This oversight
leads to overfitting issues and constrains the model's generalization ability.
In this paper, we propose a consistency regularization framework to develop a
more generalizable SFDA method, which simultaneously boosts model performance
on both target training and testing datasets. Our method leverages soft
pseudo-labels generated from weakly augmented images to supervise strongly
augmented images, facilitating the model training process and enhancing the
generalization ability of the adapted model. To leverage more potentially
useful supervision, we present a sampling-based pseudo-label selection
strategy, taking samples with severer domain shift into consideration.
Moreover, global-oriented calibration methods are introduced to exploit global
class distribution and feature cluster information, further improving the
adaptation process. Extensive experiments demonstrate our method achieves
state-of-the-art performance on several SFDA benchmarks, and exhibits
robustness on unseen testing datasets.Comment: Accepted by ICCV 2023 worksho
Evidences for interaction-induced Haldane fractional exclusion statistics in one and higher dimensions
Haldane fractional exclusion statistics (FES) has a long history of intense
studies, but its realization in physical systems is rare. Here we study
repulsively interacting Bose gases at and near a quantum critical point, and
find evidences that such strongly correlated gases obey simple non-mutual FES
over a wide range of interaction strengths in both one and two dimensions.
Based on exact solutions in one dimension, quantum Monte Carlo simulations and
experiments in both dimensions, we show that the thermodynamic properties of
these interacting gases, including entropy per particle, density and pressure,
are essentially equivalent to those of non-interacting particles with FES.
Accordingly, we establish a simple interaction-to-FES mapping that reveals the
statistical nature of particle-hole symmetry breaking induced by interaction in
such quantum many-body systems. Whereas strongly interacting Bose gases reach
full fermionization in one dimension, they exhibit incomplete fermionization in
two dimensions. Our results open a route to understanding correlated
interacting systems via non-interacting particles with FES in arbitrary
dimensions.Comment: There are 4 figures in the main text as well as a supplemental
materia
Temporal Knowledge Graph Completion: A Survey
Knowledge graph completion (KGC) can predict missing links and is crucial for
real-world knowledge graphs, which widely suffer from incompleteness. KGC
methods assume a knowledge graph is static, but that may lead to inaccurate
prediction results because many facts in the knowledge graphs change over time.
Recently, emerging methods have shown improved predictive results by further
incorporating the timestamps of facts; namely, temporal knowledge graph
completion (TKGC). With this temporal information, TKGC methods can learn the
dynamic evolution of the knowledge graph that KGC methods fail to capture. In
this paper, for the first time, we summarize the recent advances in TKGC
research. First, we detail the background of TKGC, including the problem
definition, benchmark datasets, and evaluation metrics. Then, we summarize
existing TKGC methods based on how timestamps of facts are used to capture the
temporal dynamics. Finally, we conclude the paper and present future research
directions of TKGC
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