42,847 research outputs found

    Channels of published research communication used by Malaysian authors in computer science and information technology

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    Analyse 389 records retrieved from Inspec (1990-1999), Compendex (1987-1999) and IEL (IEE/IEEE Electronic library)(1987-1999). The records comprised 159 journal articles, 229 conference papers and 1 monograph chapter. The subject coverage was computer science and information technology. The yearly output of Malaysian publications indicated a gentle upward trend. The highest contributions was 87 published in 1997. The channels used to publish differ slightly from the norm for scientists. Conference papers were preferred to journal articles. The spread of conference papers used to publish indicate three zonal distributions; the nucleus, moderate and low productivity in the ratio of 19 : 41 : 88, leading to a clustering index of 2.15. This shows that Malaysian conference contributions were concentrated in a few proceedings. No clear core journals can be identified for the journal articles and contributions were distributed in a wide variety of journal titles. Malaysian Journal of Computer Science published the highest number of journal articles. More than 83 of the articles were published in journals from the UK, USA, the Netherlands and Malaysia

    The Predictability of House Prices

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    The level and direction of autocorrelation in house price movements differ across areas and change over time. This finding reconciles the conflicting reports in the literature. When quarterly house price indices exhibit negative autocorrelation, autocorrelation shows a positive connection to volatility and a negative connection to rate of return. Autocorrelation between longer time periods is mainly positive; it exhibits a negative relationship with volatility and a positive relationship with rate of return. Volatile house price indices tend to have lower rates of return. It would be possible to obtain excess returns by following a trading strategy based on the estimated autocorrelation.

    Scattering on two Aharonov-Bohm vortices with opposite fluxes

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    The scattering of an incident plane wave on two Aharonov-Bohm vortices with opposite fluxes is considered in detail. The presence of the vortices imposes non-trivial boundary conditions for the partial waves on a cut joining the two vortices. These conditions result in an infinite system of equations for scattering amplitudes between incoming and outgoing partial waves, which can be solved numerically. The main focus of the paper is the analytic determination of the scattering amplitude in two limits, the small flux limit and the limit of small vortex separation. In the latter limit the dominant contribution comes from the S-wave amplitude. Calculating it, however, still requires solving an infinite system of equations, which is achieved by the Riemann-Hilbert method. The results agree well with the numerical calculations

    Multi-aspect, robust, and memory exclusive guest os fingerprinting

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    Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-Sommelier+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-Sommelier+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-Sommelier+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels

    Peculiar Behavior of Si Cluster Ions in Solid Al

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    A peculiar ion behavior is found in a Si cluster, moving with a speed of ~0.22c (c: speed of light) in a solid Al plasma: the Si ion, moving behind the forward moving Si ion closely in a several angstrom distance in the cluster, feels the wake field generated by the forward Si. The interaction potential on the rear Si may balance the deceleration backward force by itself with the acceleration forward force by the forward Si in the longitudinal moving direction. The forward Si would be decelerated normally. However, the deceleration of the rear Si, moving behind closely, would be reduced significantly, and the rear Si may catch up and overtake the forward moving Si in the cluster during the Si cluster interaction with the high-density Al plasma

    Minimal Permutations and 2-Regular Skew Tableaux

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    Bouvel and Pergola introduced the notion of minimal permutations in the study of the whole genome duplication-random loss model for genome rearrangements. Let Fd(n)\mathcal{F}_d(n) denote the set of minimal permutations of length nn with dd descents, and let fd(n)=Fd(n)f_d(n)= |\mathcal{F}_d(n)|. They derived that fn2(n)=2n(n1)n2f_{n-2}(n)=2^{n}-(n-1)n-2 and fn(2n)=Cnf_n(2n)=C_n, where CnC_n is the nn-th Catalan number. Mansour and Yan proved that fn+1(2n+1)=2n2nCn+1f_{n+1}(2n+1)=2^{n-2}nC_{n+1}. In this paper, we consider the problem of counting minimal permutations in Fd(n)\mathcal{F}_d(n) with a prescribed set of ascents. We show that such structures are in one-to-one correspondence with a class of skew Young tableaux, which we call 22-regular skew tableaux. Using the determinantal formula for the number of skew Young tableaux of a given shape, we find an explicit formula for fn3(n)f_{n-3}(n). Furthermore, by using the Knuth equivalence, we give a combinatorial interpretation of a formula for a refinement of the number fn+1(2n+1)f_{n+1}(2n+1).Comment: 19 page
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