Search Results for author:
Found 258 papers, 84 papers with code
Deep Nearest Class Mean Model for Incremental Odor Classification
That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time.
Deep Learning for identifying radiogenomic associations in breast cancer
Results: The best AUC performance for distinguishing molecular subtypes was 0. 65 (95% CI:[0. 57, 0. 71]) and was achieved by the off-the-shelf deep features approach.
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ
In the first approach, we adopted the transfer learning strategy, in which a network pre-trained on a large dataset of natural images is fine-tuned with our DCIS images.
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Inspired by point process in modeling temporal point process, in this paper we present a deep prediction method based on two recurrent neural networks (RNNs) to jointly model each user's continuous browsing history and asynchronous event sequences in the context of inter-user behavioral mutual infectivity.
High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data.
Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds
Existing segmentation procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of the occlusion effect of higher canopy layers.
Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds
This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method.
Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%.
Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model
The system which combined frame retaining with frame stacking could reduces the time consumption of both training and decoding.
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time.
Deep LSTM for Large Vocabulary Continuous Speech Recognition
It is a competitive framework that LSTM models of more than 7 layers are successfully trained on Shenma voice search data in Mandarin and they outperform the deep LSTM models trained by conventional approach.
Exponential Moving Average Model in Parallel Speech Recognition Training
As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently. We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model.
A robust approach for tree segmentation in deciduous forests using small-footprint airborne LiDAR data
This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests.
Agglomerative clustering and collectiveness measure via exponent generating function
A novel agglomerative clustering method is proposed by utilizing the path integral to define the affinity measure.
Implementation of Training Convolutional Neural Networks
Deep learning refers to the shining branch of machine learning that is based on learning levels of representations.
Performance Analysis on Evolutionary Algorithms for the Minimum Label Spanning Tree Problem
We also show that GSEMO achieves a $(2ln(n))$-approximation ratio for the MLST problem in expected polynomial time of $n$ and $k$.
Real-Time Traffic Signal Control for Modern Roundabouts by Using Particle Swarm Optimization-Based Fuzzy Controller
This mechanism helps to instantly respond to the current traffic condition of the roundabout so as to improve real-timeness.
Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images
Then, based on the segmentation results, a subject-specific piecewise linear mapping function was applied between the anchor points to normalize the same type of tissue in different patients into the same intensity ranges.
Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices
Sample simulation results shall be presented to verify the theoretical analysis as well as validate the effectiveness of the proposed algorithm.
Information Theory Information Theory
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning.
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed.
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples
To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples.
Session-level Language Modeling for Conversational Speech
We propose to generalize language models for conversational speech recognition to allow them to operate across utterance boundaries and speaker changes, thereby capturing conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence.
On Reproducing Kernel Banach Spaces: Generic Definitions and Unified Framework of Constructions
We explore a generic definition of RKBS and the reproducing kernel for RKBS that is independent of construction.
Topological Persistence in Geometry and Analysis
The theory of persistence modules is an emerging field of algebraic topology which originated in topological data analysis.
Algebraic Topology Classical Analysis and ODEs Symplectic Geometry 55U99, 58Cxx, 53Dxx
The Roadmap to 6G -- AI Empowered Wireless Networks
The recent upsurge of diversified mobile applications, especially those supported by Artificial Intelligence (AI), is spurring heated discussions on the future evolution of wireless communications.
Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks
A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems.
Mobile Edge Intelligence and Computing for the Internet of Vehicles
The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking.
Networking and Internet Architecture Signal Processing
Learning Clustered Representation for Complex Free Energy Landscapes
In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which yield reduced and clustered representation for the FEL.
Restricted Linearized Augmented Lagrangian Method for Euler's Elastica Model
In this paper, a simple cutting-off strategy is introduced into the augmented Lagrangian based algorithms for minimizing the Euler's elastica energy, which leads to easy parameter tuning and fast convergence.
Non-imaging single-pixel sensing with optimized binary modulation
The conventional high-level sensing techniques require high-fidelity images as input to extract target features, which are produced by either complex imaging hardware or high-complexity reconstruction algorithms.
Multi-grained Attention Networks for Single Image Super-Resolution
In this paper, we make a thorough investigation on the attention mechanisms in a SR model and shed light on how simple and effective improvements on these ideas improve the state-of-the-arts.
A Study of Data Pre-processing Techniques for Imbalanced Biomedical Data Classification
However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets.
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
Faster Activity and Data Detection in Massive Random Access: A Multi-armed Bandit Approach
In this paper, we develop multi-armed bandit approaches for more efficient detection via coordinate descent, which make a delicate trade-off between exploration and exploitation in coordinate selection.
Sparse Optimization for Green Edge AI Inference
By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem.
Communication-Efficient Edge AI: Algorithms and Systems
By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative.
DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team
Our diarization system includes multiple modules, namely voice activity detection (VAD), segmentation, speaker embedding extraction, similarity scoring, clustering, resegmentation and overlap detection.
Scene Text Recognition via Transformer
Different from previous transformer based models [56, 34], which just use the decoder of the transformer to decode the convolutional attention, the proposed method use a convolutional feature maps as word embedding input into transformer.
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
In contrast to existing works, the adversarial examples crafted by our method can also deceive recent machine learning based detectors that rely on semantic features such as control-flow-graph.
Cryptography and Security
A Perspective on Deep Learning for Molecular Modeling and Simulations
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems.
The Design and Construction of a Chinese Sarcasm Dataset
Thus, the detection and processing of sarcasm is important to social media analysis. However, most existing sarcasm dataset are in English and there is still a lack of authoritative Chinese sarcasm dataset.
Target-based Sentiment Annotation in Chinese Financial News
Based on high quality annotation guideline and effective quality control strategy, a corpus with 8, 314 target-level sentiment annotation is constructed on 6, 336 paragraphs from Chinese financial news text.
Zero-shot Text Classification via Reinforced Self-training
In this situation, transferring from seen classes to unseen classes is extremely hard.
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems
We showed that the WGAN-based method was effective to preserve image texture.
Flow Field Reconstructions with GANs based on Radial Basis Functions
Motivated by the problems of existing approaches and inspired by the success of the generative adversarial networks (GANs) in the field of computer vision, we prove an optimal discriminator theorem that the optimal discriminator of a GAN is a radial basis function neural network (RBFNN) while dealing with nonlinear sparse FFD regression and generation.
Microscope Based HER2 Scoring System
Recently, artificial intelligence (AI) has been used in various disease diagnosis to improve diagnostic accuracy and reliability, but the interpretation of diagnosis results is still an open problem.
DeFuzz: Deep Learning Guided Directed Fuzzing
Fuzzing is one of the most effective technique to identify potential software vulnerabilities.
Deep Reinforcement Learning of Transition States
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms.
Improving RNN transducer with normalized jointer network
In this work, we analyze the cause of the huge gradient variance in RNN-T training and proposed a new \textit{normalized jointer network} to overcome it.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Dynamic latency speech recognition with asynchronous revision
In this work we propose an inference technique, asynchronous revision, to unify streaming and non-streaming speech recognition models.
Automated Big Traffic Analytics for Cyber Security
Network traffic analytics technology is a cornerstone for cyber security systems.
Cryptography and Security
Ultra-Fast Accurate AoA Estimation via Automotive Massive-MIMO Radar
Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV).
Blind Data Detection in Massive MIMO via $\ell_3$-norm Maximization over the Stiefel Manifold
Specifically, we propose a novel $\ell_3$-norm-based formulation to recover the data without channel estimation.
Efficient and fair trading algorithms in market design environments
We propose a new method to define trading algorithms in market design environments.
Cores in discrete exchange economies with complex endowments
The core is a traditional and useful solution concept in economic theory.
Hawking radiation from nonrotating singularity-free black holes in conformal gravity
Besides, we investigate the dependence of the greybody factor and the sparsity of Hawking radiation on the conformal parameters.
General Relativity and Quantum Cosmology
Exploiting Behavioral Consistence for Universal User Representation
In this work, we focus on developing universal user representation model.
Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales
Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems.
Hierarchical Federated Learning with Quantization: Convergence Analysis and System Design
Hierarchical FL, with a client-edge-cloud aggregation hierarchy, can effectively leverage both the cloud server's access to many clients' data and the edge servers' closeness to the clients to achieve a high communication efficiency.
Single-photon imaging over 200 km
Long-range active imaging has widespread applications in remote sensing and target recognition.
Constrained Pseudo-market Equilibrium
We propose a pseudo-market solution to resource allocation problems subject to constraints.
Large-scale phase retrieval
In computational phase imaging, phase retrieval (PR) is required to reconstruct both amplitude and phase in complex space from intensity-only measurements.
Affine-modeled video extraction from a single motion blurred image
Recovering these sharp video frames from a single blurred image is nontrivial, due to not only its strong ill-posedness, but also various types of complex motion in reality such as rotation and motion in depth.
Supporting More Active Users for Massive Access via Data-assisted Activity Detection
Massive machine-type communication (mMTC) has been regarded as one of the most important use scenarios in the fifth generation (5G) and beyond wireless networks, which demands scalable access for a large number of devices.
HMM-Free Encoder Pre-Training for Streaming RNN Transducer
This work describes an encoder pre-training procedure using frame-wise label to improve the training of streaming recurrent neural network transducer (RNN-T) model.
Semi-Decentralized Federated Edge Learning for Fast Convergence on Non-IID Data
Federated edge learning (FEEL) has emerged as an effective approach to reduce the large communication latency in Cloud-based machine learning solutions, while preserving data privacy.
Communication-Efficient Federated Learning with Dual-Side Low-Rank Compression
Federated learning (FL) is a promising and powerful approach for training deep learning models without sharing the raw data of clients.
Joint Activity Detection and Data Decoding in Massive Random Access via a Turbo Receiver
In this paper, we propose a turbo receiver for joint activity detection and data decoding in grant-free massive random access, which iterates between a detector and a belief propagation (BP)-based channel decoder.
Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey
This survey presents the most recent findings of privacy attacks and defenses appeared in cloud-based neural network services.
Complex-domain super-resolution imaging with distributed optimization
Complex-domain imaging has emerged as a valuable technique for investigating weak-scattered samples.
Agile wide-field imaging with selective high resolution
In this work, we report an agile wide-field imaging framework with selective high resolution that requires only two detectors.
Invariant Information Bottleneck for Domain Generalization
IIB significantly outperforms IRM on synthetic datasets, where the pseudo-invariant features and geometric skews occur, showing the effectiveness of proposed formulation in overcoming failure modes of IRM.
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution
Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost.
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Recently, convolutional neural network (CNN) based image super-resolution (SR) methods have achieved significant performance improvement.
Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access
In particular, the common sparsity pattern in the received pilot and data signal has been ignored in most existing studies, and auxiliary information of channel decoding has not been utilized for user activity detection.
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test.
Primary-Auxiliary Model Scheduling Based Estimation of the Vertical Wheel Force in a Full Vehicle System
A real-world application to the estimation of the vertical wheel force in a full vehicle system are, respectively, conducted to demonstrate the effectiveness of the proposed method.
Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation
Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems.
Semantic Reinforced Attention Learning for Visual Place Recognition
(2) By exploiting the interpretability of the local weighting scheme, a semantic constrained initialization is proposed so that the local attention can be reinforced by semantic priors.
Image-free single-pixel segmentation
We envision that this image-free segmentation technique can be widely applied in various resource-limited platforms such as UAV and unmanned vehicle that require real-time sensing.
Communication-Computation Efficient Device-Edge Co-Inference via AutoML
Device-edge co-inference, which partitions a deep neural network between a resource-constrained mobile device and an edge server, recently emerges as a promising paradigm to support intelligent mobile applications.
Sk-Unet Model with Fourier Domain for Mitosis Detection
Mitotic count is the most important morphological feature of breast cancer grading.
Learn to Communicate with Neural Calibration: Scalability and Generalization
Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models.
Attentional Pyramid Pooling of Salient Visual Residuals for Place Recognition
The core of visual place recognition (VPR) lies in how to identify task-relevant visual cues and embed them into discriminative representations.
kFolden: k-Fold Ensemble for Out-Of-Distribution Detection
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP).
From em-Projections to Variational Auto-Encoder
This paper reviews the em-projections in information geometry and the recent understanding of variational auto-encoder, and explains that they share a common formulation as joint minimization of the Kullback-Leibler divergence between two manifolds of probability distributions, and the joint minimization can be implemented by alternating projections or alternating gradient descent.
Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients
Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks.
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity
By exploiting the low-latency communication among edge servers for efficient model sharing, SD-FEEL can incorporate more training data, while enjoying much lower latency compared with conventional federated learning.
Weighted Encoding Optimization for Dynamic Single-pixel Imaging and Sensing
While the network is training at a high sampling rate, the modulation patterns and corresponding weights are updated iteratively, which produces optimal ranked encoding series when converged.
A Critical Analysis of Image-based Camera Pose Estimation Techniques
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR).
Data-Driven Deep Learning Based Hybrid Beamforming for Aerial Massive MIMO-OFDM Systems with Implicit CSI
To this end, by modeling the key transmission modules as an end-to-end (E2E) neural network, this paper proposes a data-driven deep learning (DL)-based unified hybrid beamforming framework for both the time division duplex (TDD) and frequency division duplex (FDD) systems with implicit channel state information (CSI).
Stochastic Coded Federated Learning with Convergence and Privacy Guarantees
Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework, where many clients collaboratively train a machine learning model by exchanging model updates with a parameter server instead of sharing their raw data.
The Volcspeech system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge
For Track 1, we propose several approaches to empower the clustering-based speaker diarization system to handle overlapped speech.
The Gene of Scientific Success
Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success.
AugHover-Net: Augmenting Hover-net for Nucleus Segmentation and Classification
Connective nuclei may look very different from each other while some of them share a similar shape with the epithelial ones.
Communication-Efficient Federated Distillation with Active Data Sampling
Federated Distillation (FD) is a recently proposed alternative to enable communication-efficient and robust FL, which achieves orders of magnitude reduction of the communication overhead compared with FedAvg and is flexible to handle heterogeneous models at the clients.
Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack
To evaluate the attack efficacy, we crafted heterogeneous security scenarios where the detectors were embedded with different levels of defense and the attackers' background knowledge of data varies.
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images
In this paper, we propose a self-supervised learning framework for light field depth estimation.
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting
Fine-tuning attacks are effective in removing the embedded watermarks in deep learning models.
Deep learning-based approach to reveal tumor mutational burden status from whole slide images across multiple cancer types
MC-TMB algorithm also exhibited good generalization on the external validation cohort with an AUC of 0. 732 (0. 683-0. 761), and better performance when compared to other methods.
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Large-scale vision-language pre-training has achieved promising results on downstream tasks.
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization
Specifically, PEGA enables users outsourcing COPs to the cloud server holding a competitive GA and approximating the optimal solution in a privacy-preserving manner.
Federated Learning with GAN-based Data Synthesis for Non-IID Clients
A combination of the local private dataset and synthetic dataset with confident pseudo labels leads to nearly identical data distributions among clients, which improves the consistency among local models and benefits the global aggregation.
Resource-Constrained Edge AI with Early Exit Prediction
In this paper, we propose an early exit prediction mechanism to reduce the on-device computation overhead in a device-edge co-inference system supported by early-exit networks.
A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method
Geometrical shape of airfoils, together with the corresponding flight conditions, are crucial factors for aerodynamic performances prediction.
Hiding Your Signals: A Security Analysis of PPG-based Biometric Authentication
It reduces the success rate of rPPG spoofing attacks in user authentication to 0. 05.
PrivBayes： Private Data release via Bayesian networks
Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of lowdimensional marginals of D. After that, PRIVBAYES injects noise into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PRIVBAYES samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data.
Augmented Deep Unfolding for Downlink Beamforming in Multi-cell Massive MIMO With Limited Feedback
In limited feedback multi-user multiple-input multiple-output (MU-MIMO) cellular networks, users send quantized information about the channel conditions to the associated base station (BS) for downlink beamforming.
Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval
To address this problem, we propose a Text-Adaptive Multiple Visual Prototype Matching model, which automatically captures multiple prototypes to describe a video by adaptive aggregation of video token features.
DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing
Federated learning (FL) strives to enable collaborative training of machine learning models without centrally collecting clients' private data.
Deep Latent Mixture Model for Recommendation
Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications.
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design
During training, the server computes gradients on the global coded dataset to compensate for the missing model updates of the straggling devices.
Blind Performance Prediction for Deep Learning Based Ultra-Massive MIMO Channel Estimation
Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance.
Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO
In this paper, we propose a lightweight and flexible deep learning-based CSI feedback approach by capitalizing on deep equilibrium models.
Large-scale single-photon imaging
Benefiting from its single-photon sensitivity, single-photon avalanche diode (SPAD) array has been widely applied in various fields such as fluorescence lifetime imaging and quantum computing.
Machine Learning for Large-Scale Optimization in 6G Wireless Networks
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms.
Artificial intelligence for diagnosing and predicting survival of patients with renal cell carcinoma: Retrospective multi-center study
The OSrisk for the prediction of 5-year survival status achieved AUC of 0. 784 (0. 746-0. 819) in the TCGA cohort, which was further verified in the independent General cohort and the CPTAC cohort, with AUC of 0. 774 (0. 723-0. 820) and 0. 702 (0. 632-0. 765), respectively.
Scattering-induced entropy boost for highly-compressed optical sensing and encryption
In the proposed framework of single-pixel detection, the optical field from a target is first scattered by an optical diffuser and then two-dimensionally modulated by a spatial light modulator.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning
Learning communication strategies in cooperative multi-agent reinforcement learning (MARL) has recently attracted intensive attention.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
DSDP: A Blind Docking Strategy Accelerated by GPUs
In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking.
When Evolutionary Computation Meets Privacy
To this end, in this paper, we discuss three typical optimization paradigms (i. e., \textit{centralized optimization, distributed optimization, and data-driven optimization}) to characterize optimization modes of evolutionary computation and propose BOOM to sort out privacy concerns in evolutionary computation.
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization
Inspired by this, this paper intends to introduce crowdsourcing into evolutionary computation (EC) to propose a crowdsourcing-based evolutionary computation (CEC) paradigm for distributed optimization.
A Survey on Distributed Evolutionary Computation
Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment.
Grant-free Massive Random Access with Retransmission: Receiver Optimization and Performance Analysis
Specifically, by jointly leveraging the user activity correlation between adjacent transmission blocks and the historical channel estimation results, we first develop an activity-correlation-aware receiver for grant-free massive RA systems with retransmission based on the correlated approximate message passing (AMP) algorithm.
Security and Privacy Problems in Voice Assistant Applications: A Survey
The privacy issues include technical-wise information stealing and policy-wise privacy breaches.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
DABS: Data-Agnostic Backdoor attack at the Server in Federated Learning
Federated learning (FL) attempts to train a global model by aggregating local models from distributed devices under the coordination of a central server.
Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales.
Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data
Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access
Most existing studies on joint activity detection and channel estimation for grant-free massive random access (RA) systems assume perfect synchronization among all active users, which is hard to achieve in practice.
Novel deep learning methods for 3D flow field segmentation and classification
Flow field segmentation and classification help researchers to understand vortex structure and thus turbulent flow.
Towards Large-scale Single-shot Millimeter-wave Imaging for Low-cost Security Inspection
These elements are then sampled based on the ranking, building the experimentally optimal sparse sampling strategy that reduces the cost of antenna array by up to one order of magnitude.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.
CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training
We also observe the cross-modal representation extracted by CIF-PT obtains better performance than other neural interfaces for the tasks of SLU, including the dominant speech representation learned from self-supervised pre-training.
Text-only Domain Adaptation using Unified Speech-Text Representation in Transducer
Domain adaptation using text-only corpus is challenging in end-to-end(E2E) speech recognition.
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence
The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.
A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency
Without data centralization, FL allows clients to share local information in a privacy-preserving manner.
Binary Federated Learning with Client-Level Differential Privacy
To improve communication efficiency and achieve a better privacy-utility trade-off, we propose a communication-efficient FL training algorithm with differential privacy guarantee.
Feature Matching Data Synthesis for Non-IID Federated Learning
For better privacy preservation, we propose a hard feature augmentation method to transfer real features towards the decision boundary, with which the synthetic data not only improve the model generalization but also erase the information of real features.
A new solution and concrete implementation steps for Artificial General Intelligence
At present, the mainstream artificial intelligence generally adopts the technical path of "attention mechanism + deep learning" + "reinforcement learning".
Forensic Histopathological Recognition via a Context-Aware MIL Network Powered by Self-Supervised Contrastive Learning
Forensic pathology is critical in analyzing death manner and time from the microscopic aspect to assist in the establishment of reliable factual bases for criminal investigation.
FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features
However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.
Client-side Gradient Inversion Against Federated Learning from Poisoning
For the first time, we show the feasibility of a client-side adversary with limited knowledge being able to recover the training samples from the aggregated global model.
AI-Native Transceiver Design for Near-Field Ultra-Massive MIMO: Principles and Techniques
Ultra-massive multiple-input multiple-output (UMMIMO) is a cutting-edge technology that promises to revolutionize wireless networks by providing an unprecedentedly high spectral and energy efficiency.
Towards Real-Time Neural Video Codec for Cross-Platform Application Using Calibration Information
First, to solve the problem of inconsistency of codec caused by the uncertainty of floating point calculations across platforms, we design a calibration transmitting system to guarantee the consistent quantization of entropy parameters between the encoding and decoding stages.
Semantic Communications using Foundation Models: Design Approaches and Open Issues
Foundation models (FMs), including large language models, have become increasingly popular due to their wide-ranging applicability and ability to understand human-like semantics.
Mode Connectivity and Data Heterogeneity of Federated Learning
Empirically, reducing data heterogeneity makes the connectivity on different paths more similar, forming more low-error overlaps between client and global modes.
Effortless Cross-Platform Video Codec: A Codebook-Based Method
Due to the absence of autoregressive modeling and optical flow alignment, we can design an extremely minimalist framework that can greatly benefit computational efficiency.
Hyperspectral Image Fusion via Logarithmic Low-rank Tensor Ring Decomposition
On the other hand, the tensor nuclear norm (TNN)-based approaches have recently demonstrated to be more efficient on keeping high-dimensional low-rank structures in tensor recovery.
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning
Nevertheless, existing MARL algorithms ignore effective information aggregation which is fundamental for improving the learning capacity of decentralized agents.
Learning Bayes-Optimal Channel Estimation for Holographic MIMO in Unknown EM Environments
Holographic MIMO (HMIMO) has recently been recognized as a promising enabler for future 6G systems through the use of an ultra-massive number of antennas in a compact space to exploit the propagation characteristics of the electromagnetic (EM) channel.
Improving Large-scale Deep Biasing with Phoneme Features and Text-only Data in Streaming Transducer
Deep biasing for the Transducer can improve the recognition performance of rare words or contextual entities, which is essential in practical applications, especially for streaming Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Surgical Temporal Action-aware Network with Sequence Regularization for Phase Recognition
Specifically, we propose an efficient multi-scale surgical temporal action (MS-STA) module, which integrates visual features with spatial and temporal knowledge of surgical actions at the cost of 2D networks.
Green Edge AI: A Contemporary Survey
Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO
In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments.
Linear Attention via Orthogonal Memory
Given that orthogonal memory compresses global information, we further dissect the context to amplify fine-grained local information.
Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO
To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm.
Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms using Molecular Configuration Transformer
We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
We show that the generalization performance of a client can be improved only by collaborating with other clients that have more training data and similar data distribution.
MIFI: MultI-camera Feature Integration for Roust 3D Distracted Driver Activity Recognition
Distracted driver activity recognition plays a critical role in risk aversion-particularly beneficial in intelligent transportation systems.
Spatial-Aware Latent Initialization for Controllable Image Generation
Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.
FD-Vision Mamba for Endoscopic Exposure Correction
In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making.
Private Knowledge Sharing in Distributed Learning: A Survey
In this light, it is crucial to utilize information in learning processes that are either distributed or owned by different entities.
Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing System
This paper investigates resource allocation to provide heterogeneous users with customized virtual reality (VR) services in a mobile edge computing (MEC) system.
Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients.
Joint Activity-Delay Detection and Channel Estimation for Asynchronous Massive Random Access: A Free Probability Theory Approach
Grant-free random access (RA) has been recognized as a promising solution to support massive connectivity due to the removal of the uplink grant request procedures.
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
Content-aware Masked Image Modeling Transformer for Stereo Image Compression
Existing learning-based stereo image codec adopt sophisticated transformation with simple entropy models derived from single image codecs to encode latent representations.
Den-SOFT: Dense Space-Oriented Light Field DataseT for 6-DOF Immersive Experience
We have built a custom mobile multi-camera large-space dense light field capture system, which provides a series of high-quality and sufficiently dense light field images for various scenarios.
Decentralizing Coherent Joint Transmission Precoding via Deterministic Equivalents
In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission.
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme.
From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection
To address the challenges posed by system and data heterogeneities in the FL process, we study a goal-directed client selection problem based on the model analytics framework by selecting a subset of clients for the model training.
Task-Aware Encoder Control for Deep Video Compression
Prior research on deep video compression (DVC) for machine tasks typically necessitates training a unique codec for each specific task, mandating a dedicated decoder per task.
Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds
Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem.
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models
Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences.
A Self-adaptive Neuroevolution Approach to Constructing Deep Neural Network Architectures Across Different Types
One of the key settings in SANE is the search space defined by cells and organs self-adapted to different DNN types.
Attentive Multi-Layer Perceptron for Non-autoregressive Generation
Furthermore, we marry AMLP with popular NAR models, deriving a highly efficient NAR-AMLP architecture with linear time and space complexity.
Sluggish and Chemically-Biased Interstitial Diffusion in Concentrated Solid Solution Alloys: Mechanisms and Methods
Interstitial diffusion is a pivotal process that governs the phase stability and irradiation response of materials in non-equilibrium conditions.
$k$Folden: $k$-Fold Ensemble for Out-Of-Distribution Detection
For a task with $k$ training labels, $k$Folden induces $k$ sub-models, each of which is trained on a subset with $k-1$ categories with the left category masked unknown to the sub-model.
Task-Oriented Communication with Out-of-Distribution Detection: An Information Bottleneck Framework
Task-oriented communication is an emerging paradigm for next-generation communication networks, which extracts and transmits task-relevant information, instead of raw data, for downstream applications.
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model
We achieve a biomedical multilingual corpus by incorporating three granularity knowledge alignments (entity, fact, and passage levels) into monolingual corpora.
DeepIlluminance: Contextual Illuminance Estimation via Deep Neural Networks
First, the contextual net with a center-surround architecture extracts local contextual features from image patches, and generates initial illuminant estimates and the corresponding color corrected patches.
Decentralized Statistical Inference with Unrolled Graph Neural Networks
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination.
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty Quantification
Based on the adaptive aPC, a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method is proposed to reduce the training data cost and improve the surrogate model accuracy.
KERS: A Knowledge-Enhanced Framework for Recommendation Dialog Systems with Multiple Subgoals
Recommendation dialogs require the system to build a social bond with users to gain trust and develop affinity in order to increase the chance of a successful recommendation.
Efficient reinforcement learning control for continuum robots based on Inexplicit Prior Knowledge
Moreover, recent reinforcement learning methods are data-inefficient and can not be directly deployed to the robot without simulation.
MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
Federated learning (FL) is a promising framework for privacy-preserving collaborative learning, where model training tasks are distributed to clients and only the model updates need to be collected at a server.
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products.
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data
This enables client models to be updated in a shared feature space with consistent classifiers during local training.
Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems
As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains.
Evaluation of a Canonical Image Representation for Sidescan Sonar
In this paper, a canonical transformation method consisting of intensity correction and slant range correction is proposed to decrease the above distortion.
Complete Dictionary Learning via $\ell_p$-norm Maximization
Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics.
Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment
Urban environments, characterized by their complex, multi-layered networks encompassing physical, social, economic, and environmental dimensions, face significant challenges in the face of rapid urbanization.
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
While federated learning is promising for privacy-preserving collaborative learning without revealing local data, it remains vulnerable to white-box attacks and struggles to adapt to heterogeneous clients.
Context-aware Communication for Multi-agent Reinforcement Learning
Following this, agents utilize attention mechanisms in the second stage to selectively generate messages personalized for the receivers.
Accelerating Physics-Informed Neural Network Training with Prior Dictionaries
Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds.
EPI-based Oriented Relation Networks for Light Field Depth Estimation
Based on the observation that an oriented line and its neighboring pixels in an EPI share a similar linear structure, we propose an end-to-end fully convolutional network (FCN) to estimate the depth value of the intersection point on the horizontal and vertical EPIs.
Boosting Neural Representations for Videos with a Conditional Decoder
Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing, showing remarkable versatility across various video tasks.
