Clustering Frequent Navigation Patterns from Website Logs by Using Ontology and Temporal Information

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

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Probabilistic transformation of temporal information at individual synapses

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439) ◽

10.1109/iembs.2003.1279798 ◽

2004 ◽

Author(s):

Z. Wang ◽

X. Xie ◽

D. Song ◽

T.W. Berger

Keyword(s):

Temporal Information

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Temporal Brokering: A Measure of Brokerage as a Behavioral Process

Organizational Research Methods ◽

10.1177/10944281211002911 ◽

2021 ◽

pp. 109442812110029

Author(s):

Eric Quintane ◽

Martin Wood ◽

John Dunn ◽

Lucia Falzon

Keyword(s):

Temporal Dynamics ◽

Predictive Ability ◽

Temporal Information ◽

Behavioral Measure ◽

Organizational Networks ◽

Message Boards ◽

Behavioral Process ◽

Systematic Examination ◽

The Way

Extant research in organizational networks has provided critical insights into understanding the benefits of occupying a brokerage position. More recently, researchers have moved beyond the brokerage position to consider the brokering processes (arbitration and collaboration) brokers engage in and their implications for performance. However, brokering processes are typically measured using scales that reflect individuals’ orientation toward engaging in a behavior, rather than the behavior itself. In this article, we propose a measure that captures the behavioral process of brokering. The measure indicates the extent to which actors engage in arbitration versus collaboration based on sequences of time stamped relational events, such as emails, message boards, and recordings of meetings. We demonstrate the validity of our measure as well as its predictive ability. By leveraging the temporal information inherent in sequences of relational events, our behavioral measure of brokering creates opportunities for researchers to explore the dynamics of brokerage and their impact on individuals, and also paves the way for a systematic examination of the temporal dynamics of networks.

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Dissociating Representations of Time and Number in Reinforcement-Rate Learning by Deletion of the GluA1 AMPA Receptor Subunit in Mice

Psychological Science ◽

10.1177/0956797620960392 ◽

2021 ◽

Vol 32 (2) ◽

pp. 204-217

Author(s):

Joseph M. Austen ◽

Corran Pickering ◽

Rolf Sprengel ◽

David J. Sanderson

Keyword(s):

Associative Strength ◽

Ampa Receptor ◽

Reinforcement Rate ◽

Statistical Properties ◽

Temporal Information ◽

Receptor Subunit ◽

Male And Female ◽

Female Mice ◽

Ampa Receptor Subunit ◽

Numeric Information

Theories of learning differ in whether they assume that learning reflects the strength of an association between memories or symbolic encoding of the statistical properties of events. We provide novel evidence for symbolic encoding of informational variables by demonstrating that sensitivity to time and number in learning is dissociable. Whereas responding in normal mice was dependent on reinforcement rate, responding in mice that lacked the GluA1 AMPA receptor subunit was insensitive to reinforcement rate and, instead, dependent on the number of times a cue had been paired with reinforcement. This suggests that GluA1 is necessary for weighting numeric information by temporal information in order to calculate reinforcement rate. Sample sizes per genotype varied between seven and 23 across six experiments and consisted of both male and female mice. The results provide evidence for explicit encoding of variables by animals rather than implicit encoding via variations in associative strength.

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