10,792 research outputs found
Permission-Based Separation Logic for Multithreaded Java Programs
This paper presents a program logic for reasoning about multithreaded
Java-like programs with dynamic thread creation, thread joining and reentrant
object monitors. The logic is based on concurrent separation logic. It is the
first detailed adaptation of concurrent separation logic to a multithreaded
Java-like language. The program logic associates a unique static access
permission with each heap location, ensuring exclusive write accesses and
ruling out data races. Concurrent reads are supported through fractional
permissions. Permissions can be transferred between threads upon thread
starting, thread joining, initial monitor entrancies and final monitor exits.
In order to distinguish between initial monitor entrancies and monitor
reentrancies, auxiliary variables keep track of multisets of currently held
monitors. Data abstraction and behavioral subtyping are facilitated through
abstract predicates, which are also used to represent monitor invariants,
preconditions for thread starting and postconditions for thread joining.
Value-parametrized types allow to conveniently capture common strong global
invariants, like static object ownership relations. The program logic is
presented for a model language with Java-like classes and interfaces, the
soundness of the program logic is proven, and a number of illustrative examples
are presented
Multi-party Poisoning through Generalized -Tampering
In a poisoning attack against a learning algorithm, an adversary tampers with
a fraction of the training data with the goal of increasing the
classification error of the constructed hypothesis/model over the final test
distribution. In the distributed setting, might be gathered gradually from
data providers who generate and submit their shares of
in an online way.
In this work, we initiate a formal study of -poisoning attacks in
which an adversary controls of the parties, and even for each
corrupted party , the adversary submits some poisoned data on
behalf of that is still "-close" to the correct data (e.g.,
fraction of is still honestly generated). For , this model
becomes the traditional notion of poisoning, and for it coincides with
the standard notion of corruption in multi-party computation.
We prove that if there is an initial constant error for the generated
hypothesis , there is always a -poisoning attacker who can decrease
the confidence of (to have a small error), or alternatively increase the
error of , by . Our attacks can be implemented in
polynomial time given samples from the correct data, and they use no wrong
labels if the original distributions are not noisy.
At a technical level, we prove a general lemma about biasing bounded
functions through an attack model in which each
block might be controlled by an adversary with marginal probability
in an online way. When the probabilities are independent, this coincides with
the model of -tampering attacks, thus we call our model generalized
-tampering. We prove the power of such attacks by incorporating ideas from
the context of coin-flipping attacks into the -tampering model and
generalize the results in both of these areas
Sampling from a system-theoretic viewpoint: Part I - Concepts and tools
This paper is first in a series of papers studying a system-theoretic approach to the problem of reconstructing an analog signal from its samples. The idea, borrowed from earlier treatments in the control literature, is to address the problem as a hybrid model-matching problem in which performance is measured by system norms. In this paper we present the paradigm and revise underlying technical tools, such as the lifting technique and some topics of the operator theory. This material facilitates a systematic and unified treatment of a wide range of sampling and reconstruction problems, recovering many hitherto considered different solutions and leading to new results. Some of these applications are discussed in the second part
Convergence analysis of block Gibbs samplers for Bayesian linear mixed models with
Exploration of the intractable posterior distributions associated with
Bayesian versions of the general linear mixed model is often performed using
Markov chain Monte Carlo. In particular, if a conditionally conjugate prior is
used, then there is a simple two-block Gibbs sampler available. Rom\'{a}n and
Hobert [Linear Algebra Appl. 473 (2015) 54-77] showed that, when the priors are
proper and the matrix has full column rank, the Markov chains underlying
these Gibbs samplers are nearly always geometrically ergodic. In this paper,
Rom\'{a}n and Hobert's (2015) result is extended by allowing improper priors on
the variance components, and, more importantly, by removing all assumptions on
the matrix. So, not only is allowed to be (column) rank deficient,
which provides additional flexibility in parameterizing the fixed effects, it
is also allowed to have more columns than rows, which is necessary in the
increasingly important situation where . The full rank assumption on
is at the heart of Rom\'{a}n and Hobert's (2015) proof. Consequently, the
extension to unrestricted requires a substantially different analysis.Comment: Published at http://dx.doi.org/10.3150/15-BEJ749 in the Bernoulli
(http://isi.cbs.nl/bernoulli/) by the International Statistical
Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm
Samplers and Extractors for Unbounded Functions
Blasiok (SODA\u2718) recently introduced the notion of a subgaussian sampler, defined as an averaging sampler for approximating the mean of functions f from {0,1}^m to the real numbers such that f(U_m) has subgaussian tails, and asked for explicit constructions. In this work, we give the first explicit constructions of subgaussian samplers (and in fact averaging samplers for the broader class of subexponential functions) that match the best known constructions of averaging samplers for [0,1]-bounded functions in the regime of parameters where the approximation error epsilon and failure probability delta are subconstant. Our constructions are established via an extension of the standard notion of randomness extractor (Nisan and Zuckerman, JCSS\u2796) where the error is measured by an arbitrary divergence rather than total variation distance, and a generalization of Zuckerman\u27s equivalence (Random Struct. Alg.\u2797) between extractors and samplers. We believe that the framework we develop, and specifically the notion of an extractor for the Kullback-Leibler (KL) divergence, are of independent interest. In particular, KL-extractors are stronger than both standard extractors and subgaussian samplers, but we show that they exist with essentially the same parameters (constructively and non-constructively) as standard extractors
Bootstrapping bilinear models of robotic sensorimotor cascades
We consider the bootstrapping problem, which consists in learning a model of the agent's sensors and actuators starting from zero prior information, and we take the problem of servoing as a cross-modal task to validate the learned models. We study the class of bilinear dynamics sensors, in which the derivative of the observations are a bilinear form of the control commands and the observations themselves. This class of models is simple yet general enough to represent the main phenomena of three representative robotics sensors (field sampler, camera, and range-finder), apparently very different from one another. It also allows a bootstrapping algorithm based on hebbian learning, and that leads to a simple and bioplausible control strategy. The convergence properties of learning and control are demonstrated with extensive simulations and by analytical arguments
Geometric Ergodicity of Gibbs Samplers in Bayesian Penalized Regression Models
We consider three Bayesian penalized regression models and show that the
respective deterministic scan Gibbs samplers are geometrically ergodic
regardless of the dimension of the regression problem. We prove geometric
ergodicity of the Gibbs samplers for the Bayesian fused lasso, the Bayesian
group lasso, and the Bayesian sparse group lasso. Geometric ergodicity along
with a moment condition results in the existence of a Markov chain central
limit theorem for Monte Carlo averages and ensures reliable output analysis.
Our results of geometric ergodicity allow us to also provide default starting
values for the Gibbs samplers
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