text:
\section{Introduction and Related Works}
\begin{figure}
  \centering
  \includegraphics[width=0.48\textwidth]{clevrattention.png}
  \vspace{-3mm}
  \caption{Prior work reads the question and predicts a program composed of functional modules (denoted f1, f2, etc.); in contrast our model interleaves module prediction and module execution. Whereas IEP suffers an important nondifferentibility (red X), our model provides an end-to-end differentiable gradient path.}
  \vspace{-5mm}
\end{figure}

Deep learning is inherently data driven -- visual question answering, scene recognition, language modeling, speech recognition, translation, and other supervised tasks can be expressed as: given input $x$, predict output $y$.
The field has attempted to model different underlying data structures with neural architectures, but core convolutional and recurrent building blocks were designed with only general notions of spatial and temporal locality. In some cases, additional information about the problem can be expressed simply as an additional loss, but when hard logical assumptions are present, it is nonobvious how to do so in a manner compatible with backpropagation.

Discrete logic is a fundamental component of human visual reasoning: we present a general neural framework for differentiable reasoning over discrete data structures, including stacks and trees. Prior work has demonstrated some success for individual data structures and settings. StackRNN \cite{DBLP:journals/corr/JoulinM15} allows recurrent architectures to push and pop from a stack without explicit supervision. However, implicit learning only goes so far: the hardest task it was tested on is binary addition. Approaches such as recursive NN \cite{SocherEtAl2011:RNN} and TreeRNN \cite{DBLP:journals/corr/TaiSM15} enable explicit tree structure supervision, but only when the structure is also known at test time.

Our framework is flexible to differing degrees of increased supervision and demonstrates improved results when structural assumptions are available at test time. This approach is intended for maximally complex problems not feasible with minimal supervision: we are concerned with efficient incorporation of additional supervision rather than avoiding it. This paradigm enables our framework to circumvent scalability limitations commonly apparent in tasks involving discrete data structures, as demonstrated by both our RPN experiments and the restricted task scope of StackRNN.

We present our framework in the context of two broad architecture classes: Neural Module Networks (NMN, \cite{DBLP:journals/corr/AndreasRDK15}) and Neural Programmer-Interpreters (NPI, \cite{DBLP:journals/corr/ReedF15}). The original NMN allows per-example dynamic architectures assembled from a set of smaller models; it was concurrently adapted in N2NMN \cite{DBLP:journals/corr/HuARDS17} and IEP \cite{DBLP:journals/corr/JohnsonHMHLZG17} as the basis of the first visual question answering (VQA) architectures successful on CLEVR \cite{DBLP:journals/corr/JohnsonHMFZG16}. The NPI work allows networks to execute programs by directly maximizing the probability of a successful execution trace. Our framework is a superset of both approaches; DDRprog is an application thereof to CLEVR VQA. Our model interleaves program prediction and program execution by using the output of each module to predict the next module. This is an important contribution because discrete program prediction in IEP/N2NMN is not differentiable (Figure 1). Selection of modules in our model is also not differentiable, but it is influenced by the loss gradient: program execution gives a learnable pathway through the question answer loss. The second contribution of this architecture is a novel differentiable forking mechanism that enables our network to process logical tree structures by maintaining a stack of saved states. This allows our model to perform a broad range of logical operations; DDRprog is the first architecture to obtain strong performance across all CLEVR subtasks.

CLEVR has been effectively solved with and without program supervision, but it remains the best available proxy task for designing discrete visual reasoning systems because of its scale, diverse logical subtask categories, and program annotations. By effectively leveraging the additional program annotations, we improve over the previous state-of-the-art with a much smaller model  --  on the important Count and Compare Integer subtasks, we improve from 94.5 to 96.5 percent and 93.8 to 98.4 percent, respectively. However, our true objective is to enable discrete logic in neural architectures and thereby motivate more complex tasks over knowledge graphs. CLEVR is an early first step, inevitably solvable without program annotations. In the long term, human-level general visual reasoning from scratch is less reasonable than from expressively annotated data: we consider generalizing the ability of architectures to leverage additional supervision to be a likely avenue of success.

Prior work on CLEVR is largely categorized by dynamic and static approaches. IEP and N2NMN both generalized the original neural module networks architecture and used the functional annotations in CLEVR to predict a static program which is then assembled into a tree of discrete modules and executed. IEP further demonstrated success when program annotations are available for only a few percent of questions. These are most similar to our approach; we focus largely upon comparison to IEP, which performs significantly better. RN \cite{DBLP:journals/corr/SantoroRBMPBL17} and FiLM \cite{1709.07871}, the latter being the direct successor of CBN \cite{1707.03017} are both static architectures which incorporate some form of implicit reasoning module in order to achieve high performance without program annotations. In contrast, our architecture uses program annotations to explicitly model the underlying question structure and jointly executes the corresponding functional representation. As a result, our architecture performs comparably on questions requiring only a sequence of filtering operations and significantly better on questions involving higher level operations such as counting and numerical comparison.

\begin{figure}
  \centering
  \includegraphics[width=0.48\textwidth]{rpnattention.png}
  \vspace{-3mm}
  \caption{The baseline LSTM fails to learn the expression parse tree underlying reverse Polish notation; it therefore fails to generalize to expressions longer than seen at training time. In contrast, our DDRstack architecture cleanly incorporates supervision of this stack based tree representation and solves the generalization task.}
  \vspace{-5mm}
\end{figure}

We present DDRstack as a second application of our framework and introduce a reverse Polish notation (RPN) expression evaluation task. The task is solvable by leveraging the stack structure of expression evaluation, but is extremely difficult without additional supervision. DDRstack effectively solves the task by differentiably incorporating the relevant stack structure; in contrast, a much larger LSTM baseline fails the generalization test (Figure 2). Thus, we use RPN as additional motivation for our framework: despite major quantitative differences from CLEVR VQA, the RPN task is structurally similar. In the former, questions seen at training time contain programmatic representations well modeled by a set of discrete logical operations and a stack requiring at most one recursive call. The latter is an extreme case with deep recursion requiring a full stack representation, but this stack structure is also available at test time.

In summary: the DDR framework combines the interleaving program prediction and execution behavior of NPI with the learnable module structure of NMN and IEP to jointly learn branching programs and the functions composing them. Our approach resolves common differentibility issues and is easily adapted to problem specifics: we achieve a moderate improvement over previous state-of-the-art on CLEVR and succeed on RPN; a large baseline LSTM fails to generalize.
\newpage
\section{Datasets}
\subsection{CLEVR}
CLEVR is a synthetic but realistic VQA dataset that encourages discrete reasoning approaches through its inclusion of functional program annotations that model the logic of each question. The dataset consists of 100k images and 1 million question/answer pairs and has been carefully calibrated to avoid exploitable biases. Over 850k of these questions are unique. Images are high quality 3D Blender \cite{Blender} renders of scenes containing geometric objects of various shapes, sizes, colors, textures, and materials: Unlike earlier VQA datasets, no external knowledge of natural images is required. Program annotations link natural language questions to discrete logic. For example, ``How many red spheres are there?'' is represented as [\textit{filter\_red, filter\_sphere, count}]. Some questions require branching programs, such as ``How many objects are red or spheres?'', which is represented by a tree with branches [\textit{filter\_red}] and [\textit{filter\_sphere}] followed by a binary [\textit{union}] operation and a final [\textit{count}]. We include additional examples in the Supplement.

Initial benchmarks made CLEVR appear challenging, but clever architectures quickly solved the task both with and without using program annotations. One perspective is that this should motivate a return to the natural image setting without programs or with transfer learning from CLEVR. In contrast, we believe recent successes motivate more complex synthetic tasks -- perhaps involving harder logical inference over general knowledge graphs. Designing a visual Turing test with corresponding training data will likely require much iteration and experimentation. The synthetic setting is uniquely suited to this task: iterative prototyping is expensive and time consuming for natural image datasets.

\subsection{RPN}
We introduce the reverse Polish notation (RPN) expression evaluation dataset to motivate additional supervision in higher level learning tasks. The specific problem form we consider eliminates order of operations: [NUM]*($n$+1)-[OP]*$n$, that is, $n+1$ numbers followed by $n$ operations. For example, ``2 3 4 + *'' evaluates to 14. Thus the task is: given a sequence of tokens corresponding to a valid expression in reverse Polish notation, evaluate the expression and produce a single real valued answer.

This may seem like a simple task; it is not. For large $n$, expressions behave somewhat like a hash function. Small changes in the input can cause wild variations in the output -- we found the problem intractable in general. Our objective is to make stronger structural assumptions about the problem and create an architecture to leverage them. For this reason, our framework is incomparable to StackRNN, which attempts to learn a stack structure implicitly but is unable to incorporate additional supervision when the problem is likely too difficult to solve otherwise. We therefore modify the problem as such: instead of producing only the final expression evaluation, produce the sequence of answers to all $n$ intermediate expressions in the answer labels. For the example ``2 3 4 + *'', the expected output would be [7, 14] because 3+4=7 and 2*7=14. We further assume the stack structure of the problem is available to the architecture should it be capable of taking advantage of such information. The problem is still sufficiently complex -- note that to the model, $\{1, 3, 4, +, *\}$ would all be meaningless tokens: it must learn both the NUM and the OP tokens.

The dataset consists of 100k train, 5k validation, and 20k test expression with $n=10$ -- that is, 11 numbers followed by 10 operations. We also provide a 20k expression generalization set with $n=30$. The label for each question contains the $n$ solutions to each intermediate operation. During data generation, we sample NUM and OP tokens uniformly, reject expressions including division by zero, and omit expressions with $|$answer$|>100$. The NUM tokens correspond to 0, 0.1, ..., 0.9 and the OP tokens correspond to +, -, *, /; however, architectures are not privy to this information.

\section{DDR Architecture}
\begin{figure*}
  \centering
  \resizebox{1.0\linewidth}{!}{
  \includegraphics{ICLR_Final.png}}
  \vspace{-4mm}
  \caption{Visualization of the DDRprog architecture. This configuration answers "How many things are red or spheres?" by predicting $[filter\_red$, $fork$, $filter\_sphere$, $union$, $count]$}
  \vspace{-2mm}
\end{figure*}
The purpose of the DDR framework is to naturally incorporate structured information into a neural reasoning architecture. This is important when the given problem is hard without additional supervision and allows the model to perform discrete, complex reasoning. Our framework addresses the difficulty of combining discrete logic with differentiable training and is capable of interfacing with a broad range of data structures. Like IEP, we maintain a set of neural modules to allow our model to learn relevant program primitives. Like NPI, we interleave program prediction with program execution, differentiably learning modules when module arrangement is not known at test time.

This is much more general compared to either IEP/NMN or NPI independently, and the particular mechanism for combining them is a non-trivial differentiable forking operation. IEP alone lacks the ability to examine the output of intermediate operations; our CLEVR results demonstrate the importance of this. The NPI architecture can learn sequences of functions, but lacks the ability to learn the functions themselves. Our approach responds flexibly to the problem supervision: in VQA, modules are known only at train time. At each timestep, the controller therefore produces an index corresponding to a neural module, which is then executed. On the RPN task, the problem structure is also known at test time; the controller is therefore deterministic and directly executes the correct module. We refer to our VQA and RPN architecture adaptations as DDRprog and DDRstack, respectively; details are provided below.

\subsection{CLEVR Visual Question Answering: DDRprog}
DDRprog is a direct adaptation of our framework to CLEVR. We provide pseudocode in Algorithm 1, a visual example in Figure 3, and subnetwork details the Supplement.

The input data $x$ for each sample is a (image, question, program) triple; the training label $y$ is a (answer, program) pair. Our model must predict the program at test time.

The network first applies standard LSTM and ResNet \cite{DBLP:journals/corr/HeZRS15} encoders to the question/image to produce language and visual states, respectively. The ResNet encoder is unchanged from FiLM/IEP.

Both the language and visual states are passed to the controller. We use a recurrent highway network (RHN) \cite{DBLP:journals/corr/ZillySKS16} as recommended by \cite{NIPS2017_6919} instead of an LSTM \cite{Hochreiter:1997:LSM:1246443.1246450} -- both accept flat inputs. As the visual state contains convolutional maps, we flatten it with a standard classifier.

At each time step, the controller outputs a standard softmax classification prediction, which is interpreted as an index over the set of learnable neural modules. These are smaller, slightly modified variants of the modules used in IEP. The selected module is executed on the visual state; crucially, the visual state is then set to the output. The module prediction at the final timestep is followed by a small classifier network, which uses the IEP classifier. This architecture introduces a significant advantage over IEP: as modules are predicted and executed one at a time instead of being compiled into a static program, our model can observe the result of intermediate function operations -- these have meaning as filtering and counting operations on CLEVR.
\begin{algorithm}[H]
\caption{DDRprog. Note that \textbf{CNN} produces a flattened output and \textbf{Controller} also performs a projection and argmax over program scores to produce $programPrediction$}
\begin{algorithmic}
\INPUT $img, question \gets x$
\STATE $stack \gets $ \bf{Stack}()
\STATE $img, imgCopy \gets$ \bf{ResNetFeaturizer}($img$)
\STATE $langState \gets$ \bf{LSTM}($question$)
\FOR{$i=1... MaxProgramLength$}
	\STATE $visualState \gets$ CNN($img$)
	\STATE $programPrediction \gets$ Controller($visualState, langState$)
    \STATE $cell \gets$ \bf{Cells}[$programPrediction$]
    \IF{$cell$ is $Fork$}
    	\STATE $stack$.push($cell(img, imgCopy)$)
	\ELSIF{$cell$ is $Binary$}
        \STATE $img \gets cell(stack$.pop()$, img$)
    \ELSE{}
    	\STATE $img \gets cell(img)$
    \ENDIF
\ENDFOR{}
\OUTPUT \bf{Classifier}($img$)
\end{algorithmic}
\end{algorithm}
\vspace{-5mm}

\begin{figure*}
  \centering
  \resizebox{1.0\linewidth}{!}{
  \includegraphics{RPN_Final.png}}
  \vspace{-3.mm}
  \caption{Visualization of the DDRstack architecture with $n=1$. This particular configuration evaluates the [NUM][NUM][OP] formatted expression [0.4, 0.8, /], which is 0.4/0.8=0.5. NUM tokens are embedded before being passed to the LSTM. OP tokens are used as an index to select the corresponding cell. LSTM predictions at each OP token are used to predict intermediate losses (only one for $n=1$).}
  \vspace{-2.mm}
\end{figure*}

We now motivate our differentiable forking mechanism. As presented thus far, our approach is sufficient on the subset of CLEVR programs that do not contain comparison operations and are effectively linear -- indeed, we observe a large performance increase over IEP on this subset of CLEVR. However, some CLEVR questions contain a logical branching operation (e.g. \textit{are there more of ... than ... ?}) and cannot be answered by structurally linear programs. In general, programs can take the form of expressive trees, but CLEVR programs contain at most two branches; our approach handles the general case without modification. Upon encountering a program branch, denoted by a special fork module, our architecture pushes the current language and visual states to a stack and forks a subprocess. This subprocess has its own visual state (output by the special fork module) but is otherwise equivalent to the main network. After processing the last operation in the branch, a binary cell is applied to the final subprocess state and the main process states (popped from the stack), merging them as shown in Figure 3. Our architecture is likely to generalize even past the setting of tree processing, as it could interact with arbitrary hashing algorithms, heaps, priority queues, or any other problem-specific data structures.

Finally, a technical note for reproducibility: the fork module must differ from a standard unary module, as it is necessary to pass the original ResNet features (e.g. the initial visual state) to the subprocess in addition to the current visual state. Consider the question: ``Is the red thing larger than the blue thing?'' In this case, the main network filters by red; it is impossible to recover the blue objects in the subprocess given a red filtered image. We found that it is insufficient to pass only the original images to the subprocess, as the controller is small and has difficulty tracking the current branch. We therefore use a variant of the binary module architecture that merges the original ResNet features with the current visual state (see Algorithm 1). As the fork module is shared across all branch patterns, it is larger than the other binary modules and also one layer deeper -- refer to the Supplement for full architecture details on each layer.

\subsection{Expressions in Reverse Polish Notation: DDRstack}
\begin{table*}
  \small
  \centering
  \renewcommand{\arraystretch}{1.2}
\renewcommand{\tabcolsep}{1.2mm}
  \caption{Accuracy on all CLEVR question types for baselines and competitive models. The Human baseline is from the original CLEVR work. * denotes additional program supervision. SA refers to stacked spatial attention \cite{DBLP:journals/corr/YangHGDS15}}.

\resizebox{1.0\linewidth}{!}{
  \begin{tabular}{r|cc|cc|ccc|cccc|cccc|c}
Model & Parameters & Epochs & Exist & Count & Compare & Query & Compare & Overall \\
 &  &  &  &  &  Integer &  &  &  \\
\hline \hline
Q-type mode 							& - & - & 50.2 & 34.6 & 51.1 & 36.9 & 51.2 & 42.1 & \\
LSTM 									& - & - & 61.8 & 42.5 & 70.0 & 36.5 & 51.1 & 47.0 & \\
CNN+LSTM 								& - & - & 68.2 & 47.8 & 70.1 & 48.9 & 54.6 & 54.3 & \\
CNN+LSTM+SA 							& - & - & 68.4 & 57.5 & 67.7 & 87.7 & 52.0 & 69.8 & \\
CNN+LSTM+SA+MLP 						& - & - & 77.9 & 59.7 & 75.1 & 80.9 & 70.8 & 73.2 & \\
Human 									& - & - & 96.6 & 86.7 & 86.4 & 94.9 & 96.0 & 92.6 & \\
\hline
End-to-End NMN* 						& - & - & 85.7 & 68.5 & 84.9 & 89.9 & 88.7 & 83.7 & \\
IEP* 									& 41M & 12 & 97.1 & 92.7 & \bf{98.7} & 98.1 & 98.8 & 96.9 & \\
DDRprog* 								& 9M & 52 & 98.8 & \bf{96.5} & 98.4 & 99.1 & \bf{99.0} & \bf{98.3} & \\
RN 										& 500k & 1000 & 97.8 & 90.1 & 93.6 & 97.9 & 97.1 & 95.5 \\
FiLM/CBN 								& $>$50M & 80 & \bf{99.2} & 94.5 & 93.8 & \bf{99.2} & \bf{99.0} & 97.6 \\
  \end{tabular}}
  \label{tab:fullclevr}
\end{table*}
The DDRstack architecture applies our framework to the RPN task, where module arrangement is a fixed expression parse tree. One natural view of the task is: given a parse tree structure, simultaneously socket and refine the learnable NUM and OP nodes. Our model consists of an LSTM controller and a set of four learnable binary modules -- one per OP -- as well as an explicit stack. DDRstack processes one token at a time; similar to unary/binary modules in IEP, NUM and OP tokens are processed differently:

NUM: Our model embeds the token and passes it to the LSTM. It then pushes the result to the stack.

OP: Our model pops twice, calls the OP specific binary cell, and then passes the results to the LSTM. It then pushes the result to the stack. The binary cell concatenates the arguments and applies a single fully connected layer

DDRstack can be viewed as a neural analog to standard analytical RPN expression evaluation algorithm where the values of the NUM and OP tokens are unknown. We provide high level pseudocode for the model in Algorithm 2 and a visual representation in Figure 4.

We also train a baseline vanilla LSTM. DDRstack uses the same LSTM as its core controller, but includes the aforementioned stack behavior. Both models are given intermediate supervision, with predictions made in the last $n$ timesteps (Algorithm 2 shows only the final return).

\begin{algorithm}[H]
\caption{DDRstack}
\begin{algorithmic}
\INPUT $tokens \gets x$
\STATE $stack \gets$ \bf{Stack}()
\STATE $state \gets RandomLSTMInitialization$
\FORALL{$tok$ in $tokens$}
	\IF{$tok$ is a NUM}
    	\STATE $out \gets$ \bf{Embed}($tok$)
    \ELSIF{$tok$ is a OP}
		\STATE $arg2 \gets stack$.\bf{pop}()
        \STATE $arg1 \gets stack$.\bf{pop}()
        \STATE $out \gets$ \bf{Cells}[$tok$]($arg1, arg2$)
    \ENDIF
    \STATE $out, state \gets$ \bf{LSTMCell}($out, state$)
	\STATE $stack$.\bf{push}($out$)
\ENDFOR
\OUTPUT \bf{Projection}($out$)
\end{algorithmic}
\end{algorithm}

\section{DDRprog: CLEVR VQA Experiments and Discussion}

\subsection{Experiments}
We train our model with Adam \cite{DBLP:journals/corr/KingmaB14} on the full CLEVR dataset, including all program annotations. Hyperparameters and subnetwork architectures are detailed in the Supplement -- all of these are minimally configured from defaults, excepting learning rate and rough network size. The network overall has 9M parameters. We exclude the ResNet feature extractor from all calculations because it is also present in the best FiLM model. Their work further demonstrated it is replaceable with a from-scratch feature extractor with minimal loss in accuracy.

We pass the ground truth program labels to the model during training and for model selection during validation, but not during testing. We train on a single GTX 1080 TI and match the previous state-of-the-art accuracy of 97.7 percent after 35 epochs. We continue training until the 52nd epoch, dropping the learning rate to 1e-5 for the last few epochs to ensure convergence, and obtain 98.3 percent accuracy. The model predicts program cells with 99.98 percent accuracy.

\subsection{Discussion}
\begin{figure*}
\centering
\resizebox{0.48\linewidth}{!}{
\includegraphics{RPNCurve.png}}
\hspace{4mm}
\resizebox{0.48\linewidth}{!}{
\includegraphics{RPNLength.png}}
\vspace{-3mm}
\caption{Left: Training curves for DDRstack (train/val overlapping, 17k parameters) and the LSTM128 baseline (255k parameters) on RPN10. Right: Generalization performance of DDRstack and the LSTM baseline to RPN30 after training on RPN10.}
\vspace{-3mm}
\end{figure*}
FiLM and RN have both exceeded human accuracy on CLEVR without program supervision -- the task remains important for two reasons. First, both models exhibit curiously poor performance on at least one important subtask. Second, CLEVR remains the best proxy task for evaluating discrete reasoning systems because of its program annotations. We achieve a modest improvement over previous state-of-the-art in raw accuracy, but our work is more concerned with this second goal of creating of a general reasoning framework.

We presently consider RN, FiLM, IEP, and our architecture as competitive models. From Table 1, no architecture has particular difficulty with Exist, Query, or Compare questions. Count and Compare Integer are thus the most discriminative unary and binary tasks, respectively. We achieve strong performance on both subtasks and a significant increment over previous state-of-the-art on the Count subtask.

We first compare to IEP. Our model is 4x smaller than IEP (see Table 1) and resolves IEP's poor performance on the challenging Count subtask. Overall, DDRprog performs at least 2x better across all unary tasks (+1.7 percent on Exist, +3.8 percent  on Count, + 1.0 percent on Query) and closely matches binary performance (+0.2 percent on Compare, -0.3 percent on Compare Integer). We believe that our model's lack of similar gains on binary task performance can be attributed to the use of a singular fork module, which is responsible for cross-communication during prediction of both branches of a binary program tree, shared across all binary modules. We have observed that this module is essential to obtaining competitive performance on binary tasks; it is likely suboptimal to use a large shared fork module as opposed to a separate smaller cell for each binary cell.

Our model surpasses RN in all categories of reasoning, achieving a 2.6x reduction in overall error. RN achieves impressive results for its size and lack of program labels, but it is questionable whether the all-to-all comparison model will generalize to more logically complex questions. In particular, Count operations do not have a natural formulation as a comparison between pairs of objects, in which case our model achieves a significant 6.4 percent improvement. RN also struggles on the challenging Compare Integer subtask, where we achieve a 4.8 percent improvement. Furthermore, it is unclear how essential high epoch counts are to the model's performance. As detailed in Table 1, RN was trained in a distributed setting for 1000 epochs. Both our result and FiLM were obtained on single graphics cards and were only limited in number of epochs for practicality.

Both IEP and our model achieve a roughly 4x improvement over FiLM on Compare Integer questions (4.9 and 4.6 percent, respectively), the difference being that our model eliminates the Count deficiency and is also 4X smaller than IEP. The contrast between FiLM's Compare Integer and Exist/Query/Compare performance suggests a logical deficiency in the model -- we believe it is difficult to model the more complex binary question structures using only implicit branching through batch normalization parameters. FiLM does achieve strong Compare Attribute performance, but many such questions are resolvable through a series of pure filtering operations. FiLM achieves 1.5x relative improvement over our architecture on Exist questions, but this is offset by our 1.5x relative improvement on Count questions.

Given proximity in overall performance, FiLM could be seen as the main competitor to our model. However, they achieve entirely different aims: DDRprog is an application of a general framework, $>$5X smaller, and achieves stable performance over all subtasks. FiLM is larger and suffers from a significant deficiency on the Compare Integer subtask, but it uses less supervision. As mentioned in the introduction, our model is part of a general framework that better enables neural architectures to leverage discrete logical and structural information about the given problem. In contrast, FiLM is a single architecture that is likely more directly applicable to low-supervision natural image tasks.

\section{DDRstack: RPN Experiments and Discussion}
\subsection{Experiments}
For our architecture, we use hidden dimension 32 throughout the model, resulting in only 17k parameters overall. We train with Adam using learning rate 1e-3 and obtain a test L1 error of 0.17 after 63 epochs. Using the same hidden dimension in the pure LSTM baseline (9k parameters) results in test error 0.28. We overcompensate for the difference in model size by increasing the hidden dimension of the LSTM to 128 (255k parameters), resulting in an only slightly lower test error of 0.24 after nearly 3000 epochs. Figure 5 shows training curves for the LSTM baseline and DDRstack.

After training both models on problems of length $n=10$, we test both models on sequences of length $n=10$ and $n=30$. Recall that the loss is evaluated on the predicted answers to all $n$ subproblems corresponding to the outputs of each OP function. Results are shown in Figure 5.

\subsection{Discussion}
We argue that the LSTM fails the RPN task. This is not immediately obvious: from Figure 5, both the small and large LSTM baselines approximately match our model's performance on the first 5 subproblems of the $n=10$ dataset. From $n=6$ to $n=10$, the performance gap grows between our models -- as neither LSTM baseline learned deep stack behavior, performance decays sharply.

The $n=30$ dataset reveals the LSTM's failure. Performance is far worse on the first few subproblems of this dataset than on the test set of the original task. This is not an error: recall the question formatting [NUM]*($n+1$)-[OP]*$n$. The leading subproblems do not correspond to the leading tokens of the question, but rather to a central crop. For example, the first two subproblems of "12345+-*/" are given by "345+-", not "12345" -- the latter is not a valid expression. The rapid increase in error on the LSTM implies that it did not learn this property, let alone the stack structure. Instead, it memorized all possible subproblems of length $n\in \{1, 2, 3\}$ expressions preceding the first few OP tokens. Performance quickly decays to L1 error greater than 2.0, which corresponds to mostly noise (the standard deviation of answers minus the first few subproblems is approximately 6.0). In contrast, our model's explicit incorporation of the stack assumption results in a smooth generalization curve that gradually decays with increasing problem length.

We briefly address likely objections. First, one might argue that DDRstack cannot be compared to an LSTM, as the latter uses less supervision. This evaluation is precisely correct but is antithetical to the purpose of our work. There is no obvious method to include knowledge of a stack structure in an LSTM -- the prevailing approach would be to ignore it and then argue superiority on the basis of achieving good performance with less supervision. This logic might suggest implicit reasoning approaches such as StackRNN, which attempt to model the underlying data structure without direct supervision. However, we do not expect such approaches to scale to RPN: the hardest task on which StackRNN was evaluated is binary addition. While StackRNN exhibited significantly better generalization compared to the LSTM baseline, the latter did not completely fail the task. In contrast, RPN is a more complex task that completely breaks the baseline LSTM. While we did not evaluate StackRNN on RPN (the original implementation is not compatible modern frameworks), we consider it highly improbably that StackRNN would generalize to RPN, which was intentionally designed to be difficult without additional supervision. In contrast, our approach solves the task through effective incorporation of additional supervision. StackRNN is to DDRstack as FiLM is to DDRprog: one motive is to maximize performance with minimal supervision whereas our motive is to leverage structural data to solve harder tasks.

\section{Conclusion}
The DDR framework facilitates high level reasoning in neural architectures by enabling networks to leverage discrete logical information. Our approach represents a clean synthesis of the modeling capabilities of IEP/NMN and NPI through a forking mechanism that resolves common differentiability issues. We have demonstrated efficacy and ease of application to specific problems through DDRprog and DDRstack. DDRprog achieves a moderate improvement over previous state-of-the-art on CLEVR with greatly increased consistency and reduced model size. DDRstack succeeds on RPN where a much larger baseline LSTM fails to attain generalization. Our framework and its design principles enable modeling of complex data structure assumptions across a wide class of problems where standard monolithic approaches would ignore such useful properties. We hope that this increase in interoperability between discrete data structures and deep learning architectures aids in motivating higher level tasks for the continued development and progression of neural reasoning.

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text:
Star Wars UK's Biggest Film of 2015
By Alex Khalil
Even after the grand success of 007's Spectre, Star Wars has become the UK's biggest film of 2015, reaching an estimated £94.06m in just 16 days.
JJ Abrams's film has also overtaken the worldwide hit Avatar by James Cameron to become the second most successful in UK history – it reached only £94m. But while The Force Awakens has done exceptionally well, Spectre's predecessor Skyfall is still supreme ruler as the UK's highest-grossing film of all time, with takings of a staggering £103m in 2012.
The reboot of the beloved franchise sees the return of some old faces, like Harrison Ford, Carrie Fisher, and Mark Hamill, while also introducing British actors Daisy Ridley and John Boyega.
By the end of December, Star Wars: The Force Awakens became the fastest film to ever take $1bn (£674m) at the global box office.
It achieved the feat in 12 days, beating yet another film Jurassic World, which took 13 days in June 2015.
Image taken from BBC
But while Jurassic World had the benefit of record takings in China over the period, The Force Awakens had yet to open there when the figures were released.
You can read our review here!
We eagerly await the next film, and the records it will break next time!
Medium star wars
Editorials The life and career of George Lucas
When it comes to cinema, George Lucas is arguably one of the most important visionaries to ever exist. From his involvement in the development of special effects technology to crafting of some of the biggest franchises on the planet, his contribution is immense. Though best known for his large-scale epics, George Lucas began his career […]
Movies Can cinemas recover from Covid?
Cinemas were experiencing a steady revenue decline even before the pandemic. The restrictions ended up causing an almost total collapse. Tentpole franchise films ended up moving to streaming services. Blockbusters like No Time To Die, Dune and Black Widow were delayed more times than you count. Now we ask: can cinemas recover? Recommended Reading: Film […]
Editorials Netflix vs Disney+
Following Netflix's recent announcement that there will be limitations set on device sharing between households, a huge question has been posed in my mind as to whether or not my Netflix subscription is actually worth it. Other services, such as Disney+, Apple TV and Amazon Prime TV have been gaining a lot of traction recently. […]

redpajama_set_name: RedPajamaCommonCrawl


A man attacked a Muslim woman at a New York branch of Dunkin' Donuts, throwing hot coffee at her and accusing her of a being a "terrorist" last month.
Nathan Gray, 34, reportedly told his 21-year-victim he "hated Muslims" and threatened to kill her.
He then hit her with his bag and put her in a headlock, according to police. Officers were called and arrested Gray at the scene. He was charged with assault as a hate crime.
The attacker walked into the doughnut shop near Seventh Avenue in Manhattan in the afternoon and asked for a cup of coffee, said a Dunkin' Donuts.
The number of Islamophobic attacks has risen across the US in the weeks following Donald Trump's election as US President.


You're finishing up a watercolor that you're actually sort of happy with. It blows into the ocean. You run in and grab it, and of course it's drenched and diluted and unsalveagable (well, maybe if you let it dry and start over carefully... but that would take a whole lot more patience than you have). Not one to face reality, you recklessly slather on more paint. Unsurprisingly, this looks terrible. You scribble on top of all that with a pen.


text:
Q: Applying a function to a large data set I have a large dataset I am reading in R
I want to apply the Unique() function on it so I can work with it better, but when I try to do so, I get this prompted:
clients <- unique(clients)
Error: cannot allocate vector of size 27.9 Mb

So I am trying to apply this function part by part by doing this:
clientsmd<-data.frame()
n<-7316738  #Amount of observations in the dataset
t<-0
for(i in 1:200){
  clientsm<-clients[1+(t*round((n/200))):(t+1)*round((n/200)),]
  clientsm<-unique(clientsm)
  clientsmd<-rbind(clientsm)
  t<-(t+1) }

But I get this:
 Error in `[.default`(xj, i) : subscript too large for 32-bit R

I have been told that I could do this easier with packages such as "ff" or "bigmemory" (or any other) but I don't know how to use them for this purpose.
I'd thank any kind of orientation whether is to tell me why my code won't work or to say me how could I take advantage of this packages.

A: Is clients a data.frame of data.table? data.table can handle quite large amounts of data compared to data.frame
library(data.table)

clients<-data.table(clients)

clientsUnique<-unique(clients)

or
duplicateIndex <-duplicated(clients)

will give rows that are duplicates.

A: increase your memory limit like below and then try executing.
 memory.limit(4000)   ## windows specific command

A: You could use distinct function from dplyr package .
function - df %>% distinct(ID)
where ID is  something unique in your dataframe .

redpajama_set_name: RedPajamaStackExchange


The EVO series HDMI cables are the entry into the Kordz enthusiast's range of HDMI cables. It offers an impressive balance between performance, build quality and economy. EVO offers the best flexibility in the Kordz range for those tight spaces behind the AV system and TV.
Construction is based on optimized flat-cable geometry with solid OFC (Oxygen Free Copper) conductors for minimal loss and flexibility on shorter lengths. The connector uses advanced technology and precision engineering to provide a robust, secure connection with the consistency of performance designed to last a lifetime.
• Flexible, flat-profile cables allow reduced skew and shallow mounting depth.
• Ultra HD/4k video to 5.0m/16'4".
• Support for up to full HD 1080p/60 and 3D across all lengths.
• B.O.S.S termination for consistent and reliable signal integrity.
• SureFit™ gold-plated die-cast connector fits firmly into HDMI receptacle without falling out.
• Solid-core OFC(Oxygen Free Copper) TMDS conductors for high performance.
• Full Assembly 3-piece shell including 100% internal alloy shield.
• Ethernet and Audio return channel.
• Ultra-sonic automatic soldered free-air terminals create reduced capacitance and digital jitter for consistently high data rate support.
• 1080p60, 3D, and Ethernet across all lengths.


The strict sense of the word integrity – the state of being whole and unimpaired; or ethically: the quality of acting in accordance with one's own moral principles – seems to suggest that it would be simple to lead a life of integrity, if only there was no external world, no other people, no "outside" impairing us. This leads to an egocentric view, an aesthetic preoccupation with one's own self, which has to be shielded from the vicissitudes of life and the dangers of the world at large. Yet it is only in situations that require compromise and concession, that the goal of integrity – the conformity of one's actions with one's moral principles – becomes interesting, above all in politics. Here, the highest precept is no longer the purity of moral values, but instead their practical realization. Put differently: moral principles that neglect the difficulties of political practice are useless when seeking to determine a type of integrity that has to prove itself in the world. Using the example of the three maxims of the communal sense in Immanuel Kant's Critique of Judgement, "1. Thinking for oneself, 2. Thinking for all others, 3. Thinking in accordance with oneself", as one of the foundations of trustworthy behaviour in politics, Gesine Schwan will examine the difficulties that can occur in their practical application.
Gesine Schwan, studied Roman Languages, History, Philosophy and Political Science in Berlin and Freiburg/Breisgau. Extensive research stays in Warsaw and Krakow followed in preparation for her dissertation about the Polish philosopher Leszek Kolakowski. In 1971, Gesine Schwan was appointed Assistant Professor in the Department of Political Science at the Free University Berlin and in 1975 received her Habilitation there. In 1977, Gesine Schwan was named Professor of Political Science, in particular of Political Theory and Philosophy, at the Free University Berlin. Research stays in Washington D.C., Cambridge and New York followed. Her main fields of research are Political Philosophy and Theories of Democracy, recently also problems of Political Psychology and Political Culture. From October 1999 to October 2008 Gesine Schwan was president of the Europe University Viadrina in Frankfurt (Oder). She was one of the joint founders and since June 2010 also president of the Humboldt-Viadrina School of Governance. In 2004 and in 2008, she was nominated by SPD and Bündnis 90/Die Grünen as candidate for the office of the Federal President, but lost the election to the candidate of CDU/CSU and FDP. Her main publications include among many Die Gesellschaftskritik von Karl Marx. Philosophische und politökonomische Voraussetzungen (1974); Sozialismus in der Demokratie? Theorie einer konsequent sozialdemokratischen Politik (1982); Politik und Schuld. Die zerstörerische Macht des Schweigens (1997); Vertrauen und Politik. Politische Theorie im Zeitalter der Globalisierung (2006) and (together with Susanne Daschke) Allein ist nicht genug. Für eine neue Kultur der Gemeinsamkeit (2007).


A little smaller than I wanted it to be, so I will probs have to build a different one for things like flowers and crops with supports.. Either way I am happy with the way it turned out!
I see you are doing that one thing at that one place at this one time.


I found the comparisons between indirect greenhouse heaters and direct heaters, their effect on plant growth, efficiency, etc. really interesting too.
Thanks for this. I'm still very interested in setting up a fogging system for my greenhouse. I'm not in a prime climate for evaporative cooling, but I'm hoping its still worthwhile.
I just browsed the daily weather for my area from last year. If I look at midday hours where it was warm with clear skies, the relative humidity is usually around 50%. A quick browse around suggests that 50% humidity is low enough to get a significant benefit from evaporative cooling, even if its not as good as in drier areas. Using daily averaged humidity to judge effectiveness seems misleading to me, because my greenhouse only needs cooling when its sunny. Most cooling guides are about air conditioning for people's homes, and they care about maintaining temperature even at night and when its cloudy and rainy.
Mark, I just noticed you are talking about heating guides, but the link you provided is about fogger cooling. Do you have a link to the heating guide you're describing also? Maybe you pasted the wrong link.
I've been thinking a lot about heating also. My understanding is plant roots like to be warm, but foliage can be rather cold. I started dreaming of a fully hydroponic growing system using heated water, while running only minimal air heating. Would this work?
Unless you have temps in the upper 90's or 100's, considering the high RH, I'd just inject or exhaust ambient air. Whatever you do don't install a swamp cooler. They're not only useless but a PITA to maintain.
I'm still very interested in setting up a fogging system for my greenhouse.
That's a good idea. I have found some small misters but I've been stumped with the misting only for 5 seconds every 30 minutes part.
That would be easy to do. They sell on/off interval timers on amazon. You could easily have it control a valve to run water to mist lines.
One of these power a 12V power supply tied to a sprinkler valve.
If you are adding a lot of moisture and its already 50% humidity, you may end up with mold or mildew issues.
If your greenhouse is small, you maybe better off with an air conditioner. Window units or mini splits are fairly cheap.
From reading the article (and thank you for posting the article), both systems, fan and wet pad, and fan and fogging work best in hot dry locations such as Arizona, as it is written in the article -- " The high-pressure fog works best in a dry climate, he says. If the outside air is humid and hot, the cooling capacity of an evaporative cooling system is very limited to just a couple of degrees Celsius." What about a humid area such as Brian's ? Here in Colorado, the area is a dry area, and evaporative cooling works really great, and I assume fogging would also. I use what is called a wet wall evaporative system, which works wonders during the summer months. Brain, does your greenhouse have roof vents?
spaugh, my greenhouse is 575sq ft and 15ft tall. I believe cooling with regular AC unit would be extremely expensive. I do have concerns about humidity. I may simply have to experiment and see how it works out.
Millet, the limited research I've done into evaporative cooling says that Colorado has the best possible climate for this, while Pennsylvania is "reduced effectiveness", and in areas like Florida & Alabama it is totally ineffective. What I am hoping is that I will get decent results because the system would only be running when it is bright and sunny, when relative humidity is at its lowest (for the area) as compared to cloudy/rainy days and nights when humidity is 80-100%.
I do not have roof vents on my greenhouse, only two 24" high-CFM fans paired with two 36" vents.
Ah I finally saw the article you describe on the side. I've always wondered about vented (indirect) vs unvented (direct) heaters. I had read that unvented is bad because of the emitted carbon monoxide (for people) and ethylene (for plants). However... I've run unvented propane heaters for a couple years now and never set off my carbon monoxide sensor or noticed harm to plants. This article seems to claim that the unvented heaters gave better results. Though, they add a lot of humidity which is already a problem for me.
I'm still trying to think of a root zone heating system that doesn't rely on expensive electricity. Piping radiant-heating-type water pipes around root zones might work but would be a lot of work to set up.
Have you looked into rocket mass heaters?
There are some awesome setups out there. They heat up several tons of rocks with it during the day and the heat is release over the next 24hrs.
For the cooling, only one way to find out.
On my mist system, I have the pump on a humidistat set to 40%. It wont even run unless the humidity in the hoop house drops below 40. It basically kicks on only when the vents and fan are running which is maybe 20% of the time.
I had never heard of rocket mass heaters, and that is a really neat design.


So, if you read my last article you will be be jazzed to find out that I am writing a follow up article about some of my favorite brands.
There are so many 'natural' products, yet that aren't truly natural, but the products that I want to showcase in the food space along with health and beauty space are showcased below and here's why. There will be more where this is coming from.
For starters food and health and beauty are difficult to be lumped into one. So let's start with some of my favorite food brands followed up by many supplement brands that i am a huge fan of, for many reasons.
Kite Hill: They make a myriad of spreads, cheeses and dairy free yogurts made from a nut milk base, mostly almonds Ricotta cheese and cream cheese spreads made from almonds, a must try for a vegan that wants to still make a deliciously tasting lasagna or food preparation with these ingredients.
Heido Ho vegan cheeses: Veggie based vegan cheese, great to pour over veggies or for your nacho cheese fix, without the dairy with fantastic flavors and tastes.
Manini's : Great tasting and fresh gluten free pastas for your pasta fix made without the majorly processed grains.
Organic India tea and Supplements: Another reputable brand who is also a B corporation.


text:
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40 Most Powerful Celebrities in Africa
By	Art Becomes You Posted on October 17, 2011 2 2.4K views
On 12 October 2011, Forbes, famous for its powerful rankings of influential and famous people in different categories released a new ranking: The 40 Most Powerful Celebrities in Africa. This is a debut ranking in its category and I hope it will serve as a source of motivation for all Africans. More than 7,500 names were submitted and the top 40 was selected by voters. It is no surprise that the list is dominated by musicians.This only goes to strengthen my belief that music is one of the strongest mediums of communicating feelings and emotions, it is also a powerful tool that connects the singer to the listener and should never misused. Notables on the list also include authors, models, athletes, movie directors and actors.
The article went on to explain the criteria as 'determining the celebrities who exert the highest degree of influence in contemporary African pop culture involved sifting through the nominations for the individuals with the highest numbers of votes, and then measuring their media visibility (exposure in print, television, radio and online), number of web references on Google, TV/radio mentions and their general clout across the continent. Ideally, a robust social media presence would have been an invaluable yardstick in determining the intensity of influence these individuals exert over their enthusiasts.'
In intended order , here are the top 40 most powerful African celebs.
1. Chinua Achebe, 80, Nigerian, Novelist. Author of the famous novel 'Things Fall Apart' which is the most widely read book in modern African Literature.
2. Youssou N'dour, Age: 51, Senegalese, Musician. Popular for developing a new music genre known as 'mbalax'.
3. Didier Drogba, 33, Ivorian, Soccer Player. He has scored more goals for Chelsea than any other foreign player.
4. Angelique Kidjo, 51, Beninoise, Musician. Her full name is Angélique Kpasseloko Hinto Hounsinou Kandjo Manta Zogbin Kidjo. She is one of the most iconic African ladies as recognised by the BBC and the Guardian.
5. Akon, 38, Senegalese, Musician. Real name Aliaune Damala Badara Thiam. One of the wealthiest African artists.
6. Wole Soyinka, 77, Nigerian, Playwright. Awarded Nobel Prize in Literature.
7. Salif Keita, 62, Malian, Musician. Known as 'The Great Voice of Africa.' He is a a direct descendant of the founder of the Mali Empire, Sundiata Keita.
8. Yvonne Chaka Chaka, 46, South African, Musician. She was the first Black child to appear on South African television. Famous for her song 'Thank You Mr DJ.'
9. Oumou Sangare, 43, Malian, Musician. Ardent women's rights advocate opposing child marriage and polygamy.
10. Femi Kuti, 49, Nigerian, Musician. Eldest son of music legend Fela Kuti. Femi's voice is featured in the video game 'Grand Theft Auto IV'.
11. Toumani Diabaté, Malian, Musician. Excellent Kora (a kind of African harp) player.
12. Oliver Mtukudzi, 59, Zimbabwean, Musician. Most recognize artist to emerge from Zimbabwe.
13. Haile Gebrselassie, 38, Ethiopian, Athlete. Record breaking athlete with two Olympic gold medals.
14. Khaled Hadj Ibrahim, 51, Algerian, Musician. Known as the 'King of Rai'. His most famous songs are 'Aicha' and 'Didi'.
15. Samuel Eto'o, 30, Cameroonian, Soccer Player. He has won African Player of the Year a record-breaking four times. He is currently the highest paid soccer player worldwide.
16. Alek Wek, 34, Sudanese, Supermodel. She epitomizes the very definition of an ethnic model.
17. Liya Kebede, 33, Ethiopian, Supermodel. One of the world's highest paid models and a maternal health advocate.
18. Dobet Gnahoré, 29, Ivorian, Musician. World Music Award nominee.
19. Genevieve Nnaji, 32, Nigerian, Actress. CNN states she is referred to as the 'Julia Roberts of Africa'.
20. Koffi Olomidé, 55, Congolese, Musician. Real names Antoine Christophe Agbepa Mumba. He was quite a brainy student. Having graduated with a BA in Business Economics and an MA in Mathematics.
21. Neill Blomkamp, 32, South African, Movie Director. Co-writer and director of the critically acclaimed movie District 9.
22. Souad Massi, 39, Algerian, Musician. when her band. Atakor, became a political target she disguised herself by cutting her hair and wearing male clothing.
23. Baaba Maal, 58, Senegalese, Musician. One of the most internationally recognized musicians from Senegal.
24. Hugh Masekela, 72, South African, Musician. He plays the flugelhorn, the trumpet and the cornet.
25. . K'Naan, 33, Somali, Rapper. His single 'Waving Flag' was chosen as Coca Cola's anthem for the 2010 FIFA World Cup in South Africa.
26. Amadou et Mariam, Malian, Musicians. Known as 'The blind couple from Mali,' they met at Mali's Institute for the Young Blind, and found they shared an interest in music.
27. Awilo Longomba, Congolese, Musician. His CD Coupe Bibamba (1998) made him known throughout Africa, Europe and America.
28. Eric Wainaina, 38, Kenyan, Musician. He composed the music for the film, 'From A Whisper', which won the Best Director, Best Screenplay, Best Picture, Best Original Soundtrack and AMAA Achievement in Editing awards, in the African movie 2009 awards.
29. Binyavanga Wainaina, 40, Kenyan, Author. In July 2002 he won the Caine Prize for African Writing for his short story "Discovering Home."
30. Ngugi Wa Thiongo, 73, Kenyan, Author. He taught at Yale University and is regarded as a likely candidate for a Nobel Prize. He was exiled for 22 years due to his highly political work.
31. Freshlyground, South African, Musicians. Freshlyground and Shakira sang 'Waka Waka (This Time For Africa)' official anthem of the 2010 FIFA World Cup.
Lead singer, Zolani Mahola
32. Chimamanda Adichie, 34, Nigerian, Writer. Her first novel, Purple Hibiscus has received wide critical acclaim: it was shortlisted for the Orange Fiction Prize (2004) and was awarded the Commonwealth Writers' Prize for Best First Book (2005).
33. Rokia Traoré, 37, Malian, Musician. Award winning and highly acclaimed artist.
34. Tuface Idibia, 36, Nigerian, Musician. Famous for his hit 'African Queen' 2Face is currently one of the most popular artists on the African music scene.
35. P-Square, 29, Nigerian, Musicians. Award winning duo composed of identical twin brothers Peter and Paul Okoye.
36. Don Jazzy, 30, Nigerian, Music Producer. Multi award-winning record producer, singer-songwriter,musician and CEO of record label Mo' Hits Records.
37. D'Banj, 31, Nigerian, Musician. Real name Dapo Daniel Oyebanjo. Multi award winning artist. Famous for his high energy and charisma as a performer and entertainer. He refers to himself as the 'Koko master.'
38. Nneka, 31, Nigerian, Musician. Famous for her soul style songs with a political undertone. She won the award for best indigenous artist in Nigeria at the Nigerian Entertainment Awards 2010.
39. Asa, 29, Nigerian, Musician. Her single, 'Jailer' became infectious and got her a lot of attention in the music industry.
40. Patricia Amira, 33, Kenyan, TV Personality. Influential TV personality and host of M-NET's 'The Patricia Show.'
Inspirational eh? You can say that again. This is a WAKE UP CALL to everyone who has been sitting on their talent. Are you an aspiring sporting hero, author, songwriter, musician or singer? It doesn't matter what you are now, all that matters is who you will be. And that depends on what you do with yourself and your talent NOW! You can be whatever you strive to be. Physical barriers should not be a hindrance. Look at Amadou et Mariam taking the world by storm with their amazing music and making history despite the fact that they are both blind.
Never give up on your goals as long as you still alive. There is no age limit.
Who has been the most inspiration to you? Which artist is not here that you feel should have made the list? Hit the comments bar with your feedback.
And if you feel your country is under-represented instead of whining and complaining why not do something about it? Be an achiever. Be proactive. And in years to come maybe it'll be you on the Forbes list!
Good luck & Godspeed.
Holla 🙂
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Manu Dibango…Fally IPupa..Richard Bona…Oswald Boateng… Ndamukong Suh… cmmon guys…yall joking right?
wariohelena
Reblogged this on SIMPLY SAID and commented:
I couldnt agree more.. Lesson learnt from this post, Music, literature and poetry is highly influential in the thought process of Africans, Now if we would take this elements and use them as tools for mental influences all geared towards social change and awareness.
Lydia Epangue
A lifestyle & fashion influencer based in Birmingham, United Kingdom with a knack for stripes, bold colours and shoes. Has a Master's Degree in Contemporary Media. When she's not blogging, she works as a Digital Development Executive.
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The analysis of Egyptian pottery through thin section petrography is a significant tool to understanding a range of issues in Egyptian Archaeology. However, its use has remained undervalued, even though it has been available for many decades. On-site and laboratory petrography in Egypt have made its application much easier in recent years. This talk will discuss the results of such research that have shed light on Eastern Mediterranean trade over several millennia, the assimilation of non-Egyptian pottery trends in Egypt, and the specialized production of pottery beginning in the late Predynastic Period. Recent ceramic petrographic analysis has highlighted the movement of vessels throughout the Nile Valley from 3000 BC to the Medieval Period. Such information is significant to Egyptology for gaining a better understanding of technological choices, political stability, interregional connections, and the daily lives of Ancient Egyptians.
Abstract: The analysis of Egyptian pottery through thin section petrography is a significant tool to understanding a range of issues in Egyptian Archaeology. However, its use has remained undervalued, even though it has been available for many decades. On-site and laboratory petrography in Egypt have made its application much easier in recent years. This talk will discuss the results of such research that have shed light on Eastern Mediterranean trade over several millennia, the assimilation of non-Egyptian pottery trends in Egypt, and the specialized production of pottery beginning in the late Predynastic Period. Recent ceramic petrographic analysis has highlighted the movement of vessels throughout the Nile Valley from 3000 BC to the Medieval Period. Such information is significant to Egyptology for gaining a better understanding of technological choices, political stability, interregional connections, and the daily lives of Ancient Egyptians.


text:
import _jsx from "@babel/runtime/helpers/builtin/jsx";
import React from 'react';
import PropTypes from 'prop-types';
import MediaList from '../../MediaList';
import AddToPlaylistAction from '../../MediaList/Actions/AddToPlaylist';

var makeActions = function makeActions(onOpenAddMediaMenu) {
  return function (media, selection) {
    return _jsx(React.Fragment, {}, void 0, _jsx(AddToPlaylistAction, {
      onAdd: function onAdd(position) {
        return onOpenAddMediaMenu(position, media, selection);
      }
    }));
  };
};

var SearchResultsList = function SearchResultsList(_ref) {
  var results = _ref.results,
      onOpenAddMediaMenu = _ref.onOpenAddMediaMenu,
      onOpenPreviewMediaDialog = _ref.onOpenPreviewMediaDialog;
  return _jsx(MediaList, {
    className: "PlaylistPanel-media",
    media: results,
    onOpenPreviewMediaDialog: onOpenPreviewMediaDialog,
    makeActions: makeActions(onOpenAddMediaMenu)
  });
};

SearchResultsList.propTypes = process.env.NODE_ENV !== "production" ? {
  results: PropTypes.array.isRequired,
  onOpenAddMediaMenu: PropTypes.func.isRequired,
  onOpenPreviewMediaDialog: PropTypes.func.isRequired
} : {};
export default SearchResultsList;
//# sourceMappingURL=SearchResultsList.js.map


text:
Q: Adding a column of lag variables between 2 data frames in R I have two time series data frames with the same dates (lets say the data frames are called df1 and df2). I want to add multiple columns to df1 that have the lagged valeus from df2
I have tried the lag function, but it only will print out 1 lag in 1 column.
Creating 2 data frames
Date <-  seq.Date(as.Date("2004-01-01"), as.Date("2004-01-06"), by ="day")

df1 <- data.frame(
  Date,
  Price = sample(c(1:9), length(Date), replace = TRUE))

df2 <- data.frame(
  Date,
  Catch = c(sample(c(1:100), 2, replace = TRUE), NA,
            sample(c(1:100), 2, replace = TRUE), NA))

My df1 and df2 look like,
>df1
       Date     Price
>1 2004-01-01     2
>2 2004-01-02     4
>3 2004-01-03     7
>4 2004-01-04     9
>5 2004-01-05     6
>6 2004-01-06     9

>df2
       Date    Catch
>1 2004-01-01    95
>2 2004-01-02    14
>3 2004-01-03    NA
>4 2004-01-04   100
>5 2004-01-05    87
>6 2004-01-06    NA

I have tried to use the lag function:
df1$lag_catch_01 <- lag(df2$Catch, k = 1)

which gives me:
       Date     Price lag_catch
>1 2004-01-01     2        NA
>2 2004-01-02     4        95
>3 2004-01-03     7        14
>4 2004-01-04     9        NA
>5 2004-01-05     6       100
>6 2004-01-06     9        87

But I really want an output like this:
       Date      Price lag_catch_01 lag_catch_02
>1 2004-01-01     2           NA           NA
>2 2004-01-02     4           95           NA
>3 2004-01-03     7           14           95
>4 2004-01-04     9           NA           14
>5 2004-01-05     6          100           NA
>6 2004-01-06     9           87          100

I guess I could use the lag functions multiple times like this:
df1$lag_catch_01 <- lag(df2$Catch, k = 1)
df1$lag_catch_02 <- lag(df2$Catch, k = 2)

But if I wanted to make 20 columns, each with 20 day lags that would become very exhausting.

A: You could calculate the lags in their own data.frame using plyr and then combine them:
library(plyr)
library(dplyr)

df1 %>% bind_cols(data.frame(t(ldply(1:4, lag, x= (df2$Catch)))))

So ldply(1:4, lag, x= (df2$Catch) is say for the list (l in ldply) 1:4 apply the function lag and return a data.frame (d in ldply) with other named arguments at the end. The rest is just tidying up as in this case the data.frame created is the wrong way around

A: Assuming those two lags work as you intend but need to dynamically extend number of lags, simply assign new columns to a matrix output with sapply:
df1[paste0("lag_catch_", 1:20)] <- sapply(1:20, function(i) lag(df2$Catch, k = i))


text:
Arno Cost
Raving Raeanne: Upcoming Events Top Picks
Fall is yet another great season filled with EDM events. The cooler temperatures are ideal for outdoor events. With that being said, there are many outdoor events that extend from the summer season like at New York's Governors Island or Georgia where the world famous TomorrowLand festival takes the US with the first annual TomorrowWorld...
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arno cost "head up" out now
French DJ/producer Arno Cost has been a mainstay on dance music's world stages since his emergence in 2006, attracting praise from many of EDM's most formidable figures. From early releases with Norman Doray and Pierre de la Touche as The Freshmakers, to his more recent solo productions such as recent single "Lifetime" on Spinnin' Records, and remixes for the likes of David Guetta ("
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Laidback Luke, Arno Cost & Norman Doray "Trilogy" Preview
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Allison Transmission has invested in a non-controlling equity stake in Odyne.
Allison Transmission (NYSE: ALSN) is the world's largest manufacturer of fully automatic transmissions for medium- and heavy-duty commercial vehicles, and is a leader in hybrid-propulsion systems for city buses. Allison transmissions are used in a variety of applications including refuse, construction, fire, distribution, bus, motorhomes, defense and energy. Founded in 1915, the company is headquartered in Indianapolis, Indiana, USA and employs approximately 2,700 people worldwide. With a market presence in more than 80 countries, Allison has regional headquarters in the Netherlands, China and Brazil with manufacturing facilities in the U.S., Hungary and India. Allison also has approximately 1,400 independent distributor and dealer locations worldwide. For more information, visit www.allisontransmission.com.
Johnson Controls has made a strategic investment in Odyne to fund expansion and to accelerate wide-scale commercialization of Odyne's hybrid systems. Johnson Controls is a "Preferred Supplier" of advanced lithium-ion battery systems for Odyne hybrid trucks.
Johnson Controls is a global diversified technology and industrial leader serving customers in over 150 countries. Their 142,000 employees create quality products, services and solutions to optimize energy and operational efficiencies of buildings; automotive batteries; and interior systems for automobiles. Their commitment to sustainability dates back to roots in 1885, with the invention of the first electric room thermostat. Through growth strategies and by increasing market share they are committed to delivering value to shareholders and making customers successful. In 2011, Corporate Responsibility Magazine recognized Johnson Controls as the #1 company in its annual "100 Best Corporate Citizens" list.
Johnson Controls Power Solutions is the global leader in lead-acid starter batteries and advanced lead-acid batteries for Start-Stop vehicles. Their 35 manufacturing facilities supply more than one-third of the world's lead-acid batteries to major automakers and aftermarket retailers. Through innovations they are building the advanced battery industry for more fuel efficient and sustainable vehicles. Their commitment to sustainability is evidenced by world-class technology, manufacturing and recycling capabilities.
Odyne's plug-in hybrid system uses Remy's HVH250 electric motor for its robust automotive quality.
Remy Electric Motors, LLC is North America's largest independent manufacturer of advanced electric propulsion motors designed with leading edge technology and backed by the security of over a billion miles of proven reliability. Remy's electric motors provide unsurpassed power density, efficiency and durability. In business for over 100 years, today Remy is actively collaborating with companies throughout the world to develop innovations that are transforming the vehicle industry.
Terex Utilities has been appointed as distributor of Odyne hybrid systems throughout U.S. and globally.
Sales orders domestically are up substantially in 2011 and the new distribution agreement with Terex Utilities is anticipated to significantly expand opportunities. Odyne estimates an addressable market of 250,000 units worldwide per year for new and retrofit work truck hybrid systems. Joe Dalum, President and Chief Executive Officer of Odyne Systems noted "We believe that the work truck industry will be strongly affected by advancements in vehicle efficiency in the coming years and we are excited to be at the leading edge with our products. Our new distribution agreement with Terex Utilities means we will have comprehensive coverage of the North American hybrid truck market and global reach to meet customer needs for lower emission vehicles that consume less fuel and operate quietly. We are looking forward to significant growth opportunities in the hybrid truck market ahead.


text:
Giants blank Marlins behind Tyler Beede
Malaika Bobino
San Francisco – Another great performance from rookie Tyler Beede tonight. He shutout the Florida Marlins and recorded his second start of the season. The Giants rallied behind Beede and produced one run enough for the 1-0 victory over the Marlins.
"This is a time for these guys to shine and show that they belong up here," said manager Bruce Bochy. "You're hoping that they can pitch here and play here, so it was a really nice job by the three kids today. That's a close game, a one-run ballgame that didn't faze them at all."
Beede tossed six frames, allowed three hits, walked one and struck out five. And his backup didn't disappoint, Ty Rodgers spun 1.2 innings striking out three while Shaun Anderson got the save. Anderson replaced injured closer Will Smith and recored his second save of the season.
"Those guys have been super impressive all year," Beede said. "Rog, what you see from him now is what's he's done his whole career in the Minor Leagues. I'm super happy for him to be able to come up here and continue to be Tyler Rogers. The Giants are lucky to have guy like that who they can throw out there in big-time situations."
After five scoreless innings, San Francisco got on the board with a RBI single from Buster Posey. Florida's defense did a good job keeping the Giants off the board behind Sandy Alcantara's pitching. Alcantara allowed no runs until Stephen Vogt doubled and scored off Posey's single.
Vogt led off the sixth with a double. Kevin Pillar grounded out to first baseman, Neil Walker and Vogt advanced to third. Posey bounced an RBI single over third baseman, Starlin Castro and Vogt scored making it a 1-0 game.
"I thought he took some good swings today," said Bochy of Posey. "He ended up getting that big RBI for us."
The Giants had a few opportunities to control the game. Vogt and Brandon Crawford both singled in the second but Mauricio Dubon struck out swinging to end the inning. Brandon Belt who had three hits for the night, singled in the third. Evan Longoria was hit by a pitch but Vogt flew out to center field to end the threat.
Belt's ground-rule double in the fifth was just short of a home run. Mike Yastrzemski advanced to third on the hit. Longoria grounded out to end the threat, stranding two. Not the best permformance on offense for the Giants. As the season end nears, there's a big opportunity for the younger guys to prove their worth in the Majors.
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Bruce Bochy takes his final bow at Oracle Park
Giants rally in the eighth for epic win over A's
Giants beat A's in Battle of the Bay series
San Francisco – In front of a sellout crowd, Bruce Bochy tipped his cap for the last time. Bochy announced his retirement at the start of the season, so the Giants prepared a send-off that was more than the Skipper could handle.
"This was a tough a day as I've ever had," Bochy said. "I had no idea that this send-off would be like it was today."
Despite San Francisco being shutout 9-0 by the Los Angeles Dodgers. The final game of the season was more about the thirty-sixth manager of the organization than the loss. Throughout the game there were video tributes on Bochy's astounding forty-four years in the Majors. But what brought the tissues out during an emotional ceremony was who showed up to say their final farewells.
The Giants had a few surprises up there sleeves and I have to be quite honest. It blew me away as well. After the game, Bochy's retirement ceremony began with the "Forever" Giants in attendance, Willie Mays, Orlando Cepeda, Felipe Old and Juan Marichal to name a few.
Former players, including Dodgers manager Dave Roberts jogged onto the field to greet his former teammates. Barry Bonds took his cap and tossed it on the field. The crowd roared with laughter and excitement. After Roberts (who played for Bochy in 2007) embraced Bochy with a hug and handshake he headed back to the dugout.
"His hat should've been thrown," said Bochy with a smile.
"I wish we would've played better for him the entire year," Brandon Belt said.
Dereck Rodriguez didn't have the start he would've liked on Bochy's final game. Rodriguez loaded the bases after giving up a walk to Chris Taylor and two back-to-back singles to A.J. Pollock and Cory Bellinger.
Cory Seager knocked a single down the right field line and cleared the bases with a three-run double. Will Smith followed with a two-run homer to center field to make it a 5-0 game in the first frame. After Rodriguez put two on in the second, Bochy decided his time on the mound was done.
Sam Selman replaced Rodriguez who walked Bellinger to load the bases again. Selman forced Seager to ground out to end the threat. Rodriguez tossed 1.2 innings, allowed five hits, five runs, one home run, struck out two and walked two. While San Francisco's defense kept the Dodgers off the board five more frames.
They added three more runs in the seventh and the eighth inning. After loading the bases for the third time, Sam Coonrod walked Matt Beaty, gave up a single to Kristopher Negron and hit Will Smith with a pitch. Coonrod walked in a batter after he walked Gavin Lux.
By the eighth, Joc Peterson led off the frame with a single and back-to-back walks to Chris Taylor and Max Muncy loaded the bases. Austin Barnes singled Peterson and Taylor score. Muncy scored on a fielding error by shortstop Mauricio Dubon to make it a 9-0 game.
Players from the 2010 championship team walked out on the field, Nate Schierholtz, Cody Ross, Edgar Renteria, Brian Wilson, and Aubrey Huff. The 2012 team, Angel Pagan, Barry Zito, Ryan Vogelsong, Santiago Casilla and Marco Scutaro. And finally from the 2014 team, Javier Lopez, Gregor Blanco, Jake Peavy, Jeremy Affeldt and Tim Lincecum. Many of these players played all three years but it was Lincecum's arrival that really choked Bochy up.
"It was just gradually building," Bochy said on his emotions unraveling during his ceremony. "And, at the end, Timmy… When he was announced, that put me over the edge. Timmy's somebody that's really close to my heart and for him to come down here, that's a pretty big deal for me.
He's a tough one to get a hold of. I've tried to text him. I've tried to see him in Seattle."
Lincecum never returned to the Giants after leaving almost four years ago. Invites were unanswered and the guy once known as "The Freak" moved on to play for the Los Angeles Angels and the Texas Rangers. But what Lincecum did as a Giant was one for the history books.
Three World Series, four National League Pennants, and twenty-five years as a manager in the Majors. Bochy's 2010 championship team was the first to bring a World Series to the organization in fifty-two years. He's the fifth manager to to lead a team to three titles in a five year span, joining Connie Mack, Joe McCarthy, Casey Stengel and Joe Torre.
"Seeing Boch get emotional throughout the day, definitely made it hit home," Buster Posey said on Bochy retiring. "See him talk a lot. I've never seen him get emotional before. So, obviously this was a big day for him to say the least."
"The team called me about this event and I couldn't have picked a better day to make it special for him," Former Giant Pagan said. "It's his time, it's his moment and I wanted to be there to thank him personally for everything that he did, not only for me but for all the teammates that I've had and all the players that have come through his hands. So I cannot feel any more grateful to be here and this will make me come back more often."
Oakland – The Bay Bridge series is always exciting. This battle never gets old, and tonight was an epic performance. Eight runs scored in one frame led to the Giants rally in the eighth. It was something you just don't see too often in baseball especially at the Coliseum.
The A's have been consistent in their wins but tonight, the bullpen failed and former Oakland player, Stephen Vogt capped the night off with a three-run blast against his old team. San Francisco's 10-5 win over the A's was a stellar comeback.
"Felt like we did't get out for an hour," said manager Bob Melvin.
The eighth inning lasted so long, Oakland used four pitchers in one frame. Vogt had four at-bats that didn't get him on base. But his fifth at-bat, capped off a phenomenal offensive outburst for the Giants in the eighth.
After Vogt lined out, Melvin made a pitching change. Back-to-back singles from Buster Posey, and Alex Dickerson. Evan Longoria's RBI single drove in Posey to make it a 4-3 game. A.J. Puk replaced Yusmeiro Petit. A wild pitch by Puk led to Dickerson scoring to tie the game 4-4.
Puk also walked Austin Slater. Melvin made another change, Lou Trivino replaced Puk. Kevin Pillar cleared the bases with a double driving in both Longoria and Slater to extend their lead to 6-4. Then pinch-hitter Donovan Solano and Brandon Crawford hit back-to-back singles.
Melvin made his final switch in the frame with Ryan Butcher replacing Trivino. After striking out Mike Yastrzemski, Vogt went yard with a three-run home run. That gave San Francisco a good lead, making the A's work hard to dig their way out of a hole too deep.
"I think we have to just move on from this one, we've been playing well up to this point," Melvin said. "We just basically had a bad inning today."
"It's just one game, it is what it is," said Chris Bassitt. "We can make this a huge deal but it's just one game. Lick your wounds, come in tomorrow and do your job."
Oakland was in control early on, Mark Canha went deep in the second recording his 20th home run. Josh Phegley's RBI single in the second made it a 2-0 A's lead. Crawford went yard in the fifth to cut the lead in half. But Oakland's offense stayed hot.
By the sixth, Bassitt gave up a double to Dickerson and Longoria followed with a single scoring in Dickerson in to tie the game 2-2. That knocked Bassitt out of the game, and the bullpen took over. Profar Jurickson led off the frame bottom of the inning with a single. Phegley's sacrifice bunt advanced Jurickson to third. Marcus Semien's RBI single made it a 3-2 game. And Matt Olson's RBI singled drove in Semien to extend the A's lead 4-2.
The Battle of the Bay Bridge series is always exciting. Oakland opened up up Mount Davis to accommodate the 53,367 fans that showed up for the series. With both teams making a push for the postseason, the Bay Bridge series focuses on the rivalry across the bridge. Vogt a fan favorite in Oakland, had missed the entire 2018 season rehabbing from shoulder injury after he was released by the A's.
"It was really special for me with the fans," Vogt said. I was very moved by them cheering. The way that I was received the first time back, it really meant a lot to me. Obviously, this is a special place for me and for my family. To come back and have the fans say 'Thank you' or 'Hi', it was a very neat moment."
Notes – In Bruce Bochy's final farewell tour, A's manager Bob Melvin gifted him with a bottle of red wine from the team.
Photo by Michael Urakami/MLB
"It's always a lot of fun (Bay Bridge Series) especially with Mount Davis opened (upper level seating at the Coliseum)," said Melvin. "As far as Bochy goes, I guess I'll have a few more wins in my pocket once he retires. He's been so big for the Giants and what he's accomplished.
He's been a friend of mine for a long time, we talk quite a bit. Its hard not to like the opposing team whens he's over there. He's one of the greatest of all time and will be headed to the Hall of Fame, deservingly so. It'll be kinda weird knowing this is that last time he'll managing in Oakland."
Photo by Michelle Minahen/Oakland Athletics
Athletics Director and head coach, John Beam threw out the first pitch for the A's vs Giants game. Beam led his Laney College football team to the State Championship and won in 2018. He was recognized as leading the #1 team in the nation and as the California Community College Football Coach of the Year.
"I have been coaching football for 40 years," said Beam. "I stay in it because of the kids. I get paid to have fun."
Before Beam began his career at Laney, he was the head football coach at Skyline High school for 22 years. The next chapter of his life began as Laney's Running Backs coach in 2004, and in 2005 he was promoted to Offensive Coordinator where he led an explosive offense that won 3 conference championships and five straight bowl game appearances. By 2012, Beam was named head coach.
"My assistants are unbelievable, many are Skyline graduates," Beam explained. "Friendships that started off as student-teacher developed over time and that's what I like about this job because I can see those transformations. People say its a new generation but the kids still want to learn and be taught how to play the game, and we enjoy that."
The A's have been a huge contributor to Oakland schools and community colleges. They donated $100,000 to the Peralta Community College District after their offer to build a ballpark next door to Laney was shutdown. Another commitment from the A's is to support the Peralta College Workforce Development Program. Which will honor four scholarships annually for student studying business, digital media, communications and technology.
"The A's have been phenomenal partners with us (Laney College) and with Oakland," Beam said. "Our championship rings, the A's helped pay for them." I appreciate the A's bringing me here to throw out the first pitch. I wish I would've done better with that pitch but [Marcus] Jensen (A's Bullpen Coach) did good by catching my grounder."
San Francisco – The race for the postseason never gets old when these two teams are in it. Both outside looking in, the Giants and the Athletics have plenty of time to make a difference in the race for the Wild Card.
In the meantime, it's the Battle of the Bay! The Giants have dethroned the A's by defeating them 3-2 tonight in the first of a two-game series. San Francisco continues to add to their win column as they keep pace on reaching the postseason.
"The deeper the season goes, the more attention to whose in the playoff hunt," said manager Bob Melvin. "And these two teams are…"
It was a defensive game until Stephen Piscotty hit a home run to make it a 1-0. By the sixth the Giants took the lead after Buster Posey led off the first of three back-to-back doubles. Posey doubled followed by Evan Longoria's double that drove in Posey.
Kevin Pillar doubled and Longoria scored making it a 2-1 game. After Oakland made a pitching, San Francisco added one more run in the seventh. Jake Diekman walked both Aramis Garcia and Brandon Crawford. Another pitching change put Yusmeiro Petit on the mound.
Pinch-hitter Scooter Gennett hit a sacrifice fly and drove in Garcia to extend their lead 3-1. Despite the needed hits by the Giants, Madison Bumgarner has gone 4-0 with a 2.78 ERA in his last nine starts with his team going 8-1 during those nine games. Tonight was another spectacular performance.
"This is exciting baseball," said manager Bruce Bochy. "It's that time of year. Fans were in it. Players were. It was a hard fought game. We had to put some torture in it in the end."
Bumgarner allowed two hits, one home run and struck out nine over seven innings. With this win, the Giants are back at .500 for the first time since August 2nd. In order to tame Oakland, San Francisco knew their pitching had to be near perfect because the A's have a way of making a comeback late games.
Oakland made some noise in the ninth after Dustin Garneau and Khris Davis both singled to put two on with no outs. Closer Will Smith struck out Marcus Semien. But loaded the bases when Matt Chapman hit a single to right field.
Smith struck out Matt Olson. And Mark Canha was at the plate next. Canha never faced Smith until tonight. Smith walked Canha and Garneau scored. Giants manager Bruce Bochy made no change in pitching as the bases stayed loaded. With two outs, Smith struck out Chad Pinder to end the game.
"I'm trying to save as many games as I can for him in his final year," Smith said regarding Bochy. "And for him to have the trust in me is pretty cool."
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awesome! thanks I was trying to budget what my amount would be and now I know (it went down, but so did my value).
My assessment went down and we're paying just under $100 more than last year.
Considering most property owners are paying less because their building is worth less, you should be happy. I'm not cheering for the rate going up so I understand your sentiment.
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Jeff Sherman loves Milwaukee and the greater Milwaukee area. Honestly, how can't you? Therefore, he blogs about all things greater Milwaukee, mainly people, places and things in greater Downtown Milwaukee. Issues, scoops, buzz and more - he'll discuss it and do what he can to move Milwaukee forward.
What else is on Sherman's mind? Expect insight and ramblings on a ton of topics including media, Bucks basketball, leadership, random celebrity "news," movies, health and fitness stuff, urban living trends, and eventually a blog on why "Friday Night Lights" and "Real Sports with Bryant Gumbel" should be mandatory viewing for all Americans.


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Brief History of UDC
Core Business/Strategic Objectives
Corporate Values & Philosophy
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The UDC's Asset Base
Policies and Acts
UDC Focus Areas
Houses of Parliament - Public Consultation Advisory and EIA
Tax Incentive Programme
Fort Clarence Beach Park Response
THE EDITOR, Sir:
The Urban Development Corporation (UDC), in its mandate to lead sustainable urban and rural development through public-private partnerships, takes a business-like approach in achieving its mission.
While the UDC is a Government agency, it operates as a self-sustaining entity and is expected to act like any prudent corporation would in developing its growth plan. A key element of that growth plan is attracting investors who share the UDC's vision of providing world class facilities for Jamaicans.
A letter published yesterday in The Gleaner expressed concern why the UDC hasn't invested in redeveloping the Fort Clarence Beach Park. The UDC has, in fact, invested $15 million in Phase One of the redevelopment of Fort Clarence. This phase included the construction of an Administrative Building and general upgrade to the grounds and perimeter fencing.
In a bid to realise the full potential of the Fort Clarence Beach Park, an Expression of Interest was published last month. We've received responses from several parties and as part of the procurement process a Request For Proposal will now be sent to the interested investors. We will be selecting a partner by the 3rd quarter of 2017.
It is important to note that Fort Clarence is one of several divestment opportunities the UDC is actively seeking partners for. Others include Cardiff Hall in St Ann, West Street Residence in Downtown, Kingston and The Festival Village in Ocho Rios.
The UDC's long term vision for Fort Clarence is to be the premier beach park serving the Kingston Metropolitan Area, complete with first world amenities and attractions for the entire family. The UDC remains committed to this vision and will choose the most appropriate process in achieving it.
Dr Damian Graham
Urban Development Corporation
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