Introduction to Novel Temporal Adaptive

What is/are Novel Temporal?

Novel Temporal - In this paper we propose a novel temporal convolutional network (TCN) that achieves high accuracy while still being feasible for wearable platform use. ^[1] Our results provide important evidence that the exposure duration to warming may determine the impact of a pesticide under warming, thereby identifying a novel temporal aspect of stressor interactions in risk assessment. ^[2] 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. ^[3] Finally, a novel temporal correction is proposed, improving the numerical stability of the method at high density ratio. ^[4] In this research, we present a novel temporal model that allows (1) staged deployment of CCS infrastructure and (2) model parameters to change over time. ^[5] 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. ^[6] This paper proposes a novel temporal vectorization scheme for stencils. ^[7] Meanwhile, we also propose a novel temporal and spatial dual-discriminator for more robust network optimization. ^[8] A novel temporal pulse compression technique, combining stimulated Raman scattering (SRS) and laser induced breakdown (LIB) principles was presented in this work, which employing an energy controllable dual-cell setup. ^[9] In this paper, a novel Temporal-Difference Spatial Sampling and Aggregating graph neural network (TDSSA) is proposed to model spatial-temporal dependencies. ^[10] Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. ^[11] 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. ^[12] In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation (TMER), which utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations. ^[13] To this end, we developed a novel temporal conditioning paradigm utilizing repeated electric shocks. ^[14] In this work, we focused on the 18F-FDG Patlak model, and proposed a data-driven approach which can estimate the motion corrected full-dose direct Patlak images from the dynamic PET reconstruction series, based on a proposed novel temporal non-local convolutional neural network. ^[15] To address these issues, in this paper, a MI-EEG decoding framework is proposed, which uses a novel temporal-spectral-based squeeze-and-excitation feature fusion network (TS-SEFFNet). ^[16] In this work, we propose a novel Temporal Matrices Mapping (TMM) based calibration algorithm for event-driven structured light systems. ^[17] It contributes to remarkable enhancement of both electrical (by 100%) and optical output (by 30%), as well as novel temporal-spatial resolution mode for motion capturing. ^[18] To address these issues, this paper presents a novel Temporal-Structural User Representation (named TSUR) network to predict LTV. ^[19] Different from prevenient GCN, we devise a prototypical layer comprising a distance-based cross-entropy (DCE) loss function and a novel temporal entropy-based regularizer (TER) in the frameworks of SSGPN. ^[20] Finally, considering that the difference of intensity of visual stimulus between two eyes results in binocular rivalry, a novel temporal binocular rivalry inspired weighting method is designed to integrate the quality scores of left- and right-views for the final visual quality prediction of SV sequences, which is denoted as the second-stage weighting. ^[21] The role of EVs in trauma-induced coagulopathy and posttraumatic thrombosis should be studied bearing in mind this novel temporal pattern. ^[22] This paper investigates the time lag effect of carbon emissions using a novel temporal input-output approach (TIO). ^[23] In the present study, a novel temporal three-dimensional particle tracking velocimetry (3D PTV) algorithm for flow measurements with only two views is developed and validated with synthetic particles. ^[24] Then, we propose a novel temporal message propagation network to extract the graph feature from the normalized graph, and combine the graph feature with designed expert patterns to yield a final detection system. ^[25] This paper proposes a novel Temporal-Free Semantic-Guided attention mechanism (TFSG) to utilize the raw caption pre-generated by a primary decoder as the extra input to provide global semantic guidance during generation, deepening visual understanding by balancing the semantic and visual information. ^[26] In this paper we introduce an approach to learn temporally consistent features for textured reconstruction of clothed 3D human sequences from monocular video by proposing two advances: a novel temporal consistency loss function; and hybrid representation learning for implicit 3D reconstruction from 2D images and coarse 3D geometry. ^[27] In order to further speed up unsteady simulations and exploit the concurrency in temporal dimension, we propose a novel temporal parallelization strategy, i. ^[28] In order to fill the gap of obtaining multitype node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). ^[29] 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. ^[30] Our study leverages this observation, proposing a novel temporal deep learning model for head pose estimation from point cloud. ^[31] We implemented a novel temporal phenotyping algorithm pipeline to derive lines of therapy (LOT) and disease progression in CLL patients. ^[32] A novel temporal tree structure is designed to extract temporal information during the calculation of Hierarchical Softmax. ^[33] Besides, taking long-term temporal consistency into account, we define a novel temporal encoder based on a dilated convolutional network. ^[34] To ensure non-redundant data processing of deep network on a compact motion profile further, a novel temporal-shift memory (TSM) model is developed to perform deep learning of sequential input in linear processing time. ^[35] This study aimed to develop a novel temporal processing-based auditory training program for the senior users of hearing aids. ^[36] By developing a laboratory apparatus and culture media, new objects of investigation could be made to live outside the organism and be brought to behave in novel temporal ways. ^[37] We propose that understanding of time-of-day dependent vulnerability to MR signalling in the heart versus the kidney may offer the rationale for the development of novel temporal or tissue specific-MR modulators in the management of cardiovascular disease. ^[38] 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. ^[39] 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. ^[40] Therefore, in this paper, we introduce a novel temporal propagation-based fake news detection framework, which could fuse structure, content semantics, and temporal information. ^[41] We have selected the best architecture for the task and proposed a novel temporal CNN1D2D architecture that achieves overall better performance on the dataset than any referenced method. ^[42] To solve these problems, we propose a novel temporal group sparsity regression and additive model (T-GSRAM) to identify associations between longitudinal imaging QTs and SNPs for detection of potential AD biomarkers. ^[43] We present a novel temporally coherent stylized rendering technique working entirely at the compositing stage. ^[44] Furthermore, we propose a novel temporal filter that considers temporal continuity and combines it with SMCN to get our second algorithm SMCNTF. ^[45] Specifically, we present a novel temporal modeling paradigm in TIM by exploiting the temporal difference over adjacent frames along with both horizontal and vertical directions. ^[46] We used spatial variation and a novel temporal comparison to investigate the relationship between CWD and a mutation at codon 225 of the mule deer prion protein gene that slows disease progression. ^[47] To cope with this problem, a soft sensor modeling strategy of the BF wall temperature field based on a novel temporal–spatial dimensional finite-element extrapolation algorithm (TS-FEEA) is designed. ^[48] Finally, a novel temporal motif-aware community detection method is devised to find account communities over the defined high-order proximity. ^[49] We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events—events that are not mentioned explicitly in natural language text but can be inferred from it. ^[50]

novel temporal attention

To address this challenge, this paper proposes a novel temporal attention convolutional network (TACNet) for MI classification. ^[1] In the TARM, a novel temporal attention mechanism is built based on residual learning to recalibrate frames of skeleton data temporally. ^[2] We propose a novel temporal attention based neural network architecture for robotics tasks that involve fusion of time series of sensor data, and evaluate the performance improvements in the context of autonomous navigation of unmanned ground vehicles (UGVs) in uncertain environments. ^[3]

novel temporal pattern

The role of EVs in trauma-induced coagulopathy and posttraumatic thrombosis should be studied bearing in mind this novel temporal pattern. ^[1] An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters. ^[2] CONCLUSIONS In normal fetal lungs, we report a novel temporal pattern of varied morphometric and metabolic changes. ^[3]

novel temporal graph

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. ^[1] 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. ^[2] Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. ^[3]

novel temporal fusion

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. ^[1] 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. ^[2] In this paper, we propose a novel temporal fusion (TF) module to fuse the two-stream joints’ information to predict human motion, including a temporal concatenation and a reinforcement trajectory spatial-temporal (TST) block, specifically designed to keep prediction temporal consistency. ^[3]

novel temporal transformation

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. ^[1] 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. ^[2]

novel temporal clustering

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. ^[1] 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. ^[2]

novel temporal pose

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. ^[1] More specifically, we introduce a novel temporal pose convolution to aggregate spatial poses over frames. ^[2]

novel temporal adaptive

Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. ^[1] Finally, we develop a novel Temporal Adaptive Ngram (TA-Ngram) model in CSCVP to capture the dynamic and non-deterministic dependency between check-ins. ^[2]

novel temporal action

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. ^[1] Then, the actions in hand trajectories are identified with a novel temporal action localization model. ^[2]

novel temporal feature

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. ^[1] 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. ^[2]