427 research outputs found
The Development of Data Mining
Abstract Mining is the current hot spots, the most promising research areas has broad one, through data mining research status, algorithms and applications of analysis to explore data mining problems and trends, which is the development of data mining has certain reference value
InfoFlowNet: A Multi-head Attention-based Self-supervised Learning Model with Surrogate Approach for Uncovering Brain Effective Connectivity
Deciphering brain network topology can enhance the depth of neuroscientific
knowledge and facilitate the development of neural engineering methods.
Effective connectivity, a measure of brain network dynamics, is particularly
useful for investigating the directional influences among different brain
regions. In this study, we introduce a novel brain causal inference model named
InfoFlowNet, which leverages the self-attention mechanism to capture
associations among electroencephalogram (EEG) time series. The proposed method
estimates the magnitude of directional information flow (dIF) among EEG
processes by measuring the loss of model inference resulting from the shuffling
of the time order of the original time series. To evaluate the feasibility of
InfoFlowNet, we conducted experiments using a synthetic time series and two EEG
datasets. The results demonstrate that InfoFlowNet can extract time-varying
causal relationships among processes, reflected in the fluctuation of dIF
values. Compared with the Granger causality model and temporal causal discovery
framework, InfoFlowNet can identify more significant causal edges underlying
EEG processes while maintaining an acceptable computation time. Our work
demonstrates the potential of InfoFlowNet for analyzing effective connectivity
in EEG data. The findings highlight the importance of effective connectivity in
understanding the complex dynamics of the brain network
Iteration Method for Predicting Essential Proteins Based on Orthology and Protein-protein Interaction Networks
Background: Identification of essential proteins plays a significant role in understanding minimal requirements for the cellular survival and development. Many computational methods have been proposed for predicting essential proteins by using the topological features of protein-protein interaction (PPI) networks. However, most of these methods ignored intrinsic biological meaning of proteins. Moreover, PPI data contains many false positives and false negatives. To overcome these limitations, recently many research groups have started to focus on identification of essential proteins by integrating PPI networks with other biological information. However, none of their methods has widely been acknowledged.
Results: By considering the facts that essential proteins are more evolutionarily conserved than nonessential proteins and essential proteins frequently bind each other, we propose an iteration method for predicting essential proteins by integrating the orthology with PPI networks, named by ION. Differently from other methods, ION identifies essential proteins depending on not only the connections between proteins but also their orthologous properties and features of their neighbors. ION is implemented to predict essential proteins in S. cerevisiae. Experimental results show that ION can achieve higher identification accuracy than eight other existing centrality methods in terms of area under the curve (AUC). Moreover, ION identifies a large amount of essential proteins which have been ignored by eight other existing centrality methods because of their low-connectivity. Many proteins ranked in top 100 by ION are both essential and belong to the complexes with certain biological functions. Furthermore, no matter how many reference organisms were selected, ION outperforms all eight other existing centrality methods. While using as many as possible reference organisms can improve the performance of ION. Additionally, ION also shows good prediction performance in E. coli K-12.
Conclusions: The accuracy of predicting essential proteins can be improved by integrating the orthology with PPI networks
FAF: A novel multimodal emotion recognition approach integrating face, body and text
Multimodal emotion analysis performed better in emotion recognition depending
on more comprehensive emotional clues and multimodal emotion dataset. In this
paper, we developed a large multimodal emotion dataset, named "HED" dataset, to
facilitate the emotion recognition task, and accordingly propose a multimodal
emotion recognition method. To promote recognition accuracy, "Feature After
Feature" framework was used to explore crucial emotional information from the
aligned face, body and text samples. We employ various benchmarks to evaluate
the "HED" dataset and compare the performance with our method. The results show
that the five classification accuracy of the proposed multimodal fusion method
is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62%
respectively compared with that of individual modalities. The complementarity
between each channel is effectively used to improve the performance of emotion
recognition. We had also established a multimodal online emotion prediction
platform, aiming to provide free emotion prediction to more users
Exploiting Data and Human Knowledge for Predicting Wildlife Poaching
Poaching continues to be a significant threat to the conservation of wildlife
and the associated ecosystem. Estimating and predicting where the poachers have
committed or would commit crimes is essential to more effective allocation of
patrolling resources. The real-world data in this domain is often sparse, noisy
and incomplete, consisting of a small number of positive data (poaching signs),
a large number of negative data with label uncertainty, and an even larger
number of unlabeled data. Fortunately, domain experts such as rangers can
provide complementary information about poaching activity patterns. However,
this kind of human knowledge has rarely been used in previous approaches. In
this paper, we contribute new solutions to the predictive analysis of poaching
patterns by exploiting both very limited data and human knowledge. We propose
an approach to elicit quantitative information from domain experts through a
questionnaire built upon a clustering-based division of the conservation area.
In addition, we propose algorithms that exploit qualitative and quantitative
information provided by the domain experts to augment the dataset and improve
learning. In collaboration with World Wild Fund for Nature, we show that
incorporating human knowledge leads to better predictions in a conservation
area in Northeastern China where the charismatic species is Siberian Tiger. The
results show the importance of exploiting human knowledge when learning from
limited data.Comment: COMPASS 201
UATVR: Uncertainty-Adaptive Text-Video Retrieval
With the explosive growth of web videos and emerging large-scale
vision-language pre-training models, e.g., CLIP, retrieving videos of interest
with text instructions has attracted increasing attention. A common practice is
to transfer text-video pairs to the same embedding space and craft cross-modal
interactions with certain entities in specific granularities for semantic
correspondence. Unfortunately, the intrinsic uncertainties of optimal entity
combinations in appropriate granularities for cross-modal queries are
understudied, which is especially critical for modalities with hierarchical
semantics, e.g., video, text, etc. In this paper, we propose an
Uncertainty-Adaptive Text-Video Retrieval approach, termed UATVR, which models
each look-up as a distribution matching procedure. Concretely, we add
additional learnable tokens in the encoders to adaptively aggregate
multi-grained semantics for flexible high-level reasoning. In the refined
embedding space, we represent text-video pairs as probabilistic distributions
where prototypes are sampled for matching evaluation. Comprehensive experiments
on four benchmarks justify the superiority of our UATVR, which achieves new
state-of-the-art results on MSR-VTT (50.8%), VATEX (64.5%), MSVD (49.7%), and
DiDeMo (45.8%). The code is available at https://github.com/bofang98/UATVR.Comment: To appear at ICCV202
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