Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization

We study the r-complex contagion influence maximization problem. In the influence maximization problem, one chooses a fixed number of initial seeds in a social network to maximize the spread of their influence. In the r-complex contagion model, each uninfected vertex in the network becomes infected if it has at least r infected neighbors. In this paper, we focus on a random graph model named the stochastic hierarchical blockmodel, which is a special case of the well-studied stochastic blockmodel. When the graph is not exceptionally sparse, in particular, when each edge appears with probability omega (n^{-(1+1/r)}), under certain mild assumptions, we prove that the optimal seeding strategy is to put all the seeds in a single community. This matches the intuition that in a nonsubmodular cascade model placing seeds near each other creates synergy. However, it sharply contrasts with the intuition for submodular cascade models (e.g., the independent cascade model and the linear threshold model) in which nearby seeds tend to erode each others\u27 effects. Finally, we show that this observation yields a polynomial time dynamic programming algorithm which outputs optimal seeds if each edge appears with a probability either in omega (n^{-(1+1/r)}) or in o (n^{-2})

Cooperation and Stability through Periodic Impulse

Basic games, where each individual chooses between two strategies, illustrate several issues that immediately emerge from the standard approach that applies strategic reasoning, based on rational decisions, to predict population behavior where no rationality is assumed. These include how mutual cooperation (which corresponds to the best outcome from the population perspective) can evolve when the only individually rational choice is to defect, illustrated by the Prisoner’s Dilemma (PD) game, and how individuals can randomize between two strategies when neither is individually rational, illustrated by the Battle of the Sexes (BS) game that models male-female conflict over parental investment in offspring. We examine these questions from an evolutionary perspective where the evolutionary dynamics includes an impulsive effect that models sudden changes in collective population behavior. For the PD game, we show analytically that cooperation can either coexist with defection or completely take over the population, depending on the strength of the impulse. By extending these results for the PD game, we also show that males and females each evolve to a single strategy in the BS game when the impulsive effect is strong and that weak impulses stabilize the randomized strategies of this game

Incorporation of 5-fluorouracil into U2 snRNA blocks pseudouridylation and pre-mRNA splicing in vivo

5-fluorouracil (5FU) is an effective anti-cancer drug, yet its mechanism of action remains unclear. Here, we examine the effect of 5FU on pre-mRNA splicing in vivo. Using RT–PCR, we show that the splicing of a number of pre-mRNAs is inhibited in HeLa cells that have been exposed to a low dose of 5FU. It appears that this inhibitory effect is not due to its incorporation into pre-mRNA, because partially or fully 5FU-substituted pre-mRNA, when injected into Xenopus oocytes, is spliced just as well as is the unsubstituted pre-mRNA. Detailed analyses of 5FU-treated cells indicate that 5FU is incorporated into U2 snRNA at important naturally occurring pseudouridylation sites. Remarkably, 5FU incorporation effectively blocks the formation of important pseudouridines in U2 snRNA, as only a trace of pseudouridine is detected when cells are exposed to a low dose of 5FU for 5 days. Injection of the hypopseudouridylated HeLa U2 snRNA into U2-depleted Xenopus oocytes fails to reconstitute pre-mRNA splicing, whereas control U2 isolated from untreated or uracil-treated HeLa cells completely reconstitutes the splicing. Our results demonstrate for the first time that 5FU incorporates into a spliceosomal snRNA at natural pseudouridylation sites in vivo, thereby inhibiting snRNA pseudouridylation and splicing. This mechanism may contribute substantially to 5FU-mediated cell death

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral