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Found 12 papers, 5 papers with code
Dynamic Uploading Scheduling in mmWave-Based Sensor Networks via Mobile Blocker Detection
As a result, the AoI degradation arising from link blockage can be forecast and mitigated.
A Heterogeneous 6G Networked Sensing Architecture with Active and Passive Anchors
In this paper, we show that the distance between a target and its associated IRS can be indirectly estimated based on the length of the BS-target-BS path and the BS-target-IRS-BS path.
Predictive Resource Allocation in mmWave Systems with Rotation Detection
Millimeter wave (MmWave) has been regarded as a promising technology to support high-capacity communications in 5G era.
A Survey of Machine Learning-Based Ride-Hailing Planning
We further shed light on many real-world datasets and simulators that are indispensable for empirical studies on machine learning-based ride-hailing planning strategies.
Improved Target-specific Stance Detection on Social Media Platforms by Delving into Conversation Threads
In response, we address a new task called conversational stance detection which is to infer the stance towards a given target (e. g., COVID-19 vaccination) when given a data instance and its corresponding conversation thread.
Trilateration-Based Device-Free Sensing: Two Base Stations and One Passive IRS Are Sufficient
The classic trilateration technique can localize each target based on its distances to three anchors with known coordinates.
Exploiting Temporal Side Information in Massive IoT Connectivity
In particular, we propose to leverage the temporal correlation in device activity, e. g., a device active in the previous coherence block is more likely to be still active in the current coherence block, to improve the detection and estimation performance.
One model Packs Thousands of Items with Recurrent Conditional Query Learning
In this paper, we propose a Recurrent Conditional Query Learning (RCQL) method to solve both 2D and 3D packing problems.
On Massive IoT Connectivity with Temporally-Correlated User Activity
In particular, we propose to leverage the temporal correlation in user activity, i. e., a device active at the previous time slot is more likely to be still active at the current moment, to improve the detection performance.
Action Detection
Activity Detection
Information Theory
Signal Processing
Information Theory
Online Influence Maximization in Non-Stationary Social Networks
Nevertheless, the existing studies mostly investigate the problem on a one-off basis, assuming fixed known influence probabilities among users, or the knowledge of the exact social network topology.
Category Enhanced Word Embedding
In this paper, we incorporate category information of documents in the learning of word representations and to learn the proposed models in a document-wise manner.
A C-LSTM Neural Network for Text Classification
In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification.
Ranked #10 on
Text Classification
on TREC-6
