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Found 124 papers, 16 papers with code
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
ImpDet: Exploring Implicit Fields for 3D Object Detection
Conventional 3D object detection approaches concentrate on bounding boxes representation learning with several parameters, i. e., localization, dimension, and orientation.
Learning Decoupling Features Through Orthogonality Regularization
Bearing this in mind, a two-branch deep network (KWS branch and SV branch) with the same network structure is developed and a novel decoupling feature learning method is proposed to push up the performance of KWS and SV simultaneously where speaker-invariant keyword representations and keyword-invariant speaker representations are expected respectively.
Source-free Domain Adaptation for Multi-site and Lifespan Brain Skull Stripping
In this paper, we design a source-free domain adaptation framework (SDAF) for multi-site and lifespan skull stripping that can accomplish domain adaptation without access to source domain images.
Computer-Aided Road Inspection: Systems and Algorithms
Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety.
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online allocation algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order.
Seeing is Living? Rethinking the Security of Facial Liveness Verification in the Deepfake Era
Facial Liveness Verification (FLV) is widely used for identity authentication in many security-sensitive domains and offered as Platform-as-a-Service (PaaS) by leading cloud vendors.
Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation
As a result, our model can not only improve the recommendation performance of the rating platform by incorporating the sparse social data on the social platform, but also protect data privacy of both platforms.
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
To this end, PriCDR can not only protect the data privacy of the source domain, but also alleviate the data sparsity of the source domain.
Higher Order Correlation Analysis for Multi-View Learning
This can be formulated as a low rank approximation problem with the higher order correlation tensor of multi-view data.
Backdoor Defense with Machine Unlearning
First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model.
The Implicit Regularization of Momentum Gradient Descent with Early Stopping
In the present paper, we characterize the implicit regularization of momentum gradient descent (MGD) with early stopping by comparing with the explicit $\ell_2$-regularization (ridge).
Deep neural networks for solving forward and inverse problems of (2+1)-dimensional nonlinear wave equations with rational solitons
In this paper, we investigate the forward problems on the data-driven rational solitons for the (2+1)-dimensional KP-I equation and spin-nonlinear Schr\"odinger (spin-NLS) equation via the deep neural networks leaning.
Data-driven discoveries of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes
We introduce a deep neural network learning scheme to learn the B\"acklund transforms (BTs) of soliton evolution equations and an enhanced deep learning scheme for data-driven soliton equation discovery based on the known BTs, respectively.
Mathematical Models for Local Sensing Hashes
As data volumes continue to grow, searches in data are becoming increasingly time-consuming.
Practical and Light-weight Secure Aggregation for Federated Submodel Learning
Existing work fails to provide a loss-less scheme, or has impractical efficiency.
Dispensed Transformer Network for Unsupervised Domain Adaptation
To mitigate this problem, a novel unsupervised domain adaptation (UDA) method named dispensed Transformer network (DTNet) is introduced in this paper.
Joint Model and Data Driven Receiver Design for Data-Dependent Superimposed Training Scheme with Imperfect Hardware
Data-dependent superimposed training (DDST) scheme has shown the potential to achieve high bandwidth efficiency, while encounters symbol misidentification caused by hardware imperfection.
Enhanced ELM Based Channel Estimation for RIS-Assisted OFDM systems with Insufficient CP and Imperfect Hardware
Reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems have aroused extensive research interests due to the controllable communication environment and the performance of combating multi-path interference.
Wav2vec-S: Semi-Supervised Pre-Training for Speech Recognition
And there is a gap between the task-agnostic pre-training and the task-specific downstream fine-tuning, which may degrade the downstream performance.
Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans
Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i. e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan.
Structure-Enhanced Pop Music Generation via Harmony-Aware Learning
Besides, when chords evolve into chord progression, the texture and the form can be bridged by the harmony naturally, which contributes to the joint learning of the two structures.
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI.
Progressive Coordinate Transforms for Monocular 3D Object Detection
Recognizing and localizing objects in the 3D space is a crucial ability for an AI agent to perceive its surrounding environment.
Text Anchor Based Metric Learning for Small-footprint Keyword Spotting
Recently proposed metric learning approaches improved the generalizability of models for the KWS task, and 1D-CNN based KWS models have achieved the state-of-the-arts (SOTA) in terms of model size.
Privacy Threats Analysis to Secure Federated Learning
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties.
Representative Functional Connectivity Learning for Multiple Clinical groups in Alzheimer's Disease
By mapping the learned clinical group related feature vectors to the original FC space, representative FCs were constructed for each group.
AGSFCOS: Based on attention mechanism and Scale-Equalizing pyramid network of object detection
In order to solve the above problems, Experiments show that our model has a certain improvement in accuracy compared with the current popular detection models on the COCO dataset, the designed attention mechanism module can capture contextual information well, improve detection accuracy, and use sepc network to help balance abstract and detailed information, and reduce the problem of semantic gap in the feature pyramid network.
Towards a Model for LSH
Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms.
Lite-FPN for Keypoint-based Monocular 3D Object Detection
3D object detection with a single image is an essential and challenging task for autonomous driving.
Privacy-Preserving Federated Learning on Partitioned Attributes
Real-world data is usually segmented by attributes and distributed across different parties.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #8 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving
Our goal is to train a unified model for improving the performance in each dataset by leveraging information from all the datasets.
An Efficient Autocalibration Method for Triaxial Gyroscope without External Device
We empirically validate the effectiveness of the proposed method on two commonly used low-cost gyroscope and achieve real-time calibration on a low-energy microcontroller.
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection
3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control.
DM algorithms in healthindustry
Modern multi-core processors consist of several (2 to over 100) computer cores, which work independently of each other according to the principle of "multiple instruction multiple data" (MIMD).
Representing Alzheimer's Disease Progression via Deep Prototype Tree
By conducting a novel prototype learning with a deep manner, we are able to capture intrinsic relations among different clinical groups as prototypes and represent them in a continuous process for AD development.
Spectrum Sharing for 6G Integrated Satellite-Terrestrial Communication Networks Based on NOMA and Cognitive Radio
ISTCN aims to provide high speed and pervasive network services by integrating broadband terrestrial mobile networks with satellite communication networks.
Trace Ratio Optimization with an Application to Multi-view Learning
A trace ratio optimization problem over the Stiefel manifold is investigated from the perspectives of both theory and numerical computations.
Data-driven peakon and periodic peakon travelling wave solutions of some nonlinear dispersive equations via deep learning
In the field of mathematical physics, there exist many physically interesting nonlinear dispersive equations with peakon solutions, which are solitary waves with discontinuous first-order derivative at the wave peak.
Data-driven rogue waves and parameter discovery in the defocusing NLS equation with a potential using the PINN deep learning
Moreover, the multi-layer PINN algorithm can also be used to learn the parameter in the defocusing NLS equation with the time-dependent potential under the sense of the rogue wave solution.
Towards Scalable and Privacy-Preserving Deep Neural Network via Algorithmic-Cryptographic Co-design
In this paper, we propose SPNN - a Scalable and Privacy-preserving deep Neural Network learning framework, from algorithmic-cryptographic co-perspective.
Complex Relation Extraction: Challenges and Opportunities
In recent years, many complex relation extraction tasks, i. e., the variants of simple binary relation extraction, are proposed to meet the complex applications in practice.
Computer Stereo Vision for Autonomous Driving
As an important component of autonomous systems, autonomous car perception has had a big leap with recent advances in parallel computing architectures.
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Federated learning (FL) has attracted increasing attention in recent years.
Uncorrelated Semi-paired Subspace Learning
However, unpaired data can be more abundant in reality than paired ones and simply ignoring all unpaired data incur tremendous waste in resources.
Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications
In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.
A Theoretical Perspective on Differentially Private Federated Multi-task Learning
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling.
ASFGNN: Automated Separated-Federated Graph Neural Network
Specifically, to solve the data Non-IID problem, we first propose a separated-federated GNN learning model, which decouples the training of GNN into two parts: the message passing part that is done by clients separately, and the loss computing part that is learnt by clients federally.
Multi-Accent Adaptation based on Gate Mechanism
To jointly train the acoustic model and the accent classifier, we propose the multi-task learning with gate mechanism (MTL-G).
Domain Adaptation Using Class Similarity for Robust Speech Recognition
Then, for each class, probabilities of this class are used to compute a mean vector, which we refer to as mean soft labels.
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only the state (and action) but also an explicit policy representation.
Orthogonal Multi-view Analysis by Successive Approximations via Eigenvectors
We propose a unified framework for multi-view subspace learning to learn individual orthogonal projections for all views.
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy
The success of machine learning algorithms often relies on a large amount of high-quality data to train well-performed models.
A Comprehensive Analysis of Information Leakage in Deep Transfer Learning
To the best of our knowledge, our study is the first to provide a thorough analysis of the information leakage issues in deep transfer learning methods and provide potential solutions to the issue.
When Homomorphic Encryption Marries Secret Sharing: Secure Large-Scale Sparse Logistic Regression and Applications in Risk Control
In this paper, we first present CAESAR, which combines HE and SS to build secure large-scale sparse logistic regression model and achieves both efficiency and security.
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions
Building on the least squares reformulation of OPLS, we propose a unified multi-view learning framework to learn a classifier over a common latent space shared by all views.
Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge
Deep learning-based methods have achieved state-of-the-art performance; however, one of major limitations is that the learning-based methods may suffer from the multi-site issue, that is, the models trained on a dataset from one site may not be applicable to the datasets acquired from other sites with different imaging protocols/scanners.
Dual Super-Resolution Learning for Semantic Segmentation
Specifically, for semantic segmentation on CityScapes, we can achieve \geq2% higher mIoU with similar FLOPs, and keep the performance with 70% FLOPs.
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.
Deep Tensor CCA for Multi-view Learning
We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.
An Auto-Context Deformable Registration Network for Infant Brain MRI
Deformable image registration is fundamental to longitudinal and population analysis.
Large-Scale Secure XGB for Vertical Federated Learning
Privacy-preserving machine learning has drawn increasingly attention recently, especially with kinds of privacy regulations come into force.
Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
3D moving object detection is one of the most critical tasks in dynamic scene analysis.
Secret Sharing based Secure Regressions with Applications
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns.
Industrial Scale Privacy Preserving Deep Neural Network
Deep Neural Network (DNN) has been showing great potential in kinds of real-world applications such as fraud detection and distress prediction.
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
The insurance industry has been creating innovative products around the emerging online shopping activities.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
Adapted tree boosting for Transfer Learning
Secure online transaction is an essential task for e-commerce platforms.
Secure Social Recommendation based on Secret Sharing
It is well known that social information, which is rich on social platforms such as Facebook, are useful to recommender systems.
Privacy Preserving PCA for Multiparty Modeling
In this paper, we present a general multiparty modeling paradigm with Privacy Preserving Principal Component Analysis (PPPCA) for horizontally partitioned data.
Show, Recall, and Tell: Image Captioning with Recall Mechanism
SG and RWS are de-signed for the best use of recalled words.
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points
A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN.
Hetero-Center Loss for Cross-Modality Person Re-Identification
With the joint supervision of Cross-Entropy (CE) loss and HC loss, the network is trained to achieve two vital objectives, inter-class discrepancy and intra-class cross-modality similarity as much as possible.
Cross-Modality Person Re-identification
Person Re-Identification
Unsupervised Multi-stream Highlight detection for the Game "Honor of Kings"
With the increasing popularity of E-sport live, Highlight Flashback has been a critical functionality of live platforms, which aggregates the overall exciting fighting scenes in a few seconds.
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
A Self-consistent-field Iteration for Orthogonal Canonical Correlation Analysis
In this paper, we propose an alternating numerical scheme whose core is the sub-maximization problem in the trace-fractional form with an orthogonal constraint.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior
Traditional EEG/MEG Source Imaging (ESI) methods usually assume that either source activity at different time points is unrelated, or that similar spatiotemporal patterns exist across an entire study period.
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates.
Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics
Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making.
Convolutional Restricted Boltzmann Machine Based-Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
We proposed a novel convolutional restricted Boltzmann machine CRBM-based radiomic method for predicting pathologic complete response (pCR) to neoadjuvant chemotherapy treatment (NACT) in breast cancer.
From Abstractions to "Natural Languages" for Coordinating Planning Agents
We formulate this language construction problem and show that it is NEXP-complete.
Fusion-supervised Deep Cross-modal Hashing
Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages.
A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised
In this study, we perform a comparison study of credit card fraud detection by using various supervised and unsupervised approaches.
Learning Actor Relation Graphs for Group Activity Recognition
To this end, we propose to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors.
Ranked #1 on
Group Activity Recognition
on Collective Activity
Facial Feature Embedded CycleGAN for VIS-NIR Translation
VIS-NIR face recognition remains a challenging task due to the distinction between spectral components of two modalities and insufficient paired training data.
Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding
In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation.
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Skull stripping for brain MR images is a basic segmentation task.
Spherical U-Net on Cortical Surfaces: Methods and Applications
In this paper, by leveraging the regular and consistent geometric structure of the resampled cortical surface mapped onto the spherical space, we propose a novel convolution filter analogous to the standard convolution on the image grid.
CODA: Counting Objects via Scale-aware Adversarial Density Adaption
Extensive experiments demonstrate that our network produces much better results on unseen datasets compared with existing counting adaption models.
Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses
In this paper, we study algorithms for dynamically identifying a large number of products (i. e., SKUs) with top customer purchase probabilities on the fly, from an ocean of potential products to offer on retailers' ultra-fast delivery platforms.
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.
Crowd Counting with Density Adaption Networks
Crowd counting is one of the core tasks in various surveillance applications.
A practical convolutional neural network as loop filter for intra frame
It aims to design a single CNN model with low redundancy to adapt to decoded frames with different qualities and ensure consistency.
Multimedia
An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems
Multiagent coordination in cooperative multiagent systems (MASs) has been widely studied in both fixed-agent repeated interaction setting and the static social learning framework.
A Reinforced Topic-Aware Convolutional Sequence-to-Sequence Model for Abstractive Text Summarization
In this paper, we propose a deep learning approach to tackle the automatic summarization tasks by incorporating topic information into the convolutional sequence-to-sequence (ConvS2S) model and using self-critical sequence training (SCST) for optimization.
Ranked #6 on
Text Summarization
on DUC 2004 Task 1
Deep Learning based Inter-Modality Image Registration Supervised by Intra-Modality Similarity
In this paper, we propose to train a non-rigid inter-modality image registration network, which can directly predict the transformation field from the input multimodal images, such as CT and MR images.
Medical Image Synthesis with Deep Convolutional Adversarial Networks
To better model a nonlinear mapping from source to target and to produce more realistic target images, we propose to use the adversarial learning strategy to better model the FCN.
Time-sensitive Customer Churn Prediction based on PU Learning
With the fast development of Internet companies throughout the world, customer churn has become a serious concern.
TextZoo, a New Benchmark for Reconsidering Text Classification
Text representation is a fundamental concern in Natural Language Processing, especially in text classification.
Barrier-Certified Adaptive Reinforcement Learning with Applications to Brushbot Navigation
This paper presents a safe learning framework that employs an adaptive model learning algorithm together with barrier certificates for systems with possibly nonstationary agent dynamics.
Learning Hierarchical Features for Visual Object Tracking with Recursive Neural Networks
First, we learn RNN parameters to discriminate between the target object and background in the first frame of a test sequence.
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.
Ranked #1 on
Infant Brain Mri Segmentation
on iSEG 2017 Challenge
Safe Learning of Quadrotor Dynamics Using Barrier Certificates
The barrier certificates establish a non-conservative forward invariant safe region, in which high probability safety guarantees are provided based on the statistics of the Gaussian Process.
Arbitrary-Oriented Scene Text Detection via Rotation Proposals
This paper introduces a novel rotation-based framework for arbitrary-oriented text detection in natural scene images.
Evolving Boxes for Fast Vehicle Detection
We perform fast vehicle detection from traffic surveillance cameras.
Cross-model convolutional neural network for multiple modality data representation
A novel data representation method of convolutional neural net- work (CNN) is proposed in this paper to represent data of different modalities.
Probabilistic Dimensionality Reduction via Structure Learning
Based on this framework, we present a new model, which is able to learn a smooth skeleton of embedding points in a low-dimensional space from high-dimensional noisy data.
Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association
Then the Siamese CNN and temporally constrained metrics are jointly learned online to construct the appearance-based tracklet affinity models.
Shape Animation with Combined Captured and Simulated Dynamics
Instead of using the dominant surface-based geometric representation of the capture, which is less suitable for volumetric effects, our pipeline exploits Centroidal Voronoi tessellation decompositions as unified volumetric representation of the real captured actor, which we show can be used seamlessly as a building block for all processing stages, from capture and tracking to virtual physic simulation.
Recent Advances in Convolutional Neural Networks
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing.
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing.
A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation
As showcases, models that can learn a spanning tree or a weighted undirected $\ell_1$ graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously.
Video Tracking Using Learned Hierarchical Features
The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object.
Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation
In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking.
Tracklet Association with Online Target-Specific Metric Learning
In our method, target-specific similarity metrics are learned, which give rise to the appearance-based models used in the tracklet affinity estimation.
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate with the input signal, manifesting into power-supply induced intermodulation distortion (PS-IMD) and due to the memory effects of the system, there exist asymmetries in the PS-IMDs.
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over Batch Signals
Matching Pursuit LASSIn Part I \cite{TanPMLPart1}, a Matching Pursuit LASSO ({MPL}) algorithm has been presented for solving large-scale sparse recovery (SR) problems.
Towards Ultrahigh Dimensional Feature Selection for Big Data
In this paper, we present a new adaptive feature scaling scheme for ultrahigh-dimensional feature selection on Big Data.
