Cold weather in the Bolivian capital was over and above compensated by warm welcome of La Paz citizens, greeting the contestants of the Dakar Rally. The number of people on the street was impressing. There was "alive" corridor from the city suburbs to the bivouac of the race. Unlike the previous days, the top ten placed approximately within 10 minutes in the stage results. Dmitry Sotnikov's Kamaz-crew was on the 4th position, Nikolaev – on the 6th, Mardeev on 8th and Anton Shibalov finished the special 11th.
"The weather changed sharply," said Kamaz-Master Team Leader Vladimir Gennadyevich. "When we climbed on the height of more than 4500 meters, it was as if we got into a Russian winter. It was snowy, hailing. Slippery curvy roads awaited the contestants, and therefore they were very dangerous as on the right and on the left there were cliffs, canyons."
"It was important to hold out and adapt to the changed conditions, not to lose positions. You can't win – you saw how packed the finish was according to the results table. Everyone waited for the rest day, however, it met us with cold weather. But the main thing is that we had a good day. I would not say that we could improve some positions, but the most important is that we did not lose them.
He continued: "Now serious heights have begun. They are difficult not only for vehicles but for people as well. You feel the lack of oxygen. Sportsmen are hard to breath. And, of course, engines. Engine preparation to the heights is of a great importance. Especially on Bolivian parts. And the pleasant fact is that our new inline six-cylinder engine on Dmitry Sotnikov's truck performs well. We hope that in the further days the engine will work in the same stable way."
"A big maintenance and repair day is planned. Parts, that due to their resource may go out of order in the second half of the race, need to be changed. It is important to take into consideration that first after rest day stage La Pas – Uyuni is a marathon stage. Therefore we need additionally load necessary parts, check everything thoroughly. Assistance team will prepare the trucks for two days, as on the bivouac in Uyuni assistance team will have no access to the trucks. From La Pas they will straightway go to Tupiza."


text:
The Latest Florida Theme Park Attractions not to miss out on
Undoubtedly one of the best reasons to repeatedly revisit Florida is that there is always something both fresh and exciting happening, such is the vibrancy of the state's attractions. The area's theme parks are especially fun; in fact, all three of the major theme parks in the city of Orlando have recently set up new attractions that look very promising. Here's a closer look at them…
Read more about the USA:
Looking for a particular destination, see below otherwise you can go to USA Travel Guide to view current posts
Move Over Mickey- There's More To Orlando Than Just Disney!
15 Fun Touristy Things to Do in Miami
Kraken Unleashed, SeaWorld Orlando
Even if you have already often been on SeaWorld's Kraken roller coaster, you now have a good reason to try it yet again – as it has been revamped with the addition of the virtual reality experience Kraken Unleashed. You can climb into a familiar coaster train – except that, this time, the train will have fully integrated headsets that are uniquely designed and provide a custom digital overlay.
Volcano Bay, Universal Studios Florida
On 25 May, Universal Studios Florida opened its third theme park, Volcano Bay, reports TravelPulse. This is a 30-acre water park of which the focal point is the 200-foot Krakatau "fire and water volcano". It's certainly a spectacular sight, streaming waterfalls when the sky is light and fiery lava effects during the night.
Overall, Volcano Bay has 18 attractions; our favourite is definitely a multidirectional wave pool with twisting raft rides that can be enjoyed by multiple riders.
Race Through New York Starring Jimmy Fallon, Universal Studios Florida
Our choice for the strangest new ride of Florida's theme parks this year, Race Through New York lets guests join the "flying studio audience" in a racing competition with comedian Jimmy Fallon. The race takes in the hallways of The Tonight Show and midtown Manhattan's streets.
Pandora – The World of Avatar, Walt Disney World Resort
The world of Pandora, in which the blockbuster sci-fi classic Avatar is set, is brought to life by this new attraction in Walt Disney World Resort's Animal Kingdom. The attraction actually comprises two rides: Avatar Flight of Passage, which depicts banshee-riding above Pandora jungles, and Na'vi River Journey, where you can sail a boat down a river to encounter a Na'vi shaman.
The attraction was even recently visited by young cast members of the in-the-works Avatar 2, as Den of Geek! has reported. You can follow in their footsteps to Pandora – The World of Avatar after booking a hotel in Orlando, Florida. This would put you in easy travelling distance of Walt Disney World Resort.
Where to stay in San Francisco
Where to stay in Hawaii
20 Best Places to Visit in Kauai Island, Hawaii
13 Exciting Things to do in Honolulu Hawaii
The Only Honeymoon Guide to Hawaii You'll Ever Need!

redpajama_set_name: RedPajamaCommonCrawl


text:
Iran ( Arian Band, The Kamkars, Trio Chemirani, Kayhan Kalhor & Ali Akbar Moradi, Barad, Jahlè ...)
RGNET 1165
World Music Network
The traffic-choked streets of Tehran and the magnificent mosques of Esfahan, the underground DJs and the Sufi minstrels of Kurdistan, are all reflected in the vibrant musical scene in Iran. From contemporary rappers to folk legends, from great singing stars to contemporary bands, from regional traditionalists to the trance musicians of Baluchistan, this Rough Guide features a captivating cross-section of contemporary Iranian sounds. Artists include – Arian Band, The Kamkars, Hossein Alizadeh & Djivan Gasparyan, Trio Chemirani, Parvin Javdan, Abdolnaghi Afsharnia, Dastan Ensemble & Shahram Nazeri, Kayhan Kalhor & Ali Akbar Moradi, Jahlé, Chengis Mehdipour, Barad, Haj Ghorban Soleimani, Din Mohammad Zangeshâhi, O-Hum and Masters Of Persian Music
As colourful a comp as the country's history and terrain Iran is a complex, wildly varied country. Cross over from northern Iraq through a border post adorned with portraits of religious leaders, and you find yourself up among the little mountain villages of the Kurdish region. Make your way down to the plains, negotiating many more checkpoints on the way, and you're in the large, modern, busy capital, Tehran. Keep going south and you reach the magnificent mosques of Esfahan. Beyond, there stretches the coastal regions and the arid deserts leading to Afghanistan and Pakistan. It's an area with equally varied music, which has continued to flourish despite (and, at times, because of) the Islamic revolution of 1979, when Western pop was initially banned. This wide-ranging guide provides an impressive introduction. From up in the Kurdish region there are the sturdy and stirring Kamkars, famous for their Kurdish and Persian classical styles, played on long-necked lutes, fiddles and dulcimer. Then, from Tehran, there are rock bands that have survived despite official disapproval, which range from the laidback Barad to the more upbeat O-Hum, whose unlikely lyrics cover everything from personal angst to re-workings of ancient Sufi poetry. The Ayatollahs disliked rock because it symbolised the pro-Western era of the Shah, but they were keen to promote Persian culture and classical groups like the Dastan Ensemble, heard here with Kurdish singer Shahram Nazeri. The music of Iran's substantial Armenian community gets a look in, with duduk player Djivan Gasparyan playing alongside the great Iranian tar player Hossein Alizadeh. The music of black Iranians, descended from African slaves, is also featured. One of the finest tracks here is the soulful 'Lullaby', recorded for Andy Kershaw's show by the Jahlé band from the coastal city of Bandar Abbas. This is another excellent Rough Guide compilation, which comes with useful and entertaining liner notes. And I'd say all that even if it hadn't been compiled by the Songlines editor, Simon Broughton.
Robin Denselow l (from Songlines # 39)


(FOV). •Column and Row Binning Modes to Improve Image Quality when Resizing. •Simple Two-wire Serial Interface. •Programmable Controls: Gain, Frame Rate, Frame Size, Exposure. •Automatic Black Level Calibration. •On-chip Phase-Locked Loop (PLL).


text:
Q: Change position through DOM EVENT FLOW - Jquery/JS I want to change the
 position after the user stops dragging it. Also the number 10 without commas doesn't work.
It does change to a blue color, but the position doesn't. What can I do?
I have a lot of .screen divs. Thanks!
$('.screen').draggable({
  stop: function(event, ui) {
    if (parseInt(event.target.offsetTop) < -1) {
      event.target.style.backgroundColor = "blue";
      event.target.style.offsetTop = "10";
    }
  }
})

A: There's two main issues here. Firstly offsetTop is a property of the Element object, not the style of the Element.
Secondly offsetTop is readonly, so you cannot use it to update the position of the element. To do that you need to set style.top:

$('.screen').draggable({
  stop: function(event, ui) {
    if (event.target.offsetTop < -1) {
      event.target.style.backgroundColor = "blue";
      event.target.style.top = '10px';
    }
  }
})

<link rel="stylesheet" href="https://code.jquery.com/ui/1.12.1/themes/base/jquery-ui.css" />

Drag me

Also note that offsetTop returns an integer value, so parseInt() is not required.

redpajama_set_name: RedPajamaStackExchange


text:
\section{Introduction}
\input{01_intro.tex}
\section{Related Work}
\input{02_related.tex}
\section{Proposed Method}
\input{03_method.tex}
\section{Experiment}
\input{04_experiment.tex}
\section{Conclusion}
\input{05_conclusion.tex}

{\small
\bibliographystyle{ieee_fullname}

\subsection{Video Representation}
\begin{figure*}
    \centering
    \includegraphics[trim={0.5cm 3cm 1.5cm 2.5cm},width=17cm,clip]{img/GTAD_Arch.pdf}
    \caption{\textbf{Overview of G-TAD architecture.} The input of G-TAD is a sequence of snippet features. We first extract features using $b=3$ GCNeXt blocks, which gradually aggregate both temporal and multi-level  semantic context. Semantic context, encoded in semantic edges, is dynamically learned from features at each GCNeXt layer.
   Then we feed the extracted features into the  SGAlign layer, where sub-graphs defined by a set of anchors are transformed to a fixed-size representation in the Euclidean space.

    Finally, the localization module scores and ranks the sub-graphs for detection.

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

\noindent \textbf{Action Recognition}. Many CNN based methods have been proposed to address the action recognition task. Two-stream networks \cite{feichtenhofer2016convolutional,simonyan2014two,wang2015towards} use 2D CNNs to extract frame features from RGB and optical flow sequences.
These 2D CNNs can be designed from scratch~\cite{he2016deep, simonyan2014very} or adapted from image recognition tasks~\cite{deng2009imagenet}. Other methods~\cite{tran2015learning,carreira2017quo,qiu2017learning,xu2017r} use 3D CNNs to encode spatio-temporal information from the original video.
In our work, we use the pre-trained action recognition model in \cite{xiong2016cuhk,wang2016temporal} to extract video snippet features as G-TAD input, and use graph convolution as an analogue for 2D or 3D CNNs.

\noindent \textbf{Action Detection}. The goal of temporal action detection is to predict the boundaries of  action instances and their categories in untrimmed videos. Most methods~\cite{shou2016temporal,singh2016untrimmed,zhao2017temporal, zeng2019graph, chao2018rethinking, lin2018bsn} divide the task into two stages: temporal proposal generation and classification/regression of proposals. For proposal generation, they either predict action proposals using handcrafted anchors \cite{buch2017sst,caba2016fast, escorcia2016daps, gao2017turn,shou2016temporal} , or by classifying starting/ending snippets~\cite{zhao2017temporal,lin2018bsn}. Others~\cite{lin2017single,buch2017sst,lin2019bmn} tackle the problem using a single-stage model, where actions are detected directly. G-TAD is a single-stage model that scores pre-defined anchors with action confidences. We introduce a starting/ending snippet classification loss as a regularizer.

\subsection{GCN in Videos}
\noindent \textbf{Graphs in Video Understanding}. Graphs have been widely used for data representation in various video understanding tasks, such as video feature representation~\cite{liu2019learning},
video classification~\cite{wang2018videos, chen2019graph}, and action localization~\cite{zeng2019graph}.
In action recognition, Liu \textit{et al.} ~\cite{liu2019learning} view a video tensor as a 3D point cloud in the spatial-temporal space.
Wang \textit{et al.} ~\cite{wang2018videos} represent a video as a space-time region graph, in which the graph nodes are defined by object region proposals. In action detection, Zeng \textit{et al.} ~\cite{zeng2019graph} consider temporal action proposals as nodes in a graph, and refine their boundaries and classification scores based on the established proposal-proposal dependencies. Differently from previous works, G-TAD takes video snippets as nodes in a graph and form dependencies between them based on both their temporal ordering and  semantic similarity.

\noindent
\textbf{Graph Convolutions.}
Graph Convolutional Networks (GCNs)~\cite{kipf2016semi} are widely used for non-Euclidean structures. These years have also seen their successful application in  computer vision tasks due to their versatility and effectiveness, such as 3D object detection~\cite{Gkioxari2019Mesh} and point cloud segmentation ~\cite{wang2018dynamic, xie2019clouds}.
Meanwhile, various GCN architectures are proposed for more effective and flexible modelling. Busbridge \textit{et al.} ~\cite{velivckovic2017graph} propose graph attention networks that assign different weights to neighboring nodes based on local structures. Jiang \textit{et al.}~\cite{niepert2016learning} dynamically infer a graph, and apply graph convolution on the dynamic graph.
Li~\textit{et al.} ~\cite{Li_2019_ICCV} propose DeepGCNs to enable GCNs to go as deep as 100 layers by using residual/dense graph connections and dilated graph convolutions.  G-TAD uses a DeepGCN-like structure to apply graph convolutions on a dynamic semantic graph as well as a fixed temporal graph.

\subsection{Problem Formulation}
The input to our pipeline is a video sequence of $l_v$  frames  $V=\left \{ v_n \right \}_{n=1}^{l_v}$, where $v_n$ is the $n^{\textrm{th}}$ frame .
Following recent video action proposal generation methods \cite{buch2017sst, escorcia2016daps, gao2017turn,lin2018bsn}, we construct our G-TAD model using feature sequences extracted from raw video frames. We uniformly sample $L=l_v/\sigma$ frames with a sampling rate $\sigma$ and refer to each sampled frame as a  \textbf{snippet}.
Our input visual feature sequence is represented by $X=\left [ x_{l} \right ]_{l=1}^{L} \in \mathbb{R}^{C\times L}$, where $x_{l}$ is the extracted snippet feature at the ${l}$ sampled frame and $C$ is its dimension. Each video sequence has a set of $N$ annotations
$\Psi  = \left \{ \psi  _n=\left (s_{n},e_{n}, c_n \right ) \right \}_{n=1}^{N}$, where $\psi_n$ represents an action instance, and $t_{s,n}$, $t_{e,n}$, and $c_n$ are its starting time, ending time, and action class, respectively.

The temporal action detection task is to predict ${M}$ possible actions $\Phi=  \left \{ \phi _m=\left (\hat{t}_{s,m},\hat{t}_{e,m}, \hat{c}_m, {p}_m \right ) \right \}_{m=1}^{M}$ from  $X$. Here, $(\hat{t}_{s,m},\hat{t}_{e,m})$ represents the predicted temporal boundaries for the $m^{\textrm{th}}$ predicted action, and $(\hat{c}_m, {p}_m)$ are its  predicted action class and confidence score, respectively.

\subsection{G-TAD Architecture}

Our action detection framework is illustrated in Fig.~\ref{fig:arch}.
We feed snippet features $X$ into a stack of $b$ GCNeXt blocks, which is designed inspired by ResNeXt~\cite{xie2017aggregated}, to obtain context-aware features. Each GCNeXt contains two graph convolution streams. One stream operates on fixed temporal neighbors, and the other adaptively aggregates semantic context into snippet features.
Each block follows split-transform-merge strategy with multiple convolution paths.
Based on a set of pre-defined temporal anchors (see Section \ref{subsec: Details}), we define a sub-graph alignment layer named SGAlign to transform the aggregated feature of each sub-graph to a feature vector.
Multiple fully connected layers are used to predict the intersection over union (IoU) of every anchor and the ground truth action instance.
We provide a detailed description of both GCNeXt and SGAlign
in Sections \ref{subsec: GCNeXt} and \ref{subsec: SGAlign}, respectively.

\begin{figure}
    \centering
    \includegraphics[trim={0cm 0.5cm 0cm 0.5cm},width=8cm,clip] {img/GTAD_GCNeXt.pdf}
    \caption{\textbf{GCNeXt block.} The input feature is processed by temporal and semantic graphs with the same cardinality. Black and purple boxes represent Edge Convolutions and 1D Convolutions, respectively. We display (input channel, output channel) in each box. Both convolution streams follow a split-transform-merge strategy and 32 paths designed to increase the diversity of transformations. The module output is the summation of both streams and the input.}

    \label{fig:GCNeXt}
\end{figure}

\subsection{GCNeXt for Context Feature Encoding} \label{subsec: GCNeXt}
Our basic graph convolution block, GCNeXt, operates on a graph representation of the video sequence. It encodes snippets using their temporal and semantic neighbors.
Fig. \ref{fig:GCNeXt} illustrates the architecture of GCNeXt.

To build the video graph, we take as input $C$ dimensional feature vectors of $L$ snippets, denoted as $X \in \mathbb{R}^{C\times L}$.
We build a graph $\mathcal{G}=\{\mathcal{V},\mathcal{E}\}$, where $\mathcal{V}$ and $\mathcal{E}$ denote the vertex and edge set, respectively.
In this case, each vertex is a snippet (represented by its feature) and each edge shows a dependency between pairs of snippets.
We define two types of edges --- temporal edges $\mathcal{E}_t$ and semantic edges $\mathcal{E}_s$, so accordingly we define two graphs --- the temporal graph and the semantic graph. We describe each type of edge as well as the graph convolution process in the following.

\noindent
\textbf{Temporal Edges ($\mathcal{E}_t$)}. Temporal edges encode the temporal order of the video. For each node $v_i$, there is one unique forward edge to node $v_{i+1}$, and one backward edge to node  $v_{i-1}$. In this case, we have $\mathcal{E}_t=\mathcal{E}_t^{f}\cup\mathcal{E}_t^{b}$, where  $\mathcal{E}_t^{f}$ and $\mathcal{E}_t^{b}$  are  forward and backward temporal edge sets defined
as follows:
\begin{align} \label{eq:edge_t}
\mathcal{E}_t^f &=\{(v_i,v_{i+1})|~i\in\{1,2,\dots,L-1\}\}, \\
\mathcal{E}_t^b &=\{(v_i,v_{i-1})|~i\in\{2,\dots,L-1,L\}\},
\end{align}
\noindent $L$ is the number of snippets in the video.

\noindent \textbf{Semantic Edges ($\mathcal{E}_s$)}. We define $\mathcal{E}_s$ using the notion of dynamic edge convolutions \cite{wang2018dynamic}. The goal of these edges is
to collect information from semantically correlated snippets.
For each node $v_{i}$ in the input graph $\mathcal{G}$, we define a set $\mathcal{E}_s$ as follows:
\begin{align}
\mathcal{E}_s=\{(v_i,v_{n_i(k)})|i\in\{1,2,\dots,L\}; k\in\{1,2,\dots K\}\} \notag.
\end{align}
Here, $n_i(k)$ is the index of the $k^{\textrm{th}}$ nearest neighbour from $i$-th node,
$n_i$ is constructed dynamically at every layer in the node feature
space, which enables us to find the \textit{dynamic neighbors} that intrinsically carry semantic context information. Since we recompute $n_i(k)$ at each layer, $\mathcal{E}_s$ is adaptively changed to represent new levels of semantic context.

\noindent \textbf{Graph Convolution}.
A Graph Convolution transforms
the graph vertices represented as
$X=[x_1,x_2,\dots,x_L]\in \mathbb{R}^{C \times L}$
through a general graph convolution operation $\mathcal{F}$ formulated as:
\begin{equation}
    \mathcal{F}(X,A,W)=ReLU(f_{agg}(X,A,W))\notag.
\end{equation}
Here, $W\in \mathbb{R}^{2C \times C}$ are trainable weights of the aggregation function $f_{agg}$, $A\in \mathbb{R}^{L \times L}$ is the adjacency matrix without self-loops (\ie edges between a node and itself), and ReLU is the rectified linear unit as activation function.  We formally define the adjacency element $A_{i,j}$ as ${1}\{(i,j)\in \mathcal{E}\}$, where $1\{\cdot\}$ is the indicator function.
There are several choices for $f_{agg}$ in the literature.
We use a single-layer edge convolution~\cite{DBLP:journals/corr/abs-1801-07829} as our aggregation function in Eq.~\ref{eq:agg}.
\begin{align} \label{eq:agg}
   f_{agg}(X,\mathcal{E},W)&=([X^T,AX^T-X^T] W)^T \nonumber \\
   &= W_0 X + W_{1} X A.
\end{align}
We use $W_\cdot\in \mathbb{R}^{C \times C}$ with different subscripts to show different trainable weights. $[\cdot,\cdot]$ represents the matrix concatenation in columns.

\noindent \textbf{Residual Connection and Cardinality}.
We require two more graph operations. First, we use the residual connection proposed in DeepGCN \cite{Li_2019_ICCV} to improve model convergence.
Under this setup, our graph convolution block can be formulated as: \begin{align} \label{eq:block}
   \H(X,\mathcal{E},W)=ReLU[&W_\alpha X + W_\beta X A_t^f + W_\gamma X A_t^b+ \nonumber \\
                   &W_\phi X A_s+X],
\end{align}
where $A_t^f$, $A_t^b$, and $A_s$ are adjacency matrices for $\mathcal{E}_t^f$, $\mathcal{E}_t^b$, and $\mathcal{E}_s$ respectively.

The derivation of  Eq.~\ref{eq:block} is demonstrated in the \textbf{supplementary materials}, where we also prove that it can be efficiently computed by zero-padded 1D/edge convolutions.

Following ResNeXt~\cite{xie2017aggregated}, GCNeXt adopts split-transform-merge strategy to explore the applicability of group convolution by changing cardinality besides going deeper or wider.

\subsection{Sub-Graph Alignment and Localization} \label{subsec: SGAlign}

\begin{figure}
    \centering
    \includegraphics[trim={2.5cm 3.5cm 2.5cm 4.5cm}, width=8cm, clip]{img/GTAD_SGAlign.pdf}
    \caption{\textbf{SGAlign layer}. SGAlign extracts sub-graph features using a set of anchors. In the graphs above, the colored dots represent node features, grey arrows are  semantic edges, and the orange highlighted arch is the anchor. Both circles represent the same graph. SGAlign arranges node feature along the temporal/semantic graphs, and concatenates both features as output. When using the temporal graph, the order of nodes is preserved in the final representation (black lines). This is not always true for the semantic graph, since node features are represented by their feature neighbors (purple lines).

    }
    \label{fig:ga}
\end{figure}

\noindent
\textbf{Sub-Graph of Interest Alignment (SGAlign)}. Most previous action detectors perform rescaling to extract a fixed sized \textit{proposal feature vector} for  each action anchor.  Given the action anchor $a=[t_s,t_e]$, they sample the video feature sequence within $a$ through linear interpolation with $\tau$ points.
Given our graph formulation, a \textit{sub-graph feature} is extracted instead from
a Sub-Graph of Interest Alignment (SGAlign) layer that aggregates the context feature in an adaptive way, and does not rely on human priors.
Fig.~\ref{fig:ga} illustrates our new graph alignment algorithm and we present its technical details next.

\begin{algorithm}[t]
\input{08_alg.tex}
\label{alg}
\end{algorithm}

Given an input of $L$ feature vectors $X\in \mathbb{R}^{C\times L}$ and an anchor $a$, we expect to sample $\tau_1$ and $\tau_2$ vectors from the temporal and  semantic graphs, respectively. We repeat this process for all anchors.
The alignment is done in four steps. \textbf{(1)} Each snippet is projected back to the temporal order given by the temporal graph. \textbf{(2)} We run an interpolation and rescaling algorithm (Alg.~\ref{alg}) to get $\tau_1$ vectors from the temporal graph and $\tau_2$ vectors from the semantic graph. \textbf{(3)} Every node's feature is replaced with the mean  feature of its dynamic neighbors,
and then we repeat \textbf{(1)} and \textbf{(2)} to further extract features for the semantic context.
\textbf{(4)}  The $\tau_1$ and $\tau_2$ vectors are concatenated as the output of the SGAlign layer.
In Alg. \ref{alg}, the output $Y_j$ for anchor $a_j$, is the weighted average of all the nodes in the sub-graph defined by $a_j$. In the backward pass, this weighted sum means gradients will always flow to these nodes.

\noindent \textbf{Sub-Graph Localization}. For an anchor $(t_s, t_e)$, we calculate its Intersection-over-Union (IoU) with all ground truth actions $\psi$ in $\Psi$, and denote the maximum IoU $g_c$ as its label. We compute $g_c$ for all pairs of $(t_s, t_e)$.
Once we get the \textit{sub-graph feature} from SGAlign layer, we use three fully connected (FC) layers to regress it to $g_c$. The last FC layer produces a two-dimension vector, where each entry shows classification $p_{cls}$ and regression $p_{reg}$ scores.

\subsection{Training G-TAD}

In G-TAD, the sub-graph localization is used to determine the confidence scores of anchors which are regressed for final temporal action detection. We do not need to specifically classify starting and ending nodes of actions since they are predefined by the anchors. However, we noticed that adding a node classifier during training can drastically improve the model's convergence. This classification module is ignored at test time.

\noindent \textbf{Sub-Graph Localization Loss}. Sub-graph localization predicts $(p_{cls},p_{reg})$ for each anchor position.
With the training target being $g_c$, the sub-graph loss $L_{g}$ is defined as follows:
\begin{equation}
L_{g}=  \mathcal{L}_{wce}(p_{cls},1\{g_c > 0.5\}) + \lambda_1 \cdot \mathcal{L}_{2}(p_{reg},g_c),
\end{equation}
where
$\mathcal{L}_{wce}$ is the weighted  cross entropy loss. In our experiments, we take the tradeoff coefficient $\lambda_1=10$, since the second loss term tends to be smaller than the first.

\noindent \textbf{Node Classification Regularizer}. In the training process, we label a node as a start or end point if they are temporally close to $t_s$ or $t_e$, while all the other nodes are of the third class containing action and background nodes.
We add a separate branch by with 1 FC-layer after the first GCNeXt block to classify nodes to their labels $g_n$, with start/end probabilities $\boldsymbol{{p}}_n=(p_s,p_e)$.
We add a node regularizer $L_{n}= \mathcal{L}_{wbce}(\boldsymbol{{p}}_n,g_n)$, where $\mathcal{L}_{wbce}$  stands for the weighted binary cross entropy loss.

We train G-TAD in the form of a multi-task loss function, including sub-graph loss $L_{g}$, node regularizer loss $L_n$, and an $\mathcal{L}_{2}$ regularization for all the trainable parameters $\Theta$:

\begin{equation}
L = L_{g} +L_{n}+  \lambda_2 \cdot \mathcal{L}_2(\Theta).
\end{equation}
In our experiments, we set $\lambda_2=10^{-4}$.

\subsection{Inference and Post-processing}
At inference time, G-TAD predicts classification and regression scores for each anchor $(t_s, t_e)$. From the anchors, we construct predicted actions $\Phi  = \left \{  \phi  _n=(s_n, e_n, c_n, p_n) \right \}_{n=1}^{J}$, where $(s_n, e_n)$ is the action boundary in the video scale, $c_n$ is the action class, and $p_n$ is the fused confidence score of this prediction and $p_{n}=p_{cls}^\alpha \cdot p_{reg}^{(1-\alpha)}$. In our experiments, we search for the optimal $\alpha$ in each setup. We apply Soft-NMS \cite{softNMS} and select the top-$M$ predictions.

\subsection{Datasets and Metrics}

\noindent
{\bf ActivityNet-1.3} \cite{caba2015activitynet}  is a large-scale action understanding dataset for action recognition, temporal detection, proposal generation and dense captioning tasks. It contains 19,994 temporally annotated untrimmed videos with 200 action categories, which are divided into training, validation and testing sets by 2:1:1.

\noindent
{\bf THUMOS-14} \cite{jiang2014thumos} dataset contains 413 temporally annotated untrimmed videos with 20 action categories. We merge the 200 videos in validation to the training set and evaluate on the 213 annotated videos from the testing set.

\noindent
{\bf Detection Metric}. We take mean Average Precision (mAP) at certain IoU thresholds as the main evaluation metric.
Following the official evaluation API, the IoU thresholds are chosen
from $\{0.3, 0.4, 0.5, 0.6, 0.7\}$ and $\{0.5, 0.75, 0.95\}$ on THUMOS14 and ActivityNet-1.3, respectively. Following standard practise, we also report average mAP over 10 different IoU thresholds on ActivityNet-1.3.

\subsection{Implementation Details} \label{subsec: Details}

\noindent
{\bf Features and Anchors}. We use pre-extracted features for both datasets. For ActivityNet-1.3, we adopt the pre-trained two-stream network by Xiong et. al.~\cite{xiong2016cuhk}, with down-sampling ratio $\sigma = 16$.
Each video feature sequence is rescaled to $L = 100$ snippets, using linear interpolation.
For THUMOS-14, the video features are extracted from Kinetics~\cite{zisserman2017kinetics} pretrained TSN model~\cite{TSN2016ECCV} with $\sigma = 5$.
We crop each video feature sequence with a window size $L=256$ and overlap neighbouring windows with $128$ snippets. In training, we do not use any crops void of actions.

For ActivityNet-1.3 and THUMOS-14, we enumerate all possible anchors with restriction, \eg $Anchors =\{(t_s,t_e)|~ 0< t_s<t_e<L; ~t_s,t_e \in \mathcal{N}; ~t_e-t_s < D\} $, while $L$ and $D$ are $100$ and $64$, respectively.
In SGAlign, we use $(\tau_1=32,\tau_2=4)$ for ActivityNet-1.3, and $(\tau_1=\tau_2=16)$ for THUMOS-14.

\noindent {\bf Training and Inference}. We implement and compile our framework using PyTorch 1.1, Python 3.7, and CUDA 10.0.
We use $b=3$
GCNeXt blocks and train our model end-to-end, with batch size of 16.
The learning rate is $4\e{-3}/4\e{-4}$ on ActivityNet and
$6\e{-6} / 6\e{-7}$ on THUMOS14 for 5/5 epochs.
In inference, we take video classification scores by \cite{wang2017untrimmed} and \cite{xiong2016cuhk}, and multiply them to $p_n$ for evaluation.
For post-processing, the Soft-NMS threshold is $0.84$ to pick the top $M$ confident predictions, where M is 100 for  ActivityNet and 200 for THUMOS.

More details can be found in the \textbf{supplementary material}. To encourage reproducibility, the code and trained models will be made publicly available.

\subsection{Comparison with State-of-the-Art}

\noindent
{\bf ActivityNet-1.3}: Tab.~\ref{tab:sota_anet} compares G-TAD with state-of-the-art detectors. We report mAP at different tIoU thresholds, as well as average mAP. G-TAD reports the highest average mAP results on this large-scale and diverse dataset.

\begin{table}[tbp]
\centering
\caption{\textbf{Action detection results on validation set of ActivityNet-1.3}, measured by mAP ($\%$) at different tIoU thresholds and the average mAP. G-TAD achieves better performance in average mAP than other methods, even the latest work of BMN and PGCN shown in the last second block.
}
\small
\begin{tabular}{p{2.1cm}p{0.62cm}<{\centering}p{0.62cm}<{\centering}p{0.62cm}<{\centering}p{0.9cm}<{\centering}}
\toprule
Method  & 0.5  &  0.75  & 0.95 & Average\\
\hline
Wang \textit{et al.} \cite{wang2016uts}    & 43.65 & -  & - & -\\
Singh \textit{et al.} \cite{singh2016untrimmed} & 34.47 & - & - & - \\
SCC \cite{heilbron2017scc}   & 40.00 & 17.90  & 4.70   & 21.70  \\
CDC \cite{shou2017cdc} & 45.30 & 26.00 & 0.20 & 23.80 \\
TCN \cite{dai2017temporal} & 37.49 & 23.47 & 4.47 & 23.58 \\
R-C3D \cite{xu2017r} & 26.80 & - & - & - \\
BSN \cite{lin2018bsn} & 46.45  & 29.96 & 8.02  & 30.03  \\
Chao \textit{et al.} \cite{chao2018rethinking} & 38.23 & 18.30 & 1.30 & 20.22 \\ \hline
P-GCN \cite {zeng2019graph} &48.26 &33.16 &3.27 &31.11  \\
BMN \cite{lin2019bmn} & { 50.07} & \textbf{34.78} & { 8.29} & { 33.85}  \\
\hline
G-TAD (ours)  & \textbf{50.36} & {34.60} & \textbf{9.02} & {\bf 34.09} \\
\bottomrule
\end{tabular}
\label{tab:sota_anet}
\end{table}

\begin{table}[!tb]
\centering
\caption{\textbf{Action detection results on testing set of THUMOS-14}, measured by mAP (\%) at different tIoU thresholds. G-TAD achieves the best performance among all one-stage methods, and the mAP@0.7 is even higher than strong two-stage TAL-Net\cite{chao2018rethinking}.
}
\small
\begin{tabular}{p{2.3cm}p{0.7cm}<{\centering}p{0.7cm}<{\centering}p{0.7cm}<{\centering}p{0.7cm}<{\centering}p{0.7cm}<{\centering}}
\toprule
    Method                                         &  0.3 &  0.4 &  0.5 &  0.6 &  0.7 \\ \hline
    \multicolumn{6 }{c}{Two-stage Temporal Action Detection} \\ \hline

    SST \cite{buch2017sst}                 & - & -    & 23.0 & - & -\\
    CDC  \cite{shou2017cdc}             & 40.1 & 29.4 & 23.3 & 13.1 & 7.9\\
    TURN-TAP\cite{gao2017turn}                 & 44.1 & 34.9 & 25.6 & - & -\\

    CBR  \cite{gao2017cascaded}      & 50.1 & 41.3 & 31.0 & 19.1 & 9.9\\
    SSN \cite{zhao2017temporal}             & 51.9 & 41.0 & 29.8 & - & -\\
    BSN \cite{lin2018bsn}                 & 53.5 & 45.0 & 36.9 & 28.4 & 20.0 \\
    TCN \cite{dai2017temporal}            & - & 33.3 & 25.6 & 15.9 & 9.0\\
    TAL-Net \cite{chao2018rethinking}         & 53.2 & 48.5 & 42.8 & \textbf{33.8} & 20.8\\
    MGG \cite{DBLP:journals/corr/abs-1811-11524} & 53.9 & 46.8 & 37.4 & 29.5 & 21.3 \\
    DBG \cite{lin2019fast} & 57.8 & 49.4 & 39.8 & 30.2 & \textbf{21.7} \\
    P-GCN \cite {zeng2019graph}                 & \textbf{63.6} & \textbf{57.8} & \textbf{49.1} & - & -\\ \hline
    \multicolumn{6 }{c}{One-stage Temporal Action Detection} \\ \hline

    Richard \textit{et al.} \cite{richard2016temporal}     & 30.0 & 23.2 & 15.2 & - & -\\
    Yeung \textit{et al.} \cite{yeung2016end}             & 36.0 & 26.4 & 17.1  & - & - \\
    Yuan \textit{et al.} \cite{Yuan2017}                    & 36.5 & 27.8 & 17.8 & - & -\\
    Hou \textit{et al.} \cite{hou2017real}                 & 43.7 & -    & 22.0 & - & -\\
    SS-TAD  \cite{buch2017end}                & 45.7 & - & 29.2 & - & 9.6\\
    BMN \cite{lin2019bmn}                  & \textbf{56.0} & 47.4 & 38.8 & 29.7 & 20.5 \\

    \hline
    G-TAD (ours)                                 & {54.5} & \textbf{47.6} & \textbf{40.2} & \textbf{30.8} & \textbf{23.4}  \\
\bottomrule
\end{tabular}
\label{Tab:sota_thm}
\end{table}

\noindent
{\bf THUMOS14}: Tab.~\ref{Tab:sota_thm} compares the action localization results of G-TAD and various state-of-the-art methods on the THUMOS14 dataset. At 0.7 IoU, G-TAD reaches mAP of $23.42\%$, compared to the current best of $20.8\%$ from TAL-Net. At 0.5 IoU,
G-TAD outperforms all one-stage detection methods, such as SS-TAD~\cite{buch2017end}  and BMN~\cite{lin2019bmn}.
Comparing G-TAD with two-stage methods puts our method at an inherent disadvantage. For example,
P-GCN only rescores BSN proposals by mining proposal-proposal relationships and, in doing so, it increases mAP from $36.9\%$ to $49.1\%$. Our model can get such good results only from capturing more information about context.

\subsection{Ablation Study}

\noindent
\textbf{GCNeXt Module}: We ablate the three main components of GCNeXt, mainly GCN on temporal edges, GCN on semantic edges, and cardinality increase.
Tab.~\ref{tab:abl_gcnext} reports the performance obtained on ActivityNet-1.3, when each component is separately enabled/disabled. We see how each of these components contributes to the performance of the final G-TAD model. We highlight the gains from the semantic graph, showing the benefit of integrating adaptive context from semantic neighbors.

\begin{table}[tbp]
\centering
\caption{\textbf{Ablating GCNeXt Components.} We disable temporal/semantic graph convolutions and set different cardinalities for detection on ActivityNet-1.3.
}
\small
\begin{tabular}{p{0.7cm}<{\centering}p{0.7cm}<{\centering}p{0.7cm}<{\centering}|p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.55cm}<{\centering}p{0.65cm}<{\centering}}
\hline
\multicolumn{3}{c|}{ GCNeXt block} & \multicolumn{4}{c}{tIoU on Validation Set}\\
Temp. & Sem.  & Card.  & 0.5  &  0.75  & 0.95  & Avg. \\
\hline
\xmark & \xmark & 1 & {48.12} & {32.16} & 6.41 & {31.65} \\ \hline
\cmark & \cmark & 1 & {50.20} & \textbf{34.80} & 7.35 & {33.88} \\
\cmark & \xmark & 32& {50.13} & {34.17} & {8.70} & {33.67} \\
\xmark & \cmark & 32& {49.09} & {33.32} & 8.02 & {32.63} \\ \hline
\cmark & \cmark & 32& {\textbf{50.36}} & {{34.60}} & \textbf{9.02} & {\textbf{34.09}} \\
\hline
\end{tabular}
\label{tab:abl_gcnext}
\end{table}

\noindent \textbf{SGAlign Module}: This layer extracts sub-graph features by densely sampling and rescaling underlying snippet features. The sampling density is defined by factor $s$ in Alg.~\ref{alg}.
Tab.~\ref{tab:abl_sgoi} shows the effect of the sampling and feature concatenation from both temporal and semantic graphs  on ActivityNet-1.3. While sampling densely gives us minor improvements, we obtain a larger gain by including context information from the semantic graph.

\begin{table}[tbp]
\centering
\caption{\textbf{Ablating SGAlign Components
}
We disable the sample-rescale process and the feature concatenation from the semnantic graph for detection on ActivityNet-1.3. The rescaling strategy leads to slight improvement, while the main gain arises from the use of context information (semantic graph).  }
\small
\begin{tabular}{p{1.0cm}<{\centering}p{1.0cm}<{\centering}|p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.45cm}<{\centering}p{0.65cm}<{\centering}}
\hline
\multicolumn{2}{p{2cm}<{\centering}|}{ SGAlign} & \multicolumn{4}{c}{tIoU on Validation Set}\\
Samp. & Concat.  &  0.5  &  0.75  & 0.95  & Avg. \\
\hline
\xmark & \xmark     & {49.84} & {34.58} & 8.17 & {33.78} \\ \hline
\cmark & \xmark     & {49.86} & \textbf{{34.60}} & \bf{9.56} & {33.89} \\
\cmark & \cmark  & \bf{50.36} & \textbf{{34.60}} & 9.02 & \bf{34.09} \\
\hline
\end{tabular}
\label{tab:abl_sgoi}
\end{table}

\noindent\textbf{Sensitivity to Video Length}: We report the results of the sensitivity of G-TAD to different window sizes in THUMOS-14 in Tab.~\ref{tab:abl_window}. G-TAD benefits more from larger window sizes ($L=256$ vs. $128$)
for large windows mean G-TAD can aggregate more context snippets from the semantic graph. Performance degrades at $L=512$, where GPU memory limited us to use a batch size of only 2.

\begin{table}[!tb]
\centering
\caption{\textbf{Effect of Video Size.} We vary the input video size (window length $L$) and see that G-TAD performance improves with larger sizes ($L=256$). Degradation occurs at $L=512$, since GPU memory limits the batch size to be significantly reduced, leading to a noticeable  performance drop.}
\small
\begin{tabular}{p{1.5cm}<{\centering}|p{0.6cm}<{\centering}p{0.6cm}<{\centering}p{0.6cm}<{\centering}p{0.6cm}<{\centering}p{0.6cm}<{\centering}}
\hline
Window & \multicolumn{5}{c}{tIoU on Validation}  \\
    Length $L$    &  0.3 &  0.4 &  0.5 &  0.6 &  0.7 \\ \hline
    128             & 51.75 & 44.90 & 38.70 & 29.03 & 21.32\\
    256             & \textbf{54.50} & \textbf{47.61} & \textbf{40.16} & \textbf{30.83} & \textbf{23.42} \\
    512             & 48.32 & 41.71 & 34.38 & 26.85 & 19.29 \\ \hline
\end{tabular}
\label{tab:abl_window}
\end{table}

\begin{figure}
    \centering

    \includegraphics[trim={3cm 2cm 3cm 2cm},width=8cm,clip]{img/GTAD_Synth.pdf}
    \caption{\textbf{Semantic graphs and Context}. Given two untrimmed videos (left and right), we combine action frames from one video with context frames from the second (middle). We therefore create a synthetic video with no action context. As expected, the semantic graph of the synthetic video contains no edges between action and background snippets.

    }
    \label{fig:qual_results}
\end{figure}

\begin{figure*}[!ht]
    \centering
    \vspace{-0.5cm}
    \includegraphics[trim={0cm 4cm 0cm 4cm}, width=16cm,clip]{img/GTAD_Vis.pdf}
    \caption{\textbf{Qualitative results.} We show qualitative detection results on ActivityNet-1.3 (top) and THUMOS-14 (bottom).}
    \label{fig:vis_video_THUMOS}
\end{figure*}

\subsection{Discussion of Action Context}

In the ablation experiments, graph convolutions on the semantic graph improve G-TAD performance in both the GCNeXt block and in the SGAlign layer. Semantic edges connecting background to action snippets can adaptively pass the action context information to each possible action. In this section, we define 2 extra experiments to show how semantic edges encode meaningful context information.

\noindent {\bf Zero-Context Video}. How zero context between action and background leads to semantic graphs with no action-background edges is visually shown by comparing semantic graphs resulting from natural videos and synthetically compiled ones.
In Fig.~\ref{fig:qual_results} (left and right), we present two natural videos that  include actions ``wrestling" and ``playing darts", respectively. Semantic edges in their resulting graphs do exist, connecting action with background snippets, thus exemplifying the usage of context in the detection process.
Then, we compile a synthetic video that stacks action frames from the wrestling video and background frames from the darts video, feed it to G-TAD and again visualize the semantic graph (middle). As expected, the semantic graph does not include any action-background semantic edges.

\begin{figure}[!ht]
    \centering
    \includegraphics[trim={0cm 1cm 0cm 1cm},width=7cm,clip]{img/GTAD_Corr.pdf}
    \caption{\textbf{Action-Background Semantic Edge Ratio vs. Context Amount.} In the scatter plot, each purple dot corresponds to a different video graph.

     Strong positive correlation is observed between context amount and action-background  semantic  edge ratio, which means we predict on average more semantic edges in the presence of large video context.

    }
    \label{fig:ratio}
\end{figure}

\begin{figure}[!ht]
    \centering
    \includegraphics[trim={1.5cm 0.1cm 1cm 0cm},width=7.5cm,clip]{img/GTAD_Evol.pdf}
    \caption{\textbf{Semantic graph evolution during G-TAD training.} We visualize the semantic graphs at first, middle, and last layers during training epoch 0, 3, 6, and 9. The semantic edges at the first layer are always the same, while the semantic graphs at the middle and last layers evolve to incorporate more context.}
    \label{fig:evol}
\end{figure}

\noindent{\bf Correlation to Context Amount}. We also show the correlation between context edges and context as defined by human annotators.
We define the video \textbf{context amount} as the average number of background snippets which can be used to predict the video action class.
Following DETAD~\cite{detad}, we collect context amount for all videos in ActivityNet validation set from Amazon Mechanical Turk.
The scatter plot in Fig.~\ref{fig:ratio} shows the relation between \textbf{Context Amount} and
the ratio of \textbf{action-background semantic edges} over all the semantic edges.
From the plot, we observe that if a video has a higher amount of context (from human annotations), it is more likely to have more action-background semantic edges in its semantic graph. We further average context edge ratios in five context amount ranges, and plot them in green. The strong positive correlation between {Context Amount} and {action-background semantic edge ratio} indicates that our G-TAD model can effectively find related context snippets in the semantic graph.

\subsection{Visualization}
We show a few qualitative detection results in Fig.~\ref{fig:vis_video_THUMOS} on both ActivityNet-1.3 and THUMOS-14. In Fig.~\ref{fig:evol}, we visualize the evolution of semantic graphs during the training process across GCNeXt layers. Specifically, we feed a video into G-TAD and visualize the semantic graphs emerging at the first, middle, and last layers at epochs 0, 3, 6, and 9 of training. The semantic graphs at the first layer are the same, since they are built on the same input features. As we progress to different layers and epochs, semantic graphs adaptively update their edges. Interestingly, we observe the presence of more context edges as training advances. This indicates that G-TAD progressively learns to incorporate multiple levels of context in the detection process.

\subsection{Derivation and Efficient Implementation of Eq.~{\color{red} 4}}

In this section, we provide the derivation of Eq.~{\color{red} 4} in the paper (listed here in the following). We also show that Eq.~{\color{red} 4} can be  efficiently implemented by zero-padded 1D/edge convolutions. \newline

\begin{align} \label{eq:block_supp}
   ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\H(X,\mathcal{E},W)=ReLU[&W_\alpha X + W_\beta X A_t^f + W_\gamma X A_t^b+ _\phi X A_s+X]. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ \textrm{(4)}\nonumber
\end{align}

\noindent
\subsection{Derivation of Eq. 4}

\noindent
\textbf{a) Temporal Graph Convolution.} We first provide the derivation for temporal graph convolution.

The temporal forward edges $\mathcal{E}_t^f$ and backward edges $\mathcal{E}_t^b$ are formulated as
\begin{align}
    \mathcal{E}_t^f=\{(v_i,v_{i+1})|~i\in\{1,2,\dots,L-1\}\} , \mathcal{E}_t^b=\{(v_i,v_{i-1})|~i\in\{2,\dots,L-1,L\},

\end{align}

The corresponding adjacency matrices $A_t^f, A_t^b$ can be present by $L$ vectors, respectively, shown in Eq.~\ref{eq:tgc_supp}. We use $e_k\in \mathbb{R}^{L}$ to present the vector in which the $k$-th element is one but the others are zeros.
\begin{align} \label{eq:tgc_supp}
    A_t^f = &
        \begin{bmatrix}
        0 & 0 & 0 & \dots & 0 & 0 \\
        1 & 0 & 0 &   & 0 & 0 \\
        0 & 1 & 0 &  & 0 & 0 \\
        \vdots&  &  & \ddots & \vdots & \vdots \\
         0 & 0 & 0 & \dots & 0 & 0 \\
        0 & 0 & 0 & \dots & 1 & 0 \\
        \end{bmatrix}
    = [e_2,e_3,\dots, e_L, 0] \nonumber\\
    A_t^b = &
        \begin{bmatrix}
        0 & 1 & 0 & \dots& 0 & 0 \\
        0 & 0 & 1 &   & 0 & 0 \\
        0 & 0 & 0 &  & 0 & 0 \\
         \vdots&  &  & \ddots & \vdots & \vdots \\
          0 & 0 & 0 & \dots & 0 & 1 \\
        0 & 0 & 0 & \dots & 0 & 0 \\
        \end{bmatrix}
    = [0, e_1, e_2,\dots, e_{L-1}], \nonumber\\
    \Rightarrow & A_t^f = [A_t^b]^T

\end{align}

Given the input $X\in\mathbb{R}^{C\times L}$, after temporal graph convolution, the output, $X_t$, becomes
\begin{align} \label{eq:tgc}
    X_t &= f_{agg}(X,A_t^f,W_f) + f_{agg}(X,A_t^b,W_b) =W_{f,0} X + W_{f,1} X A_t^f + W_{b,0} X + W_{b,1} X A_t^b \nonumber\\
    & =(W_{f,0}+W_{b,0}) X + W_{f,1} X A_t^f + W_{b,1} X A_t^b .

\end{align}
Here $W_*$ is the trainable weights in the neural network.

\noindent
\textbf{b) Semantic Graph Convolution.}  It is straightforward to obtain Eq.~{\color{red} 4} for semantic graph convolution.

\noindent
\subsection{Efficient Implementation of Eq.~{\color{red} 4}}

In implementation of Eq.~{\color{red} 4}, we use an efficient zero-padded 1D convolution and edge convolution for temporal graph convolution and semantic graph convolution, respectively. In the following, we provide proof that our efficient implementation is equivalent to Eq.~{\color{red} 4}.

\noindent
\textbf{a) Temporal Graph Convolution.}

If a 1D convolution has kernel size 3, the weight matrix is a 3D tensor in $\mathbb{R}^{3\times C \times C}$. We denote the matrix as $W_{conv1}= [W_1,W_2,W_3],W_{1,2,3}\in \mathbb{R}^{C\times C}$.
Given the same input $X=[x_1, x_2,...,x_L]$, we pad zero on the input, $x_0=x_{L+1}=0\in \mathbb{R}^{C}$.

The output of 1D convolution can be written as $Y=[y_1,y_2,...,y_L]\in \mathbb{R}^{C\times L}$
\begin{align} \label{eq:y_k}
    y_k = [W_1 x_{k-1}+W_2 x_k+W_3x_{k+1}], k=1,2,\dots,L
\end{align}

We can prove that $X_t=Y$ by multiplying $e_k$ on both sides in Eq.~\ref{eq:proof}.  Please be noted that $W_*$ is the trainable weights in the neural network. We can assume $W_1=W_{b,1},W_3=W_{f,1}, W_2=W_{f,0}+W_{b,0}$
\begin{align} \label{eq:proof}
    X_te_k&=W_{f,0} Xe_k + W_{f,1} X A_t^fe_k + W_{b,0} Xe_k + W_{b,1} X A_t^be_k \nonumber\\
    &=(W_{f,0}+W_{b,0}) x_k + W_{f,1} X [0, e_1, e_2,\dots, e_L]^T e_k + W_{b,1} X [e_2,e_3,\dots, e_L, 0]^T e_k  \nonumber\\
    &=(W_{f,0}+W_{b,0}) x_k + W_{f,1} X e_{k+1} + W_{b,1} X e_{k-1} \nonumber\\
    &=(W_{f,0}+W_{b,0}) x_k + W_{f,1} x_{k+1} + W_{b,1} x_{k-1}  \nonumber\\
    &=W_1 x_{k-1}+W_2 x_k+W_3X_{k+1}
\end{align}

\noindent
\textbf{b) Semantic Graph Convolution.}
In the semantic graph, edge convolution is directly used, so proof is done.

\subsection{Training Details}

\noindent
\textbf{Semantic Edges from Multiple Levels.}
In G-TAD, we use multiple GCNeXt blocks to adaptively incorporate multi-level semantic context into video features. After that, SGAlign layer embeds each sub-graph by concatenating aligned features from temporal and semantic graphs. However, it is not necessary to consider \textbf{only the last} GCNeXt semantic graphs to align the semantic feature. Last row in Tab.~\ref{tab:abl_sgoi_supp} present one more experiment that takes \textbf{the union of} semantic edges from all GCNeXt blocks to aggregate the semantic feature. We can find that the semantic context also helps to improve model performance under this setup.
\begin{table}[h]
\centering
\caption{\textbf{Ablating SGAlign Components
}
We disable the sample-rescale process and the feature concatenation from the semnantic graph for detection on ActivityNet-1.3. The rescaling strategy leads to slight improvement, while the main gain arises from the use of context information (semantic graph).  }
\small
\begin{tabular}{p{1.0cm}<{\centering}p{1.0cm}<{\centering}|p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.45cm}<{\centering}p{0.65cm}<{\centering}}
\hline
\multicolumn{2}{p{2cm}<{\centering}|}{ SGAlign} & \multicolumn{4}{c}{tIoU on Validation Set}\\
Samp. & Concat.  &  0.5  &  0.75  & 0.95  & Avg. \\
\hline
\xmark & \xmark     & {49.84} & {34.58} & 8.17 & {33.78} \\ \hline
\cmark & \xmark     & {49.86} & {{34.60}} & \bf{9.56} & {33.89} \\
\cmark & \cmark  & \bf{50.36} & {{34.60}} & 9.02 & \bf{34.09} \\ \hline
\cmark & all  & {50.26} & \bf{34.70} & 8.52 & {33.95} \\
\hline
\end{tabular}
\label{tab:abl_sgoi_supp}
\end{table}

\noindent
\textbf{2D Conv. for Sub-Graph Localization.}
Once we get the \textit{sub-graph feature} from SGAlign layer, instead of using three fully connected (FC) layers regress to $g_c$, we can arrange the anchors in a 2D $L\times L$ map based on the start/end time, and set zeros to the map where is no pre-designed anchors (e.g. $t_s>t_e$). In doing so, we can use 2D CNNs to regress to a $g_c$ map that arranged by the same order. We call the predicted matrix \textbf{IoU map}.

The neighbouring anchors in the 2D IoU map have similar boundary locations. Thus we can use the proposal-proposal relationship in the 2D convolutions. We set kernel size to 1, 3, and 5, and the results are shown in Tab.~\ref{tab:abl_Conv_on_IOU_map}. We do not observe any significant benefit from 2D convolutions.

\begin{table}[h]
\centering
\caption{\textbf{The model performance when we use 3 2D convolution layers to predict IoU map.}We set kernel size to 1, 3, and 5, and collect result on ActivityNet1.3. We do not observe any significant benefit from 2D convolutions.}
\small
\begin{tabular}{p{1.5cm}<{\centering}p{0.7cm}<{\centering}|p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.65cm}<{\centering}p{0.55cm}<{\centering}p{0.65cm}<{\centering}}
\hline
\multicolumn{2}{p{3.2cm}|}{ Conv. on IoU map} & \multicolumn{4}{c}{mAP on Validation Set}\\
Kernel Size & Padding  &  0.5  &  0.75  & 0.95  & Avg. \\
\hline
(1,1) & (0,0)   & {\textbf{50.25}} & {34.66} & \textbf{9.29} & {34.08} \\
(3,3) & (1,1)   & {\textbf{50.25}} & {\textbf{34.94}} & 7.74 & {\textbf{34.10}} \\
(5,5) & (2,2)   & {49.88} & {34.39} & 8.96 & {33.77} \\
\hline
\end{tabular}
\label{tab:abl_Conv_on_IOU_map}
\end{table}

redpajama_set_name: RedPajamaArXiv


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← Back Roaster's Choice • Nov 29 Share:
Huehuetenango - December's Roaster's Choice Subscription Coffee
December's Roaster's Choice Coffee Box subscribers will be enjoying Huehuetenango, a coffee from Guatemala.
The Morales family, with over 30 years of coffee farming experience under their belts, initially bought the Los Aguacatones farm to grow avocados in 2009. Poor soil quality and difficulty accessing fresh water meant that some coffee was planted but it was primarily to facilitate experimentation rather than being considered a viable crop. The coffees however started to present unusually high levels of quality grown at this extreme altitude and when a fire on the farm wiped out the avocado plants the family refocussed Los Aguacatones on the production of specialty coffee.
Seeds were brought over from the family's origin farm and an advanced irrigation system utilising rain water catchments was set up to create healthy growing conditions for the coffees. Within a short time coffee quality was expedited and began to achieve recognition with coffees being awarded in the Guatemalan Cup of Excellence Competition.
Heavy rainfalls in 2021 have affected crops across the Huehuetenango region and labour shortages have meant that farms struggled to find pickers to complete the harvest. We're lucky that we were able to obtain this lot and thrilled to be sharing it here in the UK.
Huehuetenange, commonly referred to as 'Huehue' (pronounced whe-whe) is situated in the highlands of north-west Guatemala bordering on Mexico. Around 65-75% of the inhabitants come from indigenous Maya groups, specifically the majority Mam people, whose connection to the land dates back to 200 AD as part of the Mayan Empire.
Like many regions of Guatemala, Huehuetenango was crippled by the Guatemalan civil war. Civilians were frequently terrorised in addition to being conscripted into both joining the war and building bases for the Guatemalan army at the equivalent of $0.32 USD a fortnight. The result was a deep depression which persists today.
Around 70% of Huehuetenango's population are estimated to live in poverty with 22% of those defined as living in extreme poverty. Although historically precious metal mining and livestock were prominent economically, today coffee is the most significant export of the region.
Cool air from the high mountains and warm air from the Gulf of Mexico collide in Huehuetenango creating a sort of tropical highland that breaks down further into many unique pockets of microclimate use for growing coffees. Although Huehuetenango is the driest region in Guatemala, in terms of average rainfall, it is uniquely abundant with rivers and fresh water streams. This means that, unlike in other coffee growing regions where farmers deliver coffee cherries to centralised processing stations, many smallholders in Huehuetenango can access enough water to process their own lots.
Although the diversity of latitude and climate within the region can create a wide variety of flavours we often find coffees from here present prominent berry and stone fruit notes which, combined, with the unique combination of processing and varied microclimate create some of the most distinct flavour profiles anywhere in the world of coffee.


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Cardiovascular disease risk among older immigrants in the United States
Tina Sadarangani, Deborah Chyun, Chau Trinh-Shevrin, Gary Yu, Christine Kovner
Background: In the United States, 16 million immigrants are 50 years and older, but little is known about their cardiometabolic health and how to best assess their cardiovascular disease (CVD) risk. Aging immigrants may therefore not be benefitting from advances in CVD prevention. Objective: In this study, we estimate and compare CVD risk in a nationally representative sample of aging immigrants using 3 different measures. Methods: This was a cross-sectional analysis using National Health and Nutrition Examination Survey data. Immigrants 50 years and older with no history of CVD were eligible. The Framingham Risk Score (FRS), the American College of Cardiology/American Heart Association Pooled Cohort Risk Equation, and presence of metabolic syndrome (MetS) were used to estimate risk. Bivariate statistics were analyzed using SPSS version 23.0 Complex Survey module to account for National Health and Nutrition Examination Survey unique weighting scheme. Results: The mean age of the sample was 61.3 years; 40% had hypertension, 17% had diabetes, 10% were smokers, and 95% did not meet the recommended physical activity guidelines. Proportions at an elevated CVD risk were as follows: American College of Cardiology/American Heart Association, 42% female and 76% male; FRS, 17.4% female and 76% male; and MetS, 22% female and 24% male. Conclusions: Immigrants had a lower overall risk using MetS and the American College of Cardiology/American Heart Association equation than has been found using these tools in similarly aged samples. The opposite was true for the FRS. The discrepancy between the proportion at risk and those being treated may reflect healthcare access gaps that warrant further investigation. A more holistic approach to risk measurement is needed that accounts for determinants of health that disproportionately affect immigrants, including language and socioeconomic status.
Journal of Cardiovascular Nursing
https://doi.org/10.1097/JCN.0000000000000498
Sadarangani, T., Chyun, D., Trinh-Shevrin, C., Yu, G., & Kovner, C. (2018). Cardiovascular disease risk among older immigrants in the United States. Journal of Cardiovascular Nursing, 33(6), 544-550. https://doi.org/10.1097/JCN.0000000000000498
Cardiovascular disease risk among older immigrants in the United States. / Sadarangani, Tina; Chyun, Deborah; Trinh-Shevrin, Chau; Yu, Gary; Kovner, Christine.
In: Journal of Cardiovascular Nursing, Vol. 33, No. 6, 01.11.2018, p. 544-550.
Sadarangani, T, Chyun, D, Trinh-Shevrin, C, Yu, G & Kovner, C 2018, 'Cardiovascular disease risk among older immigrants in the United States', Journal of Cardiovascular Nursing, vol. 33, no. 6, pp. 544-550. https://doi.org/10.1097/JCN.0000000000000498
Sadarangani T, Chyun D, Trinh-Shevrin C, Yu G, Kovner C. Cardiovascular disease risk among older immigrants in the United States. Journal of Cardiovascular Nursing. 2018 Nov 1;33(6):544-550. https://doi.org/10.1097/JCN.0000000000000498
Sadarangani, Tina ; Chyun, Deborah ; Trinh-Shevrin, Chau ; Yu, Gary ; Kovner, Christine. / Cardiovascular disease risk among older immigrants in the United States. In: Journal of Cardiovascular Nursing. 2018 ; Vol. 33, No. 6. pp. 544-550.
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title = "Cardiovascular disease risk among older immigrants in the United States",
abstract = "Background: In the United States, 16 million immigrants are 50 years and older, but little is known about their cardiometabolic health and how to best assess their cardiovascular disease (CVD) risk. Aging immigrants may therefore not be benefitting from advances in CVD prevention. Objective: In this study, we estimate and compare CVD risk in a nationally representative sample of aging immigrants using 3 different measures. Methods: This was a cross-sectional analysis using National Health and Nutrition Examination Survey data. Immigrants 50 years and older with no history of CVD were eligible. The Framingham Risk Score (FRS), the American College of Cardiology/American Heart Association Pooled Cohort Risk Equation, and presence of metabolic syndrome (MetS) were used to estimate risk. Bivariate statistics were analyzed using SPSS version 23.0 Complex Survey module to account for National Health and Nutrition Examination Survey unique weighting scheme. Results: The mean age of the sample was 61.3 years; 40{\%} had hypertension, 17{\%} had diabetes, 10{\%} were smokers, and 95{\%} did not meet the recommended physical activity guidelines. Proportions at an elevated CVD risk were as follows: American College of Cardiology/American Heart Association, 42{\%} female and 76{\%} male; FRS, 17.4{\%} female and 76{\%} male; and MetS, 22{\%} female and 24{\%} male. Conclusions: Immigrants had a lower overall risk using MetS and the American College of Cardiology/American Heart Association equation than has been found using these tools in similarly aged samples. The opposite was true for the FRS. The discrepancy between the proportion at risk and those being treated may reflect healthcare access gaps that warrant further investigation. A more holistic approach to risk measurement is needed that accounts for determinants of health that disproportionately affect immigrants, including language and socioeconomic status.",
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AU - Trinh-Shevrin, Chau
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N2 - Background: In the United States, 16 million immigrants are 50 years and older, but little is known about their cardiometabolic health and how to best assess their cardiovascular disease (CVD) risk. Aging immigrants may therefore not be benefitting from advances in CVD prevention. Objective: In this study, we estimate and compare CVD risk in a nationally representative sample of aging immigrants using 3 different measures. Methods: This was a cross-sectional analysis using National Health and Nutrition Examination Survey data. Immigrants 50 years and older with no history of CVD were eligible. The Framingham Risk Score (FRS), the American College of Cardiology/American Heart Association Pooled Cohort Risk Equation, and presence of metabolic syndrome (MetS) were used to estimate risk. Bivariate statistics were analyzed using SPSS version 23.0 Complex Survey module to account for National Health and Nutrition Examination Survey unique weighting scheme. Results: The mean age of the sample was 61.3 years; 40% had hypertension, 17% had diabetes, 10% were smokers, and 95% did not meet the recommended physical activity guidelines. Proportions at an elevated CVD risk were as follows: American College of Cardiology/American Heart Association, 42% female and 76% male; FRS, 17.4% female and 76% male; and MetS, 22% female and 24% male. Conclusions: Immigrants had a lower overall risk using MetS and the American College of Cardiology/American Heart Association equation than has been found using these tools in similarly aged samples. The opposite was true for the FRS. The discrepancy between the proportion at risk and those being treated may reflect healthcare access gaps that warrant further investigation. A more holistic approach to risk measurement is needed that accounts for determinants of health that disproportionately affect immigrants, including language and socioeconomic status.
AB - Background: In the United States, 16 million immigrants are 50 years and older, but little is known about their cardiometabolic health and how to best assess their cardiovascular disease (CVD) risk. Aging immigrants may therefore not be benefitting from advances in CVD prevention. Objective: In this study, we estimate and compare CVD risk in a nationally representative sample of aging immigrants using 3 different measures. Methods: This was a cross-sectional analysis using National Health and Nutrition Examination Survey data. Immigrants 50 years and older with no history of CVD were eligible. The Framingham Risk Score (FRS), the American College of Cardiology/American Heart Association Pooled Cohort Risk Equation, and presence of metabolic syndrome (MetS) were used to estimate risk. Bivariate statistics were analyzed using SPSS version 23.0 Complex Survey module to account for National Health and Nutrition Examination Survey unique weighting scheme. Results: The mean age of the sample was 61.3 years; 40% had hypertension, 17% had diabetes, 10% were smokers, and 95% did not meet the recommended physical activity guidelines. Proportions at an elevated CVD risk were as follows: American College of Cardiology/American Heart Association, 42% female and 76% male; FRS, 17.4% female and 76% male; and MetS, 22% female and 24% male. Conclusions: Immigrants had a lower overall risk using MetS and the American College of Cardiology/American Heart Association equation than has been found using these tools in similarly aged samples. The opposite was true for the FRS. The discrepancy between the proportion at risk and those being treated may reflect healthcare access gaps that warrant further investigation. A more holistic approach to risk measurement is needed that accounts for determinants of health that disproportionately affect immigrants, including language and socioeconomic status.
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This printable, downloadable handout is intended for elementary school students at Beginner (pre-A1) and Elementary (A1) level. It can be used for strengthening your groups' Listening skills.
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Nobel-Winning Playwright Harold Pinter Dies at 78
Harold Pinter, the influential British playwright whose works earned him a Nobel Prize in 2005, died in London at age 78. He had been suffering from cancer of the esophagus since 2002. The NY Times writes his "gifts for finding the ominous in the everyday and the noise within silence made him the most influential and imitated dramatist of his generation." His most famous plays include The Birthday Party, The Caretaker, The Homecoming, The Servant and Betrayal, and the Times of London explained his work "introduced a new word to the English language – Pinteresque – to convey an atmospheric silence." (More about Pinteresque, the adjective, here.) Kenneth Tynan once wrote, "Mr. Pinter is a superb manipulator of language, which he sees not as a bridge that brings people together but as a barrier that keeps them apart. Ideas and emotions, in the larger sense, are not his province; he plays with words, and he plays on our nerves, and it is thus that he grips us."


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text:
Sweet Weasel Words
Home of author Crawford Smith
The Story of Jackrabbit
Encounter in the North Woods (2015)
Cruising with Melvin and the Furb (2016)
The Bounce (2018)
New Year, Old Story
Dillinger Exhumation: Dead or Alive?
Writer's Blockhead
Read My Shorts
Dillinger Exhumation: Dead or Alive? | Sweet Weasel Words on The Ghost of Dillinger's Corpse
Everything I've Learned About Self-Publishing, So Far | Sweet Weasel Words on Indie Bookstores Love Local Authors…but Hate Amazon Even More
Crawford on Easy Reedsy
Jennifer Huston on Easy Reedsy
Facebook Political Advertising Acrobatics | American Knucklehead on Dummies for Facebook Ads
Amazon KDP
The Jackrabbit Rests in Peace…for Now
Published by Crawford on September 18, 2019
Dillinger's body prior to burial
The September 16 date for exhuming John Dillinger's body has come and gone – and he continues to rest in peace, at least for now. In previous posts (here and here), I outlined the effort spearheaded by Dillinger nephew Michael Thompson to have his famous uncle's boy disinterred for DNA testing. Now it seems as if the planned exhumation is on indefinite hold.
The exhumation was announced shortly after the 85th anniversary of the Biograph shooting that allegedly took down the famous gangster. The Indiana State Department of Health had approved a request by Thompson to disinter Dillinger's body. The reason given was to establish once and for all whether or not it was John Dillinger in the grave. For decades, rumors had circulated that it hadn't been Dillinger who was gunned down outside the movie theater in Chicago, but rather a look-alike. This "wrong man" theory had been supported by some fascinating anecdotal evidence, and Thompson claimed that he wanted to prove or disprove the theory.
Shortly after the announcement, it was revealed that the drive to dig up the Jackrabbit was connected with a History Channel documentary on the life and crimes of John Dillinger.
Then things started to get weird. Surprise!
First, there were other Dillinger relatives who were opposed to the exhumation. Dillinger great-nephew Jeff Scalf, who has some manner of legal control over Dillinger's name and likeness, was adamantly opposed. "It's my opinion that this effort was done for 15 minutes of fame and 30 pieces of silver," he said.
Then Crown Hill Cemetery started to get cold feet. "We also have concerns that the complex and commercial nature of this exhumation could cause disruption to the peaceful tranquility of the Cemetery and those who are visiting to remember their loved ones." This is a legitimate concern, as accessing Dillinger's remains would involve a fairly robust construction effort. Concerned about grave tampering, Dillinger's father had his body reburied under huge slabs of concrete and scrap metal shortly after the initial burial in 1934.
Not to be thwarted, Thompson filed an injunction against Crown Hill to compel them to cooperate with the exhumation. Thompson's attorneys claimed that Crown Hill's objections were disingenuous. They stated, "Thompson and others in his family should not be prohibited from confirming the identity of their uncle merely because he is infamous. If identity is confirmed, Thompson and all other descendants of the deceased can put to rest their legitimate questions about identity." A hearing is scheduled for October 1 to discuss the case.
In the latest twist, the History Channel announced on September 11 that they would no longer be associated with the documentary project. No reason was given. Two days later, Thompson announced that he planned on moving forward with the project. Personally, I'm not sure if that will happen without the History Channel support. I think that the logistics involved in getting around John Sr.'s tamper-deterrent measures may require some outside financing. Perhaps some sort of "angel investor" will appear to fill the void.
The whole thing has been – and continues to be – a soap opera. I'm sure that the lack of consistent explanation from Crown Hill Cemetery and the History Channel about their sudden withdrawal from the project will fuel further conspiracy theories. I suspect that there will be a few more chapters to play out, but I think this drama is headed towards denouement.
At the end of the day, I think that maybe it wasn't that good of an idea in the first place. Of course, I'd love to really know whether or not John Dillinger is in the grave with his name on it. And as an author who has just published a book about Dillinger, I would welcome the free publicity attendant to such a spectacle. However, the idea of disturbing someone's remains for the sake of a TV documentary never seemed right.
Let Johnnie D. rest in peace.
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Frank Dunham
Being a relative I'm all for it, you get tired of people asking if it's him or not. Mikes not making anything off this, if he is it's not much, it's alright for people outside the family to make money off the Dillinger name, they have been doing it for years, Mikes not doing it for the money or anything else, just to put to rest " is it him or not " B.S. My grandpa Fields (Earl) is Lizzy's brother, Dillinger is my 1st cousin once removed, I grew up in Mooresville, Frances used to come over to our house to visit my Mom.
Scott Umsteadt
Mr. Smith, I'm very curious as to why you would be opposed to this? I don't believe there was any controversy when Jesse James' identity was confirmed. Nor Lee Harvey Oswald's. So why is John Dillinger any different?
Has "Truth" become so old fashioned that "narratives" have become more important? Are we really advocating the old adage, "When the legend becomes fact, print the legend"?
If the family wants to exhume the body for any reason it's their business. It's not like exhumations are that rare. As a matter of fact, just a few years ago a pioneer cemetery was relocated just for an I-69 highway expansion. Coincidentally, the bodies were re-interned at Crown Hill. Forensic analysis was conducted by Indiana University before the bodies were re-interned. Not to mention archeologist do scientific studies on remains everyday.
(https://www.history.com/news/digging-up-the-dead-historys-most-famous-exhumations)
Thanks for your comment! You raise some interesting points, especially about Jesse James and Oswald. Actually, I'm fairly conflicted about the entire enterprise. I can't imagine how the family members must feel, although Mr. Dunham's post does shed some light on that. I understand, however, that there are other family members who are adamantly opposed to the exhumation. I must admit to an acute interest in getting a definitive answer to the identity question – after, all I've been wondering about that since I fist read Dillinger: Dead or Alive? over forty years ago. I was therefore a little disappointed (but not especially surprised) when the proposed disinterment broke down. However, I am content to just let the legend continue of the fact isn't imminently available.


Despite gloomy predictions for automotive sales in 2018, pickup trucks led the charge to healthy year-end numbers.
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Pickup trucks continue to lead the automotive sales charge in the third quarter.
Pickup sales continue to keep vehicle sales straong for Q3 of 2018.
Between Hurricanes Harvey and Florence, pickup sales could fall short for September 2018.
What Are the Fastest-Selling Used Pickup Trucks?
Considering buying a used pickup? This study crunches used-pickup sales.
Which Pickups Had Their Best Sales Month Ever?
August pickup truck sales numbers are in and looking strong, especially for two major players.
What do July's F-Series sales figures mean for Ford's chances of selling 1,000,000 trucks in 2018?


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RAID & LTO-7. The Perfect Combo.
mRack DIT LTO-7 is a 1U rack mountable hybrid RAID and LTO-7 tape archiving solution designed to support today's digital workflows. Copy camera cards to high-speed RAID for online editing and then archive the raw footage and completed projects to LTO-7 tape to safeguard the media for the long term.
mRack DIT LTO-7 features four Thunderbolt 2 ports and connects to any Thunderbolt equipped Mac or PC. Add up to six spinning or solid state drives for integrated hardware RAID functionality. Archive assets to the on-board LTO-7 drive which provides 6TB of data storage per cartridge. LTO has a shelf life of 30+ years and is the most cost-effective storage medium on a cost per TB basis.
mRack DIT ships with a simple drag and drop Linear Tape File System (LTFS) archiving and retrieve utility for macOS. To ensure that what you archive to mRack DIT is an exact replica of your source material, the utility utilizes xxHASH64 verification. Detailed Transfer Logs are also generated with every archive.
Add six of your favorite hard drives or SSDs. mRack DIT supports drives up to 9.5mm in height. Setting up a RAID set is easy using the included utility.
The dedicated RAID-on-Chip processor and onboard 512MB of DDR3 cache memory offloads RAID processing from the host system, ensuring stable and high-performance data transfers.


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