text:
Thank you for bringing this up! Great point.
Eureka! I've solved it - How to fix ranked play!
I really like the idea. GJ Cat
Addition to the Solution:
- Show ELO in unranked for less confusion and uncertainty. It can also start the chatting in the setup process if ppl are not initiating small talk with a friendly greeting of some sorts…
Approved. I think they should have a separate ranked lobby rating.
Id be up for giving this ago, I like the map pool idea. Not sure on the scoring system.
You clearly have no clue on how Elo (or any MMR) system work.
" you're suggesting that someone will stay at a low ELO so they can just beat people more easily and not care about their rank? What's to stop someone from doing that today?"
That's called sand baggging and it's not exactly smurfing, and yes, it is already possible to do it. The issue is not that it would possible with your system, the issue is that it would be impossible NOT to sandbag in your system for people chosing a small map pool. People would get underrated and would get to bash noobs without doing anything but selecting few maps and playing the game as you intend them to.
Your error is that you are mistaking the rating system with a reward system. It is not. It is a matchmaking tool. If you use it as a reward system to the detriment of match making aspect, match making will suck. And it does not depend on which specific rating system you use.
If say you want to "punish" specialists by underrating them and "reward" generalists by over rating them matchmaking will suck. If it only impacts the speed at which you converge to your rating, well it kind of defies the purpose of a good rating system (which is knowing your level fast), and make match making momentarily suck.
One workaround might be to have two separated ranking systems, one for the reward, and a hidden one for the matchmaking. It's really convoluted and not without issues, but it's been tried more or less conclusively (Prismata and Faeria and I guess many CCG, who have both a ladder-like system, persistent or monthly refreshed, and a Elo-like system, the former being the medal you pin on your jacket and the latter being used for matchmaking and being hidden for all players but those on the last step of the ladder)
I have a more simple solution that would work just nice. Make a pool of the top 15 or 20 maps. Each map has its own ELO score and you are free to pick in which map or maps do you want to play. This way you will be facing players of your own skill level in every map ( which is the main purpose of an ELO system) Then you can have a player's global ranking, which is just the average score of a player in all the maps. This way, if your concern is the global ranking, you will be forced to play every map. But If you just want to play your favorite map against people of your own skill level, you can do it.
This has all of the same issues that were pointed out in the post right above yours and zero upsides.
Maps aren't different enough to require an entirely separate ELO calculation, so far none of the options presented on these forums are any better than the system we have now, they all attempt to solve issues and complaints with the current system but don't take into account other issues that would arise from being implemented, namely this point, again from the post right above yours:
This cannot be understated, stop focusing on your individual number as a measure of your success, it's main purpose is to find you balanced matches to play in, and it simply cannot do that if it's different for every map, nor if it has some weird multiplier based on how many maps you choose to queue for.
Maps aren't different enough to require an entirely separate ELO calculation
Well, I agree on that. Maybe they can do 4 different ELO scores instean of 16, something like:
-Open land ladder: Arabia, Gold rush, Valley, Cenotes.
-Close land ladder: Arena, Fortress, Hideout, Castros.
-Water ladder: team islands, …, whatever.
-Hybrid ladder: cross, medi, …
Map selection is just an example. You have 4 ELO scores and a total score ladder, which is just to check the most complete player. I don't see any issues with that. I read the posts above mine and still find this solution pretty much perfect for everyone.
Chances are that players will gravitate towards one of the 4 ladders as their main, and as a consequence, they'll get unbalanced games on the others should they choose to try them out, thus causing them to stick to only one ladder, there's no way a 1600 rated player on open maps is going to play like a 1000 rated zero gamer on closed maps, same goes for water/hybrid.
One map pool, with rotating maps of all variations, still makes the most sense if the goal is to find balanced matches.
Maybe it would be possible to have some dependence between the ladders, for example, winning a game on a map of the "open land" ladder could also give a small amount of points on the other ladders? So a player who only plays "open land" would also slowly increase the rating in the other ladders, accounting for the fact that the different map types play out differently to some extent, but not completely differently.
One map pool, with rotating maps of all variations, still makes the most sense if the goal is to find balanced matches.
The system we have right now is producing plenty of unbalanced games for me. I'm a decent player in arabia and gold rush but I'm a terrible player in team islands for example ( I even don't have a hotkey for docks, because I don't like water maps). If I have to play 4 or 5 games in team islands (or some of the other maps where I suck) my ELO will drop a lot. Then if next game is arabia, the other player will be facing me, a player 100 points above his current ELO. That's a total nonsense and it's not fun for anyone.
The issue you point out is just part of any ELO system. The score needs some games to adjunst itself. It happened with the current system aswell. I think it's not an issue at all and any player who plays like 10 games will be facing players at his own ELO. Some things can be done to palliate that issue though, like for example, first 10 games of any player won't affect ELO score of his rival.
You have two options here,
- Practice water maps more so that your ELO doesn't drop so drastically between maps.
- Use your ban on Team Islands so that you don't have to play it.
Personally I'd try for #1, so that you become a more balanced player.
Realistically I doubt you're getting Team Islands 4-5 times in a row (at least, not often enough for it to matter in the grand scheme of things), but this seems to simply be a case of the ranked system exposing your weaknesses, you can embrace that and work on them, or you can ignore it by banning the only water map in the pool, though you may run into issues later when the map pool rotates into a set of maps that you're not great at.
Why should anyone be forced to play maps they don't even enjoy? Some people just enjoy Arena, some people just enjoy black forest. Now they can't play their favorite maps in a competitive way, just because the game don't allow them to do so.
Imagine if we put chess players and martial artists in the same pool. They flip a coin and they have to play a boxing fight or a chess game. That's more or less what we have right now.
I like this OP's idea and DerraDeiro's initial concept and here's what id suggest.
Have the option to pick groups of maps(Land, Closed-Land, Nomad, Water,Hybrid,Etc.) You will still get a level of randomization in the maps if each group has more than 1 maps in them.
There doesnt need be any incentive in this case. You still have multiple maps to play from and this gives a level of customization to the players. Like I would prefer some nights to play a chill closed map game or on other days a frantic water/hybrid map game. In the end, if you struggle with finding games(which by the way you always will if you choose one map group simply because AOE2 doesnt have the player base), you just add more map groups and search again. People keep forgetting that average wait times in voobly were much higher. Voobly also had the problem of people controlling lobbies and creating a separation between levels. Like 2k1 TG players almost never played vs say a 1800/1900 player that they have not heard of.
Map groups are the way to go without needing to incentivize Elo because thats how the game can be played if you want people to go to the top. Players like Hera and Vivi played just Arabia for such a long time and yet you see them improve their play in other maps too. Learning to play well in one type of map is usually the way a lot of pro players improved their level.
Edit: I would suggest keeping 5 map groups as follows:
Land - Arabia, Gold Rush, Cenotes, Valley, Graveyard, Serengetti, etc.
Closed Land - Arena, Black Forest, Land Fortress, Hideout, Hill Fortetc.
Water(and Hybrid Water) - Team Islands, Medi, Rooster, Rivers, etc.
Nomadic - Nomad, Migration, Bedouins, etc.
Special Maps - Cross, MegaRandom, Chaos Pit, Land Madness, etc.
I hate this idea.
Devs, don't listen to this.
The entire article is just trying to sneak the idea: avoiding the specific maps.
What you truly want is not a ranked matchmaking system, but a old lobby like HD.
Calling meme maps like Blackforest/Nomad/Steppe the core maps? Simply a big joke.
Lower elo recieved while selecting less maps? This will only produce tons of smurfs and totally destroy the matchmaking.
The purpose of the ranked matchmaking is exactly to force the players to play various standard maps to estimate their skill level. So the point is not how much freedom should be given to the players to ban the maps, but to pick the proper maps to test how well the players are in the essential aspects of this game.
Just keep the matchmaking system as it is, kick out the meme maps and bring the regular map back. Pick the maps for the ranked map pool carefully. And rotate them periodically.
Improve the lobby, add casual elo or whatever so people can find proper players to play whatever meme maps and rules they want forever. Problem solved.
Stop ruining this game already.
Once again you haven't thought your comments through. First, an Arabia player won't only select Arabia. They will also select Gold Rush, Steppe, Ghost Lake, Cenotes, Acropolis, etc. The specialists will be people who play on clown maps like Arena and BF.
Those people who only play Arena and BF will be incentivized to play other maps, but are not forced to. Given Arena specialists may have ratings that are below their skill level, it would likely lead to less people selecting Arena in the map pool except for other Arena players. So The Solution will allow the Arena players to play each other and enjoy the game in a ranked manner that they enjoy. It should be their prerogative to do so.
It seems like you're getting hung up on this idea that people just want to keep their ratings low so they can bash noobs. It's like you're talking about the bogey man. This game is about improvement. If people select Arena in the map pool along with the other maps and get paired with someone who ratings is below their skill level, who cares? Aren't you up for the challenge? You'll lose less points anyways. There will only be a couple of maps where this happens so it's not a big deal anyways. You can always unselect BF, Arena and Islands where people may specialize.
Once again The Solution is perfect. Don't try to find one minuscule issue to bring down a solution that will allow for 95%+ of the games to be enjoyable compared to about half today.
You're really thick
It seems like you're getting hung up on this idea that people just want to keep their ratings low so they can bash noobs
no, as I said,
The issue is not that it would possible with your system, the issue is that it would be impossible NOT to sandbag in your system for people chosing a small map pool. People would get underrated and would get to bash noobs without doing anything but selecting few maps and playing the game as you intend them to.
Don't try to find one minuscule issue to bring down a solution that will allow for 95%+ of the games to be enjoyable compared to about half today.
Tell me, what part of ■■■■■■■ up matchmaking is minuscule again?
Besides, there are other simpler working solution, like the double rating system I mentioned or the ability to play rated custom games that you mentioned in a poll maybe a month or two ago (probably along with the ability to set filters based on rating while setting up or browsing lobbies).
"If people select arean in the map pool along with the other maps and get paired with someone who ratings is below their skill level, who care?"
Uh, that is the ENTIRE point of the ranked system, the only point of it, to MATCH PLAYERS OF EQUAL SKILL. There is no other purpose.
First, don't call me the same names as your GF calls me.
Second, you didn't read my response, or you don't have good reading comprehension skills. The only people that would select a single map would be Arena and BF players. They are a minority of players. Most players would not select those maps. That would mean other Arena and BF players would just be playing other Arena and BF players of the same rating who are all subject to the same rating issues you've outlined, leading to equally matched games.
The issue you're concerned about only occurs when a player who selects all the maps is paired with a Arena or BF specialists whose rating is too low. Mathematically that will be a very small percentage of the games that the user plays and if the user is bother by this, they can unselect Arena and BF and problem is solved.
The entire community stays happy. Both the Arena and BF players and the players would want to avoid those maps while the system also continues to encourage a diverse map pool.
I'm practice, once The Solution is implemented no one would ever complain about this non-issue. No one would even notice it. You're clearly both only good at BF and are just upset you won't be getting many points for only playing BF. But look on the bright side, amongst the BF clowns, you'll all have the same ratings limitations so you can still brag about your ELO if it's above others.
This point has already been made by people who understand ELO systems and their purpose (@Fano0517 and @ViscidFlea88145), but I want to touch on it again since it really is the underlying, game-breaking, flaw in the system you are suggesting.
I think the best way to illustrate the problem is with an example.
Let's image we have 2 Arena only players who are perfectly evenly matched skill-wise (each has a 50/50 chance of winning). Both of our players will start with 1000 ELO and for the sake of the example two players with even ELO will gain/lose roughly 20 points. Also, in this hypothetical scenario, our two arena-only players will keep matching up against each other. Now, according to your rules, since they both selected ONLY arena, they would gain 1 point per win, but still lose 20 points per loss on average.
As they continue to play against each other, both their ELO's would inevitably decline and no longer be representative of their skill at all, there may even be some inverse correlation between skill and ELO here since more games played should cause a lower ELO.
This is obviously a very simplified and extreme example, but the same thing would happen at a slower rate between normal players who select 16/20 maps or whatever. Winner gains ~16, loser loses ~20, over time, the more games are played, the more everyone's ELO goes down.
Again, as other's have mentioned, ELO is just there to help you find an evenly matched opponent. You can discuss how banning and picking maps should work, or what the map pool should look like, but discussing changes to the underlying ELO system is more or less pointless.


text:
VarMixup: Exploiting the Latent Space for Robust Training and Inference
The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has led to development of many defense approaches. Among them, Adversarial Training (AT) is a popular and widely used approach for training adversarially robust models. Mixup Training (MT), a recent popular training algorithm, improves generalization performance of models by introducing globally linear behavior in between training examples. Although still in its early phase, we observe a shift in trend of exploiting Mixup from perspectives of generalisation to that of adversarial robustness. It has been shown that the Mixup trained models improves robustness of models but only passively. A recent approach, Mixup Inference (MI), proposes an inference principle for Mixup trained models to counter adversarial examples at inference time by mixing the input with other random clean samples. In this work, we propose a new approach - VarMixup (Variational Mixup) - to better sample mixup images by using the latent manifold underlying the data. Our experiments on CIFAR-10, CIFAR-100, SVHN and Tiny-Imagenet demonstrate that VarMixup beats state-of-the-art AT techniques without training the model adversarially. Additionally, we also conduct ablations that show that models trained on VarMixup samples are also robust to various input corruptions/perturbations, have low calibration error and are transferable.
Keywords:Adversarial Robustness, Mixup, Mixup Inference
1 Introduction
Deep Neural Networks (DNNs) have become a key ingredient to solve many challenging tasks like classification, segmentation, object detection, speech recognition, etc. However, it is now known that they can be fooled by applying imperceptible perturbations, called adversarial perturbations, to input examples leading to wrong predictions[41, 14]. Over the past few years, various approaches [47, 22, 1, 9, 27, 14, 50, 30, 29] have been proposed to craft adversarial examples by maximizing the network's prediction error in distinct ways. As a consequence, it has become quite risky to deploy them in safety-critical applications including autonomous navigation or healthcare. Efforts [27, 14, 36, 38, 55, 35, 31, 12, 44] have been made in recent years to make models robust to these adversarial perturbations. Among the proposed methods, Adversarial Training (AT) [38, 27, 14, 56, 25] has emerged as a popular and widely uses algorithm to obtain adversarially robust models. Several variants of adversarial training have been proposed lately with motivation of inducing local linearity [35] by training networks using adversarial examples. However, training robust models using AT is not only computationally expensive but suffers from a performance degradation on clean examples [43]. Though some recent attempts [37, 53] have managed to adversarially train models in a low computational budget, they still suffer from the trade-off between standard accuracy and robustness to adversarial perturbations.
On the other hand, Mixup Training (MT) [57] has emerged as a popular technique to train models having both better generalisation and robustness. However, robustness performance of Mixup trained models is significantly lower than AT techniques [25, 27] when it comes to strong adversarial attacks like PGD [27], structural [50] or functional attacks. Taking advantage of the induced global linearity in between training examples by MT, Pang et al. [33] proposed Mixup Inference (MI) to exploit this global linearity at inference time and better defend Mixup-trained models against adversarial attacks. Though MI has been shown to significantly boost adversarial robustness of Mixup-trained models, the performance outperforms AT only when applied on top of Interpolated Adversarial Training [25], which is yet another adversarial training technique.
Although still in its early phase, the above efforts [57, 46, 33] indicate a trend to viewing Mixup from a robustness perspective.
In this work, we take another step in this direction and propose a sampling technique called VarMixup (Variational Mixup) to sample better Mixup images during training and inference to induce adversarial robustness. In particular, we hypothesize that the latent unfolded manifold underlying the data (through a generative model, a Varaitional Autoencoder in our case) is linear by construction, and hence more suitable for the linear interpolations used in Mixup. Importantly, we show that this choice of the distribution for Mixup plays an important role towards adversarial robustness (Section 3). Our experiments on 4 standard datasets- CIFAR-10, CIFAR-100, SVHN, Tiny-Imagenet shows that VarMixup when combined with Mixup training and inference, beats state-of the art adversarial training techniques (like PGD [27] and IAT [25]) under oblivious attacks without training model adversarially. We conduct several other studies to examine the proposed method, and obtain promising results across all these experiments.
Our contributions can be summarized as follows:
We propose a new sampling technique, VarMixup, which when combined with Mixup training and inference, outperforms adversarial training (by 5-10%) techniques without training the model in adversarial fashion. To the best of our knowledge, this is the first sampling technique explicitly proposed for adversarial robustness, and also the first among Mixup methods to consider the latent unfolded manifold for interpolation.
Additionally, we show that models trained using VarMixup samples are robust to common corruptions and perturbations, have low calibration error and are transferable.
We also conduct a comprehensive set of studies which show that VarMixup significantly decreases the local linearity error of the neural network and generates samples that are slightly off-distribution from training examples or mixup generated samples, to provide robustness.
2 Related Work
We briefly discuss earlier efforts in adversarial attacks and defenses, as well as Mixup-based methods, that are closest to this work.
Adversarial Attacks and Defenses Adversarial Attacks can be mainly classified into three categories: White-box attacks [30, 27, 47, 14], Black-box attacks [22, 1, 34, 9] and Oblivious attacks [8]. In the white-box setting, the adversary has full access to the network; and in the black-box setting, the adversary has no information about the network. Oblivious adversaries are not aware of the existence of the defense mechanism, and generate adversarial examples based on the unsecured classification model. With this advancement in adversarial attacks every year, various defenses have been proposed to counter them. Parseval networks [12] and Liplitchz Margin Training [44] train robust models by reducing their Lipschitz constant. LAT [38], Feature denoising [48], and Feature Scattering [55] achieve robustness by harnessing the fact that latent layers can help defend to perturbations. TRADES [56] presents a new
defense method that provides a trade-off between adversarial robustness and accuracy by decomposing the prediction error for adversarial examples (robust error) as the sum of the natural (classification) error and boundary error. Among the proposed approaches, Adversarial Training (AT) [27, 14, 25, 28] is a popular and widely opted strategy. The objective of AT is to train models on adversarial examples at every step of training to better match the adversarial distribution encountered at test time. Qin et al. [35] show that AT techniques make the loss landscapes of networks locally linear, and propose a regularizer that encourages the loss to behave linearly in the vicinity of the training data. A similar work [31] acts as a network regularizer by directly minimizing curvature of their loss surface, leading to adversarial robustness that is on par with adversarial training. Though AT is an effective way of improving the robustness of deep networks, they are usually computationally expensive. To address this drawback of AT, recent approaches [37, 54] aim to reduce
adversarial training overhead by recycling gradients and accelerating via the maximal principle respectively. For a comprehensive review of the work done in the area of adversarial examples, please refer [51].
Mixup: Mixup [57] is a recent method that trains networks on convex combinations of data pairs and their labels. By doing so, it regularizes the network to behave linearly in between training examples, thus inducing global linearity between them. A recent variant, Manifold Mixup [46], exploits interpolations at hidden representations, thereby obtaining neural networks with smoother decision boundaries at different levels of hidden representations. AugMix [19] mixes up multiple augmented images and uses a Jensen-Shannon Divergence consistency loss on them to achieve better robustness to common input corruptions [18]. In semi-supervised learning, MixMatch [4] obtains state-of-the-art results by guessing low-entropy labels for data-augmented unlabeled examples and mixes labeled and unlabeled data using Mixup. It has been shown that apart from better generalization, Mixup also improves the robustness of models to adversarial perturbations but not as significantly as AT. To further boost this robustness at inference time, Pang et al. [33] recently proposed a Mixup Inference technique which performs a mixup of input with a clean sample and passes the corresponding mixup sample () into the classifier as the processed input. Their analysis indicates that MI imposes a tighter upper bound on the potential attack ability for a crafted perturbation leading to robustness to adversarial attacks. Other efforts on Mixup [42] have shown that Mixup-trained networks are significantly better calibrated than ones trained in the regular fashion. These recent efforts have shown the potential of Mixup-based methods, which we exploit in the latent space for state-of-the-art robustness results.
Comparison with Other Mixup-related Work: Considering the recent popularity of Mixup-based methods, we address how our work is different from other recent efforts in Mixup. Xu et al. [49] use domain mixup to improve the generalization ability of models in domain adaptation. Adversarial Mixup Resynthesis [3] involves mixing latent codes used by autoencoders through an arbitrary mixing mechanism that can recombine codes from different inputs to produce novel examples. This work however has a different objective and focuses on generative models in a GAN-like setting, while our work focuses on adversarial robustness, which is a different objective. The work by Liu et al. [26] might be closest to ours in terms of approach as they introduce the adversarial autoencoder (AAE) to impose the feature representations with uniform distribution and apply the linear interpolation on latent space. However, their work deals from a generalization perspective, and results show marginal improvements. In our work, we directly exploit the manifold learned by VAE (and do not regularize it unlike the previous work) for the mixup and report improved adversarial robustness. Moreover, we also present useful insights into the working of VarMixup (leading to robustness), which is lacking in earlier work including [26], thus making our contributions different and more complete.
3 Methodology
3.1 Notations and Preliminaries
We denote a neural network as , with weight parameters .
takes an image and outputs logits, for each class . Without loss of generality, we assume the classification task, and denotes the standard cross-entropy loss function. denotes the training data distribution, and the optimal weight parameter, , obtained by training the network using standard empirical risk minimization is given by: . Here is the true label associated with input . We now briefly introduce the concepts relevant to this work, before describing our methodology.
Adversarial Attacks: In this work, we consider bounded Projected Gradient Descent Attack (PGD) [27] which is a more powerful, multi-step variant of [14] attack, and commonly considered among the state-of-the-art adversarial attacks. The adversary of input is computed as follows:
where is the step size, is the projection function, and is the space of possible perturbations. For more details, we refer the interested reader to [27].
Adversarial Training (AT): This is used to train the model in a robust manner by continuously augmenting the data with adversarial perturbations, to match the training distribution with the adversarial test distribution [14, 27]. Essentially, for AT, the optimal parameter is given by:
Here, the inner maximization is calculated using a strong adversarial attack such as PGD.
Mixup: [57] proposed a method to train models on the convex combination of pairs of examples and their labels. In other words, it constructs virtual training examples as: , where , are input vectors; , are one-hot label encodings and is a mixup coefficient, usually sampled from a distribution. Its variant, Manifold Mixup [46], a more recent method, trains neural networks on linear combinations of hidden representations of training examples.
Mixup Inference: [33], a recently developed specialized inference principle for mixup-trained models, attempts to break the locality of adversarial perturbations. Assuming that is a pre-trained mixup model, the output scores using MI, , on an input is given by:
Here, is a fixed mixup coefficient and is known as a sampling pool which differs for different variants of MI. In this work, we consider MI-OL (MI with other labels), where the sampling pool contains examples uniformly belonging to all classes except the predicted class of . The expectation can be approximated by averaging over runs.
3.2 Background: Vicinal Risk Minimization
Given the data distribution , a neural network and loss function , the expected risk (average of loss function over ) is given by . In practice, the true distribution is unknown, and is approximated by the training dataset , which represents the empirical distribution: . Here, is the Dirac delta function centered at . Using as an estimate to , we define expected empirical risk as:
Minimizing Eqn 4 to find optimal is typically termed Empirical Risk Minimization (ERM). However overparametrized neural networks can suffer from memorizing, leading to undesirable behavior of network outside the training distribution, . Addressing this concern, Chapelle et al. proposed Vicinal Risk Minimization (VRM), where is approximated by a vicinal distribution , given by:
where is the vicinal distribution that calculates the probability of a data point in the vicinity of other samples . Thus, using to approximate , expected vicinal risk is given by:
where . Popular examples of vicinal distributions include: (i) Gaussian Vicinal distribution: Here, , which is equivalent to augmenting the training samples with Gaussian noise; and (ii) Mixup Vicinal distribution : Here , where and .
From a perspective of adversarial robustness, analogous to previous definitions, we define adversarial risk in this work as:
where is the space of allowed perturbations.
Let be the solution of minimization of vicinal adversarial risk, . Then, following (cf. Eqn 11 in) [52], the quantity corresponds to the estimation of the expected adversarial risk of from its vicinal adversarial risk, and this can be bounded as:
which we call the robustness bound for VRM. Note again that this bound is analogous to the generalization bound for VRM in [52]. Motivated by this bound, we propose an approach to leverage the vicinal distribution to achieve adversarial robustness. From Eqn 8, we observe that the bound depends on two factors: (1) the choice of vicinal distribution and (2) the choice of family neural networks . In this work, we limit the choice of to ResNets [17] (specifically ResNet-34), thereby restricting the function class, and study the choice of vicinal distribution. Considering the recent success of using Mixup as a vicinal distribution for generalization [57] and Mixup inference for state-of-the-art adversarial robustness [33], we choose to consider a vicinal distribution based on Mixup-based linear interpolations. However, we propose the use of vicinal distributions from latent spaces in this work. We explain this choice below.
The use of generative models such as Variational Autoencoders (VAEs) [23] to capture the latent space from which a distribution is generated provides us an unfolded manifold (the low-dimensional latent space), where the linearity in between training examples is more readily observed. Defining vicinal distributions by using neighbours on this latent manifold (which is linear in the low-dimensional space) learned by generative models hence provides us more effective linear interpolations than the ones in input space. We use a generative model to capture the induced global linearity in between examples on a latent manifold, and define mixup vicinal distributions on this latent surface.
3.3 Our Approach: VarMixup (Variational Mixup)
To capture the latent manifold of the training data through a generative model, we opt for a Variational Autoencoder (VAE). VAE [23] is an autoencoder which is trained using Variational Inference, which serves as an implicit regularizer to ensure that the obtained latent space allows us to generate new data from the same distribution as training data.
We denote the encoding and decoding distribution of VAE as and respectively, parameterized by and respectively. Given as the desired prior distribution for encoding, the general VAE objective is given by the loss function:
Here, is any strict divergence, meaning that and if and only if , and is a scaling coefficient. The second term in the objective act as a image resconstruction loss and .
The original VAE [23] uses KL-divergence in Eqn 9, and thus optimizes the objective:
where is . However, using KL-divergence in Eqn 9 has some shortcomings. As pointed out by [11, 40, 58], the KL-divergence in Eqn 10 can be restrictive [10, 39, 6] as it encourages the encoding to be a random sample from for each , making them uninformative about the input. Also, it is not strong enough a regularizer compared to the reconstruction loss and tends to overfit data, consequently, learning a that has high variance. Both the aforementioned shortcomings can affect the encoding distribution by making them uninformative of inputs with high variance. Since we use VAEs to better capture a linear latent manifold and subsequently define interpolations there, a bad latent distribution can affect our method significantly. We hence use a variant Maximum Mean Discrepancy VAE (MMD-VAE)[58] which uses a MMD Loss [15] instead of KL-divergence, and hence optimizes the objective:
A MMD-VAE doesn't suffer from the aforementioned shortcomings [58], as it maximizes mutual information between and by matching the distribution over encodings with prior only in expectation, rather than for every input. We hence train an MMD-VAE to characterize the training distribution more effectively. We now define a Mixup vicinal distribution in the latent space of the trained VAE as:
where and . Using the above vicinal distribution, and the MMD-VAE decoder, , we construct VarMixup samples as:
From another perspective, one could view our new sampling technique as performing Manifold Mixup [46], however over the latent space of an MMD-VAE (instead of the neural network feature space) and using it for sample reconstruction. (We compare against Manifold Mixup in our results.) Figure 2 illustrates the conceptual idea behind VarMixup.
Analogous to the empirical distribution, used in Eqn 4, we also define a (non-vicinal) Empirical Risk Minimization, defined using the VAE, which we call VarERM, and is given by:
and use the MMD-VAE decoder, to construct samples which are close to the original distribution. We use this for comparisons in our experiments.
Additionally, it is easy to observe that our VarMixup sampling can also be integrated with Mixup-Inference which is given by:
We call this modified Mixup-Inference as VarMI (Variational Mixup Inference) in our experiments and show that under oblivious attacks, VarMixup and VarMI together performs significantly better than regular Mixup + MI and other adversarial training baselines.
4 Experiments and Results
We now present our experimental studies and results using our method, VarMixup, on multiple datasets. We begin by describing the datasets, evaluation criteria and implementation details.
Datasets: We perform experiments on four well-known standard datasets: CIFAR-10, CIFAR-100 [24], SVHN [32] and Tiny-ImageNet [13]. Descriptions of these standard datasets are deferred to the Supplementary section. CIFAR-10 is a subset of 80 million tiny images dataset and consists of 60,000 color images containing one of 10 object classes, with 6000 images per class. CIFAR-100 is just like CIFAR-10, except that it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. SVHN is obtained from house numbers in Google Street View images. It consists of color images belonging to 10 classes with 73257 digits for training and 26032 digits for testing. Tiny-Imagenet has 200 classes, with each class containing 500 training images, 50 validation images, and 50 test images. Each image here is of resolution .
Evaluation Criteria: Analogous to Mixup Inference [33], we evaluate trained models under the oblivious setting [8] where the adversary is not aware of the existence of the defense mechanism (e.g. MI, VarMI), and generates adversarial examples based on the classification model. We follow recent papers [33, 38, 25, 31] and use a 10-step untargeted PGD [27] attack with step-size and perturbation across all datasets. We also report accuracy on clean images and standard deviations over 10 trials. We evaluate the robustness of our trained models on the newer CIFAR-10-C, CIFAR-100-C and Tiny-Imagenet-C datasets [18] too. These datasets contain images, corrupted with 15 different distortions at 5 severity levels. Additionaly, We also perform evaluation under white-box adaptive setting [2] analogous to Mixup-Inference. In section 5, we also evaluate our models on targeted, stronger PGD and gradient-free attacks to confirm the absence of gradient masking or obfuscation [7, 2].
Implementation Details: It has been shown [21] that training a network adversarially removes irrelevant biases (e.g. texture biases) in their hidden representations, thus making them more informative. We hence hypothesize that the considered VAE, if trained adversarially, will have more informative latent encoding than its regular equivalent. This would hence help improve the empirical/vicinal distributions like VarMixup, VarERM and VarMI. Empirically, we validate this hypothesis in our subsequent experiments and use prefix adv- (eg: adv-VarMixup) to distinguish them from their regular variants. We stress that the aforementioned approach should not be confused with training a model adversarially using VarMixup (we only train the VAE adversarially). We also train a model adversarially using VarMixup, but discuss these results in the Supplementary section to avoid overcrowding of results and space constraints. Table 1 summarizes the variants of our approach that we consider with their corresponding abbreviations, which we used in subsequent sections. All models are trained on Resnet-34 [17] backbone across all datasets. A study on varying hyper-parameters is presented in Section 5.
|VarERM + VarMI
|adv-VarERM + adv-VarMI
|VarMixup + VarMI
|adv-VarMixup + adv-VarMI
Baseline Models: We compare our methods against an exhaustive set of baselines including non-VRM variants, mixup variants and state-of-the-art adversarial techniques. Below are their details:
ERM - Vanilla Empirical Risk Minimization (Eqn 4) using Adam optimizer () for 100 epochs on all datasets.
ERM + MI - Using Mixup-Inference [33] (MI-OL) on ERM with and .
Mixup - Vanilla Mixup training [57] using Adam optimizer () for 150 epochs on all datasets. Mixup coefficient is sampled from .
Mixup + MI - Using Mixup-Inference [33] (MI-OL) on Mixup with and .
IAT - Interpolated adversarial training [25] with and step-size . Interpolation coefficient is sampled from . Models are trained using Adam optimizer () for 350 epochs on all datasets.
IAT + MI - Using Mixup-Inference [33] (MI-OL) on IAT with and .
Robustness on CIFAR-10, CIFAR-100, SVHN and Tiny-Imagenet: We first evaluate the robustness and report our results in Table 2. As can be seen, without any adversarial training VarMixup + VarMI outperforms state-of-the-art adversarial training techniques by a significant margin ( over IAT + MI and over other AT techniques). Also, it can be noticed that the adversarial variant of our approach - adv-VarMixup + adv-VarMI performs better across all datasets.
|PGD10 (Clean)
|47.34 0.32 (85.58 0.14)
|22.72 0.37 (60.28 0.13)
|53.84 (87.80)
|27.03 (60.94)
|40.0 0.57 (89.7 0.33)
|20.6 0.49 (62.7 0.21)
|51.65 0.32 (88.11 0.43)
|25.5 0.35(63.3 0.32)
|54.95 (86.83)
|47.69 (84.45)
|61.57 1.30 (84.2 1.26)
|30.85 1.04 ( 61.4 1.15)
|0.0 0.0 (94.5 0.14)
|0.0 0.01 (64.5 0.10)
|0.27 0.07 (90.22 0.43)
|0.7 0.04 (63.28 0.46)
|1.01 0.02 (88.47 0.39)
|0.56 0.05 (62.74 0.26)
|Mixup [57]
|7.5 0.32 (95.5 0.35 )
|0.36 0.12 (76.8 0.41)
|12.34 0.37 (91.91 0.45 )
|0.72 0.12 (67.2 0.44 )
|17.66 0.23 (89.19 0.32 )
|0.74 0.19 (67.13 0.34)
|12.4 1.02 (60.23 0.97)
|2.4 1.11 (35.46 0.92)
|VarERM + VarMI
|30.49 1.03 (56.38 1.0)
|12.04 0.98 (33.88 1.10)
|adv-VarERM + adv-VarMI
|38.13 1.09 (53.30 0.95)
|11.45 1.01 (31.79 1.20 )
|Mixup + MI [33]
|25.76 1.12 (86.84 1.05)
|6.71 1.03 (67.8 1.06 )
|VarMixup + VarMI
|66.84 1.08 (82.15 0.92)
|35.8 1.24 (63.5 1.34)
|adv-VarMixup + adv-VarMI
|68.66 1.21 (82.06 1.19)
|40.13 1.2 (63.98 1.32)
|PGD10 (Clean)
|54.58 0.53 (91.88 0.34)
|3.78 0.21 (22.33 0.16)
|60.23 (91.65)
|54.83 0.67 (93.41 0.47)
|12.13 (51.94 0.28)
|3.83 0.12 (26.12 0.38)
|32.43 0.02 (79.28 0.01)
|60.0 1.09 (80.0 1.34)
|12.1 0.24 (18.08 0.34 )
|0.45 0.11 (95.47 0.56)
|0.0 0.0 (49.96 0.12)
|0.67 0.23 (95.60 0.55)
|0.1 0.03 (46.35 0.36)
|0.17 0.08 (95.10 0.56)
|0.04 0.01 (43.87 0.27)
|4.28 0.52 (96.54 0.49)
|0.0 0.0 (53.83 0.17)
|2.59 0.50 (96.72 0.48)
|0.01 0.01 (46.98 0.11)
|3.38 0.53 (95.64 0.50)
|0.0 0.0 (46.58 0.23)
|37.00 0.98 (71.85 1.21)
|1.54 0.05 (25.56 0.83)
|VarERM + VarMI
|38.25 0.99 (66.93 1.20)
|5.8 0.14 (23.61 0.72)
|adv-VarERM + adv-VarMI
|53.81 1.05 (69.78 1.29)
|7.27 0.19 (25.73 0.77)
|Mixup + MI
|37.05 1.30 (89.97 1.34)
|2.93 0.5 (47.2 0.23)
|VarMixup + VarMI
|78.08 1.05 (88.45 1.11)
|12.75 0.42 (42.88 0.15)
|adv-VarMixup + adv-VarMI
|87.20 1.09 (88.40 1.12)
|19.9 0.53 (44.18 0.24)
Transferability from Tiny-Imagenet to CIFAR-10 and CIFAR-100: The proposed methodology will be more scalable and time-efficient, if a VAE trained on a dataset such as ImageNet can be used to generate VarMixup samples for other datasets. This is a typical transfer learning setting, and we study the performance of training VarMixup models on CIFAR-10 and CIFAR-100 datasets using MMD-VAE trained on the Tiny-Imagenet dataset. We report our findings in Table 3. It can be seen that our approaches - VarMixup + VarMI/ adv-VarMixup + adv-VarMI still manage to outperform all adversarial training baselines (which are trained directly on the target dataset) by a good margin ( over IAT+MI and over other AT techniques).
|PGD10 (Clean)
|47.34 0.32 (85.58 0.14)
|22.72 0.37 (60.28 0.13)
|53.84 (87.80)
|27.03 (60.94)
|40.0 0.57 (89.7 0.33)
|20.6 0.49 (62.7 0.21)
|51.65 0.32 (88.11 0.43)
|25.5 0.35(63.3 0.32)
|54.95 (86.83)
|47.69 (84.45)
|61.57 1.30 (84.2 1.26)
|30.85 1.04 (61.4 1.15)
|0.0 0.0 (94.5 0.14)
|0.0 0.01 (64.5 0.10)
|0.12 0.01 (90.34 0.23 )
|0.54 0.05 (63.4 0.33 )
|0.37 0.19 (89.89 0.12)
|0.66 0.04 (62.08 0.17)
|7.5 0.32 (95.5 0.35 )
|0.36 0.12 (76.8 0.41)
|10.21 0.34 (91.09 0.21)
|0.82 0.14 (67.7 0.27)
|8.33 0.11 (90.71 0.31)
|0.44 0.09 (65.48 0.43)
|12.4 1.02 (60.23 0.97)
|2.4 1.11 (35.46 0.92)
|VarERM + VarMI
|24.46 1.23 (52.12 1.01)
|12.0 1.09 (34.84 0.98)
|adv-VarERM + adv-VarMI
|31.96 1.1 (58.94 0.96 )
|13.02 1.13 (33.04 0.92)
|25.76 1.12 (86.84 1.05)
|6.71 1.03 (67.8 1.06 )
|VarMixup + VarMI
|62.26 1.11 (82.48 1.12)
|30.3 1.03 (60.03 0.99)
|adv-VarMixup + adv-VarMI
|63.41 1.20 (82.76 1.02)
|31.3 1.15 (60.74 0.95)
Robustness to common input corruptions: Apart from adversarial perturbations, we also evaluate the trained models on various common input corruptions. Hendrycks et al. [18] recently proposed newer CIFAR-10, CIFAR-100 and Tiny-Imagenet test datasets (CIFAR-10-C, CIFAR-100-C, and Tiny-Imagenet-C) containing images corrupted with 15 different distortions (Gaussian blur, Shot Noise, Impulse Noise, JPEG compression, Motion blur, frost, etc.) and 5 levels of severity. We report the mean classification accuracy over all distortions on CIFAR-10-C, CIFAR-100-C and Tiny-Imagenet-C datasets in Table 4. From the table, our method - VarMixup/adv-VarMixup achieves superior performance by a margin of . It is also interesting to note that other variants like VarERM/adv-VarERM also perform significantly better than vanilla ERM and many adversarial training techniques, highlighting our method's effectiveness.
Comparison with Manifold Mixup: As stated in Section 3, one could view our method as performing Manifold Mixup in a latent space. It has been stated [46] that models trained using Manifold Mixup are not robust to strong adversarial attacks like PGD [27]. However, for completeness and fairness, we study the performance of the two approaches. We obtain 2.24% and 9.48% accuracy against a 10-step PGD attack (used in Table 2) using Manifold-Mixup and Manifold-Mixup + MI on the CIFAR-10 dataset. For CIFAR-100, the corresponding accuracies are 1.87% and 5.94% on Manifold-Mixup and Manifold-Mixup + MI respectively. For SVHN, Manifold mixup achieves adversarial accuracy 26.5% and 11.55% with and without MI. For Tiny Imagenet, the accuracies are 2.99% and 0.07% for Manifold Mixup with and without MI respectively. As can be compared from Table 2, VarMixup + MI performs superior to Manifold Mixup. We defer analogous results on SVHN and Tiny-Imagenet datasets to the Supplementary material due to space constraints. Our results show that the use of the latent unfolded manifold is indeed better than using hidden layer representations.
Performance under White-box Adaptive Attacks: Following Athalye et al. [2] and analogous to Mixup-Inference [33], we design adaptive white-box attacks for VarMI. From Eqn 14, VarMI is given as:
Then, the gradient of VarMI prediction w.r.t. input is given as (here, is fixed throughout inference):
Now, according to Eqn 15 above, the sign of gradients used in adaptive PGD can be approximated by:
Here, the number of adaptive samples, , refers to the execution times of sampling in each iteration step of adaptive PGD to approximate the above equation. Figure 3 shows the performance of Mixup/VarMixup/adv-VarMixup under adaptive PGD attacks on CIFAR-10/-100. As can be seen, our proposed inference VarMI along with VarMixup performs better than regular Mixup and Mixup-Inference.
|73.12 0.31
|45.09 0.31
|15.74 0.36
|75.46 0.21
|45.98 0.41
|16.20 0.23
|81.05 0.42
|50.71 0.25
|18.69 0.45
|69.29 0.21
|47.3 0.32
|17.34 0.27
|79.66 0.12
|49.26 0.31
|20.18 0.11
|79.73 0.13
|50.49 0.26
|21.62 0.09
|74.74 0.34
|52.13 0.43
|21.55 0.37
|82.57 0.42
|52.57 0.39
|24.87 0.32
|82.12 0.46
|54.0 0.41
|25.36 0.21
5 Discussion and Ablation studies
In this section, we conduct ablations to empirically characterize and analyze the efficacy of the proposed method.
Local linearity on loss landscapes: It has been shown [35] that local linearity of loss landscapes of neural networks is related to model robustness. The more the loss landscapes are linear, the more the adversarial robustness. To this fact, we analyze the local linearity of loss landscapes of VarMixup and regular mixup trained models. Qin et al. [35] defines local linearity at a data-point within a neighbourhood as: . Figure 4 shows the average local linear error (over test set) with increasing max-perturbation on CIFAR-10 and CIFAR-100 datasets. As can be seen, VarMixup/adv-VarMixup makes the local linear error significantly ( 2) lesser as compared to regular mixup, thus inducing robustness. This robustness gets further boosted by using VarMI at inference stage.
Analyzing VarMixup samples: Figure 7 shows sample data generated by regular Mixup, VarMixup, and adv-VarMixup on two images. We observe that though mixup or VarMixup samples look perceptually similar, they are quite different at statistical level. We measure the Frechet Inception Distance (FID) [20] and Kernel Inception Distance [5] between regular training data and training data generated by mixup/VarMixup/ adv-VarMixup. These scores summarize how similar the two groups are in terms of statistics on computer vision features of the raw images calculated using the Inceptionv3 model used for image classification. Lower scores indicate the two groups of images are more similar, or have more similar statistics, with a perfect score being 0.0 indicating that the two groups of images are identical. Figure 5 reports these metrics on CIFAR-10 and CIFAR-100 respectively. The greater FID and KID scores indicate that we are adding off-manifold samples (manifold characterized by training data) to the training using our approach.
Calibration error: A recent study [42] showed that DNNs trained with Mixup are significantly better calibrated than DNNs trained in a regular fashion. Calibration [16] measures how good softmax scores are as indicators of the actual likelihood of a correct prediction. We measure the Expected Calibration Error(ECE) [42, 16] of our trained networks, following [42]: predictions (total predictions) are grouped into interval bins () of equal size. The accuracy and confidence of are defined as:
where , , are the confidence, predicted label and true label of sample respectively. The Expected Calibration Error(ECE) is then defined as:
Models trained on CIFAR-10 with VarMixup and adv-VarMixup have ECE of 0.105 and 0.07 respectively whereas, model trained using regular mixup has ECE 0.13. On CIFAR-100 the ECEs are 0.135, 0.127 and 0.122 for Mixup, VarMixup and adv-VarMixup models respectively. This indicates that our VarMixup models are better calibrated than regular Mixup.
VarMixup on Interpolated Adversarial Training: We also integrate VarMixup with the more recent Interpolated Adversarial Training (IAT) [25], and call it VarIAT. On CIFAR-10, VarIAT and VarIAT+VarMI achieve adversarial robustness of 49.37% and 71.7%, respectively. For SVHN, the adversarial accuracies are 21.07% and 81.18% for VarIAT and VarIAT+VarMI, respectively. As can be seen from Table 2 (main submission), under oblivious settings, VarIAT (VarIAT + VarMI) achieves superior performance to IAT (IAT + MI) in most scenarios. The evaluations were carried out in the same setting as Table 2.
Robustness to stronger, targeted and gradient-free attacks: we also evaluate our models on a stronger 50-step PGD attack [27] (PGD50), targeted 10-step PGD attacks (t-PGD10) and Gradient free SPSA [45] attack. We use for all attacks. For targeted attacks, we chose the second most likely class as target class. SPSA results are evaluated over 1000 test samples. We report our results in Table 5. As can be seen, our methods VarMixup + VarMI outperforms the rest in most scenarios. The results also confirm the absence of any gradient masking and obfuscation [7, 2] in our training method.
|VarMixup + VarMI
|adv-VarMixup + adv-VarMI
Hyperparameter variations: We vary the beta distribution (used to sample Mixup coefficient) parameter - and train models using mixup, VarMixup and adv-VarMixup and plot their adversarial accuracies in Figure 6. Figure 6 shows the variation of adversarial accuracy when is varied from 0 to 1 in MI or VarMI. Both figures show that our VarMixup trained models perform better irrespective of hyperparameters in most cases.
Computational Overhead: We compares the computational time of our trained models using VarMixup/adv-VarMixup with AT and TRADES adversarial training techniques. VarMixup, adv-VarMixup, AT and TRADES take around 3, 5, 8.8 and 15 hours respectively. We considered the MMD-VAE training time too in these results. In addition, the transferability of a VAE trained on one dataset to do VarMixup on another makes our approach time-efficient and scalable.
6 Conclusions
In this work, we propose a Mixup vicinal distribution, VarMixup, which performs linear interpolations on an unfolded latent manifold where linearity in between training examples is likely to be preserved by construction. Analogous to Mixup Inference, we also define Variational Mixup Inference (VarMI) which when combined with VarMixup trained models provides significant gains in adversarial robustness over regular Mixup, Mixup Inference, and state-of-the-art adversarial training techniques under oblivious attacks. We also show that VarMixup trained models are more robust to common input corruptions, are better calibrated and have significantly lower local-linear loss than regular mixup models. Additionally, our experiments indicate that VarMixup adds more off-manifold images to training than regular mixup, which we believe is the primary reason for observed robustness. Our work highlights the efficacy of defining vicinal distributions by using neighbors on unfolded latent manifold rather than data manifold and we believe that our work can open a discussion around this notion of robustness and choice of vicinal distributions.
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- [52] (2018) Generalization bounds for vicinal risk minimization principle. arXiv preprint arXiv:1811.04351. Cited by: §3.2.
- [53] (2019-05) You only propagate once: painless adversarial training using maximal principle. pp. . Cited by: §1.
- [54] (2019) You only propagate once: accelerating adversarial training via maximal principle. arXiv preprint arXiv:1905.00877. Cited by: §2.
- [55] (2019) Defense against adversarial attacks using feature scattering-based adversarial training. In NeurIPS, Cited by: §1, §2.
- [56] (2019) Theoretically principled trade-off between robustness and accuracy. CoRR abs/1901.08573. External Links: Link, 1901.08573 Cited by: Figure 1, §1, §2, item 5, Table 2, Table 3, Table 5.
- [57] (2017) Mixup: beyond empirical risk minimization. CoRR abs/1710.09412. External Links: Link, 1710.09412 Cited by: Figure 1, §1, §1, §2, §3.1, §3.2, item 3, Table 2.
- [58] (2017) InfoVAE: information maximizing variational autoencoders. CoRR abs/1706.02262. External Links: Link, 1706.02262 Cited by: §3.3.

subset: commoncrawl


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Ronald L. McEnaney, 72, of South Haven, passed away Friday, February 14, 2020 at Graceway at Country Side Community in South Haven. He was born to Donald and Margaret (Calloway) McEnaney on September 23, 1947 in Chicago, Illinois. Ron married Mary Feirick on June 22, 1968 at Redeemer Lutheran Church in Chicago. Ron worked as a Security Supervisor at Palisades Nuclear Plant for 40 years, retiring in 2013. He was a member of Peace Evangelical Lutheran Church. Ron served his community by volunteering his time at Open Door, South Haven Community Hospital and Al Van Humane Society.
He is preceded in death along with his parents by his brothers Donald Leo McEnaney and James Patrick McEnaney.
Ronald is survived by his wife – Mary McEnaney of South Haven, children – Laura (Percy) Nikorawalla of Collegeville, Pennsylvania, and Steven (Jamie) McEnaney of Decatur, Michigan, Grandson – Steven McEnaney, Jr. and a brother – Thomas (Sherry) McEnaney of Barryville, Arkansas.
Visitation will be held from 10:00 AM to 11:00 AM on Thursday February 20, 2020 at Peace Evangelical Lutheran Church in South Haven. Funeral services will follow the visitation at 11:00 AM with Pastor Travis Wilson officiating. Ronald will be laid to rest at Peace Lutheran Memorial Garden. Contributions may be made in Ronald's memory to the Peace Evangelical Lutheran Church Mission Endowment Fund, 06321 Blue Star Hwy, South Haven, MI 49090 or Al Van Humane Society, PO Box 421, South Haven, MI 49090.
To plant a tree in memory of Ronald Louis McEnaney, please visit our tribute store.


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February 5, 2016
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What is fitness? The definition of physical fitness is often quoted as 'the ability of the body to meet demands imposed by the environment and everyday life.' If you look 'Fit' up in the dictionary the adjective can mean: In good health or athletic condition. We exerc...
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November 7, 2018


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bronco wont move when i switch gears, why?
Hi, I've got a 1987 Ford bronco, good condition.
It's been working fine since i've fixed it but recently it's been having
usually it only has to sit and warm up for about 10 minuets before you can
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1 Answer
If your fluid is full, you probably need a transmission.


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Latest News Headlines
Melbourne City Ballet - Bringing dance to a wider audience
Australia - Melbourne City Ballet was only formed around two years ago, but it is already proving a hit with dancers and the public alike. Members of MCB, which has recently teamed up with Harlequin Floors, spoke to Mark Rasmussen, Harlequin's global marketing manager.
As a vibrant new company MCB has provided a rallying call to dancers, still at the peak of their performing careers, who found themselves not fitting into the existing structure of more established classical ballet companies.
Principal dancer Yuiko Masukawa, who met artistic director Michael Pappalardo when they were dancing Madame Butterfly, explained: "We started talking about how we have so many beautiful dancers, but we don't have a company or a place to dance." Michael went on to explain: "So we created this space that could accommodate differences in dancers and to create work which was entertaining."
The recruitment of former dancer with West Australia and Queensland Ballet, Iona Marques as ballet mistress and senior dancer has also contributed to the success of MCB. She spoke of their ambition of growing into a major arts company in Victoria. "MCB is playing to a gap in the market for production content, dancers and the public. You get to work every day, you get to challenge yourself and somehow improve yourself as a dancer, because it doesn't feel like work at all."
When speaking about the company's choice of Harlequin floors for their studios, Iona added: "We can't do what we do without the perfect floor. The perfect floor makes for beautiful dancing." Yuiko agreed: "When I dance on a different floor I always have a fear of getting injured, but now I can dance one hundred percent."
Company manager Sean Memery pointed out, that when "choosing a floor for MCB we did try a few companies to see what was a perfect floor for us. Artistic Director, Michael Pappalardo continued: "In selecting Harlequin we went for the best product on the market and so many people have since come into the space and said we have the best floors in Melbourne."
Looking to the future Michael would like MCB to grow and is passionate about delivering great work at an affordable price to urban areas and rural areas around Victoria. "We want to create a great space for dancers to enjoy and great dance for the public to come and see," he said. "The future for Melbourne City Ballet is definitely bright," concluded Sean.
You can see highlights of the interviews as part of Harlequin Floor's "Meet the stars" video series on their YouTube Channel.
15th January 2015
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Bringing the vision to life. The City of White Plains has endured cycles of real estate boom and bust, and the early-1990s recession left troubling commercial and retail vacancy rates downtown. After VHB updated the city's Comprehensive Plan, we put the plan into practice by helping Cappelli Enterprises redevelop the former Macy's site and surrounding urban renewal properties into an $850 million mixed-use complex and then transform a vacant parking lot and surrounding properties into a luxury Ritz-Carlton hotel and residential towers. Through these and other projects, downtown White Plains is again thriving as the commercial heart of Westchester County.
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Surat Thani Town to Ao Nang
Surat Thani Town to Ao Nang:Passengers who wish to travel to Ao Nang in Krabi take our daily services from Surat Thani Town (Phantip Travel head office in Talad Kaset 1). We operate for joint minivan services from Surat Thani Town to Ao nang.The minivan services depart daily from our head office in Surat Thani Town. This combo ticket will include the minivan and transfer to Ao Nang. Minivans will take approximately 3 hours to Krabi Bus Terminal. Upon arrival customers will have to transit and take a transfer from the bus terminal to Ao Nang.
All customers must check in 30 minutes prior to departure at the ticketing office in Surat Thani Town.As this a joint minivan service, customers with oversize luggage or bags may have to purchase extra baggage space.
For more information
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Listed in:(804) 262-0035
7625 Staples Mill Rd
Henrico, VA 23228
- Home Services in Henrico
- Movers and Moving Companies in Henrico
7625 Staples Mill Rd
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general information
Current categorization of Ryder is under Home Services , Movers and Moving Companies with the location in the city of Henrico, VA . Ryder's business owner has an option to edit Ryder business listing info to promote and describe goods and service to Staples Mill Rd Henrico consumers.
Ryder has been reviewed 0 times. All Henrico-people are welcome to review and share their positive and negative experience with Ryder in Henrico, VA which promotes following services: Henrico Home Services , Henrico Movers and Moving Companies
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Senior Services
Please note that these measures may be modified as new information becomes available.
- As of Monday, March 16, all non-essential Senior Center activities will be suspended until further notice. Exceptions include:
- Senior Van Service (medical and food appointments only).
- CRT Lunch Program will continue with drop-off or pick up meals only. Call 860-844-5350 to register.
- AARP Tax Assistance. Appointments are suspended until further notice. Individuals with existing appointment will be contacted by AARP staff and provided with further instructions.
- All events (not related to the Town's FY21 budget adoption process) scheduled to be held in the Senior Center between March 13th and April 30th are postponed indefinitely. Please contact the Senior Center/Youth Services with questions at (860) 844-5350.
- Please contact the Human Services Department with questions at (860) 844-5350.
Please visit the following websites for up to date information on the developing situation:
- Centers for Disease Control and Prevention
- State of Connecticut Department of Public Health
- Farmington Valley Health District
You can also call 2-1-1 or text "CTCOVID" to 898211. These resources are available 24 hours a day, 7 days a week.
Staff Contacts
|Sandra Yost
|Director of Human Services
|(860) 844-5351
|Vanessa Berrelli
|Senior & Social Services Administrative Assistant
|(860) 844-5350


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Investing should be easy – just buy low and sell high – but most of us have trouble following that simple advice. There are principles and strategies that may enable you to put together an investment portfolio that reflects your risk tolerance, time horizon, and goals. Understanding these principles and strategies can help you avoid some of the pitfalls that snare some investors.
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For some, the social impact of investing is just as important as the return, perhaps more important.
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Tulips were the first, but they won't be the last. What forms a "bubble" and what causes them to burst?


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BiggTim replied to red baron's topic in Ready for InspectionOH man, that is cool!! Excellent paint tone variation, very realistic, and and interesting scheme too. You don't see many drones done, and this one id fabtastic!! Tim
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BiggTim replied to Madmax's topic in Works in ProgressI have to admit that I sort of like the way the stenciled one looks over the sharp perfection of the decals. As talented as you are, it seems like you could touch up the sprayed symbols with a small brush and have a much more authentic look than the decals. More like the original hand painted feel. Just my opinion, though. This is amazing work!! Tim
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BiggTim replied to boiss123's topic in Aviation DiscussionI'm no authority on Japanese aircraft, so I have to refer to things like this: http://www.detailandscale.com/attack_on_pearl_harbor.html (scroll thru the previews) It shows an explanation of second carrier group markings on page 52, I think. If that's anywhere close to accurate, then sky blue doesn't sound right to me either. And if you're new here, welcome! Tim
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Astro32 reacted to a post in a topic: I'm furloughed.....
Yikes, that sounds pretty heavy-handed. I hope they don't do that to you, though I expect we're all just a few steps away from it. My full-time job is with an architecture firm, and our biggest client is the only local hospital (the other one went bankrupt and closed a few months ago - good timing!!). Fortunately, the need is so great that they want us to keep working on their projects, so I get to work from home for the time being. The other job never stops, no matter what. Tim
I need a little advice. I do not have any good P-40 references, so I'm relying on the internet to find what I can. Trouble is, there are so many subtle differences in P-40 variants, that I am having a tough time finding solid info on all of the lights that should be on an E model. I read Thierry's old tweak list, and a few other threads here. Here's what I believe is correct so far: - Delete the fuselage side lights below the canopy. - The right wing tip lights should both be green (bluish lenses, with a warm bulb = green light) - The left wingtip lights should both be red lenses. - The tail lights should both be white or clear lenses. - The landing light on the bottom side of the left wing is clear. Here's where I get stuck: - Many photos of actual planes and lots of builds, show another white or clear light on the fuselage spine right behind the cockpit - others don't show it, and the kit does not include one. Which is right for an E model? - The kit has two formation? lights on the bottom of the fuselage right behind the wing root, but almost every E model photo I can find does not show these lights. I assume they should be deleted, is that correct? - There is also another large light molded on the bottom of the right rear wing root that I can't find good info on. What is it, and should it be there? If so, what color is it? A couple of other things not related to lights: - There is a hole in the front of the right wing root (for a cockpit vent, I think). Shouldn't there be one on the left side, too? - I believe the hole in the front of the right landing gear knuckle is BS, and should be filled. Is that correct? - I can't decide whether to add the little gun camera pod or not. It certainly doesn't show up in a lot of photos, so what do you guys think? Thanks, Tim
Ice Queen
BiggTim replied to ShelbyGT500's topic in Non-LSP WorksImpressive work, but a mighty cold way to play hockey!
NLLSP- No Longer a Large Scary Project
BiggTim replied to chrish's topic in Works in ProgressUh...WOW!!
Revell Beaufighter 1/32 1F Nightfighter
BiggTim replied to Suresh Nathan's topic in Works in ProgressWOW!!! That is, without doubt, the best build of that kit I've ever seen!!! Very impressive indeed.
No, I don't have that book, but I've been digging around and looking at enough sources and photos that I'm comfortable taking a few liberties with it and forging ahead.
Glad you might be seeing some relief. I hope there will be some sort of financial help for those affected by this. No changes as yet in my work life, so I guess that's good.
As follow up, the spring show in Seattle is officially cancelled, as the venue will not re-open until the end of April. It may be re-scheduled for a later date this year, but nothing has been settled yet, and won't be for some time, obviously.
How Do I Attach a Build to My Profile?
BiggTim replied to Dennis7423's topic in General DiscussionLooks like Brian got you squared away!
That is very intriguing, and helps to explain when the national markings were applied. Any chance I could see the preceding page as well?
Looks like the Tamiya F-51D is out at Lucky Model
BiggTim replied to Dave Williams's topic in LSP DiscussionDoes it come wrapped in a condom?