4,949 research outputs found
A Cautionary Note on Doubly Robust Estimators Involving Continuous-time Structure
Nowadays, more literature estimates their parameters of interest relying on
estimating equations with two or more nuisance parameters. In some cases, one
might be able to find a population-level doubly (or possibly multiply) robust
estimating equation which has zero mean provided one of the nuisance parameters
is correctly specified, without knowing which. This property is appealing in
practice because it suggests "model doubly robust" estimators that entail extra
protection against model misspecification. Typically asymptotic inference of
such a doubly robust estimator is relatively simple through classical
Z-estimation theory under standard regularity conditions. In other cases,
machine learning techniques are leveraged to achieve "rate double robustness",
with cross fitting. However, the classical theory might be insufficient when
all nuisance parameters involve complex time structures and are possibly in the
form of continuous-time stochastic nuisance processes. In such cases, we
caution that extra assumptions are needed, especially on total variation. In
this paper, as an example, we consider a general class of double robust
estimating equations and develop generic assumptions on the asymptotic
properties of the estimators of nuisance parameters such that the resulted
estimator for the parameter of interest is consistent and asymptotically
normal. We illustrate our framework in some examples. We also caution a gap
between population double robustness and rate double robustness
Causality of Functional Longitudinal Data
"Treatment-confounder feedback" is the central complication to resolve in
longitudinal studies, to infer causality. The existing frameworks for
identifying causal effects for longitudinal studies with discrete repeated
measures hinge heavily on assuming that time advances in discrete time steps or
treatment changes as a jumping process, rendering the number of "feedbacks"
finite. However, medical studies nowadays with real-time monitoring involve
functional time-varying outcomes, treatment, and confounders, which leads to an
uncountably infinite number of feedbacks between treatment and confounders.
Therefore more general and advanced theory is needed. We generalize the
definition of causal effects under user-specified stochastic treatment regimes
to longitudinal studies with continuous monitoring and develop an
identification framework, allowing right censoring and truncation by death. We
provide sufficient identification assumptions including a generalized
consistency assumption, a sequential randomization assumption, a positivity
assumption, and a novel "intervenable" assumption designed for the
continuous-time case. Under these assumptions, we propose a g-computation
process and an inverse probability weighting process, which suggest a
g-computation formula and an inverse probability weighting formula for
identification. For practical purposes, we also construct two classes of
population estimating equations to identify these two processes, respectively,
which further suggest a doubly robust identification formula with extra
robustness against process misspecification. We prove that our framework fully
generalize the existing frameworks and is nonparametric
Cloaking the Clock: Emulating Clock Skew in Controller Area Networks
Automobiles are equipped with Electronic Control Units (ECU) that communicate
via in-vehicle network protocol standards such as Controller Area Network
(CAN). These protocols are designed under the assumption that separating
in-vehicle communications from external networks is sufficient for protection
against cyber attacks. This assumption, however, has been shown to be invalid
by recent attacks in which adversaries were able to infiltrate the in-vehicle
network. Motivated by these attacks, intrusion detection systems (IDSs) have
been proposed for in-vehicle networks that attempt to detect attacks by making
use of device fingerprinting using properties such as clock skew of an ECU. In
this paper, we propose the cloaking attack, an intelligent masquerade attack in
which an adversary modifies the timing of transmitted messages in order to
match the clock skew of a targeted ECU. The attack leverages the fact that,
while the clock skew is a physical property of each ECU that cannot be changed
by the adversary, the estimation of the clock skew by other ECUs is based on
network traffic, which, being a cyber component only, can be modified by an
adversary. We implement the proposed cloaking attack and test it on two IDSs,
namely, the current state-of-the-art IDS and a new IDS that we develop based on
the widely-used Network Time Protocol (NTP). We implement the cloaking attack
on two hardware testbeds, a prototype and a real connected vehicle, and show
that it can always deceive both IDSs. We also introduce a new metric called the
Maximum Slackness Index to quantify the effectiveness of the cloaking attack
even when the adversary is unable to precisely match the clock skew of the
targeted ECU.Comment: 11 pages, 13 figures, This work has been accepted to the 9th ACM/IEEE
International Conference on Cyber-Physical Systems (ICCPS
Carbon dioxide and fruit odor transduction in Drosophila olfactory neurons. What controls their dynamic properties?
We measured frequency response functions between odorants and action potentials in two types of neurons in Drosophila antennal basiconic sensilla. CO2 was used to stimulate ab1C neurons, and the fruit odor ethyl butyrate was used to stimulate ab3A neurons. We also measured frequency response functions for light-induced action potential responses from transgenic flies expressing H134R-channelrhodopsin-2 (ChR2) in the ab1C and ab3A neurons. Frequency response functions for all stimulation methods were well-fitted by a band-pass filter function with two time constants that determined the lower and upper frequency limits of the response. Low frequency time constants were the same in each type of neuron, independent of stimulus method, but varied between neuron types. High frequency time constants were significantly slower with ethyl butyrate stimulation than light or CO2 stimulation. In spite of these quantitative differences, there were strong similarities in the form and frequency ranges of all responses. Since light-activated ChR2 depolarizes neurons directly, rather than through a chemoreceptor mechanism, these data suggest that low frequency dynamic properties of Drosophila olfactory sensilla are dominated by neuron-specific ionic processes during action potential production. In contrast, high frequency dynamics are limited by processes associated with earlier steps in odor transduction, and CO2 is detected more rapidly than fruit odor
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