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Found 21 papers, 5 papers with code
AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics
To achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt.
Equivalent Transformation and Dual Stream Network Construction for Mobile Image Super-Resolution
In recent years, there has been an increasing demand for real-time super-resolution networks on mobile devices.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Superhedging duality for multi-action options under model uncertainty with information delay
We consider the superhedging price of an exotic option under nondominated model uncertainty in discrete time in which the option buyer chooses some action from an (uncountable) action space at each time step.
RC-Struct: A Structure-based Neural Network Approach for MIMO-OFDM Detection
The binary classifier enables the efficient utilization of the precious online training symbols and allows an easy extension to high-order modulations without a substantial increase in complexity.
Learning to Equalize OTFS
Orthogonal Time Frequency Space (OTFS) is a novel framework that processes modulation symbols via a time-independent channel characterized by the delay-Doppler domain.
Federated Dynamic Spectrum Access
In this article, we introduce a Federated Learning (FL) based framework for the task of DSA, where FL is a distributive machine learning framework that can reserve the privacy of network terminals under heterogeneous data distributions.
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
Making Intelligent Reflecting Surfaces More Intelligent: A Roadmap Through Reservoir Computing
This article introduces a neural network-based signal processing framework for intelligent reflecting surface (IRS) aided wireless communications systems.
Harnessing Tensor Structures -- Multi-Mode Reservoir Computing and Its Application in Massive MIMO
In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC).
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks
In this paper, we consider jointly optimizing cell load balance and network throughput via a reinforcement learning (RL) approach, where inter-cell handover (i. e., user association assignment) and massive MIMO antenna tilting are configured as the RL policy to learn.
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system.
Learning for Integer-Constrained Optimization through Neural Networks with Limited Training
In this paper, we investigate a neural network-based learning approach towards solving an integer-constrained programming problem using very limited training.
RCNet: Incorporating Structural Information into Deep RNN for MIMO-OFDM Symbol Detection with Limited Training
In this paper, we investigate learning-based MIMO-OFDM symbol detection strategies focusing on a special recurrent neural network (RNN) -- reservoir computing (RC).
Equilibrium concepts for time-inconsistent stopping problems in continuous time
A \emph{new} notion of equilibrium, which we call \emph{strong equilibrium}, is introduced for time-inconsistent stopping problems in continuous time.
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.
Optimal Bookmaking
We introduce a general framework for continuous-time betting markets, in which a bookmaker can dynamically control the prices of bets on outcomes of random events.
Learning for Detection: MIMO-OFDM Symbol Detection through Downlink Pilots
Reservoir computing (RC) is a special recurrent neural network which consists of a fixed high dimensional feature mapping and trained readout weights.
Bayesian Compressive Sensing Using Normal Product Priors
In this paper, we introduce a new sparsity-promoting prior, namely, the "normal product" prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework.
Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems
Different from most existing studies that are concerned with narrowband channels, we consider estimation of wideband mmWave channels with frequency selectivity, which is more appropriate for mmWave MIMO-OFDM systems.
Information Theory Information Theory
