We present a new method to show concentration of the upper tail of random variables that can be written as sums of variables with plenty of independence. We compare our method with the martingale method by Kim and Vu, which often leads to similar results. Some applications are given to the number XG of copies of a graph G in the random graph G(n, p). In particular, for G = K4 and G = C4 we impr...

Because of the centralized, static nature of current spectrum allotment policy, wireless communication is confronting two significant problems: spectrum scarcity and deployment delays. Current systems are significantly impacted by the lack of access to unused spectrum, and existing spectrum management procedures are too inflexible to react to dynamic operational needs. The policies are static a...

Using immunocytochemical techniques and antibodies that specifically recognize xyloglucan (anti-XG), polygalacturonic acid/rhamnogalacturonan I (anti-PGA/RG-I), and methylesterified pectins (JIM 7), we have shown that these polysaccharides are differentially synthesized and localized during cell development and differentiation in the clover root tip. In cortical cells XG epitopes are present at...

Nitric oxide (NO) is involved in many physiological functions, but its role in nociceptive signaling remains uncertain. Surprisingly, little is known about how endogenous NO affects excitatory and inhibitory synaptic transmission at the spinal level. Here we determined how NO affects excitatory and inhibitory synaptic inputs to dorsal horn neurons using whole-cell recordings in rat spinal cord ...

We consider sheaves of special groups (mainly over Boolean spaces). These are connected to the sheaves of abstract Witt rings considered by Marshall in [9], used therein in particular to classify spaces of orderings with a finite number of accumulation points. Our approach allows us to show that the so-called “question 1” (see [11, 1]) has a positive answer for these spaces. We conclude these n...

T. Kampfrath,1 D. M. Beggs,2 T. P. White,2 A. Melloni,3 T. F. Krauss,2 and L. Kuipers1 1Institute for Atomic and Molecular Physics (AMOLF), Science Park 104, NL-1098 XG Amsterdam, The Netherlands 2School of Physics and Astronomy, University of St. Andrews, St. Andrews, Fife KY16 9SS, United Kingdom 3Dipartimento di Elettronica e Informazione, Politecnico di Milano, Via Ponzio 34/5, I-20133 Mila...

In this paper, we propose XG-BoT, an explainable deep graph neural network model for botnet node detection. The proposed comprises a detector and explainer automatic forensics. XG-BoT can effectively detect malicious nodes in large-scale networks. Specifically, it utilizes grouped reversible residual connection with isomorphism to learn expressive representations from communication graphs. expl...

Proof of Proposition 1. Under (H2) we have g := infω,xG(x) > 0. Now consider the sequence of random variables {κn ;n ≥ 1} defined by κn := νQ ω n−1(1)g −. From (2.8) it is straightforward to establish by induction the following semigroup property: for any ω ∈ Ω, and p, n ≥ 0, Qp+n = Q ω pQ θpω n . Combined with (H2), this gives κp+n = νQ ω pQ θpω n−1(1)g − = νQp−1Q θωQθ pω n−1(1)g − ≥ κpκ pω n ...