3,939 research outputs found
Model-Checking of Linear-Time Properties Based on Possibility Measure
We study the LTL model-checking in possibilistic Kripke structure using
possibility measure. First, the notion of possibilistic Kripke structure and
the related possibility measure are introduced, then model-checking of
reachability and repeated reachability linear-time properties in finite
possibilistic Kripke structure are studied. Standard safety property and
-regular property in possibilistic Kripke structure are introduced, the
verification of regular safety property and -regular property using finite
automata are thoroughly studied. It has been shown that the verification of
regular safety property and -regular property in finite possibilistic Kripke
structure can be transformed into the verification of reachability property and
repeated reachability property in the product possibilistic Kripke structure
introduced in this paper. Several examples are given to illustrate the methods
presented in the paper.Comment: 22pages,5 figure
End-to-End Video Captioning with Multitask Reinforcement Learning
Although end-to-end (E2E) learning has led to impressive progress on a
variety of visual understanding tasks, it is often impeded by hardware
constraints (e.g., GPU memory) and is prone to overfitting. When it comes to
video captioning, one of the most challenging benchmark tasks in computer
vision, those limitations of E2E learning are especially amplified by the fact
that both the input videos and output captions are lengthy sequences. Indeed,
state-of-the-art methods for video captioning process video frames by
convolutional neural networks and generate captions by unrolling recurrent
neural networks. If we connect them in an E2E manner, the resulting model is
both memory-consuming and data-hungry, making it extremely hard to train. In
this paper, we propose a multitask reinforcement learning approach to training
an E2E video captioning model. The main idea is to mine and construct as many
effective tasks (e.g., attributes, rewards, and the captions) as possible from
the human captioned videos such that they can jointly regulate the search space
of the E2E neural network, from which an E2E video captioning model can be
found and generalized to the testing phase. To the best of our knowledge, this
is the first video captioning model that is trained end-to-end from the raw
video input to the caption output. Experimental results show that such a model
outperforms existing ones to a large margin on two benchmark video captioning
datasets
Verifying Probabilistic Timed Automata Against Omega-Regular Dense-Time Properties
Probabilistic timed automata (PTAs) are timed automata (TAs) extended with
discrete probability distributions.They serve as a mathematical model for a
wide range of applications that involve both stochastic and timed behaviours.
In this work, we consider the problem of model-checking linear
\emph{dense-time} properties over {PTAs}. In particular, we study linear
dense-time properties that can be encoded by TAs with infinite acceptance
criterion.First, we show that the problem of model-checking PTAs against
deterministic-TA specifications can be solved through a product construction.
Based on the product construction, we prove that the computational complexity
of the problem with deterministic-TA specifications is EXPTIME-complete. Then
we show that when relaxed to general (nondeterministic) TAs, the model-checking
problem becomes undecidable.Our results substantially extend state of the art
with both the dense-time feature and the nondeterminism in TAs
Query-Efficient Black-Box Attack by Active Learning
Deep neural network (DNN) as a popular machine learning model is found to be
vulnerable to adversarial attack. This attack constructs adversarial examples
by adding small perturbations to the raw input, while appearing unmodified to
human eyes but will be misclassified by a well-trained classifier. In this
paper, we focus on the black-box attack setting where attackers have almost no
access to the underlying models. To conduct black-box attack, a popular
approach aims to train a substitute model based on the information queried from
the target DNN. The substitute model can then be attacked using existing
white-box attack approaches, and the generated adversarial examples will be
used to attack the target DNN. Despite its encouraging results, this approach
suffers from poor query efficiency, i.e., attackers usually needs to query a
huge amount of times to collect enough information for training an accurate
substitute model. To this end, we first utilize state-of-the-art white-box
attack methods to generate samples for querying, and then introduce an active
learning strategy to significantly reduce the number of queries needed.
Besides, we also propose a diversity criterion to avoid the sampling bias. Our
extensive experimental results on MNIST and CIFAR-10 show that the proposed
method can reduce more than of queries while preserve attacking success
rates and obtain an accurate substitute model which is more than similar
with the target oracle.Comment: 9 page
A Simple Analysis for Exp-concave Empirical Minimization with Arbitrary Convex Regularizer
In this paper, we present a simple analysis of {\bf fast rates} with {\it
high probability} of {\bf empirical minimization} for {\it stochastic composite
optimization} over a finite-dimensional bounded convex set with exponential
concave loss functions and an arbitrary convex regularization. To the best of
our knowledge, this result is the first of its kind. As a byproduct, we can
directly obtain the fast rate with {\it high probability} for exponential
concave empirical risk minimization with and without any convex regularization,
which not only extends existing results of empirical risk minimization but also
provides a unified framework for analyzing exponential concave empirical risk
minimization with and without {\it any} convex regularization. Our proof is
very simple only exploiting the covering number of a finite-dimensional bounded
set and a concentration inequality of random vectors
Combinatorial Constructions of Optimal Optical Orthogonal Signature Pattern Codes
Optical orthogonal signature pattern codes (OOSPCs) play an important role in
a novel type of optical code-division multiple-access (CDMA) network for
2-dimensional image transmission. There is a one-to-one correspondence between
an -OOSPC and a - packing design
admitting an automorphism group isomorphic to . In 2010, Sawa gave the first infinite class of -OOSPCs by using -cyclic Steiner quadruple systems. In this paper, we use
various combinatorial designs such as strictly -invariant -fan designs, strictly -invariant -designs and rotational Steiner quadruple systems to
present some constructions for -OOSPCs. As a consequence, our new
constructions yield more infinite families of optimal -OOSPCs.
Especially, we shall see that in some cases an optimal -OOSPC can
not achieve the Johnson bound.Comment: 24 pages. arXiv admin note: text overlap with arXiv:1312.7589 by
other author
Constraining cosmological parameters in FLRW metric with lensed GW+EM signals
We proposed a model-independent method to constrain the cosmological
parameters using the Distance Sum Rule of the FLRW metric by combining the time
delay distances and the comoving distances through a multi-messenger approach.
The time delay distances are measured from lensed gravitational wave~(GW)
signals together with their corresponding electromagnetic wave~(EM)
counterpart, while the comoving distances are obtained from a parametrized
fitting approach with independent supernova observations. With a series of
simulations based on Einstein Telescope, Large Synoptic Survey Telescope and
The Dark Energy Survey, we find that only 10 lensed GW+EM systems can achieve
the constraining power comparable to and even stronger than 300 lensed quasar
systems due to more precise time delay from lensed GW signals. Specifically,
the cosmological parameters can be constrained to ~ and
~ (1). Our results show that more precise
time delay measurements could provide more stringent cosmological parameter
values, and lensed GW+EM systems therefore can be applied as a powerful tool in
the future precision cosmology.Comment: Accepted for publication in The Astrophysical Journa
A design-driven partitioning algorithm for distributed Verilog simulation
Many partitioning algorithms have been proposed for distributed VLSI simulation. Typically, they make use of a gate level netlist, and attempt to achieve a minimal cut size subject to a load balance constraint. The algorithm executes on a hypergraph which represents the netlist. In this paper we propose a design-driven iterative partitioning algorithm for Verilog based on module instances instead of gates. We do this in order to take advantage of the design hierarchy information contained in the modules and their instances. A Verilog instance represents one vertex in the circuit hypergraph. The vertex can be flattened into multiple vertices in the event that a load balance is not achieved by instance based partitioning. In this case the algorithm flattens the largest instance and moves gates between the partitions in order to improve the load balance. Our experiments show that this partitioning algorithm produces a smaller cutsize than is produced by hmetis on a gate-level netlist. It produces better speedup for the simulation because it takes advantage of the design hierarchy.
Constructions of Augmented Orthogonal Arrays
Augmented orthogonal arrays (AOAs) were introduced by Stinson, who showed the
equivalence between ideal ramp schemes and augmented orthogonal arrays
(Discrete Math. 341 (2018), 299-307). In this paper, we show that there is an
AOA if and only if there is an OA which can be partitioned
into subarrays, each being an OA, and that there is a linear
AOA if and only if there is a linear maximum distance separable
(MDS) code of length and dimension over which contains a
linear MDS subcode of length and dimension over . Some
constructions for AOAs and some new infinite classes of AOAs are also given.Comment: 10 page
Anisotropic giant magnetoresistance in NbSb2
The extremely large transverse magnetoreistance (the magnetoresistant ratio
in 2 K and 9 T field, and in 0.4 K
and 32 T field, without saturation), and the metal-semiconductor crossover
induced by magnetic field, are reported in NbSb single crystal with
electric current parallel to the -axis. The metal-semiconductor crossover is
preserved when the current is along the -plane but the magnetoresistant
ratio is significantly suppressed. The sign reversal of the Hall resistivity in
the field close to the crossover point, and the electronic structure
calculation reveals the coexistence of a small number of holes with very high
mobility and a large number of electrons with low mobility. These effects are
attributed to the change of the Fermi surface induced by the magnetic field.Comment: 5 pages, 4 figure
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