127 research outputs found
Remarks on propagation of smallness for solutions of elliptic equations in the plane
We explore quantitative propagation of smallness for solutions of
two-dimensional elliptic equations and their gradients from sets of positive
-dimensional Hausdorff content for any .Comment: 8 page
Data Poisoning Attacks in Contextual Bandits
We study offline data poisoning attacks in contextual bandits, a class of
reinforcement learning problems with important applications in online
recommendation and adaptive medical treatment, among others. We provide a
general attack framework based on convex optimization and show that by slightly
manipulating rewards in the data, an attacker can force the bandit algorithm to
pull a target arm for a target contextual vector. The target arm and target
contextual vector are both chosen by the attacker. That is, the attacker can
hijack the behavior of a contextual bandit. We also investigate the feasibility
and the side effects of such attacks, and identify future directions for
defense. Experiments on both synthetic and real-world data demonstrate the
efficiency of the attack algorithm.Comment: GameSec 201
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
A phase field model for mass transport with semi-permeable interfaces
In this paper, a thermal-dynamical consistent model for mass transfer across
permeable moving interfaces is proposed by using the energy variation method.
We consider a restricted diffusion problem where the flux across the interface
depends on its conductance and the difference of the concentration on each
side. The diffusive interface phase-field framework used here has several
advantages over the sharp interface method. First of all, explicit tracking of
the interface is no longer necessary. Secondly, the interfacial condition can
be incorporated with a variable diffusion coefficient. A detailed asymptotic
analysis confirms the diffusive interface model converges to the existing sharp
interface model as the interface thickness goes to zero. A decoupled energy
stable numerical scheme is developed to solve this system efficiently.
Numerical simulations first illustrate the consistency of theoretical results
on the sharp interface limit. Then a convergence study and energy decay test
are conducted to ensure the efficiency and stability of the numerical scheme.
To illustrate the effectiveness of our phase-field approach, several examples
are provided, including a study of a two-phase mass transfer problem where
drops with deformable interfaces are suspended in a moving fluid.Comment: 20 pages, 15 figure
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
Kalman Filter (KF) is widely used in various domains to perform sequential
learning or variable estimation. In the context of autonomous vehicles, KF
constitutes the core component of many Advanced Driver Assistance Systems
(ADAS), such as Forward Collision Warning (FCW). It tracks the states
(distance, velocity etc.) of relevant traffic objects based on sensor
measurements. The tracking output of KF is often fed into downstream logic to
produce alerts, which will then be used by human drivers to make driving
decisions in near-collision scenarios. In this paper, we study adversarial
attacks on KF as part of the more complex machine-human hybrid system of
Forward Collision Warning. Our attack goal is to negatively affect human
braking decisions by causing KF to output incorrect state estimations that lead
to false or delayed alerts. We accomplish this by sequentially manipulating
measure ments fed into the KF, and propose a novel Model Predictive Control
(MPC) approach to compute the optimal manipulation. Via experiments conducted
in a simulated driving environment, we show that the attacker is able to
successfully change FCW alert signals through planned manipulation over
measurements prior to the desired target time. These results demonstrate that
our attack can stealthily mislead a distracted human driver and cause vehicle
collisions.Comment: Accepted by AAAI2
- …