Introduction to Novel Temporal

To address this challenge, this paper proposes a novel temporal attention convolutional network (TACNet) for MI classification.

In the TARM, a novel temporal attention mechanism is built based on residual learning to recalibrate frames of skeleton data temporally.

The role of EVs in trauma-induced coagulopathy and posttraumatic thrombosis should be studied bearing in mind this novel temporal pattern.

An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.

To efficiently tackle our problem, we first devise a novel temporal graph reduction algorithm to significantly reduce the temporal graph without losing any maximal ρ-stable (δ, ɣ)-quasi-clique.

In this paper, a novel temporal graph convolutional network (TGCN) for the representation learning network is proposed to effectively capture the graph-based spatiotemporal input features.

In this paper, we propose a novel temporal fusion and AU-supervised self-attention network (a socalled SAT-Net) to address the AU detection problem.

Additionally, a novel temporal fusion and prediction module is designed to fuse temporal information from the extracted spatial feature sequences and predict vehicle driving commands.

Furthermore, we address the output-feedback problem and show that a dynamic observer and controller can be designed based on our dual dynamic high gain scaling based design methodology along with a novel temporal transformation and form of the scaling dynamics with temporal forcing terms to achieve both state estimation and regulation in the prescribed time interval.

While prior results on prescribed-time stabilization considered systems in a normal form structure (chain of integrators with uncertainties matched with the control input), we address here a general strict-feedback-like system structure with uncertain nonlinear functions throughout the system dynamics and develop a prescribed-time stabilizing controller based on our dynamic high gain scaling technique along with a novel temporal transformation and scaling dynamics with temporal forcing terms.

In this paper we present a novel temporal clustering approach aimed at linking records of the same group (such as all births by the same mother) where temporal constraints (such as intervals between births) need to be enforced.

This is done by a novel temporal clustering algorithm, which measures motion similarity based on the curvature and torsion of a space curve formed by corresponding vertices along a series of animation frames.

In this work we propose a novel temporal pose-sequence modeling framework, which can embed the dynamics of 3D human-skeleton joints to a latent space in an efficient manner.

More specifically, we introduce a novel temporal pose convolution to aggregate spatial poses over frames.

Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins.

Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins.

To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network.

Then, the actions in hand trajectories are identified with a novel temporal action localization model.

In this paper, we propose a novel temporal feature extraction method, named Attentive Correlated Temporal Feature (ACTF), by exploring inter-frame correlation within a certain region.

In this paper, we propose a novel temporal feature extraction module, named Key Point Shifts Embedding Module ($KPSEM$), to adaptively extract channel-wise key point shifts across video frames without key point annotation for temporal feature extraction.