text:
\section{Conclusion}

The capability for machines to anticipate future concepts before they begin is
a key problem in computer vision that will enable many real-world applications.
We believe abundantly available unlabeled videos are an effective resource we
can use to acquire knowledge about the world, which we can use to learn to
anticipate future.

{\small
\textbf{Acknowledgements:} We thank
members of the MIT vision group for predicting the future on our test set.
We thank TIG for managing our computer cluster, especially Garrett Wollman for troubleshooting
many data storage issues.
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
This work was supported by NSF grant IIS-1524817, and by a Google faculty research award to AT,
and a Google PhD fellowship to CV.
}

\section{Experiments}

In this section, we experiment with how well actions and objects can be
forecasted using the predicted representations. We show results for forecasting
basic human actions one second before they start, and anticipating household
objects five seconds before they appear.

\subsection{Unlabeled Repository}

In order to train our network to predict features, we leverage a large amount
of unlabeled video.  We experimented with two sources of unlabeled videos:

\textbf{Television Shows:} We downloaded over $600$ hours of publicly available
television shows from YouTube. To pick the set of television shows, we used the
top shows according to Google. The videos we downloaded generally consist of
people performing a large variety of everyday actions, such as eating or
driving, as well as interactions with objects and other people.  We show a few
example frames of these videos in Figure \ref{fig:repository}. We use this
repository in most of our experiments.  Since we test on different datasets,
one concern is that there may be videos in the repository that also appear in a
testing set. To check this, we queried for nearest neighbors between this
repository and all testing sets, and found no overlap.

\textbf{THUMOS:} We
also experimented with using videos from the THUMOS challenge \cite{THUMOS15},
which consists of 400 hours of video from the web. These videos tend to be tutorials and sports, which
has a different distribution from television shows.
We only use THUMOS as a diagnostic dataset to quantify the performance of our method
when the training distribution is very different from the testing set.

\newcommand{\tbrow}[9]{ #1 &  #4 & #2 & #5 & #7 & #6 & #3 & #8 & #9 \\}
\newcommand{\stc}[2][c]{%
  \begin{tabular}[#1]{@{}c@{}}#2\end{tabular}}
\begin{table*}
\begin{tabular}{l | c | c c c c | c c c}
       &         & \multicolumn{4}{c|}{Regression} & \multicolumn{3}{c}{Classifier} \\
Method & Feature & Train Data & Method & Output & K & Frame & Data &  Method \\
\hline
\tbrow{ SVM Static }{ - }{ During }{ fc7 }{ - }{ 1 }{ - }{ RO }{ SVM }
\tbrow{ SVM }{ - }{ Before }{ fc7 }{ - }{ 1 }{ - }{ RO }{ SVM }
\tbrow{ MMED }{ - }{ Before }{ fc7 }{ - }{ 1 }{ - }{ RO }{ MMED }
\tbrow{ Nearest Neighbor }{ UV }{ Before }{ fc7 }{ 1-NN }{ 1 }{ fc7 }{ RI }{ SVM }
\tbrow{ Nearest Neighbor Adapted }{ UV }{ Before }{ fc7 }{ 1-NN }{ 1 }{ fc7 }{ RO }{ SVM }
\tbrow{ Linear }{ UV }{ Before }{ fc7 }{ Linear }{ 1 }{ fc7 }{ RI }{ SVM }
\tbrow{ Linear Adapted }{ UV }{ Before }{ fc7 }{ Linear }{ 1 }{ fc7 }{ RO }{ SVM }
\tbrow{ Deep K=1 }{ UV }{ Before }{ RGB }{ CNN }{ 1 }{ fc7 }{ fc7 of RI }{ SVM }
\tbrow{ Deep K=1 Adapted }{ UV }{ Before }{ RGB }{ CNN }{ 1 }{ fc7 }{ RO }{ SVM }
\tbrow{ Deep K=3 }{ UV }{ Before }{ RGB }{ CNN }{ 3 }{ fc7 }{ fc7 of RI }{ SVM }
\tbrow{ Deep K=3 Adapted }{ UV }{ Before }{ RGB }{ CNN }{ 3 }{ fc7 }{ RO }{ SVM }
\tbrow{ Deep K=3 THUMOS  }{ THUMOS }{ Before }{ RGB }{ CNN }{ 3 }{ fc7 }{ fc7 of RI }{ SVM }
\tbrow{ Deep K=3 THUMOS Adapted }{ THUMOS }{ Before }{ RGB }{ CNN }{ 3 }{ fc7 }{ RO }{ SVM }
\tbrow{ Deep K=1 ActionBank Adapted }{ UV }{ Before }{ RGB }{ CNN }{ 1 }{ ActionBank }{ RO }{ SVM }
\tbrow{ Deep K=3 ActionBank Adapted }{ UV }{ Before }{ RGB }{ CNN }{ 3 }{ ActionBank }{ RO }{ SVM }
\end{tabular}
\caption{\textbf{Overview of Models:} We compare several different ways of training
models, and this table shows their different configurations. To train the regression (if any), we
specify which source of unlabeled videos we use (UV for our repository, or THUMOS), the method, the regression target output, and the number of outputs $K$. This is then fed into the classifier, which uses labeled data.  To train the classifier,
we specify which frame to train the classifier on (during action, or before action), the regression input (RI) or output (RO),
and the classifier. During testing, the procedure is the same for all models.}
\label{tab:baselines}
\end{table*}

\begin{table}
\centering
\begin{tabular}{l l}
Method & \multicolumn{1}{c}{Accuracy}  \\
\hline
Random & $25.0$ \\
SVM Static & $36.2 \pm 4.9$ \\
SVM & $35.8 \pm 4.3$ \\
\textcolor{mod}{MMED}  & $34.0 \pm 7.0$ \\
Nearest Neighbor  & $29.9 \pm 4.6$\\
Nearest Neighbor \cite{yuen2010data}, Adapted  & $34.9 \pm 4.7$\\
Linear & $32.8 \pm 6.1$ \\
Linear, Adapted & $34.1 \pm 4.8$ \\
\hline
Deep K=1, ActionBank \cite{sadanand2012action} & $34.0 \pm 6.1$ \\
Deep K=3, ActionBank \cite{sadanand2012action} & $35.7 \pm 6.2$ \\
Deep K=1 & $36.1 \pm 6.4$ \\
Deep K=1, Adapted & $40.0 \pm 4.9 $ \\
Deep K=3 & $35.4 \pm 5.2$ \\
\textbf{Deep K=3, Adapted} & $\textbf{43.3} \pm \textbf{4.7} $ \\
Deep K=3, THUMOS \cite{THUMOS15}, Off-the-shelf & \textcolor{mod}{$29.1 \pm 3.9$} \\
\textbf{Deep K=3, THUMOS \cite{THUMOS15}, Adapted } & $\textbf{43.6} \pm \textbf{4.8}$ \\
\hline
Human (single) & $71.7 \pm 4.2$ \\
Human (majority vote) & $85.8 \pm 1.6$ \\
\end{tabular}
\caption{\textbf{Action Prediction:} Classification accuracy for predicting actions one second before they begin given only a single frame. The standard deviation across cross-validation splits is next to the accuracy.
}
\label{tab:actionresults}
\end{table}

\subsection{Baselines}

Our goal in this paper is to learn from unlabeled video to anticipate high-level concepts (specifically actions and objects) in the future.
Since our method uses minimal supervision to attach semantic meaning to the predicted representation,
we compare our model against baselines that use a similar level of supervision. See Table \ref{tab:baselines} for an overview of the methods we compare.

\textbf{SVM:} One reasonable approach is to
train a classifier on the frames before the action
starts to anticipate the category label in the future. This baseline is able to
adapt to contextual signals that may suggest the onset of an action. However,
since this method requires annotated videos, it does not capitalize on unlabeled video.

\textbf{MMED:} We can also extend the SVM to handle sequential data in order to make
early predictions. We use the code out-of-the-box provided by \cite{hoai2014max} for this
baseline.

\textbf{Nearest Neighbor:} Since we have a large unlabeled repository, one
reasonable approach is to search for the nearest neighbor, and use the
neighbor's future frame as the predicted representation, similar to
\cite{yuen2010data}.

\textbf{Linear:} Rather than training a deep network, we
can also train a linear regression on our unlabeled repository to predict
$\texttt{fc7}$ in the future.

\textbf{Adaptation:} We also examine two
strategies for training the final classifier. One way is to train the
classifier on the ground truth regression targets, and test it on the inferred output of the regression.
The second way is to adapt to the predictions by also training the
classifier on the inferred output of the regression. The latter can adapt to the errors
in the regression.

\subsection{Forecasting Actions}

\textbf{Dataset:} In order to evaluate our method for action forecasting, we require a labeled
testing set where a) actions are temporally annotated, b) we have access to
frames before the actions begin, and c) consist of everyday human actions (not sports). We use the TV Human Interactions dataset
\cite{patron2010high} because it satisfies these requirements. The
dataset consists of people performing four different actions (hand shake, high
five, hug, and kissing), with a total of $300$ videos.

\textbf{Setup:} We run our predictor on the frames before
the annotated action begins. We use the provided train-test splits with $25$-fold cross
validation. We evaluate classification accuracy (averaged across cross
validation folds) on making predictions one second before the action has
started.  To attach semantic meaning to our predicted representation, we use
the labeled examples from the training set in \cite{patron2010high}. As we make
multiple predictions, for evaluation purposes we consider a prediction to be
correct only if the ground truth action is the most likely prediction under our
model.

\begin{figure}
\centering
\includegraphics[width=\linewidth]{figs/tvactions-shorter.png}
\caption{\textbf{Example Action Forecasts:} We show some examples of our forecasts of actions one second before they begin. The left most column shows the frame before the action begins, and our forecast is below it. The right columns show the ground truth action. Note that our model does not observe the action frames during inference.}
\label{fig:actions-qual}
\end{figure}

\begin{figure}
\centering
\includegraphics[width=\linewidth]{figs/tvactions-delta.pdf}
\caption{\textcolor{mod}{\textbf{Performance vs $\Delta$:} We plot performance on forecasting actions versus number of frames before the action starts. Our model (red) performs better
when the time range is longer (left of plot).
Note that, since our model takes days to train, we evaluate our model trained for one second, but evaluate on different time intervals. The baselines are trained for each time interval.}}
\label{fig:actions-delta}
\end{figure}

\begin{figure}
\includegraphics[width=\linewidth]{figs/tvactions-multi-small.pdf}
\caption{\textcolor{mod}{\textbf{Multiple Predictions:} Given an input frame (left), our model predicts multiple representations in the future that can each be classified into actions (middle).
When the future is uncertain, each network can predict a different representation, allowing for multiple action forecasts. To obtain the most likely future action, we can marginalize
the distributions from each network (right).}}
\label{fig:actions-multi}
\end{figure}

\textbf{Results:}
Table \ref{tab:actionresults} shows the classification accuracy of different models for predicting the future action one second into
the future given only a single frame. Our results suggest that training deep
models to predict future representations with unlabeled videos may help
machines forecast actions, obtaining a
\textbf{relative gain of 19\%} over baselines.
We conjecture our network may obtain the stronger
performance partly because it can better predict the future \texttt{fc7}. The
mean Euclidean distance between our model's regressions and the actual future is about
$1789$, while regressing the identity transformation is about $1907$ and a
linear regression is worse, around $2328$.

\textbf{Human Performance:} To establish an upper expectation for the
performance on this task, we also had $12$ human volunteers study the training
sets and make predictions on our testing set. Human accuracy is good (an
average human correctly predicts 71\% of the time), but not perfect due to the
uncertain nature of the task. We believe humans are not perfect because the
future has inherent uncertainty, which motivates the need for models to make
multiple predictions. Interestingly, we can use the ``wisdom of the crowds'' to
ensemble the human predictions and evaluate the majority vote, which obtains
accuracy (85\%).

We also performed several experiments to breakdown the performance our method.
\textbf{Different Representations:} We
also tried to train a deep network to forecast ActionBank
\cite{sadanand2012action} in the future instead of $\texttt{fc7}$, which
performed worse. Representations are richer than action labels, which may
provide more constraints during learning that can help build more robust models
\cite{hinton2014distilling}. \textbf{Different Training Sets:} We also evaluated our network trained on videos
that are not television shows, such as sports and tutorials.
When we train our network with videos from THUMOS \cite{THUMOS15} instead of
our repository, we still obtain competitive performance, suggesting our method
may be robust to some dataset biases.  However, adaptation becomes more important
for THUMOS, likely because the classifier must adapt to the dataset bias. \textbf{Different Intervals:} We also evaluated our model varying the time before the
action starts in Figure
\ref{fig:actions-delta}. The relative gain of our method is often better as the prediction time frame increases.

\begin{figure}
\includegraphics[width=\linewidth]{figs/adl.pdf}
\caption{\textbf{Example Object Forecasts:} We show examples of high scoring forecasts for objects. The left most frame is five seconds before the object appears.}
\label{fig:adl-qual}
\end{figure}

\newcolumntype{Y}{D..{2.1}}
\makeatletter
\newcolumntype{B}[3]{>{\boldmath\DC@{#1}{#2}{#3}}c<{\DC@end}}
\makeatother

\begin{table*}[tb]
\centering
\setlength{\tabcolsep}{.2em}
\begin{tabular}{l | Y | Y Y Y Y Y Y Y Y Y Y Y Y Y Y }
Method & \multicolumn{1}{c|}{Mean}& \multicolumn{1}{c}{dish} & \multicolumn{1}{c}{door} & \multicolumn{1}{c}{utensil} & \multicolumn{1}{c}{cup} & \multicolumn{1}{c}{oven} & \multicolumn{1}{c}{person} & \multicolumn{1}{c}{soap} & \multicolumn{1}{c}{tap} & \multicolumn{1}{c}{tbrush} & \multicolumn{1}{c}{tpaste} & \multicolumn{1}{c}{towel} & \multicolumn{1}{c}{trashc} & \multicolumn{1}{c}{tv} & \multicolumn{1}{c}{remote}  \\
\hline
Random & 1.2& 1.2 & 2.8 & 1.1 & 2.4 & 1.6 & 0.8 & 1.5 & 2.1 & 0.2 & 0.3 & 0.6 & 1.1 & 0.5 & 0.3 \\
SVM Static & 6.4& 2.6 & 15.4 & 2.9 & 5.0 & 9.4 & 6.9 & 11.5 & 17.6 & 1.6 & 1.0 & 1.5 & 6.0 & 2.0 & 5.9 \\
SVM & 5.3& 3.0 & 8.2 & 5.2 & 3.6 & 8.3 & 12.0 & 6.7 & 11.7 & 3.5 & 1.5 & 4.9 & 1.3 & 0.9 & 4.1 \\
Scene & 8.2& 3.3 & 18.5 & 5.6 & 3.6 & 18.2 & 10.8 & 9.2 & 6.8 & 8.0 & 8.1 & 5.1 & 5.7 & 2.0 & 10.3 \\
Scene, Adapted & 7.5& 4.6 & 9.1 & 6.1 & 5.7 & 15.4 & 13.9 & 5.0 & 15.7 & 13.6 & 3.7 & 6.5 & 2.4 & 1.8 & 1.7 \\
Linear & 6.3& 7.5 & 9.3 & 7.2 & 5.9 & 2.8 & 1.6 & 13.6 & 15.2 & 3.9 & 5.6 & 2.2 & 2.9 & 2.3 & 7.8 \\
Linear, Adapted & 5.3& 2.8 & 13.5 & 3.8 & 3.6 & 11.5 & 11.2 & 5.8 & 4.9 & 5.4 & 3.3 & 3.4 & 1.6 & 2.1 & 1.0 \\
\hline
Deep K=1& 9.1& 4.4 & 17.9 & 3.0 & 14.8 & 11.9 & 9.6 & 17.7 & 15.1 & 6.3 & 6.9 & 5.0 & 5.0 & 1.3 & 8.8 \\
Deep K=1, Adapted & 8.7& 3.5 & 11.0 & 9.0 & 6.5 & 16.7 & 16.4 & 8.4 & 22.2 & 12.4 & 7.4 & 5.0 & 1.9 & 1.6 & 0.5 \\
\textbf{Deep K=3} & \multicolumn{1}{B{.}{.}{1} |}{10.7}& 4.1 & 22.2 & 5.7 & 16.4 & 17.5 & 8.4 & 19.5 & 20.6 & 9.2 & 5.3 & 5.6 & 4.2 & 8.0 & 2.6 \\
\textbf{Deep K=3, Adapted}& \multicolumn{1}{B{.}{.}{1} |}{10.1} & 3.5 & 14.7 & 14.2 & 6.7 & 14.9 & 15.8 & 8.6 & 29.7 & 12.6 & 4.6 & 10.9 & 1.8 & 1.4 & 1.9 \\
\end{tabular}
\caption{\textbf{Object Prediction:} We show average precision for forecasting
objects five seconds before they appear in egocentric videos. For most
categories, our method improves prediction performance.
The last column is the mean across all categories.}
\label{tab:adl}
\end{table*}

\textbf{Multiple Predictions:}
Since we learn a mixture of networks, our model can make diverse predictions
when the future is uncertain. To analyze this, Figure \ref{fig:actions-multi}
shows a scene and a distribution of possible future actions.  For example,
consider the first row where the man and woman are about to embrace, however
whether they will kiss or hug is ambiguous. In our model, two of the networks
predict a representation where kissing is the most likely future action, but
one network predicts a representation where the most likely action is hugging. The other rows
show similar scenarios.
Since performance drops when $K=1$,
modeling multiple outputs may be important both during learning and inference.

\textbf{Qualitative Results:}
We qualitatively show some of our predictions in Figure \ref{fig:actions-qual}. For example, in some cases our model correctly predicts that a man and
woman are about to kiss or hug or that men in a bar will high five.  The second to last row shows a comic scene where one
man is about to handshake and the other is about to high five, which our model
confuses. In the last row of Figure \ref{fig:actions-qual}, our model
incorrectly forecasts a hug because a third person unexpectedly enters the
scene.

\subsection{Forecasting Objects}

\textbf{Dataset:}
Since our method predicts a visual representation in the future,  we wish to
understand how well we can anticipate concepts other than actions.  We
experimented with forecasting objects in egocentric videos five seconds before
the object appears. We use the videos from Activities of the Daily Living
dataset \cite{pirsiavash2012detecting}, which is one of the largest
datasets of egocentric videos from multiple people. Anticipating objects in this dataset
is challenging because even recognizing
objects in these videos is difficult \cite{pirsiavash2012detecting}.

\textbf{Setup:}
In order to train our deep network on egocentric videos, we reserved three
fourths of the dataset as our repository for self-supervised learning. We
evaluate on the remaining one fourth videos, performing leave-one-out to learn
future object category labels. Since multiple objects can appear in a frame, we
evaluate the average precision for forecasting the occurrence of objects five
seconds before they appear, averaged over leave-one-out splits.

\textbf{Baselines:}
We compare against baselines that are similar to our action forecasting
experiments.  However, we add an additional baseline that uses scene features
\cite{zhoulearning} to anticipate objects. One hypothesis is that, since most objects are correlated
with their scene, recognizing the scene may be a good cue for predicting the
onset of objects. We use an SVM trained on state-of-the-art scene features
\cite{zhoulearning}.

\textbf{Results:}
Table \ref{tab:adl} shows average precision for our method versus the baselines
on forecasting objects five seconds into the future.  For the many of the
object categories, our model outperforms the baselines at
anticipating objects, with \textbf{a mean relative gain of 30\%} over baselines. Moreover, our model with
multiple outputs improves over a single output network, suggesting that
handling uncertainty in learning is helpful for objects too. The adapted and off-the-shelf
networks perform similarly to each other in the average.
Finally, we also qualitatively show some high scoring object predictions in Figure \ref{fig:adl-qual}.

\section{Introduction}

What action will the man do next in Figure \ref{fig:teaser} (left)?
A key problem in computer vision is to create machines that anticipate actions and objects
in the future, before they appear or start.  This predictive capability would
enable several real-world applications. For example, robots can use predictions of human
actions to make better plans and interactions \cite{koppula2013anticipating}.  Recommendation systems can suggest products or
services based on what they anticipate a person will do. Predictive models can
also find abnormal situations in surveillance videos, and alert emergency
responders.

\begin{figure}[tb]
\centering
\includegraphics[width=\linewidth]{figs/new_teaser.pdf}
\vspace{-1em}
\caption{\textbf{Predicting Representations:} In this paper, we explore how to anticipate human actions and objects
by learning from unlabeled video. We propose to anticipate the visual representation of frames in the future. We can apply
recognition algorithms on the predicted representation to forecast actions and objects.\vspace{-1em}}
\label{fig:teaser}
\end{figure}

Unfortunately, developing an algorithm to anticipate the future is challenging.
Humans can rely on extensive knowledge accumulated over their lifetime to infer that the
man will soon shake hands in Figure \ref{fig:teaser}. How do we give machines access to this
knowledge?

We believe that a promising resource to train predictive models are abundantly
available unlabeled videos.  Although lacking ground truth annotations, they
are attractive for prediction because they are economical to obtain at
massive scales yet still contain rich signals.
Videos come with the temporal ordering of frames ``for
free'', which is a valuable asset for forecasting.

However, how to leverage unlabeled video to anticipate high-level concepts
is unclear.  Pioneering work in computer vision has capitalized on unlabeled videos
before to visualize the future
\cite{ranzato2014video,unsupervised,walker2014patch} and predict motions
\cite{pintea2014deja,walker2015dense,yuen2010data}. Unfortunately, these
self-supervised approaches are not straightforward to apply for anticipating semantics
because, unlike pixels or motions, concepts are not readily accessible in
unlabeled video. Methods that anticipate concepts have typically required supervision \cite{kitani2012activity,lan2014hierarchical,huang2014action}, which is expensive to scale.

In this paper, we propose a method to anticipate concepts in the future
by learning from unlabeled video.  Recent progress in computer vision has built
rich visual representations
\cite{donahue2013decaf,razavian2014cnn,zha2015exploiting}.  Rather than predict
pixels or depend on supervision, our main idea is to forecast visual
representations of future frames.  Since these representations contain signals
sufficient to recognize concepts in the present, we then use recognition
algorithms on the forecasted representation to anticipate a future concept.
Representations have the advantage that they both a) capture the semantic
information that we want to forecast and b) scale to unlabeled videos because
they are automatic to compute. Moreover, representations may be easier to
predict than pixels because distance metrics in this space empirically tend to
be more robust \cite{donahue2013decaf,krizhevsky2012imagenet}.

Since we can economically acquire a large amounts of unlabeled video, we create our
prediction models with deep networks, which are attractive for this problem because their capacity can
grow with the size of data available and are trained efficiently with
large-scale optimization algorithms. In our experiments, we downloaded
$600$ hours of unlabeled video from the web
and trained our network to forecast representations $1$ to $5$ seconds in the future.
We then forecast both actions and objects by applying recognition algorithms
on top of our predicted representations.
We evaluate this idea on two datasets of human actions in television shows
\cite{patron2010high} and egocentric videos for activities of daily living
\cite{pirsiavash2012detecting}.
Although we are still far from human performance on these tasks, our
experiments suggest that learning to forecast representations with unlabeled videos may help
machines anticipate some objects and actions.

The primary contribution of this paper is developing a method to
leverage unlabeled video for forecasting high-level concepts.
In section 2, we first review related work. In section 3, we then
present our deep network to predict visual representations in the future. Since
the future can be uncertain, we extend our network
architecture to produce multiple predictions. In section 4, we show
experiments to forecast both actions and objects. We plan to make our
trained models and code publicly available.

\section{Anticipating  Visual Representations}
\label{sec:method}

Rather than predicting pixels (which may be more difficult)
or anticipating labeled categories (which requires supervision), our idea
is to use unlabeled video to learn to predict the visual representation
in the future. We can then apply recognition algorithms (such as object or action classifiers)
on the predicted future representation to anticipate a high-level concept.  In this section,
we explain our approach.

\subsection{Self-supervised Learning}

Given a video frame $x_t^i$ at time $t$ from video $i$, our goal is to predict the visual
representation for the future frame $x_{t+\Delta}^i$. Let $\phi(x_{t+\Delta}^i)$ be
the representation in the future. Using videos as training data, we wish to estimate a function $g(x_t^i)$ that
closely predicts $\phi(x_{t+\Delta}^i)$:
\begin{align}
\omega^* = \argmin_\omega \sum_{i,t} \lVert g\left(x_t^i; \omega\right) - \phi\left(x_{t+\Delta}^i\right) \rVert_2^2
\label{eqn:loss}
\end{align}
where our prediction function $g(\cdot)$ is parameterized by $\omega$.

Our method is general to most visual representations, however we focus on predicting the last hidden layer  ($\texttt{fc7}$) of AlexNet
\cite{krizhevsky2012imagenet}. We chose this layer because it empirically
obtains state-of-the-art performance on several image
\cite{razavian2014cnn,donahue2013decaf} and video \cite{zha2015exploiting}
recognition tasks.

\subsection{Deep Regression Network}

Since we do not require data to be labeled for learning, we can collect
large amounts of training data. We propose to use deep regression networks for predicting representations because
their model complexity can expand to harness the amount of data available and can be trained with large scale
data efficiently with stochastic gradient descent.

Our network architecture is five convolutional layers followed by five
fully connected layers. The last layer is the output vector, which makes the
prediction for the future representation.  In training, we use a Euclidean
loss to minimize the distance between our predictions $g(x_t)$ and the
representation of the future frame $\phi(x_{t+\Delta})$.

Our choice of architecture is motivated by the
successes of the AlexNet architecture for visual recognition
\cite{krizhevsky2012imagenet,zhoulearning}. However,
our architecture differs by having a regression loss function
and three more fully connected layers.

\begin{figure}[t]
\includegraphics[width=1\linewidth]{figs/multi.png}
\caption{\textbf{Multiple Futures:} Since the future can be uncertain, our model
anticipates multiple possibilities.}
\label{fig:multi}
\end{figure}

\subsection{Multi-Modal Outputs}

Given an image, there can be multiple plausible futures, illustrated in
Figure \ref{fig:multi}. We wish to handle the multi-modal nature of this problem
for two reasons. Firstly, when there are multi-modal outputs, the optimal least
squares solution for regression is to produce the mean of the modes. This is
undesirable because the mean may either be unlikely or off the manifold of
representations. Secondly, reasoning about uncertain
outcomes can be important for some applications of future prediction.

We therefore extend deep regression networks to produce multiple outputs.
Suppose that there are $K$ possible output vectors for one input frame.  We can
support multiple outputs by training a mixture of $K$ networks, where
each mixture is trained to predict one of the modes in the future.
Given input $x_t^i$, network $k$ will produce one of the outputs $g_k(x_t^i)$.

\subsection{Learning}

To train multiple regression networks, we must address two challenges. Firstly, videos only show
one of the possible futures (videos like Figure \ref{fig:multi} are rare).
Secondly, we do not know to \emph{which} of the $K$ mixtures each frame belongs.
We overcome both problems by treating the mixture assignment for a
frame as latent.

Let $z_t^i \in \{1, \ldots, K\}$ be a latent variable indicating
this assignment for frame $t$ in video $i$.
We first initialize $z$ uniformly at random. Then, we alternate
between two steps. First, we solve for the network weights $w$ end-to-end using backpropagation
assuming $z$ is fixed:
\begin{align}
\omega^* = \argmin_\omega \sum_{i,t} \left|\left| g_{z_t^i}\left(x_t^i; \omega\right) - \phi\left(x_{t+\Delta}^i\right) \right|\right|_2^2
\end{align}
Then, we re-estimate $z$ using the new network weights:
\begin{align}
z_t^i = \argmin_{k \in \{1, \ldots, K\}} \left|\left| g_k\left(x_t^i; \omega\right) - \phi\left(x_{t+\Delta}^i\right) \right|\right|_2^2
\end{align}
We alternate between these two steps several times, a process
that typically takes two days. We learn $w$ with warm starting, and let it train
for a fixed number of iterations before updating $z$. We illustrate this
network in Figure \ref{fig:network}.

Although we train our network offline in our experiments, we note our network can be
also be trained online with streaming videos. Online learning is attractive because the network
can continuously learn how to anticipate the future without storing frames. Additionally, the model can adapt
in real time to the environment, which may be useful in some applications.

\subsection{Predicting Categories}

Since our network uses unlabeled videos to predict a
representation in the future, we need a way to attach semantic category labels to it. To do this, we use a relatively small set of labeled examples
from the target task to indicate the category of interest. As the representation that we predict is
the same that is used by state-of-the-art recognition systems, we can
apply standard recognition algorithms to the predicted representation in order
to forecast a category.

We explore two strategies for using recognition algorithms on the predicted
representations. The first strategy uses a visual classifier trained on the
standard features (we use \texttt{fc7}) from frames containing the category of interest, but applies
it on a predicted representation. The second strategy trains the
visual classifier on the predicted representations as well. The second strategy
has the advantage that it can adapt to structured errors in the regression.

During inference, our model will predict multiple representations of the
future. By applying category classifiers to each predicted representation, we
will obtain a distribution for how likely categories are to happen in
each future representation. We marginalize over these distributions to obtain
the most likely category in the future.

\subsection{Implementation}

Our network architecture consists of 5 convolutional layers followed by 5 fully
connected layers. We use ReLU nonlinear activations throughout the network. The
convolutional part follows the AlexNet architecture, and we refer readers to
\cite{krizhevsky2012imagenet} for complete details. After the convolutional
layers, we have 5 fully connected layers each with $4096$ hidden units.

The $K$ networks (for each output) can either be disjoint or share parameters between them.
In our experiments, we opted to use the following sharing strategy in order to reduce the number
of free parameters. For the five convolutional layers and first two hidden layers, we
tie them across each mixture. For the last three fully connected layers, we
interleave hidden units: we randomly commit each hidden unit to a
network with probability $p = \frac{1}{2}$, which controls the amount of
sharing between networks.  We do this assignment once, and do not
change it during learning.

We trained the networks jointly with stochastic gradient descent. We used a
Tesla K40 GPU and implemented the network in Caffe \cite{jia2014caffe}.
We modified the learning procedure to
handle latent variables.  We
initialized the first seven layers of the network with the Places-CNN network
weights \cite{zhoulearning}, and the remaining layers with Gaussian white noise
 and the biases to a constant. During
learning, we also used dropout \cite{srivastava2014dropout} with a dropout
ratio of $\frac{1}{2}$ on every fully connected layer.  We used a fixed
learning rate of $.001$ and momentum term of $0.9$.

\begin{figure}
\centering
\includegraphics[width=\linewidth]{figs/repo-long.png}
\caption{\textbf{Unlabeled Videos:} We collected more than 600 hours of unlabeled video from YouTube. We show a sample of the frames above. We use this data to train deep networks that predict visual representations in the future.}
\label{fig:repository}
\end{figure}

\section{Related Work}

The problem of predicting the future in images and videos has received growing
interest in the computer vision community, which our work builds upon:

\textbf{Prediction with Unlabeled Videos:}  Perhaps the ideas most similar to this paper are the ones that
capitalize on the wide availability of big video collections. In
early work, Yuen and Torralba \cite{yuen2010data} propose to predict motion in a single image by
transferring motion cues from visually similar videos in a large database. Building on the rich potential of large video collections,
Walker et al.\ \cite{walker2014patch} demonstrate a compelling data-driven approach that animates the
trajectory of objects from a single frame.
Ranzato et
al.\ \cite{ranzato2014video} and Srivastava et al.\ \cite{unsupervised} also
learn predictive models from large unlabeled video datasets to predict pixels
in the future.
In this paper, we also use large video collections. However, unlike previous work
that predicts low-level pixels or motions, we develop a system to predict
high-level concepts such as objects and actions by learning from unlabeled video.

\begin{figure*}
\centering
\includegraphics[width=0.8\linewidth]{figs/network.pdf}

\caption{\textbf{Network Diagram:} We visualize the network architecture we use
in our experiments. During training, the network uses videos to learn to
predict the representation of frames in the future. Since predicting the future
is a multi-modal problem, our network predicts $K$ future representations. Blue
layers are the same for each output while green layers are separate for the $K$
outputs.  During inference, we only input the current frame, and the network
estimates $K$ representations for the future. Please see section
\ref{sec:method} for full details.}

\label{fig:network}
\end{figure*}

\textbf{Predicting Actions:} There have been some promising works on
predicting future action categories. Lan et al. \cite{lan2014hierarchical}
propose a hierarchical representation to predict future actions in the wild.
Ryoo \cite{ryoo2011human} and Hoai and De la Torre \cite{hoai2014max} propose
models to predict actions in early stages.  Vu et al.\ in
\cite{vu2014predicting} learn scene affordance to predict what actions can
happen in a static scene.  Pei et al.\ \cite{pei2011parsing} and Xie et al.\
\cite{xie2013inferring} infer people's intention in performing actions which is
a good clue for predicting future actions. We are different from these
approaches because we use large-scale unlabeled data to predict a rich visual
representation in the future, and apply it towards anticipating both actions
and objects.

\textbf{Predicting Human Paths:} There have been several works
that predict the future by reasoning about scene semantics with encouraging success.
Kitani et al. \cite{kitani2012activity} use concept detectors to predict the
possible trajectories a person may take in surveillance applications.
Lezema et al.\
\cite{lezama2011track}, Gong et al.\ \cite{gong2011multi} and Kooij et al.\ \cite{kooij2014context} also predict the
possible future path for people in the scene. Koppula and Saxena \cite{koppula2013anticipating}
anticipate the action movements a person may take in a human robot interaction scenario using RGB-D sensors.
Our approach extends these efforts by predicting human actions and objects.

\textbf{Predicting Motions:}
One fundamental component of prediction is predicting short motions, and there have been
some investigations towards this.  Pickup et al.\ in \cite{arrowoftime}
implicitly model causality to understand what should happen before what in a
video. Fouhey and Zitnick \cite{fouhey2014predicting} learn from abstract scenes to predict what
objects may move together. Lampert \cite{lampert2015predicting} predicts the future state of a probability distribution, and applies it towards predicting classifiers
adapted to future domains. Pintea et al.\ \cite{pintea2014deja} predict the optical flow from
single images by predicting how pixels are going to move in future.
We are
hoping that our model learns to extrapolate these motions automatically in the visual representation, which is helpful if we want
to perform recognition in the future rather than rendering it in pixel space.

\textbf{Big Visual Data:}
We build upon work that leverages a large amount of visual data
readily available online. Torralba et al.\ \cite{torralba200880} use millions
of Internet images to build object and scene recognition systems.
Chen et al.\ \cite{chen2013neil} and Divvala et al.\ \cite{divvalalearning}
build object recognition systems that have access to common sense by mining
visual data from the web.  Doersch et al. \cite{doersch2012makes} use large
repositories of images from the web to tease apart visually distinctive
elements of places.
Kim and Xing \cite{kim2014reconstructing} learn to reconstruct story lines in personal photos,
and recommend future photos.
Zhou et al.\ \cite{zhoulearning} train convolutional neural
networks on a massive number of scene images to improve scene recognition
accuracy. In our work, we also propose to mine information from visual media on
the web, however we do it for videos with the goal of learning a model to
anticipate semantic concepts.

\textbf{Unsupervised Learning in Vision:} To handle large-scale data, there
have been some efforts to create unsupervised learning systems for vision.
Ramanan et al.\ \cite{ramanan2007leveraging} uses temporal relationships in
videos to build datasets of human faces. Ikizler-Cinbis et al.\
\cite{ikizler2009learning} propose to use images from the web to learn and
annotate actions in videos without supervision.  Le et al.\
\cite{le2013building} show that machines can learn to recognize both human and cat faces by watching an enormous amount of YouTube
videos. Chen and
Grauman \cite{chen2013watching} propose a method to discover new human actions
by only analyzing unlabeled videos, and Mobahi et al.\ \cite{mobahi2009deep}
similarly discover objects. This
paper also proposes to use unlabeled data, but we use unlabeled video to learn to predict visual representations.

\textbf{Representation Learning:} Recent work has explored how to learn visual
representations, for example with images \cite{doersch2015unsupervised} or
videos \cite{wang2015unsupervised}. Our work is different because we do not seek to
learn a visual representation. Rather, our goal is to anticipate the visual
representation in the future. Moreover, our approach is general, and in
principle could predict any representation.

redpajama_set_name: RedPajamaArXiv


It was December 2016 when Phorest Salon Software launched Phorest FM, The Salon Owners Podcast. Two years later, we're celebrating 100 episodes aired, 100 conversations dedicated to help you grow your business, by introducing you to a brand new show of ours: The Phorest Blog Podcast! With not one, but two salon podcasts, you're sure to really get all the marketing tips and tricks you need.
Honestly, although we had never come up with a proper written-down-black-on-white mission statement for the podcast, it had always been the essence of the show. The beauty of the show though, is the impacts it has had, and continues to have on salons and spas across the industry. And once again, this is mainly thanks to you! We, as in Phorest & then Killian and myself as co-hosts, might facilitate these discussions every week, but the community around the show and its desire to have these discussions is really what drives it all.
Co-hosts Killian Vigna & Zoé Bélisle-Springer at the post-Salon Owner Summit Inside Phorest event, 2019.
Thank you for taking us on this journey week after week, allowing us to learn from you guys – hopefully – as much as you learn from us. For this, we've got a new show for you to add to your salon podcasts list!
We tried launching this podcast back in December 2016, and for multiple reasons, decided to focus our efforts in growing Phorest FM, The Salon Owners Podcast first. However, we had never let go of the idea, so if you still have no time to read but enjoy the content from the Phorest Blog, then this podcast is for you. Since January 1st, 2019, we've been steadily releasing 4 episodes per week (Tuesday through Friday) to help you grow your business.
How is this show different to Phorest FM? Well, think about this one as an audio version of the Phorest Blog. Each episode is only about 5-7 minutes long and we release more per week. If you've ever heard of the Blinkist app, then this is quite similar in format (not in the form though, as we read out our articles contrarily to Blinkist, who do book recaps).
Not every article ever written has a recording yet, but we're slowly going back on all of our work to produce audio versions of every relevant post.


Garland Police spokesperson Joe Harn addresses the media during a news conference at the Garland Police Department, Monday, May 4, 2015, in Garland, Texas. Police shot and killed two men after they opened fire on a security officer outside the Curtis Caldwell Center in Garland, which was hosting provocative contest for Prophet Muhammad cartoons Sunday night, authorities said.
In a press conference on Monday, the police department in Garland, Texas said it was investigating whether two men responsible for a shooting outside of a conference featuring a prophet Muhammad cartoon-drawing contest had ties to terrorist groups.
"We are certainly looking into that," police spokesman Joe Harn said in a press conference, which The Hill reported on. "We have not knocked that out."
Two men were killed by police after they allegedly shot a security guard outside of the event, hosted by controversial group American Freedom Defense Initiative, considered an anti-Muslim group by the Southern Poverty Law Center.
Inspired by attacks on French satirical newspaper Charlie Hebdo, the event also hosted a speech by Dutch politician Geert Wilders, who has repeatedly criticized Islam, prompting some US lawmakers to attempt to bar him from entering the country.
Speculation about the shooting's link to terrorism comes after revelations that one of the alleged shooters, Elton Simpson, had possibly attempted to join a terrorist organization. In 2010, Simpson was convicted of lying to the FBI about his plans to travel to Somalia, but a judge ruled that it couldn't be proved that he was attempting to travel there to commit terrorism.
Shortly before the shooting, an account that has spoken in support of extremist groups like ISIS tweeted that there would be a terrorist attack in Texas, the Guardian reported.


text:
Q: Limitação de Session para guardar dados Tenho um query lenta no Oracle para listar todos os acessos de um usuário, então pensei em gravar o resultado em uma Session, a media de registros retornados é 600, é muito para uma Session?
Teria alguma outra forma melhor de se guardar esses dados?

A:
A média de registros retornados é 600, é muito para uma Session?

Não. Sessions em teoria não possuem limite de armazenamento.

Teria alguma outra forma melhor de se guardar esses dados?

Sim, em um servidor de presistência chave-valor, como o Redis. Sessions te limitam a apenas um servidor, enquanto que o Redis pode atendê-lo em um contexto distribuído.

A: Não tem uma questão de ser muito para a sessão, pode ser muito para a memória que você tem disponível. Se você tiver 600 registros na sessão, é pouco. Se se você tiver 1000 sessões simultâneas, 600 mil pode ser muito, pode não ser.
Eu respondi algo sobre isto, ali falo que a melhor solução é um sistema de cache. Ele é mais adequado na maioria das situações porque pode ser liberado se necessário e dura além das sessões, que em muitos cenários pode ser um ganho interessante. Lá eu falo para não exagerar na carga da session. Mas falo de exagerar mesmo. E o tamanho do exagero depende da memória disponível.
Lembre-se que o banco de dados tem um cache também (veja como implementar na resposta do OnoSendai). Pode ser que ele seja suficiente também. Mediu para ver se realmente precisa manter algo em memória? Tem ganho razoável? Sem medir não dá para afirmar nada. Mesmo que seja intuitivo, às vezes, na prática, pode ocorrer algo diferente do esperado.
Se perceber que precisa de um outro nível de cache, procure uma solução em memória no mesmo servidor. Isto deve resolver 99,99% dos casos. Se você tem um caso raro na mão e precisa escalar horizontalmente, procure um software que ajude nisto, tipo um Memcached, Redis, etc.
Há um framework que ajuda nisto e possui diversas opções. Na página deles confirma que o cache mais simples é o mais rápido e que bancos de dados servem mais quando há necessidade de distribuição entre processos (onde a performance é absurdamente inferior, ao ponto de ser pouco útil como cache em alguns cenários). O uso de dicionário também é o que ocupa menos memória, o que é útil para dar espaço para mais conteúdo útil.

A: Como Cigano e Maniero disseram, não, não há limites.
Porém se seu problema é performance em uma base de dados Oracle, eu tenho uma sugestão: Materialized Views.
Uma Materialized View é basicamente um snapshot de uma query cacheada no banco de dados, que pode ser indexada.
Este é um exemplo de criação de Materialized View:
CREATE MATERIALIZED VIEW MV_LOGINS_USUARIO_52
NOLOGGING
CACHE
BUILD IMMEDIATE
REFRESH FAST ON COMMIT
AS
[Sua query aqui]

Uma vez criada, a view conterá o resultado da última consulta até que seja destruída ou refrescada. Como você não está executando a query de novo, mas apenas acessando dados cacheados, o tempo de resposta é muito superior.
A vantagem deste método é que você não consome recursos do servidor web.

redpajama_set_name: RedPajamaStackExchange


WIAA state track and field: What we learned from the state championships in La Crosse.
LA CROSSE - What did we learn from this past weekend's WIAA state track and field meet?
A few things quickly jump to mind: The Kimberly boys track team solidifying its status as the top Division 1 boys program in the state, and Xavier's surprise runner-up finish in Division 2 girls competition.
There were also seven event champions over the two-day meet: Cody Gebhart of Chilton won the Division 2 discus with a throw of 181 feet, 5 inches on Friday and was joined by the Hilbert/Stockbridge girls 3,200-meter relay team of Veronica Thiel, Shania Schmitz, Megan Lau and Makaylee Kuhn, who won the Division 3 title with a time of 9:47.72.
Saturday's winners included a pair from Kimberly's Division 1 team champion: the 3,200 relay team of Will Demerath, Rowen Ellenberg, Alex Jansen and Jack Fitzgerald (7:54.72), and DJ Stewart in the 300 hurdles in 38.43 seconds.
Neenah's Colin Enz was tops in the D1 400 (:49.03), with two relay teams also striking gold: Xavier's D2 400 team of Bekah Vande Hey, Vanessa Laundrie, Kylie McCormick and Stephanie Sowin (:48.87), and the Hilbert/Stockbridge 1,600 relay team of Thiel, Schmitz, Lau and Kuhn (4:05.96).
All the above were featured in previous write-ups along with the area's second-place finishers.
But there were also many area third- and fourth-place finishers, led by St. Mary Catholic's 3,200 relay team of Joseph Porter, Grant Dejno, Tip Hogerty and Ethan Unruh finishing third in Division 3 with a time of 8:19.45.
Dejno, a senior, said he was happy ending his track career on the podium with Porter and Unruh, a pair of juniors, and Hogerty, a freshman.
Also placing third Saturday was Kimberly's 800 relay team of Alex Hammen, Kevin Lind, Austin Wallace and Stewart) with a time of 1:28.26.
Clintonville's Allysin Booth has a pretty good idea of what to shoot for entering her senior track campaign in 2019: first place in the 100 hurdles.
The junior placed third on Saturday (:15.21) but was left feeling like she could have placed higher. Brooke Livingston of Adams-Friendship was first (14.79).
Also finishing third on Saturday was Fox Valley Lutheran's Louisa Marxen in the 100 (:12.46), Freedom's D2 1,600 girls relay team of Callie Genke, Rayna Stordahl, Jaci Hinz and Sam Bartels (4:01.88), Kimberly's Wallace in the D1 300 hurdles (:39.1), Stewart in the D1 110 hurdles (:14.76) and Seymour's Erin Schuh in the D2 girls pole vault (10-9).
FVL's 800 relay team of Marxen, Reagan Duwe, Olivia Verbeke and Leyna Herzog-Hanke also finished third (1:45.46).
Seymour's Erin Schuh was nervous heading into Saturday's Division 2 pole vault competition.
The junior was making her first appearance at the state meet and was battling the jitters most athletes encounter at Veterans Memorial Stadium.
"It was my first time," she said. "I was really nervous this morning just thinking about the atmosphere and everything. I knew I had to pull out my best performance but really excited too. Being in big settings like this always hypes me up."
Schuh came through when it counted, finishing third in the competition with a leap of 10-9. New Berlin West's Dailey Albino won the event with a height of 11-6.
"I was really excited (getting 10-9), because I missed my first jump at 10-6 and I knew that I had to hit it hard and I did, so I was happy with that," Schuh said.
The third attempt during a long jump competition is usually where New London's Brayden Kurth is at his best.
That's exactly what happened Saturday during the Division 2 competition when Kurth recorded a leap of 21-3.25 to finish third behind champion Ben Dunkleberger of Lake Mills (21-9.5) and Freedom's Caden Hofacker (21-6).
"Once you start getting into it, it starts getting better," Kurth said about the event progression. "You start correcting your steps, knowing where your placement is on your foot, knowing whether you have to step up or step back. The third jump is usually the sweet one for me."
Kurth, a senior, said he was pleased to finish third after entering the meet seeded third. He said he had an idea in his head of how far he had to jump to get the job done.
"I knew coming in that if I wanted to get on the podium, I'd have to get 21 feet," Kurth said. "And that's exactly what I did. My senior year coming here, it was my goal to make it to state and I'm happy I placed. I'm excited."
Emily Scott has been to the WIAA state track and field meet the past four years. She capped her career with a third-place finish in the high jump (5-6) after winning the event her junior year.
"There was a lot of consistent jumpers at those high heights," she said. "I tried my best. I haven't been jumping the greatest lately, but I wanted to do my best and work hard and do what I can. I'm just grateful."
Scott, a Louisville volleyball recruit, added that a hip strain forced her to take three weeks off earlier in the season, but that she's aware of how rare it is for an athlete to reach the state meet four consecutive years.
"It feels great," she said. "I know a lot of girls don't get the chance to make it to state and they work their butts off and, for me, I feel real fortunate that I've been able to come here every single year."
It has been a tough last few weeks for Freedom sprinter Devin Baumgart.
The junior battled an ankle injury that prevented him from competing in some of his top events in the postseason and was all but resigned to watching the state meet as a spectator.
His right ankle healed just enough, however, to allow him to run in the finals of the D2 800 relay as he helped Irish teammates Gavin Hafemann, Bryce Vandenberg and Luke Pingel record a time of 1:30.77, good for fourth place.
"The last two weeks, thinking I'm done, and then a little time trials here and there to try and get it back and it started to feel better," Baumgart said. "I mean, it's still swollen and I still have bruising, but once I started feeling better it started becoming a mental game and went from there.
"(Friday night) we started talking about the 4x2 and the handoffs weren't what we wanted, so we switched it up a little and coach told me I'm running."
The entire 800 relay returns to compete next season and Baumgart said he's looking forward to that event as well as the team.
"We feel great. Everybody is coming back and next year we get a healthy Ty Brickner back. That's a blessing right there," he said. "So we're excited for next year."
Hortonville's Aliza Van Den Elzen was making her first trip to the state meet and ended up placing fourth in the D1 high jump.
Van Den Elzen tied with Cedarburg's Abby Belschner as both cleared 5-4. Both are juniors.
"My personal best was 5-4, so I tied my personal best, which is good," she said. "And I had a clean slate up until that jump. I had cleared all the other heights on the first try up to that point.
"It was great. There are so many good athletes here. We were down to 5-4 and there were 11 people here. It was crazy. Such a close competition."
Van Den Elzen said she improved by several inches throughout the season and that has her excited about what next year will bring.
"Coming into this year, my PR was 5 feet so I improved by four inches," she said. "That helped me build some confidence. And it helped me feel like if I put my mind to it, I can do anything. It kind of pushes me into next year to go out and have fun and perform to the best of my ability."
Other fourth-place finishers included Boyd Dietzen in the discus (175-1), Caden Hofacker in the D2 100 (:11.03) and Shiocton's Reis Stingle in the D3 200 (:23.63).
Triple jump: 5, Olivia Hermsen, Little Chute, 35-0.5.
100 dash: 6, Bekah Vande Hey, Xavier, :12.73.
800: 6, Jessica Kwasny, Xavier, 2:20.18.
100: 6. Aspen Linjer, Manawa, :12.79.
400 relay: 6, Appleton North (Sheldon Achs, Terrell Williams, Ethan Dercks, Carter Robinson), :42.88.
3,200: 5, Rowen Ellenberg, Kimberly, 9:17.89.
3,200 relay: 6, New London (Noah Schiedermyer, Riley Titterton, Jeff Smolinski, Max Krause), 8:12.12.
110 hurdles: 5, Jordan Hempel, Fox Valley Lutheran, :15.36.
100: 6, Pierce Blohowiak, Xavier, :11.17.
Mother Nature cooperates: The clouds looked ominous for most of the day on Saturday, but rains that were expected do douse the competition didn't arrive until just after the team championship trophies were handed out. Mother Nature, for once, showed mercy to the thousands of fans and hundreds of competitors on hand.
Lietzke proves hard work pays off: One of the better stories coming in was Appleton West's Lauren Lietzke. The senior thrower had worked extremely hard to improve in both the discus and shot after appearing in both events last season, but failing to reach the podium.
This season, she did finish third in the discus, setting a school mark with a throw of 135-1, almost nine feet farther than her sectional seed throw. She also finished 16th in the shot put (37-5) after being among the top contenders after the first few throws.
Record breakers: Rice Lake's Kenny Bednarek set records in the 100 dash (:10.42), 200 dash (:20.43) and the 400 (:46.73) in an incredible display of sprinting prowess. He accounted for 30 of Rice Lake's 53 total team points in those events alone as the Warriors won the D2 title.
Robby Hatch broke the D2 boys 300 hurdles mark with a 37.82 in prelims with Muskego's 800 relay team breaking the D1 mark with a time of 1:26.82.
Other girls records broken at the state meet included the 300 hurdles in D1 by Wausau West's Brooke Jaworski (:41.4); the 100 dash in D2 by Wittenberg-Birnamwood's Maddy Pietz (:11.98); the D2 long jump by Edgerton's Ashlyn Oren (19-1.25) and the 1,600 relay in D2 by Wittenberg-Birnamwood (3:54.19).