In this paper we investigate the problem of secure instantiation of the random oracle, based on the concepts and methods of algorithmic randomness. We show that, for any secure signature scheme in the random oracle model, there exists a specific computable function which can instantiate the random oracle while keeping the security originally proved in the random oracle model. Our results use th...

We address the problem of polynomial time factoring RSA moduli N1 = p1q1 with the help of an oracle. As opposed to other approaches that require an oracle that explicitly outputs bits of p1, we use an oracle that gives only implicit information about p1. Namely, our oracle outputs a different N2 = p2q2 such that p1 and p2 share the t least significant bits. Surprisingly, this implicit informati...

In this talk1 we will present new results obtained in the multiple kernel ridge regression framework, which we refer to as multi-task regression. Multi-task techniques come into play when experimental limitations makes it impossible to increase the sample size n, which is the classical way to improve the performance of the estimators. However, it is often possible to have access to other closel...

s of the 6th World Congress on the Internet in Medicine (MEDNET 2001), December 2001, Udine, Italy. [8] Oracle Application Server Administration Guide (A6017201), Oracle Application Server 4.0 Documentation Set. Oracle

In this paper, we introduce oracle (finite) automata that are finite/Buchi automata augmented with oracles in some classes of formal languages. We present some testability results for the emptiness problem of oracle automata associated with various classes of oracles. Moreover, we show that some important verification problems (such as reachability, safety, LTL model-checking, etc.) for oracle ...

Conditional gradient algorithms (also often called Frank-Wolfe algorithms) are popular due to their simplicity of only requiring a linear optimization oracle and more recently they also gained significant traction for online learning. While simple in principle, in many cases the actual implementation of the linear optimization oracle is costly. We show a general method to lazify various conditi...

Often the best model to solve a real world problem is relatively complex. The following presents oracle learning, a method using a larger model as an oracle to train a smaller model on unlabeled data in order to obtain (1) a simpler acceptable model and (2) improved results over standard training methods on a similarly sized smaller model. In particular, this paper looks at oracle learning as a...

Our problem is to evaluate a multi-valued Boolean function F through oracle calls. If F is one-to-one and the size of its domain and range is the same, then our problem can be formulated as follows: Given an oracle f(a, x) : {0, 1}n × {0, 1}n → {0, 1} and a fixed (but hidden) value a0, we wish to obtain the value of a0 by querying the oracle f(a0, x). Our goal is to minimize the number of such ...

Your SAP environment is critical to business operations. Migrating your SAP systems to Oracle Solaris delivers the availability, performance, scalability, flexibility, and security you need for mission-critical operations, all at a lower total cost of ownership than other UNIX and Linux systems. And, Oracle ACS Lifecycle Migration Services and Oracle Migration Factory help you make the change q...

I. Reconstruction functions: Demonstration code: 1. xout=ADMM WT(S,b,param) Demo ADMM WT.m 2. xout=ADMM WT CT(S,b,param) Demo ADMM WT CT.m 3. xout=ADMM outer(S,b) Demo Multiscale ADMM WT CT.m. II. Sampling functions: Demonstration code: 1. S = Oracle Random Sampling( x0, sp ) Demo Oracle Random Sampling.m 2. S = Oracle Random Sampling with PCA( x0, sp, Spilot ) Demo Oracle Random Sampling with ...