12,996 research outputs found

    Efficient prime counting and the Chebyshev primes

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    The function \epsilon(x)=\mbox{li}(x)-\pi(x) is known to be positive up to the (very large) Skewes' number. Besides, according to Robin's work, the functions \epsilon_{\theta}(x)=\mbox{li}[\theta(x)]-\pi(x) and \epsilon_{\psi}(x)=\mbox{li}[\psi(x)]-\pi(x) are positive if and only if Riemann hypothesis (RH) holds (the first and the second Chebyshev function are Ξ(x)=∑p≀xlog⁥p\theta(x)=\sum_{p \le x} \log p and ψ(x)=∑n=1xΛ(n)\psi(x)=\sum_{n=1}^x \Lambda(n), respectively, \mbox{li}(x) is the logarithmic integral, ÎŒ(n)\mu(n) and Λ(n)\Lambda(n) are the M\"obius and the Von Mangoldt functions). Negative jumps in the above functions Ï”\epsilon, ϔΞ\epsilon_{\theta} and ϔψ\epsilon_{\psi} may potentially occur only at x+1∈Px+1 \in \mathcal{P} (the set of primes). One denotes j_p=\mbox{li}(p)-\mbox{li}(p-1) and one investigates the jumps jpj_p, jΞ(p)j_{\theta(p)} and jψ(p)j_{\psi(p)}. In particular, jp<1j_p<1, and jΞ(p)>1j_{\theta(p)}>1 for p<1011p<10^{11}. Besides, jψ(p)<1j_{\psi(p)}<1 for any odd p \in \mathcal{\mbox{Ch}}, an infinite set of so-called {\it Chebyshev primes } with partial list {109,113,139,181,197,199,241,271,281,283,293,313,317,443,449,461,463,
}\{109, 113, 139, 181, 197, 199, 241, 271, 281, 283, 293, 313, 317, 443, 449, 461, 463, \ldots\}. We establish a few properties of the set \mathcal{\mbox{Ch}}, give accurate approximations of the jump jψ(p)j_{\psi(p)} and relate the derivation of \mbox{Ch} to the explicit Mangoldt formula for ψ(x)\psi(x). In the context of RH, we introduce the so-called {\it Riemann primes} as champions of the function ψ(pnl)−pnl\psi(p_n^l)-p_n^l (or of the function Ξ(pnl)−pnl\theta(p_n^l)-p_n^l ). Finally, we find a {\it good} prime counting function S_N(x)=\sum_{n=1}^N \frac{\mu(n)}{n}\mbox{li}[\psi(x)^{1/n}], that is found to be much better than the standard Riemann prime counting function.Comment: 15 pages section 2.2 added, new sequences added, Fig. 2 and 3 are ne

    Extreme values of the Dedekind Κ\Psi function

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    Let Κ(n):=n∏p∣n(1+1p)\Psi(n):=n\prod_{p | n}(1+\frac{1}{p}) denote the Dedekind Κ\Psi function. Define, for n≄3,n\ge 3, the ratio R(n):=Κ(n)nlog⁥log⁥n.R(n):=\frac{\Psi(n)}{n\log\log n}. We prove unconditionally that R(n)<eÎłR(n)< e^\gamma for n≄31.n\ge 31. Let Nn=2...pnN_n=2...p_n be the primorial of order n.n. We prove that the statement R(Nn)>eγζ(2)R(N_n)>\frac{e^\gamma}{\zeta(2)} for n≄3n\ge 3 is equivalent to the Riemann Hypothesis.Comment: 5 pages, to appear in Journal of Combinatorics and Number theor

    HRM and Value Creation

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    It’s conceptually attractive to look for connection between performance, HRM and economic situation. How measure epiphenomenon’s impact when we can’t isolate that from global strategy? If casual relations maybe established, event can be interpreted in several ways (e.g. its chicken and egg situationñ€©). This paper presents the results of a research on corporate performance measured by the creation of shareholder value. To do that we test empirically forced ranking’s performance versus all other classic human resource managements’ result first with a statistical comparison of share based on fortune 100 (from 1996 to 2000); second with Standard & Poor’s (S&P) 500 value creation (from 1997 to 2000) with ñ€ƓMarakon Associatesñ€ (the growth between Market-to-book values ratio and the ROE spread (ROE – Cost of equity capital)Forced Ranking, Classic HRM, Value Creation

    Garside families and Garside germs

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    Garside families have recently emerged as a relevant context for extending results involving Garside monoids and groups, which themselves extend the classical theory of (generalized) braid groups. Here we establish various characterizations of Garside families, that is, equivalently, various criteria for establishing the existence of normal decompositions of a certain type

    Chebyshev's bias and generalized Riemann hypothesis

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    It is well known that li(x)>π(x)li(x)>\pi(x) (i) up to the (very large) Skewes' number x1∌1.40×10316x_1 \sim 1.40 \times 10^{316} \cite{Bays00}. But, according to a Littlewood's theorem, there exist infinitely many xx that violate the inequality, due to the specific distribution of non-trivial zeros Îł\gamma of the Riemann zeta function ζ(s)\zeta(s), encoded by the equation li(x)−π(x)≈xlog⁥x[1+2∑γsin⁥(Îłlog⁥x)Îł]li(x)-\pi(x)\approx \frac{\sqrt{x}}{\log x}[1+2 \sum_{\gamma}\frac{\sin (\gamma \log x)}{\gamma}] (1). If Riemann hypothesis (RH) holds, (i) may be replaced by the equivalent statement li[ψ(x)]>π(x)li[\psi(x)]>\pi(x) (ii) due to Robin \cite{Robin84}. A statement similar to (i) was found by Chebyshev that π(x;4,3)−π(x;4,1)>0\pi(x;4,3)-\pi(x;4,1)>0 (iii) holds for any x<26861x<26861 \cite{Rubin94} (the notation π(x;k,l)\pi(x;k,l) means the number of primes up to xx and congruent to lmod  kl\mod k). The {\it Chebyshev's bias}(iii) is related to the generalized Riemann hypothesis (GRH) and occurs with a logarithmic density ≈0.9959\approx 0.9959 \cite{Rubin94}. In this paper, we reformulate the Chebyshev's bias for a general modulus qq as the inequality B(x;q,R)−B(x;q,N)>0B(x;q,R)-B(x;q,N)>0 (iv), where B(x;k,l)=li[ϕ(k)∗ψ(x;k,l)]−ϕ(k)∗π(x;k,l)B(x;k,l)=li[\phi(k)*\psi(x;k,l)]-\phi(k)*\pi(x;k,l) is a counting function introduced in Robin's paper \cite{Robin84} and RR resp. NN) is a quadratic residue modulo qq (resp. a non-quadratic residue). We investigate numerically the case q=4q=4 and a few prime moduli pp. Then, we proove that (iv) is equivalent to GRH for the modulus qq.Comment: 9 page

    Meeting the Challenge of Interdependent Critical Networks under Threat : The Paris Initiative

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    NARisques à grande échelle;Gestion des crises internationale;Interdépendances;Infrastructures critiques;Anthrax;Initiative collective;Stratégie;Préparation des Etats-majors

    Understanding and modeling the small-world phenomenon in dynamic networks

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    The small-world phenomenon first introduced in the context of static graphs consists of graphs with high clustering coefficient and low shortest path length. This is an intrinsic property of many real complex static networks. Recent research has shown that this structure is also observable in dynamic networks but how it emerges remains an open problem. In this paper, we propose a model capable of capturing the small-world behavior observed in various real traces. We then study information diffusion in such small-world networks. Analytical and simulation results with epidemic model show that the small-world structure increases dramatically the information spreading speed in dynamic networks
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