Rudy Raymond

Fast and scalable algorithms for semi-supervised link prediction on static and dynamic graphs

Recent years have witnessed a widespread interest on methods using both link structure and node information for link prediction on graphs. One of the state-of-the-art methods is Link Propagation which is a new semi-supervised learning algorithm for link prediction on graphs based on the popularly-studied label propagation by exploiting information on similarities of links and nodes. Despite its efficiency and effectiveness compared to other methods, its applications were still limited due to the computational time and space constraints. In this paper, we propose fast and scalable algorithms for the Link Propagation by introducing efficient procedures to solve large linear equations that appear in the method. In particular, we show how to obtain a compact representation of the solution to the linear equations by using a non-trivial combination of techniques in linear algebra to construct algorithms that are also effective for link prediction on dynamic graphs. These enable us to apply the Link Propagation to large networks with more than 400,000 nodes. Experiments demonstrate that our approximation methods are scalable, fast, and their prediction qualities are comparably competitive.

Dependable virtual machine allocation

The difficulty in allocating virtual machines (VMs) on servers stems from the requirement that sufficient resources (such as CPU capacity and network bandwidth) must be available for each VM in the event of a failure or maintenance work as well as for temporal fluctuations of resource demands, which often exhibit periodic patterns. We propose a mixed integer programming approach that considers the fluctuations of the resource demands for optimal and dependable allocation of VMs. At the heart of the approach are techniques for optimally partitioning the time-horizon into intervals of variable lengths and for reliably estimating the resource demands in each interval. We show that our new approach allocates VMs successfully in a cloud computing environment in a financial company, where the dependability requirement is strict and there are various types of VMs exist.

(4,1)-Quantum random access coding does not exist—one qubit is not enough to recover one of four bits

An (n,1,p)-quantum random access (QRA) coding, introduced by Ambainis et al (1999 ACM Symp. Theory of Computing p 376), is the following communication system: the sender which has n-bit information encodes his/her information into one qubit, which is sent to the receiver. The receiver can recover any one bit of the original n bits correctly with probability at least p, through a certain decoding process based on positive operator-valued measures. Actually, Ambainis et al shows the existence of a (2,1,0.85)-QRA coding and also proves the impossibility of its classical counterpart. Chuang immediately extends it to a (3,1,0.79)-QRA coding and whether or not a (4,1,p)-QRA coding such that p > 1/2 exists has been open since then. This paper gives a negative answer to this open question. Moreover, we generalize its negative answer for one-qubit encoding to the case of multiple-qubit encoding

Quantum Query Complexity of Boolean Functions with Small On-Sets

The main objective of this paper is to show that the quantum query complexity Q(f) of an N-bit Boolean function f is bounded by a function of a simple and natural parameter, i.e., M = |{x|f(x) = 1}| or the size of fs on-set. We prove that: (i) For

Unbounded-error one-way classical and quantum communication complexity

poly(N)\le M\le 2^{N^d}

Analysis of transient queues with semidefinite optimization

for some constant 0 < d < 1, the upper bound of Q(f) is

Semidefinite optimization for transient analysis of queues

O(\sqrt{N\log M / \log N})

Polynomial-Time Construction of Linear Network Coding

. This bound is tight, namely there is a Boolean function f such that

(4, 1)-Quantum Random Access Coding Does Not Exist

Q(f) = \Omega(\sqrt{N\log M / \log N})

Simple and fast trip generation for large scale traffic simulation

. (ii) For the same range of M, the (also tight) lower bound of Q(f) is