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Found 54 papers, 10 papers with code
Context Matters: A Strategy to Pre-train Language Model for Science Education
However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear.
Some Properties of L-banded Matrices
Damping is commonly employed to ensure the convergence of iterative algorithms.
Mitigation of Spatial Nonstationarity with Vision Transformers
However, there is a paucity of demonstrated best practice and general guidance on mitigation of spatial nonstationarity with deep learning in the geospatial context.
Cross-Modal Mutual Learning for Cued Speech Recognition
To our knowledge, this is the first work on ACSR for Mandarin Chinese.
Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation
To this end, we propose a parameter-efficient transformer to explore intrinsic inductive bias via position information for medical image segmentation.
High Speed Rotation Estimation with Dynamic Vision Sensors
By designing a series of signal processing algorithms bespoke for dynamic vision sensing on mobile devices, EV-Tach is able to extract the rotational speed accurately from the event stream produced by dynamic vision sensing on rotary targets.
Reinforcement Learning with Prior Policy Guidance for Motion Planning of Dual-Arm Free-Floating Space Robot
However, due to the increase in planning dimension and the intensification of system dynamics coupling, the motion planning of dual-arm free-floating space robots remains an open challenge.
Capacity Optimality of OAMP in Coded Large Unitarily Invariant Systems
Under the assumptions that the state evolution for joint OAMP and FEC decoding is correct and the replica method is reliable, we analyze the achievable rate of OAMP.
Sufficient Statistic Memory Approximate Message Passing
Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems.
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML
One of the common data pre-processing operations implemented in AutoML systems is to encode categorical features as numerical features.
Ranked #1 on
AutoML
on OrdinalDataset
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm.
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics.
Attribute-Based Progressive Fusion Network for RGBT Tracking
RGBT tracking usually suffers from various challenging factors of fast motion, scale variation, illumination variation, thermal crossover and occlusion, to name a few.
Optimal LED Spectral Multiplexing for NIR2RGB Translation
The industry practice for night video surveillance is to use auxiliary near-infrared (NIR) LED diodes, usually centered at 850nm or 940nm, for scene illumination.
Sufficient Statistic Memory AMP
As an example, we construct a sufficient statistic Bayes-optimal MAMP (SS-BO-MAMP) whose state evolution converges to the minimum (i. e., Bayes-optimal) mean square error (MSE) predicted by replica methods.
Cross-Modal Object Tracking: Modality-Aware Representations and A Unified Benchmark
To promote the research and development of cross-modal object tracking, we propose a new algorithm, which learns the modality-aware target representation to mitigate the appearance gap between RGB and NIR modalities in the tracking process.
Open-Set Crowdsourcing using Multiple-Source Transfer Learning
First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks.
Cross-modal Zero-shot Hashing by Label Attributes Embedding
Cross-modal hashing (CMH) is one of the most promising methods in cross-modal approximate nearest neighbor search.
MetaMIML: Meta Multi-Instance Multi-Label Learning
Multi-Instance Multi-Label learning (MIML) models complex objects (bags), each of which is associated with a set of interrelated labels and composed with a set of instances.
AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models
Code retrieval is allowing software engineers to search codes through a natural language query, which relies on both natural language processing and software engineering techniques.
A Dialogue-based Information Extraction System for Medical Insurance Assessment
In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.
Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Specifically, we demonstrate that long-tailed recognition suffers from both sample number and category similarity.
Memory Approximate Message Passing
Approximate message passing (AMP) is a low-cost iterative parameter-estimation technique for certain high-dimensional linear systems with non-Gaussian distributions.
A Systematic Investigation of KB-Text Embedding Alignment at Scale
Knowledge bases (KBs) and text often contain complementary knowledge: KBs store structured knowledge that can support long range reasoning, while text stores more comprehensive and timely knowledge in an unstructured way.
Few-Shot Partial-Label Learning
Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label.
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means
Moreover, we evaluate our proposed model on the live streaming data and find that our proposed system can be used for real-time anomaly detection in the industrial setup.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
An Interpretable Mapping from a Communication System to a Neural Network for Optimal Transceiver-Joint Equalization
In this paper, we propose a scheme that utilizes the optimization ability of artificial intelligence (AI) for optimal transceiver-joint equalization in compensating for the optical filtering impairments caused by wavelength selective switches (WSS).
Efficient electrochemical reduction of CO2 to CO by soft functional materials
This work provides a new strategy to design novel ILs for high efficiency electrochemical reduction of CO2 to CO.
Materials Science
A molecular dynamics simulation study on the frustrated Lewis pairs in ionic liquids
Spatial distribution function (SDF) results show that toluene formed a continuum solvation shell, which hinders the interactions of (tBu)3P and B(C6F5)3 , while [Cnmim][NTf2] leave a relatively large empty space, which is accessible by (tBu3)P molecules, resulting in a higher probability of Lewis acids and bases interactions.
Chemical Physics
An LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks
In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist.
Topological Transformations in Pentagonal 2D Materials Induced by Stone-Wales Defects
We discover two distinct topological pathways through which the pentagonal Cairo tiling (P5), a structural model for single-layer $AB_2$ pyrite materials, respectively transforms into a crystalline rhombus-hexagon (C46) tiling and random rhombus-pentagon-hexagon (R456) tilings, by continuously introducing the Stone-Wales (SW) topological defects.
Soft Condensed Matter Disordered Systems and Neural Networks Materials Science
Theory of polymer diffusion in polymer-nanoparticle mixtures: effect of nanoparticle concentration and polymer length
The dynamics of polymer-nanoparticle (NP) mixtures, which involves multiple scales and system-specific variables, has posed a long-standing challenge on its theoretical description.
Soft Condensed Matter
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices
The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices.
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic.
Challenge-Aware RGBT Tracking
RGB and thermal source data suffer from both shared and specific challenges, and how to explore and exploit them plays a critical role to represent the target appearance in RGBT tracking.
W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation.
Person Re-Identification via Active Hard Sample Mining
To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts.
Identification of splicing edges in tampered image based on Dichromatic Reflection Model
Therefore we present a novel optic-physical method to discriminate splicing edges from natural edges in a tampered image.
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry.
PDANet: Pyramid Density-aware Attention Net for Accurate Crowd Counting
For this purpose, a classifier evaluates the density level of the input features and then passes them to the corresponding high and low crowded DAD modules.
Intelligent Resource Scheduling for Co-located Latency-critical Services: A Multi-Model Collaborative Learning Approach
More importantly, we observe that there are "resource cliffs" in the scheduling exploration space.
Hyperspectral Image Classification Based on Adaptive Sparse Deep Network
Sparse model is widely used in hyperspectral image classification. However, different of sparsity and regularization parameters has great influence on the classification results. In this paper, a novel adaptive sparse deep network based on deep architecture is proposed, which can construct the optimal sparse representation and regularization parameters by deep network. Firstly, a data flow graph is designed to represent each update iteration based on Alternating Direction Method of Multipliers (ADMM) algorithm. Forward network and Back-Propagation network are deduced. All parameters are updated by gradient descent in Back-Propagation. Then we proposed an Adaptive Sparse Deep Network. Comparing with several traditional classifiers or other algorithm for sparse model, experiment results indicate that our method achieves great improvement in HSI classification.
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem.
Background subtraction on depth videos with convolutional neural networks
Background subtraction is a significant component of computer vision systems.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed.
The localization of single pulse in VLBI observation
The combination of cross spectrum based detection and the localization proposed in this work then provide a thorough solution for searching single pulse in VLBI observation.
Instrumentation and Methods for Astrophysics
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator.
Learning a Rotation Invariant Detector with Rotatable Bounding Box
The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects.
Random Forests of Interaction Trees for Estimating Individualized Treatment Effects in Randomized Trials
Assessing heterogeneous treatment effects has become a growing interest in advancing precision medicine.
Hybrid Dirac Semimetal in CaAgBi Materials Family
Based on their formation mechanisms, Dirac points in three-dimensional systems can be classified as accidental or essential.
Materials Science
