Search Results for author:
Found 17 papers, 1 papers with code
Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems
The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols.
Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma
Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG).
Robust MIMO Detection With Imperfect CSI: A Neural Network Solution
Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies.
Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services
Our findings demonstrate that stakeholders, even without AI knowledge, can provide specific and critical feedback on an AI system's design and deployment, if empowered to do so.
Radio Frequency Fingerprints Extraction for LTE-V2X: A Channel Estimation Based Methodology
Then, we remove the impact of the wireless channel based on the channel estimate and obtain initial RFF features.
Understanding Practices, Challenges, and Opportunities for User-Engaged Algorithm Auditing in Industry Practice
Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems.
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification
CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians.
"Public(s)-in-the-Loop": Facilitating Deliberation of Algorithmic Decisions in Contentious Public Policy Domains
This position paper offers a framework to think about how to better involve human influence in algorithmic decision-making of contentious public policy issues.
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing
Coflow is a recently proposed networking abstraction to help improve the communication performance of data-parallel computing jobs.
TaCE: Time-aware Convolutional Embedding Learning for Temporal Knowledge Graph Completion
Temporal knowledge graph completion (TKGC) is a challenging task to infer the missing component for quadruples.
ICINet: ICI-Aware Neural Network Based Channel Estimation for Rapidly Time-Varying OFDM Systems
A novel intercarrier interference (ICI)-aware orthogonal frequency division multiplexing (OFDM) channel estimation network ICINet is presented for rapidly time-varying channels.
Hadron-quark mixed phase in the quark-meson coupling model
Using a consistent value of B in the QMC model and quark matter, we find that hadron-quark pasta phases are formed in the interior of massive stars, but no pure quark matter can exist.
Nuclear Theory
NEMR: Network Embedding on Metric of Relation
In this paper, We propose a novel method called Network Embedding on the Metric of Relation, abbreviated as NEMR, which can learn the embeddings of nodes in a relational metric space efficiently.
Symmetry energy effect on the secondary component of GW190814 as a neutron star
The constraints from the mass of 2. 6 $M_{\odot}$ and the tidal deformability $\Lambda_{1. 4}=616_{-158}^{+273}$ (based on the assumption that GW190814 is a neutron star-black hole binary) can be satisfied as the slope of symmetry energy $L \leq 50$ MeV.
Nuclear Theory High Energy Astrophysical Phenomena
Value Cards: An Educational Toolkit for Teaching Social Impacts of Machine Learning through Deliberation
In this paper, we present Value Card, an educational toolkit to inform students and practitioners of the social impacts of different machine learning models via deliberation.
Differentially Private k-Means Clustering with Guaranteed Convergence
This problem severely impacts the clustering quality and the efficiency of a differentially private algorithm.
Neural Variational Hybrid Collaborative Filtering
Specifically, we consider both the generative processes of users and items, and the prior of latent factors of users and items to be side informationspecific, which enables our model to alleviate matrix sparsity and learn better latent representations of users and items.
