This paper discusses the impact of the Affordable Care Act (ACA) on the United States. First there is discussion regarding what led up to health care reform followed by the impacts of the bill and the current trends. 

One motivation was that the "US system left more than 1 in 7 Americans without health insurance coverage in 2008." Also, there was an upward trend in how much the economy was spending on healthcare. 

The first results are shown in Figure 1. The number of uninsured dropped after the ACA. Maybe this is the most significant take away but it doesn't capture the quality and cost of care which are also addressed in the paper.

![](https://i.imgur.com/qrQ7nU9.png)

The paper states "Before the ACA, the health care system was dominated by 'fee-for-service' payment systems, which often penalized health care organizations and health care professionals who find ways to deliver care more efficiently, while failing to reward those who improve the quality of care. The ACA has changed the health care payment system in several important ways." 

The ACA modified payments for medicare services and introduced a "value-based payment" system with the goal of reducing overall cost. This is shown in Figure 4. What the plot is showing is the change over a time period. Between 2000-2005 the costs per payer were increasing in all programs. Between 2005-2010 medicaid cost to the person was decreasing and the other programs were still growing but growing slower. Between 2010-2014 the cost to both medicare and medicaid are both in decline. Private insurance costs are still increasing but they are increasing slower than before.

![](https://i.imgur.com/knBTQxK.png)

Another interesting take away is that "[t]he rate of hospital-acquired conditions (such as adverse drug events, infections, and pressure ulcers) has declined by 17%." This is reflected in the 30-day readmission rates dropping shown in Figure 6.

![](https://i.imgur.com/SmcxfoB.png)

Normally, a Deep Convolutional Network (DCN) has a conditional probabilistic model where we have some parameterised function $f_{\theta}$, and some distribution over the targets to define the loss function (categorical for classification or Gaussian for regression). But, that conditional function is treated as a black box (probabilistically speaking, it's just fit by maximum likelihood). This paper breaks the entire network up into a number of latent factors.

The latent factors are designed to represent familiar parts of DCNs; for example, max-pooling selects from a set of activations and we can model the uncertainty in this selection with a categorical random variable. To recreate every pixel you can imagine selecting a set of paths backwards through the network to reproduce that pixel activation. That's not how their model is parameterised, all I can do is point you to the paper for the real DRMM model definition.

# Inference

The big trick of this paper is that they have designed the probabilistic model so that max-sum-product message passing (also introduced in this paper) inference equates to the forward prop in a DCN. What does that mean? Well, since the network structure is now a hierarchical probabilistic model we can hope to throw better learning algorithms at it, which is what they do.

# Learning

Using this probabilistic formulation, you can define a loss function that includes a reconstruction loss generating the image from responsibilities you can estimate during the forward pass. Then, you can have reconstruction gradients _and_ gradients wrt your target loss, so you can train semi-supervised or unsupervised but still work with what is practically a DCN.

In addition, it's possible to derive a full EM algorithm in this case, and the M-step corresponds to just solving a generalised Least Squares problem. Which means gradient-free and better principled training of neural networks.

# Experimental Results

Not all of the theory presented in this paper is covered in the experiments. They do demonstrate their method working well on MNIST and CIFAR-10 semi-supervised (SOTA with additional variational tweaks). But, there are not yet any experiments using the full EM algorithm they describe (only reporting that results _appear promising_); all experiments use gradient optimisation. They report that their network will train 2-3x faster, but only demonstrate this on MNIST (and what about variation in the chosen optimizer?).

Also, the model can be sampled from, but we don't have any visualisations of the kind of images we would get.

Fawzi et al. propose an adaptive data augmentation scheme based on adversarial transformations similar to adversarial training. In particular, in each training iteration – and for each sample/batch – they compute an adversarial version by finding a transformation that maximizes the training loss. The transformation is usually constrained to a specific class of transformations – on MNIST, for example, they consider affine transformations. Additionally, only small transformations are considered, a constraint very similar to the perturbation constraint of adversarial examples. In order to find these adversarial transformations during training, they employ an iterative algorithm based on a local first-order approximation of the training loss on the transformed sample. This leads to a simple linear program that can be solved in each iteration. Overall, however, this results in a significant computational overhead during training (as does adversarial training, as well). Quantitative results on MNIST are encouraging; specifically, Figure 1 shows that the test loss converges faster when employing these adversarial transformations as data augmentation in contrast to random transformations.

https://i.imgur.com/OxuQCP9.png
Figure 1: Test loss for a model trained without data augmentation, the proposed adaptive data augmentation and random data augmentation, see paper for details.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

A biofilm is a surface-associated population of microorganisms embedded in a matrix of extracellular polymeric substances. Biofilms are a major natural growth form of microorganisms and the cause of pervasive device-associated infection. This report focuses on the biofilm matrix of Candida albicans, the major fungal pathogen of humans. We report here that the C. albicans zinc-response transcription factor Zap1 is a negative regulator of a major matrix component, soluble β-1,3 glucan, in both in vitro and in vivo biofilm models. To understand the mechanistic relationship between Zap1 and matrix, we identified Zap1 target genes through expression profiling and full genome chromatin immunoprecipitation. On the basis of these results, we designed additional experiments showing that two glucoamylases, Gca1 and Gca2, have positive roles in matrix production and may function through hydrolysis of insoluble β-1,3 glucan chains. We also show that a group of alcohol dehydrogenases Adh5, Csh1, and Ifd6 have roles in matrix production: Adh5 acts positively, and Csh1 and Ifd6, negatively. We propose that these alcohol dehydrogenases generate quorum-sensing aryl and acyl alcohols that in turn govern multiple events in biofilm maturation. Our findings define a novel regulatory circuit and its mechanism of control of a process central to infection.

A biofilm is a surface-associated population of microbes that is embedded in a cement of extracellular compounds. This cement is known as matrix. The two main functions of matrix are to protect cells from their surrounding environment, preventing drugs and other stresses from penetrating the biofilm, and to maintain the architectural stability of the biofilm, acting as a glue to hold the cells together. The presence of matrix is a contributing factor to the high degree of resistance to antimicrobial drugs observed in biofilms. Because biofilms have a major impact on human health, and because matrix is such a pivotal component of biofilms, it is important to understand how the production of matrix is regulated. We have begun to address this question in the major human fungal pathogen Candida albicans. We found that the zinc-responsive regulatory protein Zap1 controls the expression of several genes important for matrix formation in C. albicans. These target genes encode glucoamylases and alcohol dehydrogenases, enzymes that probably govern the synthesis of distinct matrix constituents. The findings here offer insight into the metabolic processes that contribute to biofilm formation and indicate that Zap1 functions broadly as a negative regulator of biofilm maturation.

They created a really nice trick to optimize the $ {L}_{0} $ Pseudo Norm - Regularization on the sorted (By magnitude) values of the optimization variable.

Their code is available at - [The Trimmed Lasso: Sparsity and Robustness](https://github.com/copenhaver/trimmedlasso).