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Apple iOS 14 review
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Top 10 by fans


How does the physical layer interference work in a computer network?
I was told that when a network interface card (NIC) wants to send signal onto wire, it will low its voltage. And thus the signal will be imposed onto the wire.
NIC 1 lowers its voltage, and thus some electric potential difference will occur between itself and other NICs, and thus electric current will be intrigued. By detecting this current, signal can be detected.
If there are many other NICs connected to a shared wire, will the electric current be weakened?
Or do all the NICs only talks directly to a central device?
Could someone give some elaboration?
How much time have you got? Seriously, this is a major topic but also one that's the subject of many standards and so doesn't really rely on someone here using their specific knowledge to write up something that is well documented elsewhere.
if you want to start with some serious basics, look up the OSI 7-layer model that defines the various functions performed by two entities communicating across some form of network media, and then zoom in on the physical layer where all the data transmission and reception takes place - below are some references. I expect this is the best you'll get here unless someone takes a long time to document what you need OR the question is closed as it's not really answerable here. The last link on CSMA/CD may help you understand how one form of multiple media access works.
Here is an article from Rice University explaining how NICs work, specifically a 10 Gbps NIC.
But question is, if there're many other NICs connected to a shared wire, will the electric current be weakened?
I don't think there is or needs to be a lot of current: Ethernet works at radio frequencies.
But yes, I think it will be weakened. A "shared wire" might be the same thing as a "passive hub": I guess there's a limit to the number of ports in a shared hub (like there's a limit to the length of a cable).
So if you have many other NICs, you'd have an active hub or a router.
Search for information about pulse-amplitude modulation. This is modulation used in Ethernet.
This assumpton misses details which thus makes this statement incorrect, add a router/hub in the equation.
Careful with words like talk (refers to a conversation) in a discussion about signals.
Data gets modulated, that signal is propagated and gets demodulated by the endpoint and stored in a buffer.
Multiplexing techniques are applied when the buffered data on a port is not yet at it's destination.
Carrier Sensing Multiple Access is used when multiple endpoints are connected with each other.
LAN <--> LAN is most likely CSMA.
LAN <--> WAN is most likely CSMA in a Hub and multiplexed in a Router.
Current flowing in CAT5 cable is negligible I believe. Data on pairs are detected differentially when the voltages are flipped indicating a bit, eight of these in a row is a byte on the wire, not necessarily a byte of data though.
To try to answer your questions: 1) No the impedance of differential inputs are typically high so low current flow - although this implies low noise thresholds. I guess the physical layer makes some compromise here. The twisted pair carrier and the differential nature of the signal detection mechanism goes a long way of negating the high impedance, I would guess.
2) Yes, typically in modern LAN's - most packet switch networks as far as I know.
All NICs conform to a standard for interoperability purposes. They do transmit information via voltage trops, which are translated as individual bits (0s and 1s) of data. Because they all do use a standard, if more than two NICs share a wire, there is the potential for more than one transmission over the same cable pair at one time. This led to CSMA/CD, as Linker3000 stated.
A long time ago, computers used to be connected in a bus or ring topology - multiple NICs would share a line. These topologies had a lot of problems (read the links that Linker3000 posted, I'm sure they describe a lot of them). Today, individual NICs are connected to a central device, as you speculated - groups of NICs connect to a central switch, allowing each NIC to have a line to itself.
1.) All NICs (nowadays) in a modern network topology talk to a central device. 2.) Each NIC has its own set of cable pairs that is only used by itself and the switch port on the other side. 3.) Because of this, you don't need to worry about NICs connecting to a shared wire (unless you run into a network that was built before 1995).
Hope this helps a bit, but you're still gonna have to do a lot of reading if you're looking for your NEtwork+ or CCNA certification (which, I'm guessing, is the reason for your question). Good luck.
Not the answer you're looking for? Browse other questions tagged networking interference or ask your own question.
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Whether you are flying solo, partying with friends, or looking for a fun night out with your sweetheart, Hollywood Blvd Crawl club crawls will show you the best of LA nightlife, VIP style.
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Let's talk about MacGyver. No, I didn't have a crush on Richard Dean Anderson in my 20's. (The only celebrity crush I ever had was Harrison Ford as Indiana Jones–no judging.) I am talking about making STILL blog photos and the low-budget studio setups I sometimes have to create. Many of my photos require a certain amount of MacGyvering. I get a ridiculous amount of satisfaction from these mini-challenges. Sometimes I hang branches from the ceiling using fishing line, sometimes I darken the white tips of feathers with ash so they don't disappear on a white background, sometimes I create seamless white backgrounds with poster paper propped up against a stack of books. Today, when I placed this great horned owl feather on white paper all the barbule details got lost when I lightened the image to bright white. So I put the feather on my diffuser in front of a window, and there was a nice soft backlight, which kept all the feather details, but my lens picked up the fabric texture of the diffuser. If I lowered the depth of field to avoid picking up the background texture I lost many of the feather details again. So, in the end I MacGyvered the photo by attaching the feather to the window, and then placing the diffuser behind the subject, outside, so it was several inches away from the feather. Only then did I have enough light and enough depth of field to properly pick up all the delicate details in this image.
gorgeous! Thank you for putting in the time to make it all work. Beautiful precision.
Just discovered your beautiful site, love it! This one is a winner, my favorite so far!


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Early returns showed the measure passing, but the lead has narrowed in King County vote counts.
The Yes vote is currently ahead by just 43 votes — 2,544 to 2,501.
Supporters say the higher minimum wage will help workers. Opponents say it will hurt businesses.
The minimum wage in Washington is $9.19 an hour, highest of any state.
The count for the latest batch of ballots will be released at 4:30 p.m. Tuesday. More mail-in ballots may be counted up until election results are certified on Nov. 26.
Published: Nov. 12, 2013, 6:41 a.m.


text:
\section{Experiments}
\label{experiments}

In this section, we evaluate our \TAAD method along with \tfa modules on \multisports and \ucftwofour datasets. We start by defining the metrics used in \refsec{subsec:metrics}
and motion category
classification in \refsec{subsec:motion_classifcation}.
First, we study the impact of different \tfa modules under different motion conditions in \refsec{subsec:tfa}.
Secondly, we compare our \TAAD method with \sotalong methods in \refsec{subsec:comparisons}.
Later, we discuss baseline model and impact of tracker on it in \refsec{subsec:baseline_exp}.
We finish with a discussion in section~\refsec{subsec:discussion}.

\subsection{Metrics}
\label{subsec:metrics}

We report metrics that measure our detector's performance both
at frame- and video-level, computing frame and video \maplong (\map),
denoted as \fmap{} and \vmap{} respectively.
These metrics are common in action detection works~\cite{kalogeiton2017action,weinzaepfel2015learning,li2020actionsas}.
A detection is correct if and only if its \ioulong (\iou) with a ground-truth
box or tube, for frame and video metrics respectively, is larger than a given threshold (e.g. $0.5$) and
the predicted label matches the ground-truth one.
From this, we can compute
\aplong (\ap) for each class and then take mean across classes, to get the desired \map metric.
Tube overlap is measured by \spatiotemporal-\iou proposed by ~\cite{weinzaepfel2015learning},
similar to ~\cite{li2021multisports}, we evaluation code from ACT\footnote{\url{https://github.com/vkalogeiton/caffe/tree/act-detector}}.

\subsection{Motion categories}
\label{subsec:motion_classifcation}
We split action into three motion categories: large, medium and small.
Computing per-motion-category metrics requires labelling the ground-truth
action tubes. We start this process by computing the \iou
between a pair of boxes separated offsets equal to $[4,8,16,24,36]$ in sliding window fashion.
We average these 5 \iou values and get the final \iou value as a measure of speed.
We then split the dataset into three equal size bins, each containing instances of actions with relative speed difference from other.
Now we can assign the motion label to each instance based on IoU of instance and splits boundaries of the dataset:
\begin{equation}
    \text{\multisports} = \begin{cases}
        \text{Large} , & \quad \text{\iou} \in [0.00, 0.21] \\
        \text{Medium} , & \quad \text{\iou} \in [0.21, 0.51] \\
        \text{Small} , & \quad \text{\iou} \in [0.51, 1.00]
      \end{cases} \\
\end{equation}
\begin{equation}
  \text{\ucftwofour} = \begin{cases}
        \text{Large} , & \quad \text{\iou} \in [0.00, 0.49] \\
        \text{Medium} , & \quad \text{\iou} \in [0.49, 0.66] \\
        \text{Small} , & \quad \text{\iou} \in [0.66, 1.00]
      \end{cases}
\end{equation}
Given these labels,
we can compute \ap metrics per motion category.
There are two options for these metrics.
The first is to compute
\ap for large, medium and small motions per action class and then
average across actions.
We call this metric \emph{\motionmap}.
The alternative is to ignore action classes and compute \ap
for large, medium and small motions, irrespective of the action,
which we call \emph{\motionap}. This essentially measures action
detection accuracy \wrt to motion speed, irrespective of class.
We compute the metrics both on a per-frame and on a per-video level,
same as above. Video metrics are denoted with a \emph{video} prefix.
We will release the code training and testing our \taad network along with evaluation scripts for \motionap{} and \motionmap.

\input{tabtex/TAB_motion_speed}
\subsection{Motion-wise (main) results}
\label{subsec:tfa}

As the main objective set out for this work, we first study how cuboid-aware baseline compares against track-based \taad under significant motion.
We compare different choices for
temporal feature aggregation. In \cref{tab:splits}, we measure the frame- and video-motion-\map,
for models trained with
different \tfa{s}, on \multisports and \ucftwofour.
Pooling features across tracks, instead of neighbouring frames,
even with a relatively simple pooling strategy, \ie Max-Pool over the \spatiotemporal dimensions,
results in stronger action detectors, with a 5.7 \%
 and
5.8 \%
frame
and video
\map
boost on \multisports.
More involved feature aggregation
strategies, such as the temporal convolution blocks (\tcn) or \aspp variant, lead to further
gains. Note that the biggest improvements on \multisports occur in the large motion category,
+ 8.4 \%  \motionmap{}, with smaller gains in meduim (+3.9\%)
and small (+5.5 \%) motions.

\input{tabtex/TAB_motion_ap}
\cref{tab:motion_ap} contains \motionap{} results on \multisports
for different choices of \tfa modules.
It is clear that \taad combined with any of the \tfa modules leads to
large performance gain. %
Larger motions' performance benefits the most, followed by medium
and small motions.
For example, the \aspp module helps more with large motions
($+7.9$) than with small motions ($+4.5$).
We observe the same trend
in \cref{tab:splits},
both for frame and video \motionmap.

The above results signify that there is a large gap between the performance on action instance with large-motion and small-motion for baseline method.
\taad combined with any of \tfa module helps reduce this discrepancy and improves overall performance on both the datasets.

\input{tabtex/TAB_Multisports}
\input{tabtex/TAB_UCF101-24}
\subsection{Comparison to the State-of-the-art}
\label{subsec:comparisons}

We compare our proposed detector with the state-of-the-art on the \multisports and \ucftwofour with approaches that focus on both frame- and tube-level action detection, unlike approaches~\cite{pan2021actor,tang2020asynchronous} which solely focus on frame-level evaluation.
It is important to note that, similar to baseline, \taad does not make use of any spatial context modelling.
So that we can understand gains made using track-aware feature aggregation rather than gains made by mixing other \spatiotemporal context modelling modules~\cite{tuberZhao2022}.

We report frame- and video-\map
for different methods in \cref{tab:multisports}, namely \slowfast variants from the original \multisports paper,
Ning \etal's \cite{ning2021person}
Person-Context Cross-Attention Modelling network and our improved baseline,
and three versions of our model,
one with \maxpool along the temporal dimensions,
the \aspp variant and the temporal convolutional network (\tcn).
\cref{tab:multisports} contains  the results of this experiments, where
we clearly see the benefit of using tracks for action detection.
The addition of feature pooling along tracks,
even with the simpler \maxpool version, outperforms
our improved baseline by 4.3 \% frame \map. Better temporal fusion strategies,
\ie \aspp and \tcn, lead to further benefits.
As a result, we set a new \sotalong on \multisports dataset.
Note that all of our TFA modules add only less than 1G FLOPS ($<2\%$) to total computation of the whole network.

Finally, we compare our proposed \taad model on the older \ucftwofour dataset in \cref{tab:ucf24}.
Our model outperforms most of existing methods, with the exception of TuebR~\cite{tuberZhao2022} and MOC~\cite{li2020actionsas}.
We think the reason for this, is TubeR make use of set prediction framework~\cite{carion2020end} with a transformer head (plus 3 layer for each encoder- and decoder-transformer) on top a CNN backbone (CSN-152), additionally they also use actor context modelling similar to~\cite{pan2021actor}.
Also, I3D based TubeR need $132M$ FLOPS, which much higher than $97M$ needed by SlowFastR5-TCN based \taad.
In case of MOC, it uses flow stream as additional input and uses DLA-34\cite{yu2018deep} as backbone network.
Note that our goal here is to analyse and improve action detection
performance across different actor motion speeds. That is why we do not
make use of any spatial attention or context modelling~\cite{tuberZhao2022} between actors.
These are certainly very interesting topics and orthogonal to our proposed approach.
Nevertheless, in fair comparison to baseline, our network consistently shows improvement in all metrics on both datasets.

Additionally, the low quality of proposals on \ucftwofour given
by \yolovfive also hampers performance final performance.
We report the corresponding \yolovfive+DeepSort metrics in
\cref{tab:trackers_recall}.
Fine-tuning the detector on each dataset
is a necessary step,
especially on \ucftwofour where the video quality is worse than \multisports.

\subsection{Building a Strong Baseline on \multisports}
\label{subsec:baseline_exp}

\input{tabtex/TAB_baseline}
Here, we investigate the effect of our proposed changes on the performance
of the baseline action detector. \cref{tab:baseline_progression} contains the \fmap{@0.5} values,
computed on \multisports,
for our re-implementation of the \resnet \slowfast network, the addition of the background
negative frames, the conversion of the multi-label to a multiclass classification and finally
the addition of the \fpn. Each component improves the performance of the detector, leading to a much
stronger baseline.

\input{tabtex/TAB_detector_recall}
\qheading{Tracker as filtering module:} Using trackers as a post
processing step for action detection has many advantages,
which we demonstrate in all the above tables, including \cref{tab:ucf24},
where we get substantial improvement in \fmap{}, labeled as ``Baseline + tracks''.
First, the tracker helps filter out
false positive person detections with high scores that appear spuriously for few frames,
which reduces the load on person detection bounding box thresholding.
Most of current \sota methods use a relatively high threshold to filter out
unwanted false positives person detections,
e.g. \pyslowfast~\cite{fan2020pyslowfast}
uses $0.8$ and mmaction2~\cite{mmaction2} uses $0.9$.
Furthermore, strict thresholds can eliminate some crucial true positives.
In contrast to standard methods,
we use a relatively liberal ($0.05$) threshold value for our track-based method.
Secondly, using a good tracker greatly simplifies tube construction.
Trackers are specifically designed to solve the linking problem,
removing the need for greedy linking algorithms
used in previous work \cite{singh2017online, kalogeiton2017action,li2020actionsas}.
The performance gains, both in \vmap{} and \fmap{}, obtained
by ``Baseline + tracks'' rows of \cref{tab:multisports} and \cref{tab:ucf24}
over the ``Baseline'' row, clearly demonstrate this.

\subsection{Discussion}
\label{subsec:discussion}

In this work, our main objective is
to study action detection under large motion.
The experiments on \multisports and \ucftwofour,
see \cref{tab:splits,tab:motion_ap},
demonstrate that \taad, \ie utilizing track information for
feature aggregation, improves performance across the board.
Nevertheless, this does not mean that there is no room for improvement.
Our method is
sensitive to the performance of tracker, since this is the first step of our pipeline.
Using
a better state-of-the-art tracker and person detector,
such as the ones employed by other contemporary methods \cite{pan2021actor,tang2020asynchronous,ning2021person,kopuklu2019you}),
should further boost performance, especially on \ucftwofour,
where \yolovfive struggles.
Further, we can improve action detection performance by incorporating spatial/actor context modelling~\cite{tuberZhao2022,pan2021actor} or long-term temporal context~\cite{tang2020asynchronous} or transformer head~\cite{tuberZhao2022} or backbone~\cite{liu2022video,li2022mvitv2} with \taad.

\input{figtex/FIG_large_visuals_three}

One could argue that our definition of motion categories is not precise.
Unlike object size categories
in \mscoco~\cite{lin2014microsoft} however,
motion categories are not easy to define.
Apart from the complex camera motion, including zoom, translation and rotation, which
is pretty common, and quick actor motion, both of which we
show in \cref{fig:reasons},
special care has to be taken to avoid mislabelling cyclic motions.
\multisports for example contains multiple actions,
\eg in aerobics,
where the actor
starts and end at the same position, which would result in a high \iou
between the boxes and thus lead to an erroneous small motion label.
To solve this problem, we use an average of \iou{s} computed
at different frame offsets.
While our motion labelling scheme is not perfect, visual
examples show that it correlates with motion speed.
Lastly, we can see visuals in Fig. ~\ref{fig:large_motion_visual} where baseline fails to detect action tubes but \taad to able to detect them.
We will provide more qualitative examples
in the \mbox{\textbf{Sup.~Mat.}}\xspace to illustrate this point.

\section{Conclusion}
\label{sec:conclusion}

In this work, we analyse and identify three coarse motion categories
in action detection datasets. We observe
that existing action detection methods struggle in the presence of
large motions, \eg
motion due to fast actor movement or large camera motion,
To remedy this,
We introduce \taadlong (\taad), a method that utilizes
actor tracks to solve this problem. \taad aggregates information
across actor tracks, rather than using a tube made of cuboid from proposal boxes.
We evaluate the proposed method on two datasets,
\multisports and \ucftwofour. \multisports is the ideal benchmark
for this task, thanks to its large number of instances
with fast-paced actions. \taad not only bridges the performance
gap between motion categories, but also sets
a new state-of-the-art for \multisports by
beating last year's challenge winner by a large margin.

\section{Introduction}
\label{sec:intro}

\Spatiotemporal action detection, which classifies and localises actions in space and time,
is gaining attention, thanks to AVA~\cite{gu2018ava} and UCF24~\cite{soomro2012ucf101} datasets.
However, most of the current state-of-the-art works~\cite{li2020actionsas,singh2017online,feichtenhofer2019slowfast,pan2021actor,tuberZhao2022} focus on pushing action detection performance
usually by complex context modelling~\cite{tuberZhao2022,pan2021actor,tang2020asynchronous}, larger backbone networks~\cite{feichtenhofer2020x3d,li2022mvitv2,liu2022video}, or by incorporating optical flow~\cite{zhao2019dance,singh2017online} stream.
These above methods use cuboid-aware temporal pooling for %
feature aggregation.
In this work, we aim to study cuboid-aware action detection under varying degree of action instance motion using \multisports~\cite{li2021multisports} dataset which contains instances with large motions unlike AVA~\cite{gu2018ava} as shown in \cref{fig:dataset_movement_stats_cumulative}.

\input{figtex/FIG_cumulative_IoUs}

\input{figtex/FIG_reasons_for_large_motions}

\input{figtex/FIG_motion_types}

Large object motion can occur for various reasons, e.g., fast camera motion, fast action, body shape deformation due to pose change, or mixed camera and action motions. These reasons are depicted in \cref{fig:reasons}.
Furthermore, the speed of motions within an action class can vary because of a mixture of the above reasons and nature of action type, e.g., pose based or interaction based action.
Either of these reason can cause sub-optimal feature aggregation and lead to error in action classification of a given reason.

We propose to split actions into three categories: Large-motion, medium-motion, and small-motion,
as shown in \cref{fig:dataset_movement_stats_cumulative,fig:movement_types}.
The distinction is based on the \iou of boxes of the same actor over time,
which we can compute using the ground truth tubes of the actors.
We propose to study the performance on different motion categories
of a baseline cuboid-aware method, without other bells and whistles like context features~\cite{pan2021actor,ning2021person,tang2020asynchronous} or long-term features
\cite{wu2019long,tang2020asynchronous}, because large-motion happens quickly in a small time window,
as seen in \cref{fig:dataset_movement_stats_cumulative} and \ref{fig:reasons}.
In large-motion cases \iou would be small (\cref{fig:movement_types} (a)), as a result
a 3D cuboid-aware feature extractor will not be able to capture
the feature that is centred on the actor's location through out the action duration.
To handle the large-motion case, we propose to track the actor over time and extract features using \toialignlong{ }(\toialign); resulting in \taadlong (\taad).
Further, we study different types of feature aggregation modules on top of TOI-Aligned features for our proposed \taad network, showing \cref{fig:main_figure}.

To this end, we make the following contributions:
(a) we are the first to study the large-motion action detection systematically, using evaluation metrics for each type of motion, similar to object detection studies on \mscoco~\cite{lin2014microsoft} based on object sizes.
(b) we propose to use tube/track-aware feature aggregation modules to handle large motions, and we show that this type of module helps in achieving great improvements over the baseline, especially for instances with such large motion.
(c) in the process, we set a new state-of-the-art for the \multisports dataset by beating last year's challenge winner by a substantial margin.

\section{Related Work}
\label{sec:related_work}

Action recognition~\cite{carreira2017quo,wang2018nonlocal,feichtenhofer2019slowfast,weinzaepfel2021mimetics,feichtenhofer2020x3d,singh2019recurrent,liu2022video,li2022mvitv2} models provide strong video representation models. However, action recognition as a problem is not as rich as action detection, where local motion in the video needs to be understood more precisely. Thus, action detection is the more relevant problem to understand actions under large motion.

We are particularly interested in the \spatiotemporal~\emph{action detection}
problem \cite{Georgia-2015a,gu2018ava,girdhar2018better,wu2019long,tuberZhao2022},
where an action instance is defined as a set of linked bounding boxes over time, called action tube.
Recent advancements in online action detection~\cite{soomro2016predicting,singh2017online,behl2017incremental,kalogeiton2017action,li2020actionsas,yang2019step}
lead to performance levels very competitive with (generally more accurate) offline action detection methods~\cite{gu2018ava,wang2018nonlocal,saha2016deep,saha2017amtnet,peng2016eccv,zhao2019dance,singh2018tramnet,singh2018predicting,van2015apt} on \ucftwofour~\cite{soomro2012ucf101} dataset.

\ucftwofour has been a major benchmark for \spatiotemporal action detection (i.e. action tube detection), rather than \ava ~\cite{gu2018ava}. The former is well suited for action tube detection research, as it provides dense action tube annotations, where every frame of the untrimmed videos is annotated (unlike \ava~\cite{gu2018ava}, in which videos are only annotated at one frame per second).
More recently, Li~\etal~\cite{li2021multisports} proposed the \multisports dataset, which resolves two main problems with the \ucftwofour dataset.
Firstly, it has more fine-grained action classes.
Secondly, it has multiple actors performing multiple types of action
in the same video.
As a result, the \multisports dataset is comparable to \ava in terms of diversity and scale.
Moreover, the \multisports dataset is densely annotated, every frame at a rate of 25 frames per second,
which makes it ideal to understand action under large motion, as shown in Fig.~\ref{fig:dataset_movement_stats_cumulative}.

At the same time, there have been many interesting papers~\cite{feichtenhofer2019slowfast,feichtenhofer2020x3d,tang2020asynchronous,pan2021actor,chen2021watch} that focus on keyframe based action detection on \ava \cite{gu2018ava}.
\ava has been helpful in pushing action detection research on three fronts. Firstly, backbone model representations are much better now thanks to works like~\cite{feichtenhofer2019slowfast,feichtenhofer2020x3d,liu2022video,wang2018nonlocal,chen2021watch}.
Secondly, long-term feature banks (LBF) ~\cite{wu2019long} came to the fore~\cite{zhang2019structured,tang2020asynchronous,pan2021actor}, capturing some temporal context, but without temporal associations between actors.
Thirdly, interactions between actors and object have been studied~\cite{pan2021actor,zhang2019structured,tang2020asynchronous,ning2021person}.
Once again, the problem we want to study is action detection under large motion, which happens quickly at a small temporal scale. All the above methods use cuboid-aware pooling for local feature aggregation, which - as we will show - is not ideal when the motion is quick and large.
As a result, we borrow the \slowfast~\cite{feichtenhofer2019slowfast} network
as the baseline network for its simplicity and \spatiotemporal representational power.
Also, it has been used for \multisports~\cite{li2021multisports} as baseline
and in many other works on \ucftwofour as a basic building block.

The work of Weinzaepfel~\etal~\cite{weinzaepfel2015learning}
is the first to use tracking for action detection.
That said, their goal was different than ours.
They used a tracker to solve the linking
problem in the tube generation part,
where action classification was done on a frame-by-frame basis given the bounding box proposals from tracks. We, on the other hand,
propose action detection by pooling features from within
entire tracks.
Gabriellav2~\cite{dave2022gabriellav2} is another method that makes use of tracking to solve the problem of temporal detection of co-occurring activities, but it relies on background subtraction which would fail in challenging in-the wild videos.
Singh~\etal~\cite{singh2018tramnet},
Li~\etal~\cite{li2020actionsas} and Zhao~\etal~\cite{tuberZhao2022} are the only works generating flexible micro-tube proposals without the help of tracking. However, these approaches are limited to few number of frames (2-10),
and without the possibility to scale to larger time windows of 1-2 seconds as it would require multi-frame tube anchors/query to regress of box coordinates on large number of frames together, and performance drops after few frames.

\section{Methodology}
\label{sec:method}
\input{figtex/FIG_tracking_action_detector}

In this section, we describe the proposed method to handle actions
with large motions, which we call \taadlong (\taad).
We start by tracking actors in the video,
using a tracker described in Section~\ref{subsec:tracker}.
At the same time, we use a neural network
designed for video recognition,
\slowfast \cite{feichtenhofer2019slowfast},
to extract features from each clip.
Using the track boxes and video features, we pool per-frame features
with a \roialign operation \cite{he2017mask}.
Afterwards, a \tfalong (\tfa) module
receives the per-track features and
computes a single feature vector,
from which a classifier predicts the final action label.
\refFig{fig:main_figure} illustrates
each step of our proposed approach.

\subsection{Baseline Action Detector}
\label{subsec:backbone}

We select a \slowfast \cite{feichtenhofer2019slowfast} network
as our video backbone.
The first reason for this choice is that its performance
is still competitive to larger scale transformer models,
such as \videoswin \cite{liu2022video} or
\mvit \cite{fan2021multiscale,li2022mvitv2}, on the task of
\spatiotemporal action detection.
Furthermore, \slowfast is more computationally efficient
than the transformer alternatives, with a cost of
65.7 \gflops compared to 88, at least, and 170
for \videoswin \cite{liu2022video} and \mvit \cite{fan2021multiscale} respectively,
and offers features at two different temporal scales.
Having different temporal scales is important, especially since
we aim at handling fast and/or large motions, where a smaller
scale is necessary.
Finally, \slowfast is the default backbone network of choice for
\multisports and \ucftwofour datasets, which are the main benchmarks in this work, facilitating comparisons with existing work.

We implement our baseline using \pyslowfast\cite{fan2020pyslowfast} with a
\resnet~\cite{he2016deep} based \slowfast~\cite{feichtenhofer2019slowfast}
architecture, building upon the works of
Feichtenhofer \etal \cite{feichtenhofer2019slowfast}
and Li \etal \cite{li2021multisports}.
First, we add background frames (+bg-frames),
i.e. frames erroneously detected by our detector,
\yolovfive, as extra negative samples for training the action detector.
Next, we replace the
multi-label with a multiclass classifier, switching
from a binary cross entropy per class to a cross entropy loss (CE-loss).
Finally, we also added a downward \fpn block (see Sup.Mat. for details).
Through these changes, we aim to build strongest possible baseline.

\subsection{Tracker}
\label{subsec:tracker}

We employ class agnostic version of
\tracker~\cite{yolov5deepsort2020} as our tracker,
which is based on \yolovfive \cite{yolov5,redmon2016you} and
TorchReID\cite{torchreid}.
We fine-tune the medium size version of \yolovfive as the detection model for `person` classes.
A pretrained OsNet-x0-25~\cite{zhou2021osnet} is used as re-identification (ReID) model.
As we will show in the experiment section,
a tracker with high recall, \ie small number of missing associations,
is key for improving performance action tube detection.
We will also show that the fine-tuning the detector is a necessary step,
particularly for \ucftwofour, where the quality and resolution of the videos
is small.

Tracker can also be used as bounding box proposal filtering module.
Sometimes, the detector produces multiple high scoring detection which are spurious and lead to false positives but these detection does not match to any of the tracks being generated because they are not temporally consistent.
The proposals generated by tracks can be used with the baseline methods at test-time, as a result it helps improve the performance of the baseline method.

\subsection{Temporal Feature Aggregation}
\label{subsec:tpa}

\qheading{\toialignlong{ }(\toialign):} The \slowfast video backbone processes the input clip and produces
a $T \times H \times W$ feature tensor, while our tracker returns
an array with size $N_t \times T \times 4$ that contains the boxes
around the subjects. An \roialign \cite{he2017mask} takes these two arrays as input
and produces a feature array of size $N_t \times T \times H \times W$, \ie
one feature tube per track. In case the length of the track is smaller than length of the input clip then we replicate the last available bounding box in temporal direction, which occurs around $~3\%$ of input clips in \multisports dataset.

\qheading{Feature aggregation:} In order to predict the label of a bounding box in a key-frame,
we need to aggregate features across space and time, first we apply average pooling is spatial dimensions on features extracted by \toialign, then \tfalong role performed by
one of the following variants considered:

\setlist{itemsep=0pt}
\begin{enumerate}
    \item Max-pooling over the temporal axes (\maxpool).
    \item A sequence of temporal convolutions (\tcn).
    \item A temporal variant of Atrous Spatial Pyramid Pooling (\aspp) \cite{deeplabv3plus2018}. We modify \detectron{'s} \cite{wu2019detectron2} ASPP implementation, replacing 2D with 1D convolutions.
\end{enumerate}

We also tried temporal version of \convnext~\cite{liu2022convnet} and \videoswin~\cite{liu2022video} blocks, however these resulted in unstable training,
even with the tunning of learning rates and other hyperparameters. In our experiments, we only used one layer of temporal convolution for our \tcn module, adding more layer did not help. See the \mbox{\textbf{Sup.~Mat.}}\xspace for more details.

\subsection{Tube Construction}
\label{subsec:tube_con}

Video-level tube detection requires the construction of action tubes from per-frame detections.
This process is split into two steps~\cite{saha2016deep}, one to link the proposal to form tube hypothesis (i.e. action-tracks), second, these hypothesis need to be trimmed to the part where action is present in these tracks.
One can think of these two steps as tracking step plus temporal (start and end time) action detection step.
The majority of the existing action tube detection methods \cite{li2021multisports,singh2022road,li2020actionsas,singh2018tramnet}
use a greedy proposal linking algorithm first proposed by in~\cite{singh2017online,kalogeiton2017action} for the first step. %
For baseline approach,  we use the same method for tube linking process from~\cite{singh2017online}.
Whereas for our method (\taad), we already have tracks, as a result, the linking step is already complete.
While the temporal trimming of action-tracks is performed using label-smoothing optimisation~\cite{saha2016deep}, which is used by many previous works~\cite{kalogeiton2017action,li2020actionsas}, specifically, we use class-wise temporal trimming implementation provided by~\cite{singh2017online}.

\subsection{Datasets}
We evaluate our idea on two densely annotated datasets (\multisports~\cite{li2021multisports} and \ucftwofour \cite{soomro2012ucf101}) with frame- and tube-level evaluation metrics for actions detection, unlike \ava \cite{gu2018ava}, which is sparsely annotated and mostly used for frame-level action detection.

\qheading{\multisports~\cite{li2021multisports}}
is built using 4 sports categories, collecting $3200$ video clips annotated at 25 \fps,
and annotating 37701 action-tubes instances with $902$k bounding boxes.
Although it contains $66$ action classes, we follow the official evaluation
protocol~\footnote{\url{https://github.com/MCG-NJU/MultiSports/}} that uses $60$ classes.
Due the fine granularity of the action labels the length of each action segment
in a clip is short, with an average
tube length of $24$ frames, equal to one second,
while the average video length is $750$ frames.
Each video is annotated with multiple instances of multiple action classes,
with well defined temporal boundaries.
\multisports contains action instances with large motions around actors, as shown in \reffig{fig:dataset_movement_stats_cumulative}

\qheading{\ucftwofour \cite{soomro2012ucf101}}
consists of $3207$ videos annotated at $25$ \fps with $24$ action classes from different sports,
$4458$ action-tube instances with $560$K bounding boxes.
Videos are untrimmed for in nature,
where average video length
is $170$ frames and the average action tube length is $120$ frames.
The disadvantages of \ucftwofour are the presence
of only one action class per video and the low quality of its images,
due to compression and the small resolution, namely $320 \times 240$ pixels.
Even though \ucftwofour has less diversity,
less motion, less classes and more labelling noise compared to \multisports,
it is still useful for evaluating action detection performance,
thanks to its temporally dense annotation.

\subsection{Implementation Details}
\label{subsec:training_hparams}
We use 32 frames as input with sampling rate of 2, which means more 2 seconds of video clip.
We use Slowfast-R50-$\tiny{8\times8}$~ \cite{feichtenhofer2019slowfast}, meaning  speed ratio $\alpha=8$ and channel ratio $\beta=1/8$.
We use stochastic gradient descent (SGD) to optimise the weights, with a learning rate of
$0.05$ and batch size of $32$ on $4$ GPUs.
We use 1 epoch to warm up the learning rate linearly,
followed by a cosine learning rate schedule \cite{cosine_lr}, with an final learning rate
of $0.0005$, for a total of 5 epochs.
Note that we only train for $3$ epochs on \ucftwofour.
All our networks are trained with a batch size equal to $32$ on $4$ Titan X GPUs.
We use frame-level proposal released by \cite{li2021multisports} with \multisports dataset for fair comparison. More details can be found in \mbox{\textbf{Sup.~Mat.}}\xspace.

\section{Overview}
\label{experiments}

Here, we aim to provide additional details about the certain parts of main paper.
First, we show architecture of backbone network, where feature pyramid network structure (FPN) incorporated into  \slowfast~\cite{feichtenhofer2019slowfast} network in figure~\reffig{fig:backbone}.
Second, we show the structure of \tfa modules used in our \taad model in \refsec{sec:tfa_structure}.
Then, we present frame-level \motionmap and \motionap on individual time scales in \refsec{sec:indiv_scale}
Finally, we show visual results of detected action-tube instances under different motion types in \refsec{subsec:visuals}.

\input{figtex/FIG_slowfast_backbone}
\input{text_supp_mat/01_tfa}

\section{Individual time scales results}
\label{sec:indiv_scale}
Frame-level \motionmap and \motionap{} on individual time scales are shown in
following tables:
\begin{enumerate}[label=(\arabic*)]
    \item \motionap{} on \multisports in \reftab{tab:motion_ap_ms}
    \item \motionap{} on \ucftwofour in \reftab{tab:motion_ap_ucf24}
    \item \motionmap{} on \multisports in \reftab{tab:motion_map_ms}
    \item \motionmap{} on \ucftwofour in \reftab{tab:motion_map_ucf24}
\end{enumerate}

\input{tabtex/TAB_supp_motionAP_indivScaleMS}
\input{tabtex/TAB_supp_motionAP_indivScaleUCF24}
\input{tabtex/TAB_supp_motion-mAP_indivScaleUCF24}
\input{tabtex/TAB_supp_motion-mAP_indivScaleMS}

The main take-away from these tables is that results are consistent across different time scales
compared to the average over all time scales.
Through the \motionap{} metric,
we show that
that large-motion action instances are harder to detect compared to medium motions,
which in turn are even harder to detect compared to small-motion action instances, or in other
words performance of large-motion $<$ medium-motion $<$ small-motion.
This result is consistent across both our benchmarks, \ie
\multisports and \ucftwofour.
Such pattern is desirable and intuitive to understand, it is missing in \motionmap{} because some class might not have any (or very few) ground-truth instances in one or two motion type categories resulting very small value of \map or zero \map, since medium is middle motion category it has more classes with some instances with medium motion class, hence fewer classes with zero \motionap{} resulting in higher mean-AP i.e. \motionmap{} for medium motion type.

\section{Motion-wise visual results}
\label{subsec:visuals}
In this section we show visual results obtained using our baseline and \taad model.
We discuss some interesting observation in the caption of figures. The figures are
best viewed in colour. The qualitative results contain the following scenarios:
\begin{enumerate}[label=(\arabic*)]
    \item \lmotion due to fast execution of actions in \reffig{fig:large_motion_action}.
    \item \lmotion due to fast camera motion in \reffig{fig:large_motion_camera_plus}.
    \item \mmotion action instances in \reffig{fig:medium_motion}.
    \item \smotion action instance in \reffig{fig:small_motion}.
    \item An action instance where \taad fails due to tracking error shown in \reffig{fig:tracking_error}.
\end{enumerate}

In all the captions,
``Overlap'' denotes the spatiotemporal overlap of the detected tube with the ground-truth tube,
as defined by Weinzaepfel \etal \cite{weinzaepfel2015learning}.
Ground-truth boxes and frames (dot at the bottom of the frame) are shown in green colour,
while the detected track is shown in red colour.
We use ``baseline+tracks'' in these figure as ``baseline'' method.
Since all the methods use same set of tracks, red box is used to annotate track boxes.
Each method's score has a separate colour, described in the sub-caption.

\input{figtex_supp_mat/FIG_large_action_motion}
\input{figtex_supp_mat/FIG_large_camera_motion_plus}
\input{figtex_supp_mat/FIG_medium_motion}
\input{figtex_supp_mat/FIG_small_motion}
\input{figtex_supp_mat/Fig_tracking_error}
\section{Structure of \tfalong Modules}
\label{sec:tfa_structure}

\refListing{lst:maxpool}, ~\ref{lst:tcn},
and ~\ref{lst:aspp} contain the \mbox{PyTorch}\xspace implementation
of our MaxPool, \tcn and \aspp \tfa modules,
used with our \taadlong (\taad)
method.
Similar to 1D-\aspp module (see Listing-Fig.~\ref{lst:aspp}),
In addition to these blocks,
we tried
1D-ConvNeXt~\cite{liu2022convnet} and 1D-\swin~\cite{liu2022video} blocks as well,
but observed that training was unstable and that the final performance was worse.
For example,
1D-ConvNext could only reach up to 44\% \fmap{} compared to 53.3\% using MaxPool module (Listing-Fig.~\ref{lst:maxpool}).
In all the listings that follow, $C$ is the number of channels and $T$ is the number
of input frames.

\begin{figure*}
\begin{lstlisting}[caption={MaxPool module with input feature of size $T \times C$ and output of $1 \times C$.},captionpos=b,label={lst:maxpool}]

    {
    (tube_temporal_pool): AdaptiveMaxPool1d(output_size=1)
    }
\end{lstlisting}
\end{figure*}

\begin{figure*}
\begin{lstlisting}[caption={TCN module with input feature of size $T \times C$ with output of $1 \times C$.},captionpos=b,label={lst:tcn}]

    {
    (TCN): Conv1d(576, 576, kernel_size=3,
            stride=1, padding=2, dilation=2)
    (tube_temporal_pool): AdaptiveMaxPool1d(output_size=1)
    }

\end{lstlisting}
\end{figure*}

\begin{figure*}
\begin{lstlisting}[caption={1D-ASPP~\cite{deeplabv3plus2018} module with input feature of size $T \times C$ with output of $1 \times C$.}, captionpos=b, label={lst:aspp}]

    {
    (ASPP): ASPP1D(
        (convs): ModuleList(
          (0): Conv1d(576, 256, kernel_size=1,
                      stride=1)
          (1): Sequential(
            (0): Conv1d(256, 576, kernel_size=1,
                        stride=1)
            (1): ReLU()
          )
          2: ASPPConv1D(
            (0): Conv1d(256, 576, kernel_size=3,
                stride=1, padding=1)
            (1): ReLU()
          )
          (3): ASPPConv1D(
            (0): Conv1d(256, 576, kernel_size=3,
                stride=1, padding=3, dilation=3)
            (1): ReLU()
          )
          (4): ASPPConv1D(
            (0): Conv1d(256, 576, kernel_size=3,
                stride=1, padding=(5), dilation=5)
            (1): ReLU()
          )
          (5): ASPPPooling1D(
            (0): AdaptiveAvgPool1d(output_size=1)
            (1): Conv1d(256, 576, kernel_size=1,
                stride=1)
            2: ReLU()
          )
        )
        (project): Sequential(
          (0): Conv1d(2880, 576, kernel_size=1,
                        stride=1, bias=False)
          (1): ReLU()
          )
    )

    (tube_temporal_pool): AdaptiveMaxPool1d(output_size=1)

    }

\end{lstlisting}
\end{figure*}

redpajama_set_name: RedPajamaArXiv


After enquiries with Sedbergh Post office and the Royal Mail, I can confirm that is was the Royal Mail contractors that have painted the Post Box at the bottom of Deepdale Lane, Dent black and removed the door. This is to ensure that people know the mail box is disused, apparently it was just a sheet metal door not a cast iron door so this is was why it was removed.
I recall for the 2012 Olympics, the Royal Mail painted Post Boxes gold if you won a medal - I thought they may be painting this one gold for my arrival but clearly not!
Many thanks to the residents that got in touch with me over this.


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7 CFR 14.7 - Non-Federal programs and payments. § 14.7 - Non-Federal programs and payments.
§ 14.7 - Non-Federal programs and payments.
(a) Definition of non-Federal programs. Non-Federal program means any program of a State, a possession of the United States, a political subdivision of any State or possession of the United States, the District of Columbia, or a combination of any of the foregoing.
(b) Applicability. Payments received through non-Federal programs under which payments are made primarily for the purpose of conserving soil and water resources, protecting or restoring the environment, improving forests, or providing a habitat for wildlife may be considered for exclusion from gross income under part 14.
(c) Determining the primary purpose of non-Federal payments. The determination of the primary purpose for which non-Federal payments are made with respect to their potential for exclusion from gross income shall be made by using the criteria set forth in part 14 for determining the primary purpose of Federal payments.


Trainers, Deoxys raids are live! You can read all about Deoxys counters in our Deoxys Counters Guide (EX Raid, Normal Form) and all about Deoxys in the meta in our Deoxys, Deoxys forms and their place in the Pokemon GO meta article, but right now we just want to share a few incredible videos of two Ultra friends beating the hell out of Deoxys. Yes, Deoxys duo raids are super easy and super popular right now!
A pair of Team Instinct players have managed to easy duo a Deoxys EX raid without even abusing the "empty party slot" glitch. Stacked with Bite Crunch Tyranitars, Scizors, Gengars and Mewtwo's, Th6EvilDead and BigSheesh managed to defeat Deoxys with around 80 seconds left on the timer. After winning the battle both Trainers received 16 Premier Balls as a reward. The capture encounter is very similar to Mewtwo and it's fairly easy to land an Excellent throw.
Two Team Mystic treainers, DrStefan87 and DARPanda, abused the empty party trick to get a few more seconds and went in with a team of high level Bite Crunch Tyranitars, Ultra friends boost and under the influence of Fog weather. They managed to complete the raid with 100 seconds left on the timer.
We are still waiting for a video of a weather boosted Deoxys duo raid (under Windy weather), but we're hoping someone can actually do it. Let's go!


Accountancy Practice recruitment specialists Clark Wood are currently working with an established and well-known firm in the Woking area who are seeking to recruit a Bookkeeper to join their team.
This well-known practice is seeking a candidate who will have strong experience of undertaking bookkeeping work and preparation of VAT returns & management accounts within a practice environment.
You will have gained a solid foundation in double entry bookkeeping and keen on pursuing a career in this area. The role offers the chance to work with a great team and manage your own portfolio of clients. There will be lots of client contact so you will need strong communication skills.


text:
The video for this story is not available, but you can still read the transcript below.
Positive April Job Growth Tempered by Market Uncertainty in Europe
May 7, 2010 12:00 AM EST
https://www.pbs.org/newshour/show/positive-april-job-growth-tempered-by-market-uncertainty-in-europe
Employers added 290,000 jobs last month, marking the biggest hiring increase in four years. Jeffrey Brown talks to two financial experts about the pace of the economic recovery and gets an update from Susie Gharib of PBS's Nightly Business Report about the brief but deep stock market plunge on Thursday afternoon.
JEFFREY BROWN:
U.S. employers revved up hiring last month, even as more people entered the workforce, causing the overall unemployment number to rise.
In the meantime, Wall Street continued its losses. The Dow Jones Industrial Average lost nearly 140 points to close at 10,380. The Nasdaq fell 54 points to close at 2,265.
That came amid uncertainty over the situation in Greece and questions over what led to yesterday's sudden 1,000-point plunge.
NewsHour correspondent Kwame Holman begins our coverage.
KWAME HOLMAN:
Payrolls expanded by 290,000 in April, the most in four years. Of that, private employers added 231,000 new jobs, with widespread gains across a number of industries, including manufacturing, construction, retail, education, and health services. Another 66,000 temporary government workers were hired to help conduct the Census.
Despite the job growth, the unemployment rate still rose .2 of a point to 9.9 percent, as more than 800,000 jobless Americans resumed their search for work.
At the White House, President Obama called today's report very encouraging news.
U.S. PRESIDENT BARACK OBAMA:
Yes, we've got a ways to go, but we've also come a very long way. And we can see that the difficult and, at times, unpopular steps that we've taken over the past year are making a difference.
On Capitol Hill, reaction to the jobs report from members of the Joint Economic Committee was mixed, with Republicans contending Democrats had not done enough to create jobs.
Kevin Brady represents Texas' Eighth District.
REP. KEVIN BRADY, R-Texas:
A painfully slow recovery is better than no recovery. But for the 15.3 million Americans who are out of work and waiting for Washington Democrats to finally focus on jobs, this report is disheartening.
The jobs numbers also did not stop the jitters on Wall Street a day after one of the most volatile trading sessions in history. A computerized sell-off midday yesterday sent the Dow Jones Industrial Average plummeting nearly 1,000 points in about 30 minutes.
MAN:
It's like "Terminator," machines taking over.
Some reports indicate the activity might have been caused by a simple typographical error.
Meanwhile, some members of Congress now are pressing government regulators to ensure that high-technology trading is well-monitored.
Mr. Obama said yesterday's events are being investigated.
BARACK OBAMA:
The regulatory authorities are evaluating this closely, with a concern for protecting investors and preventing this from happening again. And they will make findings of their review public, along with recommendations for appropriate action.
Even before the sharp drop in the market, stocks had been lower. The main cause was investors' concerns the $140 billion bailout for Greece by the European Union and International Monetary Fund might not be enough to stop the debt crisis from spreading throughout Europe.
Today, G-7 finance ministers were teleconferencing on the crisis, while the German parliament approved its share of the rescue package.
Chancellor Angela Merkel spoke before travelling to Brussels for a meeting of the EU.
ANGELA MERKEL, German chancellor (through translator): This is a very important decision that makes clear that we are protecting the currency in the interests of our citizens. But it can only be effective in combination with the ambitious Greek austerity plan which was passed in the Greek parliament yesterday.
President Obama said he spoke with Merkel this morning about the Greece crisis and made clear the U.S. supports the rescue efforts. But even with the support of the international community for Greece, anxiety over the country's debt issues rattled markets in Europe and Asia.
And we turn next to two economy watchers: Mark Zandi, chief economist at Moody's Economy.com and Mohamed El-Erian, CEO and co-chief investment officer at PIMCO, a global investment management firm.
Mark Zandi, starting with you, what do the jobs number tell you?
MARK ZANDI, chief economist, MoodysEconomy.com: A great report. A lot of jobs, very broad-based, across lots of different industries.
You know, we have been in recovery for six to nine months, but the missing link was job growth. And I think we found it today. A very positive report indicates that the economy should do much better going forward.
And you see it, you said, widespread among key sectors.
Fill that in a little bit.
MARK ZANDI:
Yes, it was very broad-based. It was manufacturing, it was construction. And, of course, those are two sectors that have been losing lots of job during the recession.
It was leisure and hospitality, it was retailing, it was parts of financial services. It was, of course, the federal government because of the Census hiring. Almost every sector added a bit during the month, and that is a very good sign.
And Mohamed El-Erian, what do you see in the jobs market.
MOHAMED EL-ERIAN, COO and chief investment officer, PIMCO: Same as Mark, a very encouraging report. We're finally seeing all these cyclical tailwinds translate into job creation.
And the question is, can this continue given what else is going on in the global economy? But on a standalone basis, today's report was strong not only in terms of the headline number, but, also, there were past revisions that created even more jobs over the last few months.
And just to explain, Mr. El-Erian, why the unemployment number went up, more people are coming back to the workforce. In theory, that's a positive, right?
MOHAMED EL-ERIAN:
Correct. As people see their neighbors getting jobs, they are encouraged to start looking for jobs again. So, ironically, you get both job creation, but you also get more people looking for jobs, so the unemployment rate goes up. In this case, it went up to 9.9 percent.
So the question, Mark Zandi, is the pace, the rate of growth of jobs. Can it sustain these people coming in and the number of jobs that we need to create to get the economy really moving?
Right. I think we need approximately 150,000 jobs each and every month just to stabilize unemployment. So, to bring unemployment down in a meaningful way, we would need job growth of 200,000, 250,000, 300,000.
Now, we got that in the month of April. We got a similar kind of job growth in March. We now need to see that over the next six, 12, 24 months. We need consistent job growth.
Then, the preconditions for that are in place. Businesses are doing much better. Corporate profits are up. Their balance sheets are in better shape.
So, they are — it's no longer a question of, are they able to hire?
It's really a question of, are they willing?
Well, Well, Mr. El-Erian, you just raised that big uncertainty about what's happening around the globe, particularly starting in Greece. Spin that out for us a bit. I mean, how worried are you, and how does that then play into the positive signs for the economy we've just been talking about?
It is worrying.
This week, Greece went from being a country problem to being a problem for Europe as a whole. And it started morphing into a global problem.
So, if you are in the United States, there are different implications.
First and foremost, unfortunately, things are going to slow down in Europe as a whole. This is a deflationary shock. It's going to reduce activity. It's going to reduce demand. And therefore, it's going to reduce the ability of Europe to buy exports from the United States.
Secondly, what's happening in Greece, which is fundamentally a solvency issue, people are worried whether Greece will pay back its debt.
Despite the $140 billion package that was referred to earlier, the market is still worried about solvency. It's worried about Greece's ability to pay off its debt over time.
Because of that, there has been an increased risk aversion. People are getting nervous about, what's next? And in particular, what happens to the banks that are exposed to Greece?
Now, as you know, banks are like the oil in a car. They connect things. If the oil in your car does not work, the car doesn't go forward. And what we started seeing this week is that the European banks are getting under pressure. So that tended to disrupt sentiment, and that is why we have seen sell-off in markets.
There is one silver lining in all of this, which is capital that would have otherwise gone to Europe may well be diverted into the United States. And that's why our interest rates have been coming down this week.
You mean the United States has a safe haven amidst all the problems elsewhere?
Yes. My former colleague says sometimes you want to wear your cleaner shirt, but there are times where you're going to have to settle for your cleanest dirty shirt. There are times when you live in a relative world. And relative to Europe today, the United States looks attractive to foreign investors.
Well, so, Mark Zandi, how does all of that temper your more positive view, especially with these job numbers for the U.S.
Yes. Right.
Well, if the Greek debt crisis continues on, if it's not quelled by policymakers, then it would change my view. I mean, I think the Greek situation, if unchecked, will undermine our stock market. And the stock market has been key to strong economic growth here. It's the reason why consumers are outspending. It's why businesses are starting to hire again.
So, it's very, very important for policymakers, the European Central Bank, the European Union, the IMF, to really engage here and quell the crisis. And they have the tools to do it.
I mean, the ECB can provide more liquidity to the banking system.
Mohamed mentioned troubles the banks are having. They can help with that. They can also lower interest rates if the European economy does, in fact, begin to struggle.
And the IMF, they put forward $140 billion, but clearly that's not enough. They can increase the size of package and get creative about how they provide the aid. So this is really a policy decision on their part, and I think they need to put an end to this so that it doesn't become a larger global economic problem.
And Mr. El-Erian, we talked about this, actually, last night, with Paul Krugman and Robert Barbara (ph). I wanted to ask you the same question.
To what extent do the problems that you see in Greece, and perhaps spreading elsewhere in Europe, does that raise questions about our own long-term debt problems here? Is that an issue here, or is it really the more immediate, as you were laying out before, the impact on our exports and the connections there?
It's both. In the short term, it's a contamination from Europe. But over the longer term, what is happening in Greece tells you three things that have to be taken very seriously in our country.
First, deficits and debt matter. You can't simply ignore them year after year.
Secondly, it's important to have flexibility in the budgetary system to respond. One of the problems that Greece has is, even if it's willing to adjust, it's finding itself not able to adjust. And that's because the situation has gotten so bad, that there is limited flexibility.
And the third lesson that Greece tells us all is things can change very quickly. Who would have thought that within a few weeks, not only would there be doubts about whether Greece could make its debt payments, but that there would be doubts about what the eurozone would look like in six months to a year? So things can change very quickly, and it's important that we don't waste time to get our act together on the public finances.
And Mark Zandi, just very briefly, so do you expect more market volatility based on that uncertainty?
Yes, because to get out of the financial panic and great recession, every country had to engage in massive fiscal stimulus. I think it was absolutely necessary that they did that, because we would still likely be in recession if they hadn't. But the downside is now every country, including our own, has a very large deficit, a very large debt load. And each country is going to face this problem unless they make the hard choices and clean up their fiscal situation.
All right. Mark Zandi and Mohamed El-Erian, thank you both very much.
And now, what did happen yesterday to briefly cause the stock market to plunge 1,000 points? And could it happen again?
For that, we're joined once again by Susie Gharib, anchor of PBS's "Nightly Business Report."
Susie, thanks for joining us again.
What more is known today about what happened yesterday?
SUSIE GHARIB:
You know, it's hard to believe, but they're still trying to figure out what happened. It turns out that decoding what happened yesterday is not so easy. Very complex systems at work here.
So, officials here at the New York Stock Exchange, as well as the Nasdaq and regulators, are investigating. Here's what we know tonight.
At first, it was thought it was human error. Now it turns out that's too simple an explanation.
The focus now is on computerized trading. It appears that some dramatic moves in the currency markets yesterday may have triggered computerized program selling in the stock markets. And once that happened, the stock transactions were like a domino effect. Anything that was available to sell was sold.
Also today, a lot of finger-pointing going on between the New York Stock Exchange and the Nasdaq, the blaming, who was really at fault here. The Chicago Mercantile Exchange said everything was properly functioning at our exchange. So, ,the exact origin of what went wrong, still, it's going to probably take a couple of weeks until we know when these investigations are all wrapped up.
But there is still the how in the world — you know, we all remember this happening in the past when there were some really huge drops. And then, my understanding was they put into place what they call circuit breakers so that there would be a kind of cooling-off period before things really got out of hand.
Now, does this suggest the circuit breakers aren't there or they don't work? Do you think people are getting around it? What's going on?
No, the circuit breakers exist, but there are certain situations that have to happen for a circuit breaker to kick in. At the New York Stock Exchange, for example, the Dow has to drop something like 10 percent for the circuit breakers to kick in. But after 2:30 p.m., the Dow has to drop something like 20 percent. Well, all of this commotion happened after 2:30, and the Dow did not hit that 20 percent threshold.
The other thing is, there are circuit breakers for individual stocks.
And that did happen yesterday, because Procter & Gamble, for example, dropped 10 percent. And a circuit breaker kicked in. Trading on that stopped.
However, the electronic traders, traders on electronic exchanges, bypassed that and they still went on to do trading. And so the selling was continuing outside of the New York Stock Exchange.
Now, the New York Stock Exchange believes that investors would be best served if everybody had circuit breakers in place, and that they all kicked in together in a universal point of view. But the electronic exchanges say we want to have continuous trading. So this is an ongoing debate. And so, yesterday, the circuit breakers didn't really have an impact.
And briefly, as you said, there's finger-pointing going on. And the SEC is now involved, looking in to make sure — see what happened and make sure it doesn't happen again.
Right. And, you know, until we know what happened, it's kind of hard to know who is going to fix this problem.
If it has to do with the exchanges, then the Securities and Exchange Commission will get involved. If it had to do with the futures contracts, on futures exchanges, then the Commodities Futures Trading Commission will get involved. One way or another, all of the exchanges are going to have to have a say-so in how to fix this problem.
And, of course, there are lawmakers in Washington. Already, today, a House subcommittee said they are going to have hearings on Tuesday to figure out what was behind this whole market drop and what should be done to fix it. You know that, already, Congress is talking about financial reform because of the financial crisis and "too big to fail."
Now it looks like Wall Street regulation is also going to be on the table.
Susie Gharib with Nightly Business Report. Thanks again.
housing bubble
jobless benefits

redpajama_set_name: RedPajamaCommonCrawl


Runs on WordPress, PHP, and MySQL backend with Amazon Cloudfront providing a fleet of CDN edge cache servers for high-availability and for HTTPS encryption. There is content going back to 2004.
Runs on Google Blogger platform, to showcase and celebrate home cooked food with photos and recipes.


text:
Q: Newbie Neo4j issue about Match/with clause So Imagine that in my you have a relationship [event] --> [person]
and this event can have many relationships to the same person. For example:
   /-----------\
  /---------v   v
event----->[person]
  \---------^

In this case we have an event have 3 relations with this [person] node.
(There may be many occurrences of this)
My problem is only selecting the ones with more then x of the same relationship.
Right now I have the query.
MATCH (e:Event)-[r:FOLLOWS]->(f:Follows { name: "ExamplePerson"})
with e, count(r) as alias
where alias > 3
return e, alias

But this is returns total relationships for example:
   /------------- \
  /------------v   v
event----->[person1]
  \---------^
   \---------->[person2]

= 4 instead of 3.
 I have also tried the following:
MATCH (e:Event)-[r:FOLLOWS]->(f:Follows { name: "ExamplePerson"})
with e, count((e:Event)-[r:FOLLOWS]->(f:Follows { name: "ExamplePerson"})) as alias
where alias > 3
return e, alias

Without success what  am I doing wrong?

A: Try grouping for the same person, such as this change into your 1st attempt:
MATCH (e:Event)-[r:FOLLOWS]->(f:Follows { name: "ExamplePerson"})
with e, f, count(r) as alias
where alias > 3
return e, f, alias

redpajama_set_name: RedPajamaStackExchange


On May 29, 2005, EDWARD C. RATHELL; beloved husband of Deborah Jean (nee Manager); devoted father of Matthew Paul and Erik Lee Rathell; loving grandfather of Hailey Elaine Rathell; dear brother of Robert W. Rathell, Charlotte T. Hebrank, David G. Rathell and the late Joseph M. Rathell and Sally A. Janson; loving son-in-law of Paul W. and Marie V. Manager; dear brother-in-law of Judy and Art Hiob, Bonnie and Mike Rogers, Lou Ann and Gary Jasper. Also survived by many nieces and nephews. Friends may call at the family owned Leonard J. Ruck Inc. Funeral Home, 5305 Harford Road (at Echodale), on Wednesday, 2 to 4 and 7 to 9 P.M. A Funeral Mass will be celebrated at St. Dominic Church, on Thursday, 10 A.M. Interment Holly Hill Memorial Park.


picture frames / photo frames: Preferred by galleries nation-wide our natural blond wood frame is elegant and an easy style to decorate with. Now in a wall size Crafted of fine hardwood with tailed corners and exact fitting back. Glare resistant glass lens. Dual easel back for portrait or landscape tabletop display. Recessed wall mounts included for easy hanging. Frame edge is a very contemporary .50" all-around. Quality crafted by Dennis Daniels® of California.


text:
Q: Google map displaying in jquery pop up window I use jQuery 1.7 my web site, i need to show a google Map in a lightbox.
for example : http://dev.visualdrugs.net/mootools/gmapsoverlay/
Can you help me to do the same with jQuery.
Thanks

A: Here is a link to some instructions on one way to do this using jQuery Colorbox:
http://www.primecut.gr/2011/06/colorbox-with-google-maps/
Here is some simple code to this. This requires jQuery and the Colorbox plugin (JavaScript and CSS).
HTML
<a class="googleMapPopUp" href="https://maps.google.com.au/maps?q=south+australia" target="_blank">
    View location map
</a>

JavaScript
$('.googleMapPopUp').each(function() {
    var thisPopup = $(this);
    thisPopup.colorbox({
        iframe: true,
        innerWidth: 400,
        innerHeight: 300,
        opacity: 0.7,
        href: thisPopup.attr('href') + '&ie=UTF8&t=h&output=embed'
    });
});

Demo


DISCUSSION: REVISITING PANAMERICA: a conversation with artist Pablo Helguera and critical theorist Adetty Pérez de Miles, moderated by curator Sara Reisman, to celebrate the launch of Mobilizing Pedagogy: Two Social Practice Projects in the Americas by Pablo Helguera and Suzanne Lacy, published by Amherst College Press. The conversation will be followed by Helguera reading a selection of Panamerican Addresses drafted by various cities. Presented with The Shelley & Donald Rubin Foundation.
NADA Presents is a series of conversations, performances, and events during NADA Miami, December 6–9, 2018, showcasing unique voices in contemporary art and culture. Join us as they share the stage in an eclectic line-up of public programs.
NADA Presents is made possible with Major Support by Snark.art, a digital laboratory for art and technology that taps into the creative possibilities of the blockchain.


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Welcome to Astoria on the beautiful Coast of Oregon. We hope you will enjoy your visit.
Go here to find the distance from Astoria to another city on the Oregon Coast.
Get directions to Astoria from anywhere in the United States.
Here is an interactive map of Astoria, Oregon.
Things you may want to check out when you visit Astoria, Oregon.
Some places you can sleep when you visit Astoria, Oregon.
Here are some suggestions about where to eat in Astoria, Oregon.
Before Seattle, before Portland, there was Astoria. The rest of the country is just beginning to discover Astoria, Oregon, that historic gem of a town at the mouth of the Columbia River west of Portland, and the oldest European-American settlement west of the Rockies. The author provides a chronological look dating back to the 1500s, including European exploration, Native American life, logging, fishing, Chinese laborers in the salmon industry, a giant cheese in the Civil War, Oregon's first female surgeon, Victorian architecture, and valiant Coast Guard rescues. A chronology of events from the 1500s to present day can be found in the appendix, along with self-guided tours of downtown and the riverfront, and a listing of things to do and places to visit. Whether you're a born and bred Astorian or just passing through, Astoria's history is rife with drama that will captivate and inspire you. So settle into a comfortable chair and enjoy this journey through time.
The Lewis and Clark Expedition is a symbol of our nation's heritage, of the spirit of discovery and exploration that identifies our past. Seeking to honor the Corps of Discovery's incredible journey to the Pacific Ocean, the community of Astoria, Oregon built a memorial in the form of the Fort Clatsop replica. But on October 3, 2005, flames blazed high in the sky. Local officials and townspeople looked on in horror as Fort Clatsop burned to the ground.


Building on the strengths of its affiliates and partners, the Center for Financial Security at the University of Wisconsin–Madison has been awarded a five-year cooperative agreement with the Social Security Administration's Retirement and Disability Research Consortium.
The multidisciplinary center, housed in the School of Human Ecology, received the award along with three other RDRC centers at Boston College, the National Bureau of Economic Research, and the University of Michigan. Read the full press release here.


Outdoor trials are a unique way to enjoy the warm weather with your pup.
Last week I wrote about getting your pup in shape for summer fun, which focused a lot of hiking and long walks. The spring also marks the start of another activity I love—outdoor dog sport trials. It's such a fun way to enjoy the warm weather while bonding with your pup and meeting fellow dog lovers.
If you're interested in trying a new sport, start by searching for your local dog training club by looking online, asking around at the park, or getting recommendations from your veterinarian or groomer.
There are also clubs that focus on one dog sport, often affiliated with national organizations. Their web sites often list local clubs and chapters that can connect you with beginner classes. You can also search to find trials, tournaments, and events in your area to get a better idea of what the sport is all about.
Here are some web sites to get you started.
Dogs run with their handlers through a timed obstacle course.
Dogs compete in jumping for distance or height over a body of water.
Dogs race across hurdles to grab a tennis ball and return to the start line in a relay race.
Dogs work under their handler's guidance to herd animals, typically ducks or sheep.
Dogs chase a mechanically operatured lure.
Dogs search for scents in different environments.
Dogs complete a specific routine of behaviors to showcase their training.
A test of obedience-like behaviors, but in a course format.
Dog sports are a great way to enjoy the outdoors in a unique way. The time I spend with my dogs training for agility or rally obedience is really special. It's unlike any other activity we do together.


We went to over-winter our 2007 Lunar Quasar 524 and the bathroom roof light needs replacing. I have found somewhere to buy but what size do I buy?
The specs aren't in the user manual and I can't find details on internet.
It was the window roof light that had blown off in the wind. I managed to find a replacement for £65 and took the other one out (with a little help from YouTube) it was easy to replace and fix.
Hopefully it will still be there for many years to come.
Did you manage to find the correct bathroom light yet?
Wasn't sure if you meant the light bulb or cover?
Also if it is a bulb you are after try replacing with an LED as they last 30,000 hours or more and use 90% less energy.
If you did manage to find the bathroom light can you please post a comment to help our readers.


text:
Cosmos » Climate » Melting glaciers are adding more than water to our oceans
20 January 2015 /
Bill Condie
Melting glaciers are adding more than water to our oceans
As glaciers melt, they release significant volumes of organic carbon, with unknown impacts on marine life. Credit: IStock
Most concerns about melting glaciers focus on how they contribute to rising sea levels, but scientists are now looking at another problem – what happens to all the organic carbon that was trapped under the ice and is released as it melts?
"This is the first attempt to figure out how much organic carbon is in glaciers and how much will be released when they melt," says Florida State University assistant professor Robert Spencer of a new study he has been part of.
"It could change the whole food web. We do not know how different ecological systems will react to a new influx of carbon."
Glaciers and ice sheets contain about 70% of the Earth's freshwater but they also store organic carbon derived from both primary production on the glaciers and deposition of materials such as soot or other fossil fuel combustion byproducts.
Spencer, along with colleagues from Alaska and Switzerland, studied measurements from ice sheets in mountain glaciers globally, the Greenland ice sheet and the Antarctic ice sheet to measure the total amount of organic carbon stored in the global ice reservoir.
They found that, as glaciers melt, the amount of organic carbon exported will increase 50% over the next 35 years – the equivalent to half the organic carbon in the Mississippi River being added each year to the ocean.
"This research makes it clear that glaciers represent a substantial reservoir of organic carbon," said Eran Hood, the lead author on the paper published in Nature Geoscience, and a scientist with the University of Alaska Southeast.
"As a result, the loss of glacier mass worldwide, along with the corresponding release of carbon, will affect high-latitude marine ecosystems, particularly those surrounding the major ice sheets that now receive fairly limited land-to-ocean fluxes of organic carbon."
Spencer says that the study is a wake-up call, emphasising that there is a host of issues besides water levels.
"The thing people have to think about is what this means for the Earth," Spencer said. "We know we're losing glaciers, but what does that mean for marine life, fisheries, things downstream that we care about?"
Originally published by Cosmos as Melting glaciers are adding more than water to our oceans
Bill Condie is a science journalist based in Adelaide, Australia.
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Tracking earthquakes deep underground


text:
Harry William Brockbank (born 26 September 1998) is an English footballer who plays as a defender for St Patrick's Athletic in the League of Ireland Premier Division.

Early life
Brockbank was educated at Turton School.

Club career

Bolton Wanderers
Harry Brockbank joined the Bolton Wanderers academy at the age of seven.

Loans
Brockbank joined Hyde United on loan alongside team mate Ryan White on 16 December 2016. He made no appearances during this loan spell. Two years later he was loaned to Salford City on 30 November 2018 and made his debut on 18 December, starting and playing the full 90 minutes in a 3–1 win against Gateshead in the FA Trophy first round, helping Salford reach the second round for the first time in their history. He played one more game, on 1 January 2019, coming on as a late substitute for Matt Green in a 2–0 league win against Wrexham.

First Team
He made his debut for Bolton on 19 April 2019, starting in a 0–2 defeat against Aston Villa. This result meant Bolton were relegated to League One. Brockbank was originally released by the club when his contract expired on 30 June 2019, however he signed a new two-year contract on 3 August 2019 despite interest from Burnley and Sunderland. When the senior players refused to play against Coventry City due to unpaid wages, Brockbank played as Captain, and lead the youngest team in Bolton Wanderers history, with an average age of 19, to a 0–0 draw. Brockbank described captaining the club as his dream come true and that Bolton are the only team he wants to play for.

On 2 June 2021 the club announced that Brockbank had signed a new two year deal which would keep him at the club until at least June 2023. On 19 January 2022, the club announced that he had been released from the remainder of his contract to take up a potential playing opportunity in America.

El Paso Locomotive
On 21 January 2022, Brockbank signed for USL Championship side El Paso Locomotive. He made his debut on the opening day of the season in which he came on as a half–time substitute for Shavon John-Brown in a 3–1 defeat against Sacramento Republic. On 24 March he scored the first goal of his career in a 5–4 defeat against Las Vegas Lights.
 On 21 May, he left El Paso by mutual consent later stating he left as he was homesick.

St Patrick's Athletic
It was announced on 1 July 2022 that Brockbank had signed for League of Ireland Premier Division club St Patrick's Athletic. In signing for St Patrick's Athletic, he reunited with former Bolton team mate Eoin Doyle with Brockbank stating it was Doyle who had arranged for Brockbank to have a trial at the club. His debut came on 15 July 2022, in a 1–1 draw against Dundalk at Richmond Park. Brockbank made his first appearance in European football on 21 July 2022 in a 1–1 draw with Slovenian side NŠ Mura in the UEFA Europa Conference League. After making 15 appearances in his first half season at the club, Brockbank signed a new 1 year contract with the club on 8 November 2022.

Personal life
In January 2021 Brockbank took on the role as a mental health ambassador for Bolton Wanderers and spends part of his free time helping to support people battling with mental health problems. On 21 May 2021, he was given an award by the Professional Footballers' Association for his work in helping people that struggle with their mental health.

Style of play
He can play in every defensive position and can even play as an emergency goalkeeper though his natural position is as a right-back.

Career statistics

Notes

Honours
Bolton Wanderers
EFL League Two third-place (promotion): 2020–21

References

English footballers
1998 births
Living people
Footballers from Bolton
Association football defenders
Bolton Wanderers F.C. players
Hyde United F.C. players
Salford City F.C. players
El Paso Locomotive FC players
St Patrick's Athletic F.C. players
English Football League players
National League (English football) players
USL Championship players
League of Ireland players
English expatriate sportspeople in the United States
Expatriate soccer players in the United States
English expatriate footballers
Expatriate association footballers in the Republic of Ireland

redpajama_set_name: RedPajamaWikipedia