Popul ar myt hs t hat cheaper memor y, hi ghs peed l i nks , and hi ghs peed pr oces s or s wi l l s ol ve t he pr obl emof con i n comput er networ ks ar e s hown t o be f al s e . A s i mpl e de ni t i on f or conges t i on bas ed on s uppl y and dema r es our ce s i s pr opos ed and i s t hen us ed t o c l as s i f y var i ous conges t i on s chemes . The i s s ues t hat make t he pr obl ema...

Abstract Alkalimetallsalze des Typs M + [Ter( i Pr)P−C(=S)−P( Pr) 2 S] .− (M=Na, K; _ ; Ter=2,6‐bis‐(2,4,6‐trimethylphenyl)phenyl) mit einem raumtemperaturstabilen Thioketylradikalanion wurden durch die Reduktion Thioketons Ter( S ( 1 ) Alkalimetallen (Na, K) erhalten. Einkristall‐Röntgenstrukturanalyse sowie ESR‐Spektroskopie konnten das Vorliegen Thioketylradikalanions sowohl im Festkörper al...

Bayesian Reinforcement Learning in MDPs: MDP: (S,A, T,R) • S: Set of states •A: Set of actions • T (s, a, s′) = Pr(s′|s, a), the transition probabilities •R(s, a) ∈ R, the immediate rewards Assume transition function T is the only unknown. •Define prior Pr(T ) •Maintain posterior Pr(T |s1, a1, s2, a2, . . . , at−1, st) via Bayes rule. •Act such as to maximize expected return given current poste...

We analyze t he issue of generalization in systems that learn from examples as a problem of representation of functions in finite fields. It is shown th at it is not possible to design algorit hms wit h uniformly good generalization pr opert ies in the space of all functions. Th erefore th e pr oblem of achieving good generalization properties becomes meaningful only if the functions being st u...

In this paper, we pr es ent s ome pr act i cal r es ul t s concer ni ng t he i mpl ement at i on of t he al gor i t hmdes cr i bed i n [DMT] whi ch comput es dynami cal l y t he Del aunay t r i angul at i on of a s et of s i t es i n t he pl ane i n l ogar i t hmi c expect ed updat e t i me. Mor e pr ec i s e l y, we s howt hat t he hypot hes es of non degener at e pos i t i ons can be dr opped...

• Distribution 2: Pr(0) = Pr(50) = Pr(100) = 1/3. Both have the same expectation: 50. But the first is much less “dispersed” than the second. We want a measure of dispersion. • One measure of dispersion is how far things are from the mean, on average. Given a random variable X , (X(s) − E(X)) measures how far the value of s is from the mean value (the expectation) of X . Define the variance of ...

background: p. atrosepticum is a commercially important pathogen. it causes blackleg in the field and soft rot of tubers after the harvest. this effect is due to secretion of depolymerases and other virulence factors by several mechanisms including t3ss objectives: the effect of bacterial t3ss on solanum tuberosum (s. tuberosum) varieties and its relationship with s. tuberosum resistance gene e...

A set S of vertices in a graph G is a paired-dominating set of G if every vertex of G is adjacent to some vertex in S and if the subgraph induced by S contains a perfect matching. The maximum cardinality of a minimal paired-dominating set of G is the upper paired-domination number of G, denoted by pr(G). We establish bounds on pr(G) for connected claw-free graphs G in terms of the number n of v...

where we would need to substitute consistent estimates of the conditional probability Pr(Y |X = x, S = s) and the marginal probability Pr(S = s). It is sometimes useful to short-cut having to estimate the marginal probability by using the law of large numbers. Notice that, with respect to the sum over s, Pr(Y |X = x, S = s) is just some function of s. If we have an IID sample of realizations of...

The present work continues the study of the stability of the functional equations of the type f(pr, qs) + f(ps, qr) = f(p, q) f(r, s) namely (i) f(pr, qs)+f(ps, qr) = g(p, q) g(r, s), and (ii) f(pr, qs)+f(ps, qr) = g(p, q) h(r, s) for all p, q, r, s ∈ G, where G is an abelian group. These functional equations arise in the characterization of symmetrically compositive sumform distance measures.