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

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 (m,n,4,2)(m, n,4,2) 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 $(m, n, w, \lambda)$-OOSPC and a $(\lambda+1)$-$(mn,w,1)$ packing design admitting an automorphism group isomorphic to $\mathbb{Z}_m\times \mathbb{Z}_n$. In 2010, Sawa gave the first infinite class of $(m, n, 4, 2)$-OOSPCs by using $S$-cyclic Steiner quadruple systems. In this paper, we use various combinatorial designs such as strictly $\mathbb{Z}_m\times \mathbb{Z}_n$-invariant $s$-fan designs, strictly $\mathbb{Z}_m\times \mathbb{Z}_n$-invariant $G$-designs and rotational Steiner quadruple systems to present some constructions for $(m, n, 4, 2)$-OOSPCs. As a consequence, our new constructions yield more infinite families of optimal $(m, n, 4, 2)$-OOSPCs. Especially, we shall see that in some cases an optimal $(m, n, 4, 2)$-OOSPC can not achieve the Johnson bound.Comment: 24 pages. arXiv admin note: text overlap with arXiv:1312.7589 by other author

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 $90\%$ of queries while preserve attacking success rates and obtain an accurate substitute model which is more than $85\%$ similar with the target oracle.Comment: 9 page

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 ~$k=0.01_{-0.05}^{+0.05}$ and ~$H_0=69.7_{-0.35}^{+0.35}$ (1$\sigma$). 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$(s,t,k,v)$ if and only if there is an OA$(t,k,v)$ which can be partitioned into $v^{t-s}$ subarrays, each being an OA$(s,k,v)$, and that there is a linear AOA$(s,t,k,q)$ if and only if there is a linear maximum distance separable (MDS) code of length $k$ and dimension $t$ over $\mathbb{F}_q$ which contains a linear MDS subcode of length $k$ and dimension $s$ over $\mathbb{F}_q$. 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 $\sim 1.3\times10^5\%$ in 2 K and 9 T field, and $4.3\times 10^6\%$ in 0.4 K and 32 T field, without saturation), and the metal-semiconductor crossover induced by magnetic field, are reported in NbSb$_2$ single crystal with electric current parallel to the $b$-axis. The metal-semiconductor crossover is preserved when the current is along the $ac$-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