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Found 14 papers, 3 papers with code
Physical Layer Design for Ambient IoT
One way to serve such applications is through backscatter devices, which communicate using energy harvested from ambient sources such as radio waves transmitted by a reader.
A Machine Learning based Hybrid Receiver for 5G NR PRACH
Random Access is a critical procedure using which a User Equipment (UE) identifies itself to a Base Station (BS).
A Quick Guide to Quantum Communication
This article provides a quick overview of quantum communication, bringing together several innovative aspects of quantum enabled transmission.
Enhancements for 5G NR PRACH Reception: An AI/ML Approach
The UE identifies itself by embedding a Preamble Index (RAPID) in the phase rotation of a known base sequence, which it transmits on the Physical Random Access Channel (PRACH).
Causally-guided Regularization of Graph Attention Improves Generalizability
Graph attention networks estimate the relational importance of node neighbors to aggregate relevant information over local neighborhoods for a prediction task.
Granger causal inference on DAGs identifies genomic loci regulating transcription
When a dynamical system can be modeled as a sequence of observations, Granger causality is a powerful approach for detecting predictive interactions between its variables.
Consenting to Internet of Things Across Different Social Settings
Devices connected to the Internet of Things (IoT) are rapidly becoming ubiquitous across modern homes, workplaces, and other social environments.
Cryptography and Security
Ultra-dense Low Data Rate (UDLD) Communication in the THz
Using distributed device-to-device (D2D) communication in the THz, we can cater to these Ultra-dense Low Data Rate (UDLD) type applications.
MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) application
One solution is to maximize the user coverage for a single AP, which might be dependent on multiple parameters.
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia.
Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference
A regression-based BNN model is proposed to predict spatiotemporal quantities like hourly rider demand with calibrated uncertainties.
Deep Reinforcement Learning for Green Security Games with Real-Time Information
Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.
Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research.
Designing the Game to Play: Optimizing Payoff Structure in Security Games
We study Stackelberg Security Games where the defender, in addition to allocating defensive resources to protect targets from the attacker, can strategically manipulate the attacker's payoff under budget constraints in weighted L^p-norm form regarding the amount of change.
