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Found 28 papers, 13 papers with code
A network model for cellular aging
Our model predicts that the rate of aging, defined by the Gompertz coefficient, is approximately proportional to the average number of active interactions per gene and that the stochastic heterogeneity of gene interactions is an important factor in the dynamics of the aging process.
Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph
The redundant features existing in high dimensional datasets always affect the performance of learning and mining algorithms.
Preference-based performance measures for Time-Domain Global Similarity method
To obtain the general expressions of performance measures based on the preferences of tasks, the mapping relations between performance of TDGS method about physical similarity and correctness of data sequences are investigated by probability theory in this paper.
Improvement of training set structure in fusion data cleaning using Time-Domain Global Similarity method
Traditional data cleaning identifies dirty data by classifying original data sequences, which is a class$-$imbalanced problem since the proportion of incorrect data is much less than the proportion of correct ones for most diagnostic systems in Magnetic Confinement Fusion (MCF) devices.
Nationality Classification Using Name Embeddings
Through our analysis of 57M contact lists from a major Internet company, we are able to design a fine-grained nationality classifier covering 39 groups representing over 90% of the world population.
Machine learning and serving of discrete field theories -- when artificial intelligence meets the discrete universe
A method for machine learning and serving of discrete field theories in physics is developed.
Pointfilter: Point Cloud Filtering via Encoder-Decoder Modeling
In this paper, we propose a novel deep learning approach that automatically and robustly filters point clouds with removing noise and preserving sharp features and geometric details.
Graphics
Explicit Structure-Preserving Geometric Particle-in-Cell Algorithm in Curvilinear Orthogonal Coordinate Systems and Its Applications to Whole-Device 6D Kinetic Simulations of Tokamak Physics
The code has been applied to carry out whole-device 6D kinetic simulation studies of tokamak physics.
Plasma Physics Computational Physics
Deep Patch-based Human Segmentation
3D human segmentation has seen noticeable progress in re-cent years.
A Plug-and-play Scheme to Adapt Image Saliency Deep Model for Video Data
With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.
GmFace: A Mathematical Model for Face Image Representation Using Multi-Gaussian
The GmNet is then designed using Gaussian functions as neurons, with parameters that correspond to each of the parameters of GmFace in order to transform the problem of GmFace parameter solving into a network optimization problem of GmNet.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection
Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely.
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections.
Data-Level Recombination and Lightweight Fusion Scheme for RGB-D Salient Object Detection
Existing RGB-D salient object detection methods treat depth information as an independent component to complement its RGB part, and widely follow the bi-stream parallel network architecture.
Depth Quality Aware Salient Object Detection
The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D).
Ranked #16 on
RGB-D Salient Object Detection
on NJU2K
A Deeper Look at Salient Object Detection: Bi-stream Network with a Small Training Dataset
Compared with the conventional hand-crafted approaches, the deep learning based methods have achieved tremendous performance improvements by training exquisitely crafted fancy networks over large-scale training sets.
Exploring Rich and Efficient Spatial Temporal Interactions for Real Time Video Salient Object Detection
In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter
In sharp contrast to the state-of-the-art (SOTA) methods that focus on learning pixel-wise saliency in "single image" using perceptual clues mainly, our method has investigated the "object-level semantic ranks between multiple images", of which the methodology is more consistent with the real human attention mechanism.
Geometric Electrostatic Particle-In-Cell Algorithm on Unstructured Meshes
We compare the energy conservation property of the geometric PIC algorithm derived from the discrete variational principle with that of previous PIC methods on unstructured meshes.
Plasma Physics
From Semantic Categories to Fixations: A Novel Weakly-Supervised Visual-Auditory Saliency Detection Approach
Thanks to the rapid advances in the deep learning techniques and the wide availability of large-scale training sets, the performances of video saliency detection models have been improving steadily and significantly.
Learning Continuous Face Representation with Explicit Functions
First, we propose an explicit model (EmFace) for human face representation in the form of a finite sum of mathematical terms, where each term is an analytic function element.
Knowledge-inspired 3D Scene Graph Prediction in Point Cloud
Unlike conventional methods that learn knowledge embedding and regular patterns from encoded visual information, we propose to suppress the misunderstandings caused by appearance similarities and other perceptual confusion.
Weakly Supervised Visual-Auditory Fixation Prediction with Multigranularity Perception
Moreover, we distill knowledge from these regions to obtain complete new spatial-temporal-audio (STA) fixation prediction (FP) networks, enabling broad applications in cases where video tags are not available.
Shifting Trends of COVID-19 Tweet Sentiment with Respect to Voting Preferences in the 2020 Election Year of the United States
COVID-19 related policies were extensively politicized during the 2020 election year of the United States, resulting in polarizing viewpoints.
Cointegration of SARS-CoV-2 Transmission with Weather Conditions and Mobility during the First Year of the COVID-19 Pandemic in the United States
We examined the cointegration of the effective reproductive rate, Rt, of the virus with the dewpoint at two meters, the temperature at two meters, Apple driving mobility, and Google workplace mobility measurements.
Salient Object Detection via Dynamic Scale Routing
First, instead of using the vanilla convolution with fixed kernel sizes for the encoder design, we propose the dynamic pyramid convolution (DPConv), which dynamically selects the best-suited kernel sizes w. r. t.
WinDB: HMD-free and Distortion-free Panoptic Video Fixation Learning
The main reason is that there always exist "blind zooms" when using HMD to collect fixations since the users cannot keep spinning their heads to explore the entire panoptic scene all the time.
Robust Zero Level-Set Extraction from Unsigned Distance Fields Based on Double Covering
We show that the computed iso-surface is the boundary of the $r$-offset volume of the target zero level-set $S$, which is an orientable manifold, regardless of the topology of $S$.
