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Belize Fisheries Department join in celebrating World Oceans Day!
Special Envoy and Ministry of Agriculture officials discuss collaboration on school gardens, feeding programs
By Zoila Palma: Today, representatives from the Ministry of Agriculture, Food Security, and Enterprise met with the Special Envoy for the Development of Families and children, Rosanna Briceno.
Discussions included collaboration between the Ministry and the Office of the Special Envoy, especially for school feeding programs and school gardens.
A presentation on how School gardens can be used as a pedagogical tool in schools through partnerships with the National Food and Nutrition Security Commission, the Ministry of Education, the Ministry of Health & Wellness, and the Office of the Special Envoy was done.
The Special Envoy highlighted the main goal of empowering the most vulnerable families and children to fully contribute and to successfully participate in achieving Belize's development goals.
Strategic priorities include strengthening engagement to support the most vulnerable, increasing citizen participation and ownership, and promoting effective and accountable governance.
"The mission of the Office of the Special Envoy aligns with the program areas of the National Food and Nutrition Security Commission and with the overall goals of Plan Belize," a statement said.
Representing the Ministry of Agriculture were Servulo Baeza, CEO in the ministry; Luis Pook, director of communications; Clifford Martinez, director of extension; Gary Ramirez, national horticulture coordinator; and Emilio Montero, coordinator for the National Food and Nutrition Security Commission.

redpajama_set_name: RedPajamaCommonCrawl


text:
THE WORD ALIVE To Release 'Monomania' Album In February
THE WORD ALIVE will release its new new album, "Monomania", on February 21 via Fearless Records. The disc was produced by Eric Ron (PANIC! AT THE DISCO, GODSMACK).
The official music video for the "Monomania" title track can be seen below.
"With 'Monomania', I wanted to capture the emotional roller coaster we go through as we continually kill our true selves battling the 'madness' we feel as we struggle with who we are, wondering at times if there's any point to all of this," says vocalist Telle Smith. "Sometimes, we're so desperate to be heard that we get lost attempting to fit in or to stand out. It's fucking hard learning what feels like hard lessons over and over. We fail to take ownership of our thoughts and actions, pulling us further away from facing who we are. You only really overcome that by trusting in yourself, because that's the only way you can break the cycle."
The Arizona-based band previously released the anthemic track "Burning Your World Down".
"When you listen to our new music, I hope you have a better understanding of our entire career," Smith says about the album as a whole. "Maybe you can see the whole story and realize that we're not different from you. We've been through it all, but we're always trying to learn, grow, and leave something behind we feel will help people. This is our purest work. This is everything we've been through in the last couple of years. It's the best conclusion of the last 10 years and the best opening to the next 10."
"Monomania" track listing:
01. Monomania
02. No Way Out
03. Searching For Glory
04. Another Year In The Shadows
05. Greatest Almost
06. Thank You
07. Numb Love (Misery II)
08. K.F.
09. Burning Your World Down
10. Comfort & Chaos
11. I'm Sorry You're Sorry Now
12. Death Is Only The End If You Assume The Story Is About You
Since emerging in 2009, THE WORD ALIVE's first decade comprised "Deceiver" (2010), "Life Cycles" (2012), "Real." (2014) and "Dark Matter" (2016), reaching a high watermark on "Violent Noise" in 2018. Bringing their total streams past 100 million, the latter included "Why Am I Like This?" (9.7 million Spotify streams) and "Red Clouds" (4.7 million Spotify streams).
Tags: the word alive
JUDAS PRIEST's ROB HALFORD: 'I Still Go Through Moments Of Depression'
ANTHRAX's FRANK BELLO Thanked METALLICA's JAMES HETFIELD For Organizing 'Big Four' Shows: They 'Didn't Have To Do That'
BRUCE DICKINSON Says IRON MAIDEN Will Perform Entire 82-Minute 'Senjutsu' Album On Future Tour
SAMMY HAGAR On His Las Vegas Residency: 'I Can Stand Right In The Middle Of The Room' And 'It's Awesome'
Ex-FEAR FACTORY Frontman BURTON C. BELL On His Singing Style: 'It's All About Vocalizing For Feeling And Vocalizing A Moment'
KING BASTARD
It Came From The Void
FURIS IGNIS
Decapitate The Aging World
WORM SHEPHERD


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3D printing architectural models has become a well-established practice within that industry, with an increasing number of firms purchasing their own printers to test their designs and display them for clients. It's a logical application for the technology, and one that has already yielded excellent results. But are there other areas where 3D printing could be utilised in this way? We would argue that any industry where complex designs must be tested and displayed would benefit from exploring 3D printing in depth.
A strong example of this would be Japanese interior design specialists id.arts' recent use of 3D printing to create attractive scale models of their kitchen designs. Starting with a highly detailed model of a kitchen, id.arts envisage this technology being useful for both industrial designers and developers, helping them test multiple iterations of their designs and providing clients and internal stakeholders with a striking representation of a design.
Of course, one huge asset for interior designers who're ready to dive head-first into 3D printing would be the ability to design and print custom parts on an as-needed basis, without the associated costs of ordering one-off parts from traditional manufacturers — something that forward-thinking designers are already exploring. With the right technology and a willingness to explore its capabilities, it is possible to produce designs that would be challenging, or even impossible, to produce with traditional methods. If effectively implemented, this could easily turn into a powerful USP for an interior design agency.
The potential is enormous, but as with many other industries, 3D printing is still finding its feet in interior design. It will take time for interior design agencies to establish the technology within their individual workflows, and for the concept of ordering 3D printed parts for the interior of one's home to catch on amongst the general public. This will take time to resolve itself, but the more agencies that begin exploring 3D printing technology, and the more high-profile projects they successfully apply it to, the sooner we will see 3D interior design enter the next stage of its evolution.


CBR 308: Developing Real Solutions to Real Problems Policy Analysis from a Community Perspective.
8 Icebreaker: What does good policy look like? What do you think of these policy options? Are they options? Option 1 Children from better-off families do better in school. To make university and college access more equitable, we need to address inequalities as they build up from early childhood by intervening early to offer lower-income children educational, health and social programs to encourage development that will ultimately allow them to move on to post-secondary education. Option 2 Have tuition increases affected the enrolment of students from different income levels?
16 1.Timeframe of governments business/election cycle –make the tough decisions early 2.Difference between election/post-election periods – move from firm to more fluid ideology 3.Short attention span of politics, short shelf life of policy – In two years, its not my problem 4.Governments policy agenda/priorities – we want to do this 5.Governments communications agenda/priorities – we want to say that Around the Cabinet Table Before the First Step: What drives political/public policy decisions?
20 Crafting the Foundation The Second Step: Positioning research to frame your options To make the case, apply and distinguish - whats same/similar, whats different, and why? How is the issue framed? Who is affected? What is the political and policymaking environment? How is the solution communicated? Who benefits, who doesnt? What alternatives were considered? What is the outcome, is it relevant? What are the next steps? What would we have to change for it to work here?
21 Crafting the Foundation The Third Step: Leveraging community Create communities of interest - build and sustain networks, alliances, coalitions inside/outside your community Look for opportunities to build consensus and credibility, even on relatively small issues – build capacity to disagree, negotiate, compromise, and move forward Work from the grassroots – generate issues and solutions that are of the community, with voices from the community Practise collaborative consultation - travel in groups Develop parallel advocacy strategies – demos. and docs. Other?
22 Case Study #1 Whats the Issue? The facts You are the Minister of Health and Long-Term Care in Ontario. The Chief Medical Officer of Health for the province, who is a public servant, has suggested that all fish sold to the public in Ontario must be frozen before it is sold in a grocery store, market, or restaurant. It is known that some fish contains parasites, usually roundworms that can make people sick if they eat contaminated fish. With the recent emphasis on public health following the Walkerton water crisis and deaths, SARS, and mad cow disease, Ministry public health staff want to ensure that the Ontario public is protected. Your group task What are the issues here? How do you, as Minister, see them? How could others see them? Whats the story?
29 Case Study #2 Constructing a Policy Options Menu Again, the facts of the Case Study You are the Minister of Health and Long-Term Care in Ontario. The Chief Medical Officer of Health for the province, who is a public servant, has suggested that all fish sold to the public in Ontario must be frozen before it is sold in a grocery store, market, or restaurant. It is known that some fish contains parasites, usually roundworms that can make people sick if they eat contaminated fish. With the recent emphasis on public health following the Walkerton water crisis and deaths, SARS, and mad cow disease, Ministry public health staff want to ensure that the Ontario public is protected.
30 Case Study #2 Constructing a Policy Options Menu Your group task Construct a policy options menu to deal with the issues these facts present. Keep in mind our earlier plenary discussion about framing problems and developing options. Take a strategic approach: consider each from a policy perspective, as well as from communications, fiscal, community/partnership development. Finally, think about the impact – the pros and cons – of each option.
36 Developing Recommendations What turns a policy option into a decision? It reflects consensus or compromise – its the best deal It works – it solves the problem or at least makes it go away It manages risk well – its relatively safe It can lead to more change – its incremental It gives your community and the government an opportunity to engage - it carries the power of partnership It fits – it delivers on the governments policy, communications, and/or fiscal agenda Other?
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text:
\section{Introduction}
\vspace{-0.1cm}

Generative models typically aim to capture the natural statistics while isolating independent factors of variation. This can be beneficial if such factors correspond to variables of interest in tasks to be instantiated {\em post-hoc}, or if the model is to be used for image synthesis where the user wants to independently control the outcome.
Generative models learned from large image collections, for instance variational auto-encoders (VAEs) or generative adversarial networks (GANs) do isolate independent factors of variation, but those affect the global statistics of the image.
We are interested in spatially-localized factors of variation, so that manipulation of image statistics can occur at the level of {\em objects}, rather than of the whole image.
While one could learn conditional generative models, this usually requires annotation of the independent factors.
We aim to learn spatially and semantically independent latent factors without the need for any annotation.
We call these {\em object-centric generative factors}.

\begin{figure}[!t]
\begin{center}
\footnotesize{}
  \includegraphics[width=0.38\textwidth]{figures/only_change_one.png}
\end{center}
\vspace{-0.3cm}
\caption{Perturbing the factors learned without knowing objects affects the synthesized scene globally (middle). Object-centric generative factors enable changing the color of the pillar from green (left) to yellow without affecting the other objects (right).}
\vspace{-0.2cm}
\label{fig:global-effect-betaVAE}
\end{figure}

The existing literature on object-centric generative models is restricted to piece-wise constant or smooth images.
We introduce two variational constraints, one derived from the Contextual Information Separation (CIS) principle \cite{yang2019unsupervised}, but extended to multiple objects, and one derived by enforcing perceptual cycle-consistency, which is that the partition of the image into independently controlled region is stable with respect to perturbations of the latent factors.
We illustrate the characteristics of our model on existing datasets, and introduce two new datasets of increased complexity.

\vspace{-0.05cm}
\section{Related Work}
\vspace{-0.1cm}

There are two approaches to representation learning, {\em task driven}, where the goal is to learn a function of the data that captures all information relevant to the task and discards everything else (sufficient invariant) \cite{achille2018emergence}, and {\em disentangled}, where the goal is to reconstruct the data while separating the independent factors of variation \cite{bengio2013representation,tran2017disentangled}.
Technically speaking, the latter is a special case of the former, when the task variable is the data itself, and the independence of latent factor can be framed as the secondary task.
However, the literature has largely progressed on separate tracks.
An independent taxonomy can be devised based on the level of supervision.
While disentangled representations usually refer to unsupervised learning, task-driven representations can be unsupervised (if the task is, for instance, prediction, or reconstruction), semi-supervised, or fully supervised.
Among unsupervised representation learning methods, variational autoencoders (VAE) \cite{kingma2013auto} attempt to extract ``meaningful'' latent factors by forcing a variational bottleneck in the generative model. This can also be seen as a special case of task-driven representation, where the task is the data itself.
It was shown by \cite{achille2018emergence,press2018emerging} that the latent factors tend to be independent, and can be easily manipulated in a generative setting.
A different approach uses an adversarial loss \cite{goodfellow2014generative} to map a known distribution (typically a Gaussian) to an approximation of the data distribution, so the input distribution can be considered a set of independent factors.
To encourage the alignment of the learned representation with the underlying generative factors, several constraints have been proposed to enforce the disentanglement of the latent codes \cite{higgins2017beta,burgess2018understanding,achille2018emergence,press2018emerging,kim2018disentangling,chen2018isolating,bouchacourt2018multi,hsu2017unsupervised}. InfoGAN \cite{chen2016infogan} promotes disentanglement by explicitly maximizing the mutual information between a subset of latent variables and the generated images. Also, domain-specific knowledge could be utilized to learn disentangled representations \cite{tulyakov2018mocogan,tran2017disentangled,karras2019style}.

In all these methods, disentanglement is sought in latent space, with no grounding on the domain where the data is defined. For the case of images, we would like the independent factors to correspond to compact and simply-connected regions of the image, corresponding to {\em objects}. In all these methods, manipulation of the independent factors typically has global effects on the image, rather than enabling manipulation of one object at a time. We would like to develop models that naturally disentangle the spatial domain, in addition to other latent factors of variation.

Of course, one could partition the image and independently represent each region, but doing so would require the ability to detect objects in the first place.
Recent methods on structured representation learning have been proposed to enable reasoning about objects in the scene. AIR \cite{eslami2016attend} proposes a structured generative model for the image generation process.
SQAIR \cite{kosiorek2018sequential} proposes an additional state-space model to enforce temporal consistency such that the decomposition within a sequence is consistent. The capacity of their structured models and the assumption of known/trivial background restrict both AIR and SQAIR.
DRAW \cite{gregor2015draw} employs attention to generate images with objects in a structured manner.
In \cite{yao20183d}, an image is decomposed into semantic mask, texture, and geometry, requiring heavy manual supervision.
And \cite{kulkarni2015deep} proposes an auto-encoder that de-renders images into graphics code, which is trained by explicitly specifying the variations.
Similarly, \cite{wu2017neural} employs a graphics engine as the decoder to enforce an interpretable representation, which is, however, not backward-differentiable.
NEM \cite{greff2017neural,van2018relational} construct spatial mixture models to cluster pixels into objects, but only for grayscale images.
IODINE \cite{greff2019multi} also employs a spatial mixture model to jointly infer the segmentation and object representation. \cite{greff2019multi} does not perform well on textured or cluttered scene, culprit the assumption that pixels can be grouped into objects according to the low dimensional spatial mixture models.

Semantic segmentation has improved considerably since the advent of deep neural networks  \cite{long2015fully,chen2017deeplab,zhang2018context} and so has instance segmentation, where the network has to distinguish between different instances of the same semantic class \cite{romera2016recurrent,hu2017maskrnn,liu2018path,chen2018masklab}.
However, these methods depend on densely annotated ground-truth segmentation masks.
On the other hand, unsupervised object segmentation has been a long-standing problem, but most of the methods require complicated optimization during inference \cite{yang2015self}.
Recently, unsupervised learning methods for object segmentation have shown promise: UMODCIS \cite{yang2019unsupervised} proposes contextual information separation for binary moving object detection, with end-to-end training without manual supervision or pseudo masks.
Later, \cite{arandjelovic2019object} proposes Copy-Pasting GAN to discover binary object masks in images; however, special care has to be taken to prevent trivial solutions.

There is only a handful of work dealing with both segmentation and object-centric representation learning in a unified framework.
Besides the few employing variational inference with a structured model, MONet \cite{burgess2019monet} introduces a recurrent segmentation network within the VAE framework, and trains them jointly to provide segmentation and learned representation.

In the next section, we describe our method and in the following Sect. \ref{sec:experiments} we test the model's ability to capture the statistics of the data while enabling independent control of latent factors corresponding to objects in the scene.

\vspace{-0.05cm}
\section{Method}
\vspace{-0.1cm}
\label{sec:method}

Let $\mathbf{x} \in \mathbb{R}^{H\times W\times 3}$ be a color image, and $\mathbf{z} \in \mathbb{R}^{N}$ be the generative factor of $\mathbf{x}$, which represents different characteristics of the data.
Our model uses as an inference criterion the Information Bottleneck when the task is the data itself,
whereby a representation (encoder) $q_{\phi}(\mathbf{z}|\mathbf{x})$ describes the latent factors (bottleneck) $\mathbf{z}$, and a decoder $p_{\theta}(\mathbf{x}|\mathbf{z})$ allows sampling images from the latent factors $\mathbf{z}$.
The encoder and decoder are trained by minimizing the Information Bottleneck Lagrangian (IBL) \cite{achille2018emergence}:
\begin{multline}
\mathcal{L}(\phi, \theta; \mathbf{x}, \beta) = -\mathbb{E}_{\mathbf{z} \sim q_{\phi}(\mathbf{z}|\mathbf{x})}[\mathrm{log} p_{\theta}(\mathbf{x}|\mathbf{z})] \\ +\beta\mathbb{KL}(q_{\phi}(\mathbf{z}|\mathbf{x}) \| p(\mathbf{z}))
\label{eq:beta-vae}
\end{multline}
where $\mathbb{KL}$ is the Kullback–Leibler divergence, and $p(\mathbf{z})=\mathcal{N}(\mathbf{0},I)$ is usually a zero mean unit variance Gaussian.
When $\beta = 1$ the IBL reduces to the Evidence Lower Bound (ELBO). For $\beta>1$, \cite{achille2018emergence} shows analytically and  \cite{higgins2017beta} validates empirically, that the latent factors are disentangled.
However, the factors always affect the generated images globally as shown in Fig. \ref{fig:global-effect-betaVAE}. Our goal here is to infer object-centric generative factors, such that we can perturb the factors associated with a single object, and the perturbation will not affect other objects or entities in the scene. In other words, our goal is to make the generative factors disentangled not only statistically, but also spatially.

\begin{figure}[!t]
\begin{center}
  \includegraphics[width=0.48\textwidth]{figures/objcentric_system_overview.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it System Overview.} Our method works by partitioning the image domain into mutually independent regions using the Contextual Information Separation criterion, which entails an inpainting network, and then extracting the generative factors disentangled both spatially and statistically with the identity consistency enforced by the perceptual cycle-consistency constraint. We omit the masks for simplicity.}
\vspace{-0.2cm}
\label{fig:system-overview}
\end{figure}

To this end, we construct a segmentation network to map a color image to $K$ segmentation masks $\Omega_k$'s, with $K$ the maximum number of objects (including background) in the scene:
\begin{equation}
    \Omega: \mathbb{R}^{H\times W\times 3}\rightarrow[0,1]^{H\times W\times K}; \sum_k\Omega(i,j,k)=1, \forall i,j
\end{equation}
Note that $\sum_{i,j}\Omega_k(i,j)=0$ simply means that there is no object in the $k$-th channel. If all the non-zero channels in $\Omega$ represent exactly the segmentation masks of the objects, then we can presumably learn the object-centric generative factors $\mathbf{z}_k$'s as follows:
\begin{equation}
    \mathcal{L}(\phi^\alpha, \phi^s, \theta; \Omega, \mathbf{x}, \beta, \lambda) = \sum_k \mathcal{L}(\phi^\alpha, \phi^s, \theta; \Omega_k, \mathbf{x}, \beta, \lambda)
\label{eq:object-centric-beta-VAE-sum}
\end{equation}
with
\begin{multline}
    \mathcal{L}(\phi^\alpha, \phi^s, \theta; \Omega_k, \mathbf{x}, \beta, \lambda) = \\
    -\mathbb{E}_{ \mathbf{z}_k \sim q_{\phi^\alpha}\cdot q_{\phi^s} } \mathrm{log} p_{\theta}( [\mathbf{x}\cdot\Omega_k,\Omega_k] \mid \mathbf{z}_k ) \\
    + \beta\mathbb{KL}( q_{\phi^\alpha}(\mathbf{z}_k^{\alpha} \mid \mathbf{x}\cdot\Omega_k) \| p(\mathbf{z}_k^\alpha)) \\
    + \lambda\mathbb{KL}( q_{\phi^s}(\mathbf{z}_k^s \mid \Omega_k) \| p(\mathbf{z}_k^s))
\label{eq:object-centric-beta-VAE}
\end{multline}
where $q_{\phi^\alpha}$ and $q_{\phi^s}$ are the encoders for appearance and shape related factors of objects in $\mathbf{x}$ respectively.
Then the joint decoder $\theta$ reconstructs the appearance and mask for each object using $\mathbf{z}_k = \{ \mathbf{z}_k^\alpha, \mathbf{z}_k^s \}$, which is the union of the appearance and shape related factors.
Note that, Eq. \eqref{eq:object-centric-beta-VAE-sum} is a summation of the Information Bottleneck Lagrangians Eq. \eqref{eq:object-centric-beta-VAE} defined on individual segments or ``objects''.
A similar loss is also used in \cite{burgess2019monet} to learn object related representations in an unsupervised manner. However, a question arises from Eq. \eqref{eq:object-centric-beta-VAE-sum}: {\it Why would minimizing the above loss yield a segmentation network $\Omega$ that partitions the image domain into objects?} Given a small enough encoding capacity, it may be true that $\Omega$ will be biased to partition the image $\mathbf{x}$ into pieces that are easier to encode and decode than the full image, while minimizing the first term in Eq. \eqref{eq:object-centric-beta-VAE}, which represents the reconstruction error.
Then $\Omega$ may succeed when objects happen to be constant color blobs, as they appear in some datasets, \eg, Multi-dSprites and Objects Room~\cite{multiobjectdatasets19}, used for experiments in \cite{burgess2019monet,greff2019multi}. However, what if we want to apply our method on textured objects or cluttered scenes, which are simply not color blobs?

\begin{figure}[!t]
\begin{center}
  \includegraphics[width=0.4\textwidth]{figures/edge-condition.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it The average inpainting error using context conditioned on binarized edge map is a good measure of the contextual information computed with the edge conditionals}: Given the binary edge map of an unknown image, the average inpainting error of the masked out region (question mark) will be larger when the context contains less mutual information (the first row), smaller with more mutual information in the context (second row).}
\vspace{-0.3cm}
\label{fig:edge-based-CIS}
\end{figure}

{\bf Spatial Disentanglement.} To endow our method with the ability to learn object-centric generative factors in realistic scenarios, we adapt the Contextual Information Separation (CIS) criterion of \cite{yang2019unsupervised}, which obviates the shortcomings of Eq. \eqref{eq:object-centric-beta-VAE}.
Instead of a binary segmentation, we extend CIS to multiple objects with the number of objects unknown, and also combine it with the representation learning loss in Eq. \eqref{eq:object-centric-beta-VAE-sum}, which in turn imposes additional regularization through the representational bottleneck.
This way, statistical and spatial disentangling of the generative factors occur simultaneously during learning.

The basic idea of CIS is that, when the context contains no information about a sub-region of an image, the (conditional) reconstruction or ``inpainting'' error will be maximized, as shown in Fig. \ref{fig:edge-based-CIS}.
In order to measure the mutual information, a joint distribution between a region and its context has to be specified. Here, we choose to use the conditional distribution $p(\mathbf{x} \mid \overline{\nabla\mathbf{x}})$ of an image $\mathbf{x}$ on the binarized edge $\overline{\nabla\mathbf{x}}$. Note that, one could also use the marginal distribution of images $p(\mathbf{x})$, which may result in degraded performance in general since the mutual information between pixels computed using $p(\mathbf{x})$ depends on the spatial proximity instead of the structure of the scene. By instantiating a conditional inpainting network $\psi$, our CIS-based spatial disentanglement loss becomes:
\begin{equation}
    \mathcal{L}_{SD}(\psi; \Omega, \mathbf{x}) = \sum_k \dfrac{
    \langle  \Omega_k, \|\psi(\Omega_k, \mathbf{x}\cdot(1-\Omega_k); \overline{\nabla\mathbf{x}})-\mathbf{x}\| \rangle }{\langle \Omega_k, \|\mathbf{x}\| \rangle + \epsilon }
\label{eq:CIS-disentangle-loss}
\end{equation}
where $\langle \cdot, \cdot \rangle$ represents the dot product, and $\|\cdot\|$ is the element-wise $L_1$ norm, which keeps the dimension of the input, $\epsilon$ is a small positive constant that prevents division by zero.
In \cite{yang2019unsupervised}, it is shown that, under the assumption of Gaussian conditionals, the mutual information can be approximated with the inpainting error.
Note the conditional inpainting network $\psi$ takes in the condition $\overline{\nabla\mathbf{x}}$ and the context $\mathbf{x}\cdot(1-\Omega_k)$ of the image being masked out by $\Omega_k$, and outputs the inpainted image.
If $\Omega_k$'s perfectly separate the context from each object, this spatial disentanglement loss will be maximized, thus minimizing the mutual information between the inside and outside of $\Omega_k$'s.

{\bf Perceptual Cycle-Consistency.} Given that the decoder $p_{\theta}$ generates images from the object-centric generative factors $\mathbf{z}_k$'s, we can perturb the factors of an appointed object $\hat{\mathbf{z}_k} \sim \mathcal{N}(\mathbf{z}_k, I)$ (Eq. \eqref{eq:PCL-perturb}),
and synthesize the perturbed image $\hat{\mathbf{x}}$ with $\{ \mathbf{z}_k\}_{\{1,..K\}\backslash k} \cup \hat{\mathbf{z}_k}$ (Eq. \eqref{eq:PCL-syn}),
and then extract the object-centric generative factors of the perturbed image (Eq. \eqref{eq:PCL-seg},\eqref{eq:PCL-disentangle}).
If not only the factors are well disentangled statistically and spatially, but also the identities of the disentangled factors are robust to local perturbations, we would expect that the factors extracted from the perturbed image will be unchanged in $\mathbf{z}_k$'s of the other objects, and, also synchronize well with $\hat{\mathbf{z}_k}$, which suggests the following perceptual cycle-consistency loss to further promote disentanglement and identity consistency:
\begin{align}
    k &\sim \mathrm{Uniform(1,K)} \\
    \hat{\mathbf{z}_k} &\sim \mathcal{N}(\mathbf{z}_k, I)
    \label{eq:PCL-perturb}\\
    \hat{\mathbf{x}}   &\leftarrow p_{\theta} ( [\mathbf{x},\Omega]|\{ \mathbf{z}_k\}_{\{1,..K\}\backslash k} \cup \hat{\mathbf{z}_k} )
    \label{eq:PCL-syn}\\
    \hat{\Omega} &= \Omega(\hat{\mathbf{x}})\label{eq:PCL-seg}\\
    \{ \bar{\mathbf{z}_k} \} &\xleftarrow[]{q_{\phi^\alpha}, q_{\phi^s}} \hat{\mathbf{x}}, \hat{\Omega}
    \label{eq:PCL-disentangle}\\
    \mathcal{L}_{PC}(\phi^{\alpha}, \phi^s, \theta, \Omega, \mathbf{x} ) &=\sum_{l\neq k}\| \mathbf{z}_l - \bar{\mathbf{z}_l} \| + \| \hat{\mathbf{z}_k} - \bar{\mathbf{z}_k} \|
    \label{eq:perceptual-consistency-loss}
\end{align}
Note this characteristic is also desired when we need to track the status of different objects for temporal analysis.
By combining Eq. \eqref{eq:object-centric-beta-VAE-sum}, \eqref{eq:CIS-disentangle-loss} and \eqref{eq:perceptual-consistency-loss} we have the final training loss for our model:
\begin{multline}
    \arg\max_{\psi}\min_{\phi^\alpha, \phi^s, \theta, \Omega} \mathcal{L}(\phi^\alpha, \phi^s, \theta; \Omega, \mathbf{x}, \beta, \lambda) \\
    - \gamma\mathcal{L}_{SD}(\psi; \Omega, \mathbf{x}) + \eta\mathcal{L}_{PC}(\phi^{\alpha}, \phi^s, \theta, \Omega, \mathbf{x} )
\label{eq:final-training-loss}
\end{multline}
Note that, the segmentation network $\Omega$ appears now in three terms, which encourage $\Omega$ to partition the image (first term) while minimizing contextual information (second term) to prevent over-segmentation; \ie, pixels belonging to the same object should be grouped together. Moreover, it has to be robust to perturbations introduced by the third term, which not only imposes perceptual consistency, but can also prevent identity switching; \ie, the object assigned to the $k$-th mask $\Omega_k$ (identified as $k$) should be assigned to $\Omega_k$ again after the perturbations, especially the spatial ones.
This is particularly useful for applications involving video, since temporal consistency will be automatically achieved after training, and we will show its effectiveness in the experimental section.

\begin{figure}[!t]
\begin{center}
  \includegraphics[width=0.3\textwidth,trim={0 0.1cm 0 2.4cm},clip]{figures/multi_dSprites_seg.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it Spatial Disentanglement on Multi-dSprites}: Our method can segment images containing various numbers of constantly colored objects with heavy occlusions (last two rows).}
\vspace{-0.2cm}
\label{fig:multi_dsprite_seg}
\end{figure}

\begin{figure}[!t]
\begin{center}
  \includegraphics[width=0.35\textwidth,trim={0.05cm 2.0cm 0 0},clip]{figures/objects_room_seg.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it Spatial Disentanglement on Objects Room}: Our method works on 3D scenes with smoothly colored objects, complex shapes, and different lighting conditions.}
\vspace{-0.2cm}
\label{fig:objects_room_seg}
\end{figure}

\begin{figure}[!ht]
\begin{center}
  \includegraphics[width=0.4\textwidth,trim={0.3cm 0 0 0},clip]{figures/multi_dsprites_dis4.png}
\end{center}
\vspace{-0.3cm}
\caption{  {\it Traversing the object-centric generative factors on Multi-dSprites}: the input image and the corresponding spatial disentanglement (first row). The rest: traversing the statistically disentangled generative factors of a specific object. Note that the perturbations only affect the object been targeted, and the color of the background can also be modified.}
\vspace{-0.3cm}
\label{fig:multi_dsprites_dis}
\end{figure}

\vspace{-0.05cm}
\section{Experiments}
\vspace{-0.1cm}
\label{sec:experiments}

We first describe the datasets used for evaluation and then elaborate on the implementation details and the training procedure, after which qualitative and quantitative comparisons are provided.

\subsection{Datasets}
\vspace{-0.1cm}

{\bf Multi-dSprites}: dSprites~\cite{matthey2017dsprites} consists of binary images of a single object that varies in shape (square, ellipse, heart), scale, orientation, and position. In Multi-dSprites~\cite{multiobjectdatasets19}, 1-4 shapes are randomly selected from dSprites, randomly colorized and composed on a randomly colored background with occlusions and independent variations in position, scale, and rotation; see sample images in Fig. \ref{fig:multi_dsprite_seg}.

\begin{figure}[!ht]
\begin{center}
  \includegraphics[width=0.35\textwidth,trim={0.4cm 0.1cm 0 0.2cm},clip]{figures/objects_room_dis2.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it Traversing the object-centric generative factors on Objects Room}: We can change the scale, position, and color of the green pillar continuously without affecting the others. Also, its shape can deform from a circle to a triangle.}
\vspace{-0.2cm}
\label{fig:objects_room_disentangle}
\end{figure}

\begin{figure}[!h]
\begin{center}
  \includegraphics[width=0.35\textwidth,trim={0 2.6cm 0 0},clip]{figures/Tex-dSprites-seg-1.png}
  \includegraphics[width=0.35\textwidth,trim={0 0 0 6.4cm},clip]{figures/Tex-dSprites-seg-1.png}
\end{center}
\vspace{-0.3cm}
\caption{{\it Spatial Disentanglement on Multi-Texture}: with explicit spatial disentanglement via contextual information separation, our method can segment objects with complex textures.}
\vspace{-0.2cm}
\label{fig:Tex_dSprites_seg}
\end{figure}

{\bf Objects Room} \cite{multiobjectdatasets19} contains rendered images of 3D scenes consisting of 1 to 3 randomly chosen 3D objects that vary in shape, color, size, and pose independently. The wall and the floor of the 3D scene are also colorized randomly. Once projected, objects can exhibit significant appearance variability, depending on lighting and viewpoint. Examining the images from Objects Room in Fig. \ref{fig:objects_room_seg}, we can still see that the images are far from realistic, even though the objects are not uniformly colorized.

{\bf Multi-Texture}: To test whether our proposed method works on complex appearance, for example, textured objects, we create the Multi-Texture dataset. To generate this dataset, 1 to 4 shapes are randomly selected, and independently textured using randomly colorized chessboard patterns. Then, these textured objects are randomly placed on a randomly colorized wooden texture background (Fig. \ref{fig:Tex_dSprites_seg}).

{\bf Flying Animals}: Although the Multi-Texture is more complex in the object appearance compared to the Objects Room dataset, the homogeneously textured objects still look unnatural in the image statistics, far from the images that would be seen in the real world. For this reason, we come up with the Flying Animals dataset. We collect two sets of natural images. One contains background images from 10 different landscapes, e.g., mountain, desert, and forest, each with 10 different instances; the other set contains clean foreground images of 24 different kinds of animals, each with 10 different instances. We select 1 to 5 objects, randomly scale and position them on a random background image with occlusions. Moreover, we perturb the intensity of each component to simulate different lighting conditions. For sample images please refer to Fig. \ref{fig:flying_animal_seg}.

\begin{figure}[!t]
\begin{center}
  \includegraphics[width=0.35\textwidth]{figures/Tex_dSprites_dis.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it Traversing the object-centric generative factors on Multi-Texture}: Although the objects are not constantly colored, disentangled object-centric generative factors can still be learned with explicit modeling of the spatial disentanglement.}
\vspace{-0.2cm}
\label{fig:Tex_dSprites_dis}
\end{figure}

\vspace{-0.05cm}
\subsection{Training Details}
\vspace{-0.1cm}

{\bf Segmentation Network $\Omega$}: Similar to the DeepLabV2 architecture \cite{chen2017deeplab}, we use ResNet50 \cite{He2016IdentityMI} as the backbone for our segmentation network, which is followed by four dilated convolution layers in parallel, whose responses are aggregated to generate the K segmentation masks. The total number of trainable parameters is 24M.

{\bf Inpainting Network $\psi$}: We adapt the inpainting network from \cite{yang2019unsupervised}. It consists of two symmetric encoders that encode binarized edge map and masked image (context), respectively; and a joint decoder with skip-connections from the two encoders. The total number of parameters in the inpainting network is 13M.

{\bf Encoder and Decoder $\phi^\alpha, \phi^s, \theta$}: We adapt the VAE structure proposed in \cite{kim2018disentangling}.
Instead of a single encoder for images, we instantiate two symmetric encoders $\phi^s$ and $\phi^\alpha$, where $\phi^s$ encodes the one-channel object mask which is the output of the segmentation network, and $\phi^\alpha$ encodes the masked object to get the appearance-related generative factors.
The decoder takes in the object-centric generative factors and generates the objects' appearance and masks, which are then concatenated and fused through four convolutional layers with relu and sigmoid activations to synthesize images over the whole image domain.
The total number of parameters in our encoder-decoder is 1.7M.

{\bf Training}: Adam is used \cite{kingma2014adam} for all modules with initial learning rate 1e-4, epsilon 1e-8, and beta (0.9,0.999).
As in \cite{yang2019unsupervised}, we find that a pretrained inpainting network will stabilize the training. We randomly crop the input images using rectangular masks with varying height, width, and position, and train the inpainting network to minimize the inpainting error ($L_1$) within the masked region. The training stops after 50K steps.
Then, we update the segmentating network and the inpainting network adversarially to speed up the spatial disentanglement before the joint training of all modules that is performed in an adversarial manner as shown in Eq. \eqref{eq:final-training-loss} and stops after 4M iterations.
The capacity constraint is adjusted during training following the scheme proposed in \cite{burgess2018understanding}.

\begin{figure*}[!ht]
    \centering
    \includegraphics[width=0.75\linewidth,trim={0 2.6cm 0 0},clip]{figures/flying_animals_seg2.png}
    \vspace{-0.0cm}
    \caption{ {\it Spatial Disentanglement on Flying Animals.} Our method can spatially disentangle images with natural statistics, where the objects and background are highly non-homogeneous, and also the shape of the objects exhibits more variations than squares or ellipses.}
    \vspace{-0.3cm}
    \label{fig:flying_animal_seg}
\end{figure*}

\vspace{-0.05cm}
\subsection{Results}
\vspace{-0.1cm}

The closest method to ours is MONet~\cite{burgess2019monet}, which is, to the best of our knowledge, the only one to learn segmentation and representation in a unified framework for non-constantly colored objects. Since we do not have access to the native implementation, we re-implemented MONet by training the same K-way segmentation network in our framework but using the same loss as in \cite{burgess2019monet}. This also eliminates the structural bias that could prevent a fair comparison. Note that we set $K=6$, which is larger than the maximum number of objects that could appear in the training of MONet. In the following, we show the segmentation and learned object-centric generative factors on each dataset. We will also show quantitative evaluations of the jointly learned object segmentation masks.

{\bf Multi-dSprites}: As shown in Fig. \ref{fig:multi_dsprite_seg}, our approach manages to separate different objects such that the VAE can learn representations for every single object and background. Note that the unsupervised segmentation works well with an unknown number of objects and heavy occlusions. Given that spatial disentanglement is achieved through segmentation, we forward each masked object and background into the encoder-decoder and obtain the object-centric factors at the bottleneck. We observe that some dimensions diverge from the prior Gaussian distribution during the training process. As explained in \cite{burgess2018understanding}, these dimensions exhibit semantic meanings aligned with the independent generative factors of dSprites. Fig. \ref{fig:multi_dsprites_dis} displays the segmentation and object-centric disentanglement for an image with three objects. Objects can be manipulated one at a time by perturbing one's latent factors while keeping other objects' representation unchanged. For each object, we can control its independent factors, including positions along two orthogonal axes, rotation, scale, shape, and color, by traversing one dimension in the latent space one at a time.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.9\linewidth]{figures/flyingani_spatial_dis3.png}
    \caption{ {\it Traversing the object-centric generative factors on Flying Animals}: The spatial disentanglement of the input image (top-left) is shown in the first column and the second column displays the reconstructed objects by the decoder. The other columns show the traversal on each object. Still, we can change the shape or appearance of each spatially disentangled object individually. For example, in the second row, when perturbing the badger's representation, the appearance of the animal in the yellow box interpolates from owl-like to fox-like while the other four animals and the background remain unchanged.}
    \vspace{-0.3cm}
    \label{fig:flying_animal_disentangle}
\end{figure}

{\bf Objects Room}: Our approach also performs well on the Objects Room dataset, even with shading effects on different objects under various lighting conditions, as shown in Fig. \ref{fig:objects_room_seg}. Object-centric statistical disentanglement is presented in Fig. \ref{fig:objects_room_disentangle}. Similarly, we can edit the scene by changing the position, shape, and color of different objects individually, which shows the applicability on 3D scene editing.

{\bf Multi-Texture}: We experiment on the Multi-Texture dataset to demonstrate that our proposed method enables spatial disentanglement on textured images. As shown in Fig. \ref{fig:Tex_dSprites_seg}, our approach can accurately segment out squares and ellipses with chessboard texture, confirming that the  Contextual Information Separation constraint prevents the network from naively splitting the chessboard into two different colors which correlate with each other. Fig. \ref{fig:Tex_dSprites_dis} shows the disentanglement and the object-centric manipulation results. Even with complex textured objects, our method still enables learning the factors that allow us to change the object consistently, including the background.

{\bf Flying Animals}: To verify that our method is not restricted to synthesized images but can also deal with natural ones, we further test our method on the Flying Animals dataset with real landscapes and animals. As shown in Fig. \ref{fig:flying_animal_seg}, even with complex appearance and shape, our approach can again segment out animals from the natural landscapes, which is far more challenging than segmenting uniformly colored objects as in Multi-dSprites and Objects Room. Similarly, our method can learn disentangled representations for every single animal in the scene and then edit the animals one at a time, shown in Fig. \ref{fig:flying_animal_disentangle}. However, due to the complexity of the appearance and shape, and the trade-off between reconstruction quality and bottleneck capacity for disentanglement, the $\beta$-VAE framework is not powerful enough to conduct statistical disentanglement for each animal while maintaining the details of the object. We will discuss this further in the next section.

\begin{table}
\begin{center}
\footnotesize{}
    \hskip-.32cm
    \begin{tabular}{|c|c|c|c|c|}
    \hline
    Dataset & M-dSprites & Obj-Room & M-Texture & F-Animals \\
    \hline
    MONet & $0.84\pm6.4\delta$ & $0.80\pm8.3\delta$ &  $0.37\pm0.3\delta$ & $0.18\pm2.8\delta$ \\
    \hline
    Ours  & $0.92\pm6.6\delta$ & $0.85\pm5.6\delta$ & $0.88\pm2.6\delta$ & $0.81\pm5.5\delta$ \\
    \hline
    \end{tabular}
    \vspace{-0.0cm}
\caption{ {\it Quantitative evaluation of the segmentation quality between MONet \cite{burgess2019monet} and our method.} Performance measured in the mean intersection-over-union score, reported with mean and variance, where $\delta=10^{-3}$. MONet performs well on Multi-dSprites and Objects Room (constantly or smoothly colored), but its performance drops significantly on Multi-Texture and Flying Animals (textured or complex natural appearance). Our method performs robustly well across different datasets.}
\vspace{-0.4cm}
\label{tb:quantitative}
\end{center}
\end{table}

\begin{table}
\begin{center}
\footnotesize{}
    \begin{tabular}{|c|c|c|}
    \hline
    Perceptual Cycle-Consistency & No & Yes \\
    \hline
    rate of identity switching & 21/255 &  0/255 \\
    \hline
    \end{tabular}
    \vspace{0.0cm}
\caption{ {\it Quantitative evaluation on identity switching}: Note the identity switching decreases to 0 out of 255 by imposing the perceptual cycle-consistency constraint.}
\vspace{-0.6cm}
\label{tb:quantitative_cyc}
\end{center}
\end{table}

{\bf Quantitative Evaluation}:
We compare our method with MONet~\cite{burgess2019monet} re-implemented on the four datasets mentioned above. We report the mean-intersection-over-union score (mean$\pm$variance). As shown in Table \ref{tb:quantitative}, our approach achieves better scores than MONet on all four datasets. Particularly, in Multi-Texture and Flying Animals, where objects have complex texture, without explicit information separation (CIS) constraint, MONet tends to segment the images based mainly on color information naively. At the same time, CIS enables our model to ``see'' objects as entities with different parts correlated with each other. To illustrate this, we present the segmentation results in Fig. \ref{fig:cmp_Monet}. Note that MONet dissects the black-and-white panda into different channels based on color, while our method successfully detects it without being biased by its color.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.9\linewidth]{figures/compare-monet-flying-animal_supp.png
    \caption{{\it Comparison of the segmentation} between our approach and MONet, which fails to capture objects with natural statistics due to the lack of explicit spatial disentanglement.}
    \vspace{-0.2cm}
    \label{fig:cmp_Monet}
\end{figure}

{\bf Perceptual Cycle-Consistency}
We expect our model to exhibit the perceptual consistency mentioned in Section \ref{sec:method}, which means that in a temporally coherent sequence, each segmentation channel should keep track of the same object without identity switching. To verify the effectiveness of the perceptual cycle-consistency constraint, we train two segmentation networks on the Multi-Texture dataset, one with perceptual cycle-consistency but not the other. Then, we generate 256 sample sequences with varying positions and occlusions. Fig. \ref{fig:Perceptual Consistency} compares the two networks' behavior by visualizing their first output channels on the sequence in the top row. Without perceptual cycle-consistency, the first output channel mainly detects the ellipse but can switch to the square occasionally, especially when the two objects come close. However, with the perceptual cycle-consistency constraint enabled, the segmentation network can have each output channel focus on a fixed target throughout the whole sequence, with no identity switching. We evaluate the two network's performance by counting the number of target switches, shown in Table \ref{tb:quantitative_cyc}, which verifies that the proposed consistency constraint improves the temporal coherence of the generative factors.

\begin{figure}[!t]
    \centering
    \includegraphics[width=0.7\linewidth,trim={0 0 0 0.1cm},clip]{figures/Perceptual_Consistency4.png}
    \caption{ {\it Effectiveness of the Perceptual Cycle-Consistency}: Note that an object will be assigned to different channels of the segmentation network from time to time (second row), showing temporal incoherence in the spatial disentanglement, however, the proposed perceptual cycle-consistency eliminates this incoherence, making the status of objects trackable.}
    \vspace{-0.3cm}
    \label{fig:Perceptual Consistency}
\end{figure}

\begin{figure}[!ht]
\begin{center}
  \includegraphics[width=0.48\textwidth]{figures/occlusion_flying_animal.png}
\end{center}
\vspace{-0.3cm}
\caption{ {\it Occlusion affects the accuracy of the spatial disentanglement on the Flying Animals dataset.} Orange boxes highlight the regions where occlusion happens and the affected objects. }
\vspace{-0.3cm}
\label{fig:occlusion-flying-animal}
\end{figure}

\vspace{-0.05cm}
\section{Discussion}
\vspace{-0.1cm}

The evaluation of a method that aims at ``disentanglement'' is subjective, since we do not know what the model will be used for: It is common to hope that the hidden variables correspond to known components of the image-formation process, such as pose, scale, color, and shape. However, making that a quantitative benchmark may be misleading since, if that were the goal, we would simply capture those factors explicitly, for instance, via a conditional generative model. What we do observe is that the perceptual cycle-consistency, explicitly enforced in our model, enables the persistence of the representation, so identities of objects are not switched in different views. This would enable temporal consistency when the model is used as a prior in a sequential setting as shown in Fig. \ref{fig:Perceptual Consistency}.

Our model has limitations. The use of a VAE forces a hard trade-off between capacity, which affects the quality of the reconstructed image, and disentanglement, which is forced by the bottleneck. For complex scenes, there may not be a broad range of the trade-off parameter over which the model both captures the image statistics faithfully, and separates the hidden factors. Another limitation is the power of the inpainting model. For highly textured or complex scenes, the un-occluded region requires capturing the fine-grained context at a level of granularity higher than what our model affords, which may make it difficult for the segmentation network to learn perfect segmentation when occlusion happens, as shown in Fig. \ref{fig:occlusion-flying-animal}.

\section*{Acknowledgements}

Research supported by ONR N00014-17-1-2072 and N00014-19-1-2229.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Jaded Blossom: Guest Designer ~ Katya Bess!
Katya Bess from Kattyxotica's Kreation here.
I am very excited to be a guest designer. Woohoo!
Thank you Candace and Michelle for giving me a chance.
I have four children but one in particular is a little bit mischievous... his name is Tiberius.
He would have almost have all "no's" but the elf was grading on the curve this time.
to get his act together before his birthday and before Christmas.
for a "no lines" coloring method and used Prismacolor pencils to color him up.
Here is a close up of the elf.
to check out the other guest designers.
Katya this just beautiful!!! I love your coloring style!!!!
This is so fun! LOVE how detailed your coloring is on the elf!
WOW! Your card / coloring is amazing!!
Such a cute card! Your elf is darling and the colors are perfect!! Congrats on your GD spot!
Wow! The elf is so awesome!
This is ADORABLE!! Your elf is just PRECIOUS!! Congrats on your GD spot!
What a darling card Katya! My daughter doesn't listen either, she's 17. LOL!
OH MY GOODNESS.....will you please come teach me to color like that? Your elf is amazing!
Wow, that is beautiful...I love your coloring!
Love your coloring - such a cute elf!!


You are here: Home / News / 2018 / What is Life Now?
Our Life Now program offers free yoga, exercise and meditative classes for cancer patients and their carers at many locations across Perth and some regional areas.
Life Now was established in 2007 and is based on research which links regular exercise and stress-reduction programs to reduced treatment-related side effects and improved general well-being for people with cancer.
"Including regular exercise as part of a patient's cancer treatment helps to improve recovery before, during and after cancer treatment, providing a range of mental and physical benefits," said our Life Now Project Officer, Stephanie Lamb.
Life Now Exercise classes are open to people who have been diagnosed with cancer within the last two years and are delivered by experienced facilitators.
"The Life Now Exercise program aims to introduce exercise in a safe and supportive environment and empower people living with cancer to manage their own physical activity," Stephanie said.
Life Now Exercise participant, Helen Moffat, attributes her improved strength and mobility to the program.
"I am so amazed at what I have achieved in mobility and strength and how it took away the pain I had in my joints. I am determined to keep it going to improve my chances of not letting cancer come back into my body forever," she said.
Last year, 1,345 people registered to participate in 98 Life Now programs across the state.
Our Life Now program offers yoga, meditation, tai chi, exercise, mindful art and mindfulness classes to cancer patients and their carers.
Programs are run free of charge thanks to generous donations from the West Australian community.
Registrations for 2018 are now open. Find a class near you here.
To get involved in any of our programs please submit a Life Now Expression of Interest form or get in touch with one of our helpful Cancer Nurses by calling 13 11 20.
•	Can't make it to one of our classes? Try these 22 different exercises at home.
•	If you would like more information about cancer or cancer-related issues, call our Cancer Nurses on 13 11 20.


Old Dresser Drawer Shelves I Have The Perfect Drawer For Something Drawer Shelves Uploaded by treelifedesigns on Friday, October 5th, 2018 in category Interior Design.
Here we have another image Unique Dresser Drawer Shelves Drawer Shelves featured under Old Dresser Drawer Shelves I Have The Perfect Drawer For Something Drawer Shelves. We hope you enjoyed it and if you want to download the pictures in high quality, simply right click the image and choose "Save As". Thanks for reading Old Dresser Drawer Shelves I Have The Perfect Drawer For Something Drawer Shelves.


Make this nifty Grit Stitch crocheted table setting for an even lovelier dining room. The simple and clean design will definitely make your table look even more inviting.
Craft in your favorite color for a personal touch!
In fact, this pattern is a great project for experienced beginner crocheters looking to advance their crochet skills.
The pattern includes detailed written instructions on how to make the placemat and coaster in one size. As always, you can tweak the directions to make them any size you desire.
This crochet pattern is another lovely design by Nicola Schofield.
In fact, you can access the FREE pattern for this crocheted table setting from the Ink Flamingos website.
Featured image: Ink Flamingos/ Neschof – thank you!


Louisa Watson is a conundrum. I cannot find her anywhere and right now she is my biggest brick wall. As I was browsing through my tree a few days ago I noticed one of my many dead ends. You know the kind, you look at a pedigree and it just stops...while it's fellow lines continue on. Well I despise those dead ends, especially when I am at my wits end to resolve them.
The problem with Louisa is that she is from Nebraska. Nebraska is a beautiful state and I especially love Peony Park (is that still there?). But I don't like the issues I am having with early record searches there. I found Louisa on her daughter's death certificate and that was the first, and the last, time I saw her in print. Her daughter was Ella Caroline Webb, born 1864 in Wahoo, Nebraska (according to her death certificate). Ella's death certificate states that her father's birthplace is Kentucky and her mother's was Wahoo, Nebraska. The problem with being born in Wahoo, Nebraska is that it did not exist when Louisa was born, which I'm conjecturing was between 1840-1845. In fact, Wahoo didn't exist until 1870, long after Ella was born as well.
I know Ella's father was Charles M. Webb, who was actually born in Indiana. I have been able to track Charles through census records from age 10 to 86, except for 1860. In 1850 he was living with his mother in Indiana and in 1870 he was in Ray County, Missouri with 6-year-old Ella. Apparently, a lot happened to him in those 20 years. He never remarried and his death certificate lists him as widowed, a good indication that he and Louisa were actually married...but you never know. Nebraska is not on the route of travel between Indiana and Missouri, so what led Charles there? And more importantly, where is Louisa?
I contacted the Saunders County Museum and Historical Society, where Wahoo is located and they were unable to find any record of a marriage for Charles and Louisa. Because Saunders County was formed from Cass County, they suggested I try there.
I contacted the Cass County Historical Society, but they found no record of Louisa's headstone or anything about her in their newspaper indexes. Of course, the newspaper for the area didn't begin printing until 1865 so that may not have helped anyway.
I contacted the Cass County Clerk, who would have had any early records for the county, but she found nothing. She suggested I contact the state archives, but they only maintain birth records from 1904/1909 to present.
I also sent an email to the Nebraska Historical Society to see if they had any early records for Cass County, Nebraska. I have yet to hear back from them.
Looked at FamilySearch.org and found no microfilm options for Cass or Saunders Counties that were helpful. A search of Nebraska marriages came up empty handed as well.
So now what? Louisa's maiden name was Watson and I have found several Watsons in the Saunders County area, but there is no way to determine if they are her ancestors. Her husband, Charles, left a document trail of censuses, but not much more, so I found nothing on Louisa there. I'm at a loss and looking for tips. Any help is appreciated!
I believed that Wahoo would be what it was called when the person died - it's current name at the time of death. I am not familiar with Nebraska and it's county history. Find out what the area of Wahoo was called in the time frame you are researching and be sure you are researching in the historically correct county for that time period.
Theresa- That is a good point to figure out what they called the area during that time (if something other than just the county). It was a part of Cass County at the time, but they don't have any records on the family. Such a brick wall!


This is an excellent deal on this ready-to-wear dream catcher style necklace. Each one is complete with a 18" cable style chain. The center of each silver color dream catcher has a turquoise color epoxy accent to add to the attraction and a feather dangle. Actual size of dangle is 20x40mm.


La Jolla Library Lecture: "I Sleep Therefore I Am"
Please join us for this month's Wet Lab Citizen Science Lecture at the La Jolla Library presented by Dr. Indrani Ganguly-Fitzgerald.
There is little argument that without sleep, we are functional basket cases. In this lecture, Dr. Indrani Ganguly-Fitzgerald will attempt to shed light on the function of sleep, by diving into the neuro-genetic investigations that reveal a surprising connection between sleep, waking experience, and aging brain health.


Is your athlete prepared for the upcoming season?
Creating a pre-game routine requires more than just prepping a sports bag the night before. Getting sports gear ready is just one piece of being properly prepared for a game.
Good preparation starts from the inside out and teaching your athlete the importance of creating their own pre-game plan that relaxes and focuses your athlete can help calm their nerves and prepare them to play at their best.
How an athlete performs is dependent on many things, but especially on how they go about physically and mentally preparing for a game. Preparing for peak healthy performance requires smart training, nutrition, and rest.
Physical preparation in training is a given, but mental toughness in preparation can't be underestimated as having a strong mindset can improve athletic performance.
Athletes aren't the only ones that need to be properly prepared for the sport season. Coaches and parents need to get themselves ready to support their athletes.
This month, we asked Roberta Kraus, Ph.D., sport psychologist and president of the Colorado-based Center for Sports Psychology, how parents can best support their athletes as they begin their sport season.
Sport parents and coaches, should be properly prepared to be the support that their athlete's can count on as the season progresses. Parents and coaches need to keep themselves accountable to send their athletes off to a game with a good mindset, so they can continue to build on that positive foundation that will continue to grow outside of sports.
Staying hydrated during the sport season is just as important for your young athlete as proper nutrition.
Just think of healthy foods as the fuel to your athlete's sports-playing engine and water as the oil that makes that engine run smoothly.
Youth athletes should make sure they are more hydrated than their non-sports playing friends if they want to feel and perform their best on and off the field. Their high-level of activity causes them to burn through more calories and sweat more.
Whether you're a coach or a parent, (or both!), you know that keeping your athlete hydrated during exercise generally aids in their sport performance and makes them feel better than any dehydrated athlete. Learn about the signs of dehydration and how to hydrate your athlete correctly for top performance during practice and in competition.
This month, we're launching a new segment called Ask the Expert that answers the questions that sports parents and coaches may have regarding youth sport. If you've ever wondered if coconut water was just a trend or if it aids in the rehydration process, we've consulted with an expert dietician and USADA's very own supplement advisor.
Calling all youth sport coaches and parents! We want to hear from you!
If you have questions related to your athletes, send us an email at TrueSport@truesport.org and we'll get in touch with our experts for you!
Educators and coaches can enjoy the hydration lesson plan and brand new educational handouts that TEACH the importance of proper hydration in and out of sport.
Kids will love the new activities in our PLAY App that will not only teach them all about their nutritional needs, but allows them to win cool awards!
The importance of learning about proper hydration during the sport season will help power your athlete's healthy performance.
Our friends at True Sport have launched a terrific campaign called, The Ride Home, aimed at positively changing the way parents interact with children after sporting events. This is some great stuff and is must-read for all sports moms and dads.


WASHINGTON--(BUSINESS WIRE)--President Obama recently signed the Military Construction, Veterans Affairs, and Related Agencies Appropriation Bill into law. This new law removed barriers to employment for Licensed Marriage and Family Therapists (LMFTs) to work for the U.S. Department of Veterans Affairs (VA). This important legislation will significantly improve the availability of qualified clinicians who can treat veterans seeking mental health services.
In 2006, a law was enacted to permit the VA to hire LMFTs. However, the job specifications required LMFT applicants to have graduated from a program accredited by the Commission on Accreditation for Marriage and Family Therapy Education (COAMFTE). Many of the California marriage and family therapy programs preceded the establishment of COAMFTE and are, instead, part of regionally accredited institutions. This was an insurmountable employment barrier for 95% of California's LMFTs qualified to treat our nation's veterans.
The California Association of Marriage and Family Therapists (CAMFT), an organization representing 32,000 Marriage and Family Therapists, appealed to the VA and advocated for legislation to remove the COAMFTE requirement and allow LMFTs to serve the rising number of veterans with mental health needs in the VA.
"California has the largest veteran population in the nation and over 40,000 LMFTs. Given the documented challenges with veterans' access to mental health services, CAMFT is pleased to have LMFTs be part of the solution," said CAMFT's Executive Director Jill Epstein.
LMFTs are qualified to treat severe mental illnesses including, but not limited to, post-traumatic stress disorder, schizophrenia, schizoaffective disorder, bipolar disorder, major depressive disorders, panic disorder, obsessive-compulsive disorder, and alcohol and substance abuse. Thus, LMFTs are qualified to provide the mental health services necessary within the Veterans Medical Centers.
According to the RAND Center for Military Health Policy Research, 18.5% of the veterans who served in Iraq or Afghanistan suffer from either major depression or post-traumatic stress disorder. Nineteen and a half percent of veterans in these two categories have experienced a traumatic brain injury.1 Additionally, the Department of Veterans Affairs estimates that nearly 13,000 Iraq and Afghanistan veterans have alcohol dependence syndrome.2 These disorders alone have an enormous impact on the demand for veteran mental health treatment services. Approximately 50 percent of returning veterans who need treatment for mental health conditions seek counseling, but only slightly more than half who receive treatment get adequate care.3 This new legislation is critical in the effort to increase the number of licensed clinicians that are eligible to treat veterans within the VA healthcare system.
To assist LMFTs in providing high-quality mental health services to veterans, CAMFT supports PsychArmor Institute's program to provide free on-line training courses to enhance LMFTs' understanding of the unique needs of veterans. The on-line library of over 100 educational courses include subjects such as "Understanding Invisible Wounds of War," "Comprehensive PTSD," "Suicide in the Military," and many more. "PsychArmor Institute is proud to have CAMFT promote these training courses that will be key in preparing LMFTs to more effectively treat veterans and support their families," said Marjorie Morrison, CEO and Founder of PsychArmor Institute.
CAMFT is an independent professional organization of over 32,000 members representing Marriage and Family Therapists. It is dedicated to advancing the profession, maintaining high standards of professional ethics, upholding the qualifications for the profession, and expanding awareness of the mental health treatment provided by Marriage and Family Therapists for individuals, couples, and families. For more information about CAMFT, follow us on Facebook, Twitter, or visit www.camft.org.
PsychArmor Institute is a national nonprofit that provides free education and support to all Americans who work with, live with, or care for military veterans and their families. PsychArmor recruits nationally recognized subject matter experts to create and deliver online courses on relevant issues related to veterans and their families. Our online training developers use engaging and interactive technology to deliver this critical information to the learner through a sophisticated learning management system. In addition, our "Helping You Help Veterans" support line provides continual assistance and vetted resources to civilians, ensuring they are armed with all the tools needed to effectively assist military veterans and their families. For more information about PyschArmor, follow us on Facebook or Twitter (@PsychArmor) or visit PsychArmor.org.


Those are the profound words of Barnesville High School sophomore, Nicole Herbranson. Nicole is a four-sport varsity athlete, involved in basketball, volleyball, softball, and track. In reading her words, it is clear that Nicole is very mature for her age, and truly something special.
Nicole is one of five children in the Herbranson family. To say she grew up in the world of sports would be an understatement. With four of the five children being athletes, Joanne, Nicole's mother, says they brought Nicole to her sister's basketball game just three days after she was born. Nicole's sister Laura played volleyball at Minnesota State Community and Technical College–Fergus Falls, her sister Jaynie played basketball at Concordia College in Moorhead, and her younger brother plays basketball, football, and baseball.
Along with the support of her family, Nicole also grew up with the leaders before her reminding her and her teammates to always work their hardest, play every game like it is their last, and never take it for granted. Now, Nicole is in a position to positively influence and lead her teams.
Regardless of the sport, Nicole is the type of athlete any coach wants on their team; not just because of her work ethic and athletic ability, but also because of what she brings to the team in terms of leadership, motivation, and support.
Despite being young, Nicole is a vital part of her teams, and has had great success in her high school career thus far. Winning All-Conference Honorable Mention in both volleyball and basketball, being named MVP of the basketball team in the 2014 season, and named a Captain for the volleyball team this past season, are just a few of Nicole's accolades. She is growing into a great athlete and leader, and shows no signs of slowing down.
You can never be sure what the future holds, but one thing you can be sure of is that Nicole's future is bright. Her level of maturity, selflessness, optimistic mindset, and fundamental desire to succeed will take her far, not only in sports, but in life as well.


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You can now make bookings at our spa online!
For the first time you can now make spa bookings online.
Come and see our new look reception area!
Birmingham Marriott leisure club has under gone a make over.
Get into the sports season spirit!


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Published On: Fri, Dec 18th, 2020
Kingdom News / Opinions / Views | By The Publisher
A very bad situation
bad situationHilbert Haarliquidity supportno dealOpinion Piece
By Hilbert Haar
Now they've really done it. I never thought that the government would be so daft to put the livelihood of its citizens at risk by stubbornly refusing to accept the Dutch conditions for liquidity support and – let's not forget that one – the refinancing of a 50 million-guilders loan.
Minister Plenipotentiary René Violenus blamed the Dutch in Friday's meeting of the Kingdom Council of Ministers for the situation St. Maarten finds itself in. Did Violenus act on his own? Of course not. He is the responsibility of Prime Minister and Minister of General Affairs Silveria Jacobs. Violenus was just following orders and now those order have come back to bit the government in its arrogant behind in a ferocious manner.
There is, at the time of this writing, no deal about continued liquidity support. No deal about the refinancing of that loan.
Instead, the government will have distance itself from Violenus' statements if it wants to save the day. It has to distance itself from the pipe dreams of the Pro Soualiga Foundation. And it will have to distance itself from the insults parliamentarians launched at state Secretary Knops last Monday.
What were those insults again? "Mafia" and "gangster-style negotiations" (MP Grisha Heyliger), "Hitler-thinking" (MP Emmanuel) and "A little Napoleon without a sense of direction" (MP Buncamper).
Right. How would, for instance, MP Emmanuel react if Knops would compare him to, say, Idi Amin? Not that Knops would ever do something like that of course but still, it would not be much different from Emmanuel's Hitler-thinking. Next thing you know, if there is no deal by the end of the weekend, somebody will say that the Netherlands is putting St. Maarten in a concentration camp.
What is the point of all this? And what do this government and this parliament think to achieve? What are they protecting? Autonomy? I thought we had that already.
The only thing this little country does not have is money – at least, not enough to keep the government apparatus going for a very long time or to provide the services its citizens are entitled to.
No matter how you look at it, this is a very bad situation. The Netherlands is not to blame for it. The blame is squarely and fairly in the court of the government and the parliament.
If these people do not come to their senses very soon businesses will suffer. Citizens will suffer. Come to think of it, they are already suffering. And for what? I am tempted to think that Hitler, Napoleon and the mafia combined had more vision than our seriously misguided politicians.
Frontal collision: no deal about liquidity support
No fundamental changes after debate about IPKO-statement


Ultrashock.com is celebrating its 5 year anniversary with a contest including a grand prize of a 60Gb video iPod. Additionally they've launched a new series of articles on Flash 8, Photoshop and Illustrator well worth a read! I've contributed an article on new features in Flash 8, giving a quick overview changes that should hopefully persuade you to upgrade if you haven't done so by now.


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Gratitude is the heart's memory. Such a powerful idea. For families, memories can be so special and gratitude can have such a positive impact on family life.
Once a week, our family has a meeting where we share appreciations, compliments or things we are thankful for – we do this year round – it's simply a great way for us to build memories while giving each family member a time to reflect and have their very own chance to share (if they want) their thoughts with the whole family about, well, anything really.
Hearing my children appreciate the smallest of things is such a spark for me to appreciate everything, to feel incredibly grateful and blessed. Finding gratitude in the simplest of things and sometimes even in the hardest moments, each and every week truly connects us and is so special.
Gratitude is powerful! How do you cultivate a heart of gratitude with your family?


Sustainable choices benefit the environment, economy, and us too. These are just some of the ways being sustainable is good for your health.
Here are some compelling reasons why you should drink and eat local in Hawaii, whether you're visiting, or live here year-round.
These "dirty dozen," fruits and vegetables are foods you should always buy organic, even if you can't afford to eat a 100% organic diet.


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15 Lowell Street, Portland, Maine 04102
161 Marginal Way, Portland, ME 04101
Richard A. Bazarian, M.D., F.A.C.S.
Jeffrey L. Berman, M.D.
R. Samuel Cady, M.D.
Sirus Hamzavi, M.D., FACS
Natan D. Kahn, M.D.
Curtis M. Libby, M.D.
Brooke E. Miller, M.D.
Jeffrey K. Moore, M.D.
Margaret C. Pollard, MD
Brynn N. Wajda, M.D.
Jessica S. Watson, M.D.
Charles M. Zacks, M.D.
Holly K. McCarthy, O.D.
Matthew A. Thees, O.D.
John Walters, O.D.
Cataract Center
LASIK Center
Retina Center
Cornea Center
Glaucoma Center
Pediatric Center
Oculoplastics Center
Billing Insurances
Lowell Campus
Marginal Way Campus
What is Dry Eye Syndrome (Keratoconjunctivitis sicca)
At Maine Eye we are committed to helping our patients manage dry eye syndrome. Dry eye is a general term used to describe a heterogeneous group of diseases resulting from inadequate wetting of the cornea and conjunctiva by the precorneal tear film (PCTF). Millions of people worldwide suffer from dry eye.
Despite its high prevalence, dry eye is not always easy to diagnose. The vast majority of patients have symptoms that are mild to moderate in severity. Although these patients are genuinely suffering discomfort, objective signs of dry eye may be missed, and without proper diagnosis, patients may not receive the attention and treatment that this condition warrants.
The signs and symptoms of dry eye can be misinterpreted as evidence of other conditions such as infectious, allergic, or irritative conjunctivitis.
What Causes Dry Eye?
Dry eye conditions are classified as various types of abnormalities that can lead to insufficient wetting of the corneal surface. These classifications are:
Abnormalities of the aqueous layer
Abnormalities of the mucin layer
Abnormalities of the lipid layer
Abnormalities of the corneal epithelium
Abnormalities of the lids
What are the Signs and Symptoms of Dry Eye?
The symptoms of dry eye vary considerably from one individual to another. Most patients complain of a foreign body sensation, burning and general ocular discomfort. The discomfort is typically described as a scratchy, dry, sore, gritty, smarting or burning feeling. Discomfort is the hallmark of dry eye because the cornea is richly supplied with sensory nerve fibers.
A significant percentage of patients also experience photophobia and intermittent blurring or other problems with visual acuity.
Individuals with dry eye commonly remark that their eyes tire easily, making it difficult for them to read or watch television. The reason for this difficulty is that the frequency of blinking typically decreases during tasks that require concentration. As blink frequency decreases, there is more time for the tear film to evaporate. If blinking is infrequent enough, the duration of exposure will exceed the BUT, resulting in the formation of one or more dry spots on the corneal surface.
Contact lens intolerance can also be a symptom of dry eye. Sometimes, a patient with mild to moderate dry eye may not experience symptoms until contact lenses are fitted. The placement of a contact lens can upset the delicate balance of tear film production and distribution, leading to lens intolerance.
Content: Alcon Labs
Oculoplastic
Lowell Campus:
15 Lowell St, Portland, Maine 04102
Marginal Way Campus:
161 Marginal Way, Portland, Maine 04101
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