![]() U, for instance, the map screen will show selected messages other users have left after playing specific levels. These Miiverse messages can also be integrated into the games themselves. It has also been interesting to use the message counts as a rough gauge of the popularity of various games: Thousands more people are talking about ZombiU than the Wii U version of Ninja Gaiden 3, for instance. So far, reading through these threads has felt like a more-focused, more-polite, much-less-cynical version of an average gaming message board. You can even show your approval for specific notes with a “Yeah!” that acts like Facebook's Like function. There's a system in place to flag inappropriate content, or to note content that contains spoilers (which will then have to be actively expanded to view). There are huge content threads devoted to each game and app on the system, where people can post typed messages or hand-drawn notes. Miiverse is probably best described as a simplified cross between Facebook and a massive, Nintendo-focused message board. Hopefully this is only a one-off issue and other games will let players send friends the kind of direct match invites that gamers are used to on other gaming services. Only if both players enter the same match code in the online menu at the same time will they be set up against each other. ![]() The only way to do this is to come up with an arbitrary “match code” and share it with your friend by some other means. Setting up a match with a specific person is another matter. Playing with strangers over the Wii U was a pretty smooth experience: the game recognized my Nintendo Network ID immediately and placed me in a relatively lag-free friendly match after just a few seconds of matchmaking. Multiplayer mishapsThe only Wii U game I have access to that supports actual online multiplayer, rather than the Miiverse messaging functions mentioned above, is Tekken Tag Tournament 2. You can easily add any interesting-looking Miis you find to your local Mii Maker, or click through a specific message to find out more about your fellow players in Miiverse. Nintendo’s filtering police seem to be doing a good job on that score). I encountered a lot of fan art and a lot of overzealous praise for each game, but also a few messages that were surprisingly negative about Nintendo's launch software (though not obscene. Still, it’s nice feeling like part of a community of Wii U players, and I had a great time clicking around the various Miis and seeing what they had to say about each game. ![]() I can’t help but feel that Nintendo is using its plaza not just for expanded social networking, but also as a form of ad space for retail games. At first, this screen (known officially as WaraWara Plaza) was filled with preloaded robots from Nintendo talking excitedly about features like “System Settings.” By the next day, though, my plaza filled up with real people gathering around icons for games I owned and a few I didn’t. Once you’re connected to the Nintendo Network, your Wii U home screen will fill up with Miis from around the world, gathering around large icons representing games and apps they’ve played. You can protect your ID with a password that’s required each time you use it, or set it up to log you in automatically every time you turn on the system. Signing up for an ID takes a few minutes and requires some very basic personal information (like an e-mail address). The Wii U represents Nintendo’s biggest push into the online space yet, and part of that push is replacing the inconvenient, frustrating, and game-specific Wii Friend Codes with a unified online infrastructure called the Nintendo Network ID. We spent a little over an hour downloading that update and a few more days tinkering with the new features it unlocked, so we can now report on how the Wii U handles some important functions aside from playing games. When we reviewed the Wii U earlier this week, we were forced to give it an “Incomplete” verdict, as we were waiting on a day-one system update that would unlock a large number of the system’s promised features.
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![]() This isn’t just important before a sale closes, either – increasing customer lifetime value involves building and maintaining customer trust once the deal is done and they’re signed up to use your product. For sales teams, it’s now more important than ever to find a way to connect with customers personally and cut through the endless noise of transactional messages. Buyers are more informed than ever, and face-to-face engagement forms only a tiny part of the buyer-seller relationship. As sales trainer John Barrows recently told us, “We have to stop spamming people, both on the sales and the marketing side.”īuyer/seller dynamics have shifted in many ways thanks to messaging, and especially in SaaS. It feels lazy, looks spammy, and it more than likely isn’t going to elicit a response because – guess what – everyone else is doing it too. Untargeted outreach like this is the business equivalent of getting a “‘sup?” text from an acquaintance you barely know and haven’t seen in ages. ![]() Let me know if I can help with something!” On the other hand, it’s now really easy to spam 50 people. On the one hand, it’s now really easy to send out a message to 50 of your customers to say “Hey Eric, just checking in to see how you’re getting on. When it comes to engaging with customers, modern messaging tools can be a blessing and a curse to relationship management teams. ![]() Thus, at large numbers of particles, the number of events per time step can become very large, and SSSAs become prohibitively slow. However, a more significant drawback is the fact that SSSAs are event-driven algorithms. For example, there are spatial resolution limits under which artefacts in particle interactions might occur, and also some effects at boundaries might not be accurately captured. While widely used, SSSAs suffer from several drawbacks. It is possible to generate sample paths consistent with the RDME using a variety of spatial Stochastic Simulation Algorithms (SSSAs). Mean-field approaches provide some analytical tools to help understand systems with bimolecular reactions, but these do not provide exact solutions. However, there do exist some closed form solutions for systems involving monomolecular reactions. The RDME is generally analytically intractable. Diffusion can then occur between different voxels, and reactions can occur within voxels on the assumption that reactants are well-mixed. A widely used approach to study stochastic spatial dynamics is the Reaction Diffusion Master Equation (RDME), in which space is partitioned discretely into a number of voxels. Spatial-stochastic effects are increasingly found to play important roles throughout a range of biological scales, from intracellular and intercellular processes, to ecological and epidemiological scales. However, there exist parameter sets where both the qualitative and quantitative behaviour of the SCLE can differ when compared to the RDME, so care should be taken in its use for applications demanding greater accuracy. This becomes very useful in search of quantitative parameters yielding desired qualitative solutions. The SCLE provides a fast alternative to existing methods for simulation of spatial stochastic biochemical networks, capturing many aspects of dynamics represented by the RDME. ![]() However, areas of the parameter space in the Gray-Scott model exist where either the SCLE and RDME give qualitatively different predictions, or the RDME predicts patterns, while the SCLE does not. As expected, the SCLE captures many dynamics of the RDME where deterministic methods fail to represent them. All approaches match at the leading order, and the RDME and SCLE match at the second leading order. For non-linear reaction networks, differential equations governing moments do not form a closed system, but a general moment equation can be compared term wise. Resultsįor linear reaction networks, it is well known that the first order moments of all three approaches match, that the RDME and SCLE match to the second moment, and that all approaches diverge at third order moments. ![]() We consider the Gray-Scott model, a well-known pattern generating system, and a predator–prey system with spatially inhomogeneous parameters as sample applications. Sample paths are generated computationally by the Next Subvolume method (RDME) and the Euler-Maruyama method (SCLE), while a deterministic solution is obtained with an Euler method. We investigate moment equations and correlation functions analytically, then we compare sample paths and moments of the SCLE to the RDME and associated deterministic solutions. Here we investigate an uncommon, but much faster alternative: the Spatial Chemical Langevin Equation (SCLE). However, simulating sample paths from the RDME can be computationally expensive, particularly at large populations. A popular method of representing such stochastic systems is the Reaction Diffusion Master Equation (RDME). ![]() It has been established that stochastic effects play an important role in spatio-temporal biochemical networks. |