text:
\section{Acknowledgment}
This work was supported by Huawei Zurich Research Center; by the National Centre of Competence in Research (NCCR) Robotics (grant agreement No. 51NF40-185543) through the Swiss National Science Foundation (SNSF), and the European Research Council (ERC) under grant agreement No. 864042 (AGILEFLIGHT).

\clearpage
\section*{\Large \bf Appendix}
\input{sections/appendix.tex}

{\small
\bibliographystyle{ieee_fullname}

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

Time matters for object detection.
In 30 milliseconds, a human can run 0.3 meters, a car on public roads covers up to 1 meter, and a train can travel over 2 meters.
Yet, during this time, an ordinary camera captures only a single frame.

Frame-based sensors must strike a balance between latency and bandwidth.
Given a fixed bandwidth, a frame-based camera must trade-off camera resolution and frame rate.
However, in highly dynamic scenes, reducing the resolution or the frame rate may come at the cost of missing essential scene details, and, in safety-critical scenarios like automotive, this may even cause fatalities.

In recent years, event cameras have emerged as alternative sensor that offers a different trade-off.
Instead of counterbalancing bandwidth requirements and perceptual latency, they provide visual information at sub-millisecond latency but sacrifice absolute intensity information.
Instead of capturing intensity images, event cameras measure changes in intensity at the time they occur.
This results in a stream of events, which encode time, location, and polarity of brightness changes \cite{Gallego20pami}.
The main advantages of event cameras are their sub-millisecond latency, very high dynamic range ($>120$ dB), strong robustness to motion blur, and ability to provide events asynchronously in a continuous manner.

\input{figures/first.tex}

In this work, we aim to utilize these outstanding properties of event cameras for object detection in time-critical scenarios.
Therefore, our objective is to design an approach that reduces the processing latency as much as possible while maintaining high performance.
This is challenging because event cameras asynchronously trigger binary events that are spread of pixel space and time.
Hence, we need to develop detection algorithms that can continuously associate features in the spatio-temporal domain while simultaneously satisfying strict latency requirements.

Recent work has shown that dynamic graph neural networks (GNNs) \cite{Li2021, Schaefer21cvpr} and sparse neural networks \cite{Messikommer2020, Zhang_2022_CVPR, Yao2021TemporalwiseAS, Cordone2022ijcnn} can theoretically achieve low latency inference for event-based object detection.
Yet, to achieve this in practical scenarios they either require specialized hardware or their detection performance needs to be improved.

An alternative thread of research approaches the problem from the view of conventional, dense neural network designs \cite{Iacono2018iros, Chen2018cvprw, Jiang2019icra, Perot2020neurips, Li2022tip}.
These methods show impressive performance on event-based object detection, especially when using temporal recurrence in their architectures \cite{Perot2020neurips, Li2022tip}.
Still, the processing latency of these approaches remains beyond 40 milliseconds such that the low-latency aspect of event cameras cannot be fully leveraged.
This raises the question: How can we achieve both high accuracy and efficiency without requiring specialized hardware?

We notice that common design choices yield a suboptimal trade-off between performance and compute.
For example, prior work uses expensive convolutional LSTM (Conv-LSTM) cells \cite{Shi15nips} extensively in their feature extraction stage \cite{Perot2020neurips, Li2022tip} or relies on heavy backbones such as the VGG architecture \cite{Li2022tip}.
Sparse neural networks instead struggle to model global mixing of features which is crucial to correctly locate and classify large objects in the scene.

To achieve our main objective, we fundamentally revisit the design of vision backbones for event-based object detection.
In particular, we take inspiration from neural network design for conventional frame-based object detection and combine them with ideas that have proven successful in the event-based vision literature.
Our study deliberately focuses on \emph{macro} design of the object detection backbone to identify key components for both high performance and fast inference on GPUs.
The resulting neural network is based on a single block that is repeated four times to form a multi-stage hierarchical backbone that can be used with off-the-self detection frameworks.

We identify three key components that enable an excellent trade-off between detection performance and inference time.
First, we find that interleaved local- and global self-attention \cite{Tu2022eccv} is ideally suited to mix both local and global features while offering linear complexity in the input resolution.
Second, this attention mechanism is most effective when preceded by a simple convolution that also downsamples the spatial resolution from the previous stage.
This convolution effectively provides a strong prior about the grid-structure of the pixel array and also acts as a conditional positional embedding for the transformer layers \cite{CPE2021}.
Third, temporal recurrence is paramount to achieve strong detection performance with events.
Differently from prior work, we find that Conv-LSTM cells can be replaced by plain LSTM cells \cite{Hochreiter1997} that operate on each feature separately\footnote{equivalent to $1\times 1$ kernel in a Conv-LSTM cell}.
By doing so, we dramatically reduce the number of parameters and latency but also slightly improve the overall performance.
Our full framework achieves competitive performance and higher efficiency compared to state-of-the-art methods.
Specifically, we reduce parameter count (from 100M to 18.5 M) and inference time (from 72 ms to 13 ms)
up to a factor of 5 compared to prior art \cite{Li2022tip}.
At the same time, we train our networks from scratch, showing that these benefits do not originate from large-scale pretraining.

Our main contributions can be summarized as follows:
(1) We re-examine predominant design choices in event-based object detection pipelines and reveal a set of key enablers for high performance in event-based object detection.
(2) We propose a simple, composable stage design that unifies the crucial building blocks in a compact way.
(3) We propose a hierarchical multi-stage backbone that is fast, lightweight and still offers performance comparable to the best reported so far.
(4) We present state-of-the-art object detection performance of 47.5\% mAP on the Gen1 detection dataset \cite{gen12020} and highly competitive results on the 1 Mpx detection dataset \cite{Perot2020neurips} while training the proposed architecture from scratch.
In addition, we also provide insights into effective data augmentation techniques that contribute to reaching these results.
\section{Related Work}\label{sec:relwork}

\subsection{Object Detection for Event Cameras}

Object detection in the event camera literature can be broadly classified into three emerging research directions.

Recent work explores \textbf{graph neural networks} to dynamically construct a spatio-temporal graph \cite{Li2021, Schaefer21cvpr, Mitrokhin_2020_CVPR}.
New nodes and node edges are established by sub-sampling events and finding existing nodes that are close in space-time.
The main challenge is to design the architecture such that information can propagate over vast distances in the space-time volume.
This is relevant, for example, when large objects move slowly with respect to the camera.
Furthermore, aggressive sub-sampling of events can lead to the removal of potentially crucial information, but is often required to maintain low-latency inference.

A second line of work employs \textbf{spiking neural networks} (SNNs) that propagate information sparsely within the network \cite{Zhang_2022_CVPR, Yao2021TemporalwiseAS, Cordone2022ijcnn}.
SNNs are closely related to dense recurrent neural networks (RNNs) in that each spiking neuron has an internal state that is propagated in time.
Differently from RNNs, neurons in SNNs only emit spikes whenever a threshold is reached.
This spike generation mechanism is not differentiable, which leads to substantial difficulties in optimizing these networks \cite{Neftci2019, Shrestha18nips, Lee_2016, Zenke_2018, Lee_2020, Bellec_2020, Taherkhani_2020}.
One workaround is to avoid the aforementioned threshold and instead propagate features throughout the receptive field \cite{Messikommer2020} .
The downside of this mechanism is that the sparse-processing property is lost within deeper layers of the network.
Overall, the design and training of SNNs still requires fundamental investigation before competitive performance can be reached.

A third research direction is concerned with exploring \textbf{dense neural networks} for object detection with event cameras.
The first step is the creation of a dense tensor (event representation) that enables compatibility with dense operations such as convolutions.
Early work directly uses a single event representation generated from a short temporal window of events to infer detections \cite{Iacono2018iros, Chen2018cvprw, Jiang2019icra}.
These approaches discard relevant information from beyond the considered temporal window such that detecting slowly moving objects becomes difficult or impossible.
Followup work addresses this issue by incorporating recurrent neural network layers \cite{Perot2020neurips, Li2022tip} which drastically improved the detection performance.
We follow this line of work but revamp dominant architecture choices to build a canonical framework that is fast, lightweight and highly performant.

\subsection{Vision Transformers for Spatio-Temporal Data}
The success of attention-based models \cite{TF2017neurips} in NLP has inspired the exploration of transformer-based architectures in computer vision \cite{dosovitskiy2021an}.
Attention-based models have recently also been explored in video classification \cite{tong2022videomae, MaskedAutoencodersSpatiotemporal2022, Arnab_2021_ICCV, gberta_2021_ICML} where the models are applied directly to a set of frames.
While these approaches have shown promising results in spatio-temporal modelling, they are optimized for offline processing of stored video data.

In event-based vision, attention-based components have found applications in classification \cite{Sabater_2022_CVPR,Wang2022icip} and image reconstruction \cite{Weng2021}, but their use in event-based object detection has yet to be investigated.
\section{Conclusion}\label{sec:conl}
We introduced a novel backbone architecture for object detection with event cameras.
The architecture consists of a stage design that is repeatedly applied to create a multi-stage hierarchical neural network.
Each stage compactly incorporates convolutional priors, local- and sparse global attention and recurrent feature aggregation.
Our experiments highlight that recurrent vision transformers can be trained from scratch to reach state-of-the-art performance
in object detection with event cameras.
The resulting canonical stage-design is directly compatible with existing detection frameworks, and paves the way to low-latency object detection with event cameras on conventional hardware.
Nonetheless, we hope that this work also inspires novel designs in future neuromorphic systems.
\section{Method}\label{sec:method}
Our object detection approach is designed to process a stream of events sequentially as they arrive.
Incoming events are first processed into tensors that represent events in space and time.
In every timestep, our network takes a new event representation as input as well as the previous states of the recurrent neural network layers.
After each pass through the backbone, the output of the RNNs are used as input to the detection framework.
The following sections elaborate on each one of these steps.
Figure \ref{fig:overview} shows an overview of the RVT architecture.

\paragraph{Event Processing}\label{sec:ev_repr}
Each pixel of an event camera can independently trigger an event when a significant log brightness change occurs.
An event can be positive or negative depending on the sign of the brightness change.
We characterize an event with polarity $p_k\in\{0,1\}$ as a tuple $e_k=(x_k,y_k,t_k,p_k)$ that occurs at pixel $(x_k, y_k)$ at time $t_k$.
Modern event cameras can produce 10s of millions of events per second which renders event-by-event processing out of reach on conventional processing units.%

In this work we opt for a very simple preprocessing step to enable compatibility with convolutional neural network layers which, as we will show later in Sec. \ref{sec:ablatation}, are an important contributor to the performance of our model.

Our preprocessing step starts with the creation of a 4-dimensional tensor $E$.
The first dimension consists of two components and represents the polarity. The second dimension has $T$ components and is associated with $T$ discretization steps of time. The 3rd and 4th dimension represent height and width of the event camera.
We process set of events $\mathcal{E}$ within a time duration $[t_a,t_b)$ the following way:
\begin{align*}
    E(p, \tau, x, y) &= \sum_{e_k\in\mathcal{E}}\delta(p - p_k)\delta(x-x_k,y-y_k)\delta(\tau-\tau_k),\\
    \tau_k &= \left\lfloor \frac{t_k-t_a}{t_b - t_a}\cdot T\right\rfloor
\end{align*}
In words, we create $T$ 2-channel frames where each pixel contains the number of positive or negative events within one of the $T$ temporal frames.
As a final step, we flatten the polarity and time dimension to retrieve a 3-dimensional tensor with shape $(2T, H, W)$ to directly enable compatibility with 2D convolutions.
We implement the presented algorithm with byte tensors to save memory and bandwidth.
Other, more sophisticated representations are possible \cite{Cannici2019cvprw, Zhu19cvpr, Gehrig19iccv, Baldwin2022, Li2022tip, Tulyakov_2019_ICCV, Barchid2022}, but their thorough evaluation is not our focus.

\subsection{Mixing Spatial and Temporal Features}
The main difficulty of object detection with event cameras is that at any given time, the neural network should be able to efficiently
(1) extract local- and global task-relevant features in pixel space because objects can cover both very small regions or large portions of the field of view;
(2) extract features from very recent events (e.g. moving edges) as well as events from several seconds ago. This is necessary because some objects or moving slowly with respect to the camera such that they generate very few events over time.
These observations motivate us to investigate transformer layers for spatial feature extraction and recurrent neural networks for efficient temporal feature extraction.
Figure \ref{fig:block} illustrates the components of a single stage.

\paragraph{Spatial Feature Extraction}
The spatial feature extraction stage should incorporate a prior about the fact that pixels are arranged in a 2D grid as early as possible in the computation graph.
We enable this by using a convolution with overlapping kernels on the input features that at the same time spatially downsamples the input or features from the previous stage.
This convolution also endows our model with a conditional positional embedding \cite{CPE2021} such that we do not require absolute \cite{TF2017neurips,dosovitskiy2021an} or relative \cite{liu2021Swin} positional embeddings.
Our ablation study in Sec. \ref{sec:ablatation} shows that overlapping kernels lead to a substantial boost in detection performance.

In a subsequent step, the resulting features are transformed through multi-axis self-attention.
We quickly summarize the steps but refer to Tu et. al \cite{Tu2022eccv} for an elaborate explanation.
Multi-axis attention consists of two stages using self-attention.
The first stage performs local feature interaction while the second stage enables dilated global feature mixing.
More specifically, the features are first grouped locally into non-overlapping windows:
Let $X\in\mathbb{R}^{H\times W\times C}$ be the input feature tensor.
We reshape the tensor to a shape $(\frac{H}{P}\times \frac{W}{P}, P\times P, C)$ where $P\times P$ is the window size in which multi-head self-attention \cite{TF2017neurips} is applied.
This \emph{block attention} (Block-SA in Fig. \ref{fig:block}) is used to model local interactions.
As a next step, we would ideally be able to extract features globally.
One straightforward way to achieve this would be applying self-attention on the whole feature map.
Unfortunately, global self-attention has quadratic complexity in the number of features.
Instead, we use \emph{grid attention} (Grid-SA in Fig. \ref{fig:block}).
Grid attention partitions the feature maps into a grid of shape $(G\times G, \frac{H}{G}\times\frac{W}{G}, C)$ using a $G\times G$ uniform grid.
The resulting windows are of size $\frac{H}{G}\times\frac{W}{G}$.
Self-attention is then applied to these windows which corresponds to global, dilated mixing of features.

We study alternative designs as part of our architecture in the ablation studies in Sec. \ref{sec:ablatation}.

\paragraph{Temporal Feature Extraction}
We opt for temporal feature aggregation with LSTM \cite{Hochreiter1997} cells at the end of the stage.
Differently from prior work \cite{Perot2020neurips, Li2022tip} we find that temporal and spatial feature aggregation can be completely separated.
This means that we use plain LSTM cells such that the states of the LSTMs do not interact with each other.
By avoiding Conv-LSTM units \cite{Shi15nips}, we can drastically reduce the computational complexity and parameter count.
I.e. a Conv-LSTM with kernel size $k\times k$ and stride 1 demands $k^2$ the number of parameters and compute compared to the original LSTM cell.
We examine this aspect in the experimental Sec. \ref{sec:ablatation}.

\paragraph{Model Details}
We apply LayerNorm \cite{Lei16arxiv} before and LayerScale \cite{Touvron_2021_ICCV} after each attention and MLP module, and add a residual connection after each module.
We found that LayerScale enables a wider range of learning rates.

\subsection{Hierarchical Multi-Stage Design}
We compose multiple RVT blocks together to form a multi-stage hierarchical backbone.
The overall architecture is shown in Fig. \ref{fig:overview}.

At first, a local temporal slice of events is processed into a 2D tensor format as formulated in the beginning of this section.
Subsequently, each stage takes the previous features as input and optionally uses the LSTM state from the last timestep to compute features for the next stage.
By saving the LSTM states for the following timestep, each recurrent stage can retain temporal information for the whole feature map.

We follow prior work and use features from the second to the fourth stage for the object detection framework.
To do so, we reshape the hidden states of the LSTMs into 2D feature maps.
\section{Discussion and Limitations}\label{sec:limit}
We use a very simple event representation which does not leverage the full potential of event-based data. For example, we only have a weak prior on the order of events because we process the temporal dimension directly with fully connected layers. Recent work has shown substantial gains by introducing temporal convolutions in early layers \cite{Li2022tip}. Efficient low-level processing of event data is still an open research problem that we have not addressed in this work.

Our approach currently only uses event streams to detect objects.
Frames yield complementary information that, when properly incorporated, will yield significantly enhanced detection performance.
For example, in Fig. \ref{fig:qual_gen4} (d) we can see that our model can retain information over some period when no events are available.
Still, the memory of the network will fade and detection performance deteriorates.
High quality frames even at low frame-rate could provide the missing complementary information.
Hence, we believe that a multi-modal extension of our method on a suitable dataset is a promising next step.

\section{Overview}
Here we supplement the main paper with details regarding data augmentation parameters in section A-\ref{sec:data_augm} (A- refers to the sections in this appendix) and additional experiments in section A-\ref{sec:add_exp}. More specifically, section A-\ref{sec:add_exp_lstm} studies skip connections for the LSTM cells and section A-\ref{sec:add_exp_dsec} discusses the cross-dataset generalization capabilities with a deployment on the DSEC \cite{gehrig2021dsec} dataset.
\section{Data Augmentation Details}\label{sec:data_augm}
We apply three data augmentation techniques to train our models from scratch.
Table \ref{tab:augm} summarizes the probability of each augmentation being used on an individual sample.

For each sample, we may apply horizontal flipping and also apply a zoom augmentation subsequently.
For the zoom augmentation we draw from a Bernoulli distribution to indicate whether we apply the augmentation at all.
If zoom augmentation shall be applied, we randomly choose between zoom-in or zoom-out augmentation based on the respective probability.
For the zoom augmentations, there is also the parameter that defines the \emph{magnitude} with which the augmentation is applied.
A magnitude of 1 means that no zoom is applied while a magnitude greater than 1 indicates how much zooming in or zooming out is applied with respect to the original resolution.
The magnitude that we finally apply is drawn from a continuous uniform distribution with bounds \emph{min} and \emph{max}.
\begin{table}[h]
    \centering
    \begin{tabular}{@{}llll@{}}
        \toprule
                        &              & \multicolumn{2}{c}{Magnitude} \\ \cmidrule(l){3-4}
        Augmentation    & Probability  & min           & max           \\ \midrule
        horizontal flip & 0.5          &               &               \\
        apply zoom      & 0.8          &               &               \\
        zoom in         & 0.8          & 1             & 1.5           \\
        zoom out        & 1-P(zoom in) & 1             & 1.2           \\ \bottomrule
    \end{tabular}
    \caption{\textbf{Data Augmentation Parameters}. The probability defines the Bernoulli distribution from which we draw the decision whether to apply this augmentation on a given sample.}
    \label{tab:augm}
\end{table}
\section{Additional Experiments}\label{sec:add_exp}
This section provides two additional experiments that did not fit into the main paper.
First, we briefly discuss an ablation on the possibility of using residual LSTM layers.
Second, we follow with a qualitative study of cross-dataset generalization using our model trained on the 1 Mpx dataset.

\subsection{Residual LSTM Ablation}\label{sec:add_exp_lstm}
Our model employs LSTM cells \cite{Hochreiter1997} without skip/residual connections in the model.
We also experimented with adding skip connections to the LSTM cells on the Gen1 \cite{gen12020} dataset.
Table \ref{tab:lstm_residual} shows that adding a residual connection to the LSTM cells leads to worse results.
We hypothesize that this residual connection hampers the LSTM's ability to control the mixture of incoming (current timestep) and retained temporal features (previous timesteps).
For example, it would be difficult for the residual-LSTM combination to ignore the incoming feature because the output of the LSTM is simply added to this feature.
Without the residual connection, the LSTM could simply set the input gate to 0 to ignore the input.

\begin{table}[h]
    \centering
    \begin{tabular}{@{}llll@{}}
        \toprule
        LSTM residual & mAP & AP$_{50}$ & AP$_{75}$ \\ \midrule
        \underline{without residual}     & \textbf{47.6}    & \textbf{70.1}          & \textbf{52.6} \\
        with residual & 46.0    & 69.8          & 50.3          \\ \bottomrule
    \end{tabular}
    \caption{\textbf{LSTM with and without residual connection}. Using a skip connection over the LSTM cells leads to worse results.}
    \label{tab:lstm_residual}
\end{table}
\subsection{Cross-Dataset Generalization: From 1 Mpx to DSEC}\label{sec:add_exp_dsec}
DSEC is a dataset that features event cameras and global shutter cameras close to each other.
Unlike the 1 Mpx dataset \cite{Perot2020neurips} which was recorded mostly urban scenarios in Paris, DSEC \cite{gehrig2021dsec} provides recordings from urban and rural regions in Switzerland.
Furthermore, the event camera used in the DSEC dataset is a Gen 3 prophesee event camera instead of a Gen 4 camera as in the 1 Mpx dataset.
In this section, we qualitatively show that our model can be successfully deployed in a different environment and using different event cameras.

We deploy RVT-B, trained on the 1 Mpx dataset , on several sequences of the DSEC dataset to qualitatively assess the cross-dataset generalization.
While \emph{DSEC does not provide object detection labels}, we can visually assess the quality of the detections by using the provided calibration files to map the frames of the global shutter camera to the event camera view.

Figure \ref{fig:dsec_interlaken} and \ref{fig:dsec_zurich} show predictions of our model together with the images closest in time to the detections.
Figure \ref{fig:dsec_interlaken} shows our model can successfully detect cars in mountainous environments.
In particular, Figure \ref{fig:dsec_interlaken} (a) shows an HDR scene where the global shutter frame is overexposed such that the approaching car is barely visible.
Due to the high dynamic range of the event camera, our model can detect the approaching car without any difficulty.
Figure \ref{fig:dsec_zurich} features more urban environments where our model also manages to detect objects correctly.

\begin{figure*}[ht]
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/preds/000776.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/preds/000662.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/preds/000279.png}\\
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/imgs/000777.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/imgs/000663.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/interlaken_00_b/imgs/000280.png}\\
    \includegraphics[width=0.33\linewidth]{imgs/supp/a_subcap_hack.pdf}
    \includegraphics[width=0.33\linewidth]{imgs/supp/b_subcap_hack.pdf}
    \includegraphics[width=0.33\linewidth]{imgs/supp/c_subcap_hack.pdf}
    \caption{Prediction examples on the DSEC dataset featuring a \textbf{mountainous environment}. Frames are shown only for visualization purposes and are not used by the model. Column (a) shows a typical high-dynamic range (HDR) scenario where the vehicle is exiting a tunnel with a car approaching from outside the tunnel. The HDR capabilities of the event cameras enables our model to detect the approaching car. Column (b) shows a scenario with a wet road and challenging reflections.}
    \label{fig:dsec_interlaken}
\end{figure*}

\begin{figure*}[ht]
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_00_b/preds/000445.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_07_a/preds/000159.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_04_f/preds/000718.png}\\
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_00_b/imgs/000446_no_plate.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_07_a/imgs/000160.png}
    \includegraphics[width=0.33\linewidth]{imgs/supp/dsec/zurich_city_04_f/imgs/000719.png}\\
    \includegraphics[width=0.33\linewidth]{imgs/supp/a_subcap_hack.pdf}
    \includegraphics[width=0.33\linewidth]{imgs/supp/b_subcap_hack.pdf}
    \includegraphics[width=0.33\linewidth]{imgs/supp/c_subcap_hack.pdf}
    \caption{Prediction examples on the DSEC dataset featuring a \textbf{(sub-)urban environments}. Frames are shown only for visualization purposes and are not used by the model. Column (a) illustrates a typical urban situation where pedestrians, two-wheelers, cars and other road users occupy the street simultaneously. In column (c), we show a failure case of our model where a street pillar is erroneously detected and classified as a two-wheeler.}
    \label{fig:dsec_zurich}
\end{figure*}

\paragraph{Discussion of Failure Cases}
By and large, our model can successfully detect objects on DSEC even though it has only been trained on the 1 Mpx dataset.
Still, we found failure cases that might stem from distribution shift between the datasets.
For example, Figure \ref{fig:dsec_zurich} (c) shows the erroneous detection of a two-wheeler that instead is a pillar on the street.

Overall, the model is good at detecting cars but is less confident and accurate at detecting two-wheelers and pedestrians.
This effect likely stems from the fact that the 1 Mpx dataset has almost twice as many car labels as pedestrian and two-wheeler labels combined.
\textbf{The submission comes with two videos} showing the predictions on two separate sequences on DSEC for a better qualitative assessment.
\balance

\section{Dataset Licenses}
\paragraph{Gen1 \cite{gen12020}}
``Prophesee Gen1 Automotive Detection Dataset License Terms and Conditions'': \url{https://www.prophesee.ai/2020/01/24/prophesee-gen1-automotive-detection-dataset/}
\paragraph{1 Mpx \cite{Perot2020neurips}}
``Prophesee 1MegaPixel Automotive Detection Dataset License Terms and Conditions'': \url{https://www.prophesee.ai/2020/11/24/automotive-megapixel-event-based-dataset/}
\paragraph{DSEC \cite{gehrig2021dsec}}
``Creative Commons Attribution-ShareAlike 4.0 International public license (CC BY-SA 4.0)'': \url{https://dsec.ifi.uzh.ch/}
\section{Experiments}\label{sec:exp}

\input{tables/architecture-params.tex}

We conduct ablations and evaluation our model on the Gen1 \cite{gen12020} and 1 Mpx \cite{Perot2020neurips} event camera datasets.
We train two variants of our model: RVT-B the base model and a small model RVT-S on both datasets.
Parameter details for both models are shown in Tab. \ref{tab:arch_variants}.

\subsection{Setup}
\paragraph{Implementation Details}

We initialize all layers randomly except LayerScale which is initialized to 1e-5 for each module.
We train our models for 400k iterations with the ADAM optimizer \cite{Kingma15iclr} with a OneCycle learning rate schedule \cite{onecyclelr} using a 2000 warmup iterations and linear decay from a maximum learning rate or both models on both datasets and all ablation studies.
For comparison with prior work, we use backpropagation through time (BPTT) over a sequence length of 21 for the Gen1 dataset and 11 for the 1 Mpx dataset.
We use a batch size of 8 and a maximum learning rate of 2e-4 for the Gen1 dataset and train the model on a single Tesla V100 GPU which takes approximately 2 days.
For the 1 Mpx dataset, we use an effective batch size of 12, a maximum learning rate of 2.45e-4 and train the model on four Tesla V100 GPUs which takes up to 3 days.
Our data augmention includes random horizontal flipping, zooming in and zooming out applied over the BPTT sequence.
More details on data augmentation are available in Sec. \ref{sec:ablatation} and the supplementary material.

For evaluation, we feed the model full sequences and only reset the state of the recurrent layers at the beginning of a new sequence.
This is different from the training setting where we only train on chunks of randomly sampled shorter sequences for BPTT.

Finally, we use the YOLOX framework \cite{yolox2021}, which includes the IOU loss, class loss and regression loss.
These losses are averaged both over the batch and sequence length for each optimization step.
We discretize time into $T=10$ bins and summarize 50 milliseconds of events into an event representation.

\paragraph{Datasets}
The Gen1 Automotive Detection dataset \cite{gen12020} consists of 39 hours of event camera recordings at a resolution of $304 \times 240$.
In total, the Gen1 dataset contains 228k car and 28k pedestrian bounding boxes available at 1, 2 or 4 Hz.
We follow the evaluation protocol of prior work \cite{Perot2020neurips, Li2022tip} and remove bounding boxes with a side length of less than 10 pixels and a diagonal of less than 30 pixels.

The 1 MPx dataset \cite{Perot2020neurips} also features driving scenarios but provides recordings at a higher resolution of $720 \times 1280$ over a period of several months at day and night.
It consists of approximately 15 hours of event data labeled at a frequency of 30 or 60 Hz with a total amount of 25 million bounding box labels for three classes (car, pedestrian, and two-wheeler).
We follow the evaluation protocol of prior work \cite{Perot2020neurips, Li2022tip}.
That is, we downsample the input resolution by 2 to nHD resolution ($640\times 360$) and remove bounding boxes with a side length of less than 20 pixels and a diagonal of less than 60 pixels (at the original resolution).
We provide qualitative examples of this dataset together with predictions of our base model in Fig. \ref{fig:qual_gen4}.

For both datasets, mean average precision (mAP) is the main metric \cite{Lin14eccv} that we consider.

\subsection{Ablation Studies}\label{sec:ablatation}
This section examines the two main contributors to the final performance of the proposed model.
First, we investigate key components and design choices of the proposed backbone.
Second, we study the influence of different data augmentation techniques that are compatible with our sequential problem setting.

The ablation studies are performed on the Gen1 dataset, unless stated otherwise, and the training sequence length for BPTT is set to 11 instead of 21 to reduce the training time.
We set the window size of window-based attention models to $8\times 10$ at all scales in the backbone.
This means that the window at the fourth stage covers the whole feature map.
Our ablation studies are performed on the validation set where the best performing model is taken after 400k iterations.

\input{tables/ablation/ablation-block.tex}

\subsubsection{Model Components}

\paragraph{Spatial Interaction}
In Tab. \ref{tab:block_type}, we study different spatial aggregation techniques.
For a fair comparison, we keep the LSTM and convolutional downsampling layers identical and only exchange the attention and MLP modules.
We compare multi-axis attention with ConvNext blocks \cite{liu2022convnet} and Swin transformer blocks \cite{liu2021Swin}.
ConvNext is a convolutional neural network architecture that has shown competitive performance with transformer-based models on a wide range of tasks, including object detection.
We use the default kernel size of $7 \times 7$ as originally suggested and place three ConvNeXt blocks in each stage to approximately match the number of parameters of the reference model.
Swin, instead, is an attention-based model that applies local self-attention in windows that interact with each other through cyclic shifting.

We find that our Swin variant achieves better performance than the ConvNext variant, however, both are outperformed by multi-axis self-attention \cite{Tu2022eccv} on both the Gen1 and 1 Mpx dataset.
This experiment suggests that global interaction at every stage (multi-axis) is advantageous to purely local interaction (Swin, ConvNext).

\input{tables/ablation/ablation-patch.tex}

\paragraph{Convolutional Downsampling}
The original vision transformer \cite{dosovitskiy2021an} architecture does not perform local feature interaction with convolutional layers.
Some popular hierarchical counterparts also choose to apply downsample features without overlapping kernels \cite{chu2021twins,liu2021Swin}.
In Tab. \ref{tab:patch_type}, we compare overlapping and non-overlapping convolutional kernels in both the input layer (patch embedding) and feature downsampling stage.
While non-overlapping convolutions reduce the number of parameters, they cause a substantial drop in performance.
Consequently, we choose overlapping kernels in all stages of the network.

\input{tables/ablation/ablation-lstm.tex}

\paragraph{LSTM with Convolutions}
Prior state-of-the-art approaches on object detection with event cameras heavily rely on convolutional LSTM cells \cite{Perot2020neurips,Li2022tip}.
We revisit this design choice and experiment with plain LSTM cells and a depthwise separable Conv-LSTM variant \cite{Pfeuffer2019}.
The depthwise separable Conv-LSTM first applies a depthwise separable convolution on both the input and hidden state before a point-wise ($1\times 1$) convolution is applied.
Our results in Tab. \ref{tab:lstm_type} suggest that plain LSTM cells are sufficient in our model and even outperform both variations.
This is to some degree surprising because both variants are a strict superset of the plain LSTM.
We decide to use a plain LSTM cell based on these observations.

\input{tables/ablation/ablation-lstm-placement.tex}
\input{tables/sota-table.tex}

\paragraph{LSTM Placement}
In this ablation we study the influence of using temporal recurrence only in a subset of stages or not at all.
For all comparisons, we leave the model exactly the same but reset the states of the LSTMs at selected stages in each timestep.
This way, we can simulate the absence of recurrent layers while keeping the number of parameters constant in the comparisons.

The results in Tab. \ref{tab:lstm_placement} suggest that using no recurrence at all leads to a drastic decline of detection performance.
Enabling the LSTMs in each stage, starting from the fourth consistently leads to enhanced performance.
Surprisingly, we find that adding an LSTM to the first stage also leads to improvements, albeit the increase in mAP is not large.
In general, this experiment suggests that the detection framework benefits from features that have been augmented with temporal information.
Based on our observations, we decide to keep the LSTM also in the first stage.

\input{tables/ablation/ablation-augm.tex}
\subsubsection{Data Augmentation}

While data augmentation is not directly related to the model itself, it greatly influences the final result as we will illustrate next.
Here, we investigate three data augmentation techniques that are suitable for object detection on spatio-temporal data:
Random (1) horizontal flipping, (2) zoom-in, and (3) zoom-out.

Zoom-in augmentation randomly selects crops that contain at least one full bounding box at the final timestep of the BPTT sequence (i.e. during training).
This crop is then applied to the rest of the sequence before the crops are rescaled to the default resolution.
This procedure ensures that we have at least a single label to compute the loss function while maintaining the same resolution during training.

Zoom-out augmentation resizes the full input to a lower resolution and randomly places the downscaled input in a zero-tensor initialized at the default resolution.
This procedure is then applied in an identical way to the remaining BPTT sequence.

Table \ref{tab:augm} shows that our model is performing poorly if no data augmentation is applied.
Overall, we find that data augmentation is important to combat overfittig not only on the Gen1 sequence but also on the 1 Mpx dataset.
The most effective augmentation is zoom-in, followed by zoom-out and horizontal flipping.
Based on these results, we decide to apply all data augmentation techniques.
We report the specific hyperparameters in the supplementary material.

\input{figures/qual_gen4.tex}

\subsection{Benchmark Comparisons}

In this section, we compare our proposed neural network architecture against prior work on both the Gen1 \cite{gen12020} and 1 Mpx dataset \cite{Perot2020neurips} and summarize the results in Tab. \ref{tab:sota}.
We train two models, a base model (RVT-B) with approximately 18.5 million parameters and a small variant (RVT-S) with 4.4 million parameters by reducing the channel dimensions by two in each stage.
Their architectural hyperparameters are outlined in Tab. \ref{tab:arch_variants}.
To compare with prior work, we choose the models based on their best performance on the validation set and evaluate them on the test set.

From Tab. \ref{tab:sota} we can draw multiple conclusions.
First, we observe that models using recurrent layers consistently outperform other approaches, both sparse (GNNs, SNNs) and dense feed-forward models without recurrent layers (Inception+SSD, RRC-Events, YOLOv3 Events) by an mAP of more than 10 on both datasets.
One notable exception is MatrixLSTM \cite{Cannici19cvprw} which applies LSTM cells directly at the input.
In contrast, RED \cite{Perot2020neurips} and ASTMNet \cite{Li2022tip} employ recurrent layers only in deeper layers.

Our base model achieves a new state-of-the-art performance of 47.5 mAP on the Gen1 dataset and 47.0 mAP on the 1 Mpx dataset.
ASTMNet claims comparable results on both datasets albeit at the cost of using a much larger backbone and increased inference time.
The RED model, also reports favorable results, but achieves 7.5 lower mAP on the Gen1 and 4 mAP lower mAP on the 1 Mpx dataset compared to our model.
Finally, our small model is amongst the smallest in our comparison.
Still, it achieves 4.3 higher mAP on the Gen1 dataset than the RED model while using 5 times fewer parameters.

\paragraph{Inference Time}

We also compute the inference time of our model in PyTorch eager mode on a T4 GPU with a batch size of 1.
Unfortunately, both RED and ASTMNet are not open source such that we cannot directly compare inference time on the same GPU model.
Instead, we use the timings provided by the authors that conducted their timing experiments on comparable GPUs (e.g. Titan Xp).

We report the timing results of both our base and small model in Tab. \ref{tab:sota} and also visualize them in Fig. \ref{fig:first}.
With an inference time of 10.4 ms on the Gen1 dataset ($304\times 240$) our base model yields a latency reduction of approximately 6 milliseconds compared to RED and over 3 times lower inference time than ASTMNet.
On the the 1 Mpx dataset, using a higher resolution of $640\times 360$, our base model runs 3 times faster than RED and over 5 times faster than ASTMNet.
Our small model keeps the inference time on both datasets below 10 milliseconds.
We also report the inference time of the backbone alone, by excluding computation dedicated to the detection framework.
The numbers in brackets in the timing column indicate that the backbone typically occupies about half of the total inference time.

The inference times of both model variants are quite similar, although the small model uses approximately 4 times less compute.
This suggests that a proper deployment (e.g. with TensorRT) could reduce the overhead of PyTorch eager execution and further reduce the overall latency.

redpajama_set_name: RedPajamaArXiv


text:
/* @flow */

import { AppSettings } from './AppSettings';
import uuidv4 from 'uuid/v4';

//import snoowrap from 'snoowrap';
const snoowrap = window.snoowrap;

export default class SubredditService {
  authCode: string;
  agent: snoowrap;
  userRefreshToken: ?string;

  constructor() {
    let userRefreshToken: ?string = localStorage.getItem(AppSettings.locStorRefTokKey);
    if (userRefreshToken) {
      this.userRefreshToken = userRefreshToken;
    }
    else {
      let code = new URL(window.location.href).searchParams.get('code');

      if (code) {
        this.authCode = code;
      }
      else {
        let authenticationUrl = snoowrap.getAuthUrl({
          clientId: AppSettings.appClientId,
          scope: AppSettings.permissionsScope,
          redirectUri: AppSettings.redirectUri,
          permanent: true,
          state: uuidv4() // a random string, this could be validated when the user is redirected back
        });

        window.location = authenticationUrl; // send the user to the authentication url
      }
    }
  }

  getAgent() : Promise<snoowrap> {
    if (this.agent) {
      return Promise.resolve(this.agent);
    }
    else {
      if (this.userRefreshToken) {
        return this.createPromiseFromRefreshToken();
      }
      else if (this.authCode) {
        return snoowrap.fromAuthCode({
          code: this.authCode,
          userAgent: AppSettings.appUserAgent,
          clientId: AppSettings.appClientId,
          redirectUri: AppSettings.redirectUri
        }).then(snooAgent => {
          this.userRefreshToken = snooAgent.refreshToken;
          localStorage.setItem(AppSettings.locStorRefTokKey, snooAgent.refreshToken);
        }).then(() => {
          return this.createPromiseFromRefreshToken();
        });
      }
      else {
        return Promise.reject("No auth code received. Authenticate via reddit first.");
      }
    }
  }

  createPromiseFromRefreshToken() : Promise<snoowrap> {
    return new Promise((resolve, reject) => {
      let agent = new snoowrap({
        userAgent: AppSettings.appUserAgent,
        clientId: AppSettings.appClientId,
        clientSecret: AppSettings.appClientSecret,
        refreshToken: this.userRefreshToken
      });

      if (agent) {
        this.agent = agent;
        resolve(this.agent);
      }
      else {
        reject("Failed to create agent.");
      }
    });
  }
}

redpajama_set_name: RedPajamaGithub


Copyright-evidenceEvidence reported by scanner-Liz-Ridolfo for item therockofagesors00bickuoft on Sep 8, 2006; no visible notice of copyright and date found; exact publication date unknown; not published by the US government; a copyright renewal record could not be found.
This book was republished later as "The Trinity," and is useful in responding to the views of groups like Jehovah's Witnesses (and other Arian type groups). It is a classic defense of the Trinity.


There are a few signs that summer is coming to a close: Labor Day weekend cook outs, cooler temperatures, Q3 winding down, and the return of football. For football coaches and sales leaders, the long road to the "big games" of the year begins – whether it's meeting end of the year goals or making it to post-season playoffs. Every good coach – in sales and sports – knows that a strong playbook is the key to success, combined with good coaching and great players. In this post, we discuss the benefits of SalesWise as your game film, playbook, and coaching guide to lead you to success.
In any meeting, it's crucial to know when – and the outcome of — the last time you met. CRM acts as a great highlight reel, but it doesn't complete the larger picture. In fact, we found in our research that 76% of events aren't ever captured within CRM. With SalesWise, we show graphically when the last meeting was, and most importantly, who was in attendance. As a sales manager, you can see if the star players were in the meeting, or if they were sitting on the bench. The absence of these key decision-makers may indicate that an account needs some attention, and allows you to step in for further coaching.
Whether it's in sports or sales, fumbles are inevitable. But having the ability to "review the film" allows leaders to analyze why it happened, and better coach their sales representatives so that the same mistake doesn't happen again. And while pipeline meetings have traditionally been an effective coaching strategy, being able to see the actions, rather than the representative's description of the actions, allows more effective and efficient coaching. On the flip side, once you develop the recipe for success, it's important to replicate it – especially when new members are added to the team. With SalesWise, you're able to see the play-by-play of every relationship, including the calendar events, email messages exchanged, contact information for key players and crucial documents needed to close business.
When it comes to winning championships, it takes more than just a good offense. The combination of offense, defense and special teams performing at their best makes the organization as a whole successful. We know this at SalesWise, which is why our platform enables better communication between functional groups after the touchdown. Without per-seat licensing, you can easily share the details of every account with Finance, Customer Success, or anyone else that may have an interest. You always want your customers to have the most positive experience possible, and a more informed team provides that. In addition, this historical information, SalesWise helps you easily see up- and cross-sell opportunities that may have been lost in CRM. In fact, SocialToaster uncovered about $1 million in opportunities that were previously overlooked.
Coaching is key when it comes to setting your players – in this case, the sales reps – up for success. By providing the right tools and visibility into accounts and historical data, SalesWise enables sales leaders to be more efficient and effective coaches than ever before.
SalesWise recently introduced the first-ever Relationship Graph – a visual representation of account relationships and interactions occurring on both the buy-side and the sell-side.
In Part 1, we explained how the Relationship Graph answers sales leaders' most pressing questions about the buy-side of an opportunity. Today, Part 2 details how the Graph provides key insights from the sell-side of deals.
When was the last time we engaged?
Too often, the answers tend to be "just last week" or some other subjective response that paints a more optimistic picture. Even pulling reports on recent activities can be cumbersome. Or worse, the inte ractions are not entered into CRM leaving reporting to be severely challenged. Credit is given when it was not deserved, or credit is not given where it is.
The Relationship Graph organizes all of the email, meeting, and CRM interactions instantly enabling easy understanding of the engagement timeline. Leaders are able to quickly see whether clients have been engaged recently, or if too much time has passed with no (or infrequent) touch-points. Now, managers can intervene before it's too late. No other visualization provides this degree of accuracy, especially, not one requiring no additional effort from the sales team.
Who from my company is interacting with customers?
Less than half of companies give CRM access to non-sales departments, according to Capterra. While non-sales departments have important communications with clients, many of those interactions are never captured in CRM. The shift toward account-based selling only drives up the number of folks involved on the selling side. Orchestration of account-based sales, then, remains a huge challenge for companies before the Relationship Graph.
The Relationship Graph instantly completes the missing picture of sales by automatically aggregating all interactions between the selling company and the prospects and customers. This especially improves the effectiveness of enterprise, global, and outside sales teams.
Management would no longer have to constantly ping other people in order to get the answers they need. Did Susan from accounting send over the contract? Did Bill from engineering get their log-in set up? Did Mike from client success follow up? With the Relationship Graph, the answers are at your fingertips and no longer a walk across the office or an interrupting phone call away. As a sales leader, this is also critical for you to ensure that your team isn't dropping hand-offs and making sure that the customer is getting their needs met.
Looking at the frequency of outbound messages provides a better idea of how engaged a prospect is, but seeing how often and what the sentiment is of the responses completes the picture of an interaction. Tracking email opens and clicks is useful, but if all of the activity with an account is solely the sales rep pushing communication out to no response, it becomes clear that this is not an engaged prospect – and likely not a deal about to close.
The Relationship Graph visually shows the break down of internal communication and prospect communication with any account or individual. This allows leaders to immediately see how interested a prospect is, and as a sales leader, may be an indication that a given deal needs management intervention.
The new Relationship Graph from SalesWise provides instant, unprecedented visibility into the crucial accounts that matter most, both on the buy- and sale-side of your business.
Learn how you can unlock all of the contacts buried in email across the enterprise, and get Relationship Graphs for your most important deals – with no data entry and completely free for 14 days. Click here to learn more.
Congrats! You've implemented your Account-Based Selling (ABS) Strategy. You have identified your target accounts, and defined the different functional groups that will influence the purchasing decision. Your team has begun reaching out to this group, and you're sending out tailored messaging to each contact ("persona"). But what comes next? How do you take your ABS strategy from good to great?
We've compiled 3 tips to help you optimize your ABS Strategy, and get the most out of your sales and marketing efforts.
This first tip is something that you should already be doing, due to the individualized nature of ABS. The more you can personalize your communications to each individual, the more effective outreach will be. Save the email templates and CRM tags for later in your pipeline – every point of contact should be unique to the individual account and individual within the account. Make sure you talk to their specific challenges in their industry and in their job role. Offer helpful solutions, not just a sales pitch for your own solution. Providing accounts with valuable, but not annoying, content helps to establish trust and build the relationship. For more high-involvement communications further along your ABS process, you can even create custom reports providing ROI. This shows thoughtful consideration into their needs and goes deeper than just walking through an ROI calculator in a sales call.
Today's marketing and sales professionals have an abundance of ways to reach out to their prospects. If you're not capitalizing on them, you're missing out. Breaking through the noise in today's marketing environment can be challenging, especially if the primary forms of communication are just emails and voicemails. McKinsey found that, on average, a B2B customer will regularly use six different interaction channels throughout the decision journey. It's important to provide dialogue through multiple channels to accommodate this.
Use a tool that provides greater visibility.
Whether you're taking the lead on the account or you're trying to pick back up a deal that has fallen through the cracks, having visibility into the activity on the account is crucial to the success of ABS. And while tools like Salesforce and other CRMs are essential to keeping your contacts organized, they rely on your representatives entering in the necessary information. Here at SalesWise, we have been able to uncover 2.5x more customer contacts in email and calendar data that have not made their way into Salesforce. Furthermore, ABS is more than just the initial sale transaction – it continues through to the hand off to the client success team, and eventually to the renewals representatives. "Whenever a process crosses teams, a 'baton' is passed. These handoffs are the cause of 80% of the problems and defects your processes," says Aaron Ross of Predictable Revenue. Using a tool like SalesWise enables any member of your team to see the direct relation between your team and your prospect or client's team.
ABS, when leveraged to its maximum potential, can provide phenomenal Return on Investment – in a study by BizBible, over half of those in the study report a revenue lift of 10% or more after a year of use. With the right techniques, like personalization and different interaction channels, and the right tools, like SalesWise for increased visibility, organizations using an ABS Strategy can grow revenue, increase opportunity for expansion and renewals, and develop valuable client relationships.
Business Relationship Intelligence: What Is It?
You've heard of Business Intelligence (BI), the term that refers to software applications that analyze an organization's raw data to mine it, process it, query it, and/ or report on it. But, have you heard about Business Relationship Intelligence (BRI)?
In an ever-changing environment, business, and sales are about much more than the historical, transactional relationships. Today, it's about the relational relationships you have with your customers, partners, and clients. In fact, 89% of customers have said they will (or have) stopped doing business with a company after a single poor customer experience (CMO).
So, how can we expect our sales teams to properly build and manage customer relationships with traditional CRM systems? As it stands, sales professionals are bogged down by data entry with 71% believing they spend too much time on data entry (Toutapp). Yet, Salesforce reports 91% of CRM data is still incomplete. Data is key to the success of any relational relationship. SalesWise believes no sales team should be limited by the data (or lack thereof) in their CRM system. That's why we decided to build a solution intelligently designed around the way in which relationships really work! With a full 360-view, our Cross Application Relationship Data (CARD) platform aggregates data from every message, meeting, document, and person involved so that you gain a clear view into the existing relationships as well as new relationship opportunities.
As many sales leaders know, relationships don't exist solely outside the walls of your enterprise with your customers. Sure, your sales team is central to that relationship, but so are your legal, finance, and onboarding functions. Rarely are sales ever one-to-one any more. Today, sales are many-to-many with multiple individuals involved in the sales cycle and customer experience processes. Gartner found 7-8 people are typically involved in all areas of the buying process for enterprise tech sales; while another 5-6 people occasionally participating. That's a lot of relationships within the business relationship.
According to the 2015 IDC Workforce Transformation Survey, employees spend an average of 7.36 hours per week trying to find information and an average of 8.15 hours per week trying to find people or experts. SalesWise, as a leading BRI solution, believes in greater productivity through the democracy of data. The only way to empower your relationships is to ensure your sales team and non-sales staff are working effectively and efficiently together to shorten the sales cycle and enhance the customer experience. A true BRI solution ensures everyone has access to your relationship data. In the end, not only are your staff empowered; but, the sales leader is empowered with complete and comprehensive data that keeps them in-the-know and better prepared to lead.
A major factor in the feeling of empowerment for any sales leader or team member is access to insights. With traditional data-entry systems, we're relying on a sales professional to assess and enter the information (a.k.a. data) they believe is the most beneficial… after the fact. The truth is relationships are happening in real-time. This antiquated system is flawed.
A BRI solution doesn't rely on data entry, nor does it rely on subjectivity. A sales professional solely focused on his/ her relationship with a customer will never see the connections that customer may have with other individuals in your organization (e.g., procurement, legal, etc.), or the additional customer connections they may find within the customer's business to further the relationship. With SalesWise, our customers discover an average of 5 – 8 times more contacts and increase visibility by 300% beyond their CRM system. These insights inspire actions that lead to stronger relationship connections.
Relationships are all around you. So, why shouldn't your sales technologies work the same way? With SalesWise, we gather relationship data from your CRM system, inbox, calendar, contacts, messages, documents, and more to share real-time critical intelligence. Business Relationship Intelligence (BRI) is the newest industry made up of innovative software applications that analyze an organization's data to mine, process, query, and report on relationship insights for greater business and sales success. To learn more about SalesWise, the leading BRI solution, visit www.saleswise.com/the-platform/.
http://www.saleswise.com/wp-content/uploads/2016/12/business-relationship-intelligence.jpg 997 1000 Daryl //try.saleswise.com/wp-content/uploads/2016/04/logo_color-300x114.png Daryl2016-12-14 21:53:452016-12-14 21:58:00Business Relationship Intelligence: What Is It?
We are very excited to be co-hosting a webinar with our friends at Cirrus Insight on Democratizing Relationship Data next Tuesday, Nov. 29th from 1:30PM-2:30PM, so sign up today! We will be giving away some fantastic gifts to randomly selected attendees (just in time for the holidays).
Technology advances the cause of sales automation, but doesn't always result in equal access to information for everyone in the enterprise. The way software-as-a-service applications are provisioned often leads to fragmented data sources and siloed access. This can make the sales force the de facto administrators of data, as they field inquiries from others across the company without access to Salesforce. In this webinar, SalesWise demonstrates the power of democratizing relationship data for improved productivity and insights throughout the organization.
This is completely free, and you'll learn about the importance of transparency in today's business – modern sales teams, customer success efforts, executive leadership, and more! Plus, you'll get to learn about the great stuff Cirrus Insight is up to.
Register today and make sure to share with your friends and colleagues — the more, the merrier!
The key for most successful endeavors in business is being in the right place at the right time with the right person. So, why should your sales process function any differently? We've written before about the importance of your CRM system to your organization. But, what's worth noting is that 91% of CRM data is incomplete (Salesforce). So, how can your sales team achieve success if they don't have the appropriate data available to access the right person at the right time in the right place? Let's take a closer look.
You can read a dozen sales books that will tell you that it's best to make cold calls in the late afternoon, Thursday is the best day to prospect or that it takes an average of eight cold call attempts to reach a prospect (The Brevet Group). But, these are generalities and every prospect is different. The importance of time is knowing when your prospect or customer is the most responsive and/or prefers to be contacted. It's the time it takes to build the connection. Communication is key to the sales process but so is persistency. In fact, according to the Marketing Donut, 80% of sales require five follow-up calls after the meeting while 44% of sales representatives give up after only one follow-up. Seamlessly tracking your interactions with a prospect or customer as well as the quality of those communications can help you to consistently connect and build a successful sales relationship.
What PLACE should we meet?
It's not just about the right time. But, where should you engage with your prospects? Phone calls and especially check-in calls can help you to maintain relationships or learn about new company developments that could open sales opportunities. But, sales interactions aren't only taking place by phone, with the rise of our digital lives both personally and professionally, Forbes reports that 78% of salespeople using social media outsell their peers. But, don't worry, the tried and true staple of email won't be going anywhere soon. Email performs nearly 40 times better than Facebook and Twitter when it comes to acquiring new customers (McKinsey). And, meetings, whether in person or virtually always offer a unique opportunity to engage more personably.
Getting time with a prospect in-person is a very good thing. But, of even greater significance, it's about getting time with the right person. According to Gartner, at the typical firm with 100 – 500 employees, an average of seven people are involved in most buying decisions. No longer are the days of sales one-to-one. Today, it's many-to-many. Whether within your own company with people in your sales, marketing, accounting and procurement departments involved in the prospect-to-sale journey; or, on the customer's side with multiple decision makers with a variety of areas of expertise, knowing who the right person(s) may be has never been more crucial.
How do you possibly manage it all successfully? Tracking so many interactions on a number of different channels can be a challenge. Limited CRM data and access across your enterprise can also present many barriers. That's why SalesWise tracks all your emails, key contacts involved, meetings, and documentation conveniently all in one place. We also believe in democratizing data so that everyone in your enterprise can access the information they need to enable successful customer relationships free of per-seat license fees. Relationship Intelligence has now reached a new level ensuring you can be in the right place at the right time with the right person. If you don't believe me, just #CheckTheCard.
SaaSquatch – Time for a Change in the Pay Per Seat SaaS Model?
Salesforce.com was founded in February of 1999. Best I can tell, that was the beginning of the last real innovation in the business of software. The pay as you go, pay per seat business model directly challenged the prevailing model of large up front software license fees and perpetual maintenance fees. Folks my age remember the bright red circle and line through the word "Software" that was integral to the early messaging and positioning of SFDC. Needless to say – Salesforce.com and Marc Benioff were right, and through a ton of hard work and market making transformed the Software industry into what we affectionately now call the "Cloud".
That was 17 years ago. Other than a few derivative business models such as "Freemium" coined in 2006, there has been a complete absence of innovation in the business model of how SaaS companies charge for their products and services. For a while, the per-seat model made a ton of sense and was good for everyone. Enterprises enjoyed massive reductions in upfront fees and eventually could materially reduce CapEx as solutions moved more and more to the cloud. The SaaS providers enjoyed stable recurring monthly revenues, easier and more frequent product iterations, and far quicker enterprise sales cycles. Even the economics made sense regarding the cost of goods to deliver SaaS – each big customer required more storage, more processing, more servers and co-locations. But something changed. And the business model of SaaS did not not keep up with innovation.
2) The cost of building software, storage, and processing plunged as AWS and others created today's modern cloud infrastructure.
There is tons of data on the proliferation of cloud apps and it all points up and to the right with regard to how fast SaaS (used to be ASP and now it's "Cloud") applications are growing inside mid to large enterprises. It's not just Salesforce.com and Box anymore. To name just a few SaaS segments that have exploded over the past five years: CRM, ERP, Marketing Automation, Document Management, Finance, HR, Cloud storage, Disaster recovery, Customer Service, and many more. It is not uncommon for an enterprise to have 30 or even 50 SaaS providers. That's a lot.
For many, the proliferation of SaaS solutions creates a data mess. While each platform may serve its purpose well, companies with any scale are discovering that data is trapped in SaaS silos and difficult to use in support of the "rest of the enterprise". For example, SFDC may work well for sales and customer service reps who live in the application all day long, but it fails to provide easy access to Product, Marketing, Finance, Integration teams, Legal, or even the C-suite. Many companies invest hundreds of thousands, if not millions, to create customer solutions that work across the enterprise but with the pace of SaaS innovation, will likely find themselves back in the SaaS data disaster that has been created.
Interestingly, SFDC understands this issue and hopes to solve it by becoming the center of the SaaS universe with their ecosystem – AppExchange. Smart. However, for now, most integrations create a "dumping ground" of data that exacerbates the difficulty of non-sales functions to find what they need to better perform their jobs. Meanwhile,the once innovative SFDC per-seat business model has now become near-universally despised for taxing growth and success.
The reality is that Sales reps are the only ones that need all of SFDC, marketing folks are the only ones that need all of Marketo, Finance same for NetSuite, and the list goes on. However, almost everyone in the enterprise needs a little something from nearly every system to arm themselves with the data required to better perform their jobs. This is where the pay per-seat business model falls down. Compounding this pain is the reality that no one wants to learn and use multiple UI's to get all the data they need.
So…. perhaps SFDC continues to conquer the world and becomes the center of the enterprise data universe? Maybe. But now we are back to the reality that per-seat pricing does not work for everyone in an enterprise. Perhaps it's time for innovation in the business of SaaS.
What if each functional area of an enterprise could cherry pick the data they require to better perform their jobs from across the enterprise SaaS landscape? What if you could reduce your current per-seat SaaS costs by up to 30% and pay a simple flat monthly fee for enterprise wide access to all your data in your SaaS investments? This is what we are building at SalesWise — delivering each person all the data they need to better perform while eliminating up to 30% of the cost of SaaS seat licenses like Salesforce.com.
Take the free Salesforce Audit here to see how much you could save while driving better performance across the enterprise.
http://www.saleswise.com/wp-content/uploads/2016/05/no-per-seat.png 400 700 Gregg //try.saleswise.com/wp-content/uploads/2016/04/logo_color-300x114.png Gregg2016-05-09 18:02:232016-08-12 16:04:43SaaSquatch - Time for a Change in the Pay Per Seat SaaS Model?
In an earlier blog, we talked about the biggest problem with Salesforce according to Forrester Research. To recap, studies found that a "rigid and inflexible pricing model" registered as the top complaint among companies that hoped to do more with this excellent CRM.
While the pricing structure remains a problem, there's an even more significant issue from a productivity standpoint: uneven Salesforce adoption by sales teams and the resulting data corruption. Your CRM can only be as good as the data inside it, which will degrade rapidly without constant updates.
Successful Salesforce adoption has always faced challenges from reluctant sales teams that are under intense pressure to shorten the sales cycle. From the point of view of these sales professionals, it's easy for repetitive data entry to get pushed way down on the priority list when there are quotas to hit and active prospects on the line.
When sales people don't have time to update the CRM, or they speed through the task so fast that errors creep in, the inevitable result will be inaccurate, out of date, no good data. This has been precisely the case for many firms, and it severely impairs the effectiveness of their CRM. If you already have Salesforce in place, you need better control over your data. If you don't, you need to be certain that your sales teams will actually use Salesforce effectively before you commit to substantial investments of time, resources and training.
Here is the answer to all of the above complications. SalesWise can open up a single Salesforce access point to every single person in your company for zero additional costs. That means everyone in your organization gains the ability to work better together without having to worry about inflexible pricing. Meanwhile, your sales professionals don't have to waste their critical sales time in updating the CRM. With increasing global competition and the accelerating speed of business, a few minutes can be the difference between a close and a wasted effort. And, over the course of a year adds up to serious revenue.
Picture how much more organized and productive your sales teams will be once they are liberated from busy work like data entry and reporting. This elegant, cloud-based enterprise intelligence platform will do just that. The gorgeous SalesWise UI was developed from the ground up to clear away all distractions and allow sales teams the freedom to do what they do best: sell.
For a substantially lower investment, you can get access to the most powerful tools in Salesforce, share them all across your organization for closer collaborations and ramp up productive sales time. What else could you want? Actually, quite a bit more.
To secure higher revenues in the new economy, you will need to share more than just access to Salesforce. Your sales team should be knowledgeable about every interaction and every document relating to a specific client. Nothing should be lost inside Dropbox, Evernote, email attachments, social media exchanges or messaging apps.
SalesWise acts like an intelligent personal assistant that immediately lays out a true picture of what's going on with any deal and any prospect. In practice, that translates into instant, transparent deal visibility for effective pipeline management. Not coincidentally, that's exactly what CRM was supposed to do in the first place.
Is it possible to share too much information? Absolutely. Anyone who's ever gotten stuck inside an email chain where everybody automatically hits "Reply All" knows the frustration of TMI. SalesWise keeps you one step ahead.
The unique SalesWise platform was carefully designed to allow you to share what you want with who you want. Maybe the CEO needs to be brought in on the most important deals. Maybe Production has to get involved on a personalization or localization issue. Undoubtedly, Finance will need to be involved on some deals but definitely won't want details on all of them. The power to dial sharing up and down is in your hands.
This facilitates team selling, vacation coverage, field office reporting, mobile presentations, new hire training and a host of issues related to today's more complex buyer's journey.
Try out SalesWise Enterprise Intelligence for free and see how good it feels to make your entire sales team more productive with one application to connect everything. How much of your day do you want to spend toggling between windows just to figure out what you're doing next? In a glance, the SalesWise dashboard informs you the stage the customer or prospect is in at the moment, when they were last contacted and the probability to close.
To start your sales team off in the right direction, sign up for Wise & Shine. This early morning daily email gives a comprehensive overview of the day's meetings, goals, notes and all the documents your team will need to organize a more successful day. You'll break out of the gates with direction and purpose, while the competition is still doing their data entry from yesterday.
education school on Business Relationship Intelligence: What Is It?
SalesWise, the pioneer in Business Relationship Intelligence (BRI), is an Atlanta-based technology company that builds innovative solutions to empower team leaders and enterprises with real-time visibility and critical intelligence into customer relationships to drive alignment and productivity.
Contact us today at hello@saleswise.com.


text:
Fighting Crime and Raging Hormones in Shadoweyes in Love
January 22, 2011 Press Release	Leave a comment
Second volume of critically-acclaimed series by Ross Campbell due out in April
Press Release: Scout Montana has just started to come to terms with turning into a mutant and becoming a superhero, but fate decided that isn't enough complication in the life of the teenage vigilante. In the second volume of Ross Campbell's critically acclaimed SLG Publishing series Shadoweyes, as Scout grapples with ethics and tries to balance friendship with crime-fighting she must face a truly formidable opponent: love.
Shadoweyes in Love, set to be released in April 2010, follows up the debut book of the series, 2010's Shadoweyes, which was on the American Library Association's Great Graphic Novels for Teens and Popular Paperbacks for Young Adults lists and received a starred review in Booklist. While volume one focused on Shadoweyes' origin story, Shadoweyes in Love encompasses more characters and larger themes.
Shadoweyes in Love - Ross Campbell
"Shadoweyes is figuring out where she stands or doesn't stand as a hero or antihero and dealing with the violence that comes with being a vigilante, but it involves the other characters a lot more," said Campbell. "Everything from volume one gets kind of amped up, which was both a blast and difficult to write. Definitely some new territory for me and I'm having a lot of fun with it."
Among the other characters whose lives form part of Shadoweyes' world are Sparkle, a bubbly girl who has vigilante dreams of her own; Noah, a young man Shadoweyes is drawn to who brutally combats crime with a crowbar; Kyisha, Shadoweyes' former best friend and Noah's girlfriend; as well Shadoweyes' mother Max, who must support her crime-fighting daughter as Scout continues to transform and find her place as Shadoweyes.
Shadoweyes in Love features the black-and-white artwork depicting the decaying cityscape of Dranac, Campbell's diverse character designs, and dynamic action. A third book in the series, Shadoweyes Unmasked, is planned for 2012.
Established in 1986, SLG Publishing is a San Jose, California-based publisher of comic books and graphic novels. Some of SLG's more notable comics and creators have included Johnny the Homicidal Maniac by Jhonen Vasquez, and Zombies Calling by Faith Erin Hicks. More information on the company can be found at the official website www.slgcomic.com
graphic novelsindie comicssci-fi comicsSlave Labor Graphics
Previous PostInterview: Digital Pimp Online at NYCCNext PostInterview: Comic Artist Stephen Silver at NYCC
So Very Tired… #4: Blade Runner 2019 and the Trouble with Trench Coats

redpajama_set_name: RedPajamaCommonCrawl


text:
What's happening in Asian Church
and what does it mean for the
rest of the world?
Subscribe No, Thanks
From the Vatican
UCAN Store
UCAN Directory
News Features Commentary Series Specials Video Stories PODCAST UCAN Store
SOUTH ASIA▾ ?
SOUTHEAST ASIA▾ ▾
EAST ASIA▾ ▾
ASIAN CATHOLIC DIRECTORY
Myanmar students seize political freedom in classrooms and on the streets
Some urge cautious approach to reforms, while others are less patient ahead of November elections
Students on the campus of the University of Yangon last week review their lessons prior to an examination (AFP photo/Soe Than Win)
By Jerome Taylor for AFP, Yangon
Published: March 23, 2015 05:23 AM GMT
Updated: March 22, 2015 06:52 PM GMT
Every morning Su Wai Phyo wakes before dawn to do something that was until recently banned in Myanmar: study politics.
The 23-year-old is one of a handful of students taking part in a new political science diploma for postgraduates at the University of Yangon — a course that would have been unthinkable under Myanmar's brutal junta.
"Young people need to understand politics," she enthuses after a recent early morning class. "They need to know about those who are ruling us, whether those in power are doing good things for us or doing bad things."
She will be among millions of new voters able to choose who governs them for the first time in decades when Myanmar goes to the polls later this year.
It is a decision she will exercise amid a huge flowering of public political discussion after harsh military rule gave way to a quasi-civilian reformist government in 2011.
But while Myanmar's turnaround initially won widespread praise, prompting a flood of foreign investment, there are fears it is now backsliding on key reforms.
Those fears were heightened earlier this month when police violently cracked down on student-led protesters calling for education reform, arresting at least 130 and sparking international condemnation.
Myanmar's former military rulers are historically suspicious of students, who have been at the forefront of several major uprisings, including a mass 1988 demonstration that ended in a bloody military assault and saw the rise of Aung San Suu Kyi's opposition.
The months of student rallies have rattled the current government, fearful that the demonstrations could spiral into a wider destabilizing protest movement ahead of elections expected in November.
The violent scenes in the town of Letpadan, where students were charged by baton-wielding security forces after they tried to break through police lines, is far removed from the leafy confines of the University of Yangon where optimism and political discourse abounds for the first time in years.
Once celebrated as one of Asia's leading universities, it was shuttered for much of the last two decades by the junta, its colonial-era buildings languishing in fields of long grass.
It was reopened in late 2013 as part of government attempts to overhaul an education system that calcified under army rule.
"If I had tried to study political science before 2011, my parents would be worried about me going to prison," Su Wai Phyo says.
Instead, every weekday before heading to her job at a local NGO, she attends two hours of lectures and seminars on the same campus that gave birth to the 1988 protests.
Fellow student Ko Ko Aung, 28, marvels at freedoms out of reach just four years ago.
"We were very afraid of the military government at the time," he says. "We think everywhere there are spies, that's why we didn't talk about politics."
Professor Chaw Chaw Sein, head of the international relations department that runs the course, says her students burn with an appetite to learn the subject.
"We can talk and discuss, not only about politics around the world but also the political changes and politics in Myanmar," she says.
The academic proudly describes herself as one of the "88 generation". But when that movement was crushed she decided to work within the system.
For years, teaching international relations involved little more than studying the "Burmese Path to Socialism" espoused by the country's secretive and paranoid military rulers.
But Chaw Chaw Sein believes a gradual transition towards democracy is the right route for Myanmar — fearful of the chaos that could be unleashed if full freedoms were granted overnight.
"Although [the] international community are criticizing that this is just a semi-democracy, a semi-civilian government, we have to take lessons from the Soviet Union's disintegration," she says. "Gradual change is better for our political culture."
But many young people feel change needs to come quicker.
In a rickety wooden building in downtown Yangon before the latest crackdown, activists from the All Burma Student Federation Union met to strum guitars, play with the union's dog "Lucky" and plan their protests.
The students say a 2014 education law curbs academic freedom and want it altered to include free and compulsory schooling until children reach their early teens, permission to form unions, and teaching in ethnic minority languages.
"What we are demanding is autonomous universities, universities that are independent of the government," says Min Thawe Thit, a leader in the All Burma Student Federation Union who has since been arrested.
"We don't want top-down policies imposed, we want decisions to be made from the bottom up," he adds.
Nicholas Farrelly, a Myanmar expert at the Australian National University, says the latest crop of student protesters have come of age during "a very significant and abrupt set of transformations in Myanmar society".
But they are also political ingénues compared to their forebears from the '88 generation.
"[They] don't always appreciate just how dark the darkest of the years in Myanmar were," he says.
Neither generation wants the dark days to return.AFP
Lend your helping hand!
Unequal Christians of Asian Churches is a new series of features aimed to help us see prejudice and bias that are at work in our Church. They also help us see the struggles of Catholics to live out their faith.
Such features come to you for FREE, but it cost us to produce them.
Help UCA News publish such great stories.
In praise of martyrs, ancient and contemporary
Christian leaders welcome Rahul Gandhi's India march
Pakistan Church leaders deplore Quran burning
Timor-Leste hero blasted for attending pedophile's birthday
Filipinos mourn Italian priest who pioneered catechetical works
Accused Indian cardinal gets bail in land sale case
Indian bishops need to be serious about their meetings
Indian parishioners stage stir to reopen Cathedral
Catholic epidemiologist appointed Taiwan's new premier
Asian Dioceses
Diocese of Manokwari-Sorong
The diocese of Manokwari-Sorong is located in West Papua province. It covers an area of 111.835 square kilometers, with
Eparchy of Parassala
Parassala is a town in Trivandrum in Kerala state and is located at the southern end of Kerala
Diocese of Naha
In a land area of 2,276 square kilometers, the diocesan territory covers Kagoshima
Diocese of Dipolog
In a land area of 7,205.92 square kilometers, the diocesan territory covers the entire civil province of Zamboanga del
Asian Pilgrim Centers
India's Santa Cruz Cathedral, a repository of Portuguese heritage
Santa Cruz Cathedral Basilica at Fort Kochi is one of the finest churches and a historic but also a...
Malaysian Church pays tribute to miraculous Saint Anthony
The Church of St. Anthony of Padua at Teluk Intan in Malaysia is a wonderful tribute to the wonder...
Indian Church relishes miracles of Saint Thomas, the Apostle
On every first Friday of the month thousands of Catholics flock to Holy Cross Church of Cherpunkal...
Malaysia's St. Michael Church holds relics of Padre Pio
Asian Catholics who cannot travel to Italy to venerate the relics of Padre Pio now has an...
SPECIAL EBOOKS
Can't read the image? click here to refresh.
UCA News monitors the content of the comments and reserves the right to remove posts that contains abusive, offensive, racist, threatening or harassing comments or personal attacks of any kind.
UCA News
Support Asia's largest network of Catholic journalists and editors
Contribute Get Rewarded
Overcoming Inequality
Church & Society
Catholic Dioceses in Asia
Sign up for UCA News Newsletters
Support UCA News
© Copyright Union of Catholic Asian News. All rights reserved. No part of this publication may be reproduced without prior permission.


Our Volunteer Matching Team will be in contact with you within 72 hours of your application being submitted to help guide you to activities that best fit your commitment level, availability, and interests, all while helping us meet our organizational goals. Orientation will cover the goals and policies of the organization, as well as explain volunteer opportunities.
Submit an online application, which includes signing a volunteer agreement and a Release and Waiver of Liability form.
Get matched by our Volunteer Matching Team.
Complete an online general volunteer orientation session.
Be able to work independently and meet the volunteer requirements – including the essential capabilities of our volunteers.
Treat all animals with kindness and concern for their welfare.
Represent themselves and TSDP in a professional manner and treat clients, staff, and other volunteers with respect.
Responsibilities: Transport volunteers use their personal vehicles to transport animals and/or supplies (i.e. rescue pick up, chauffeuring animals to/from medical or therapy visits, or helping with special event transport). Travel is primarily in the Dallas/Fort Worth area.
Requirements: Must have a reliable vehicle that is licensed and insured; the vehicle must have enough space for kenneled animals. Requires a valid driver's license.
Time Commitment: Best suited for a flexible schedule.
Responsibilities: Responsible for the daily care of a foster animal, including feeding, exercising, playtime, socialization, grooming, reinforcing basic obedience commands and, of course, providing patience, love and security. TSDP will provide all necessary medical care and our Foster Teams will provide full support throughout the foster period. You may also need to transport the foster animal to medical appointments and/or adoption events/photo shoots.
Requirements: Love and patience! A fenced yard is preferred, but not necessary for dog foster parents.
Responsibilities: Help supervise the planning and execution of adoption, fundraising and special events such as booths at local festivals and community events to promote TSDPs message and community participation.
TSDP has a volunteer opportunity for everyone! The activities range from marketing and promoting our dogs to working on a training plan with them. Every activity is important and crucial to our ability as an organization to save each of their lives. Please fill out the below form and we'll be in touch soon!


Royal Cross buys into Governor Services for training support.
Follow the links to find opportunities for training and contact school if you have any questions. You must be a Governor at Royal Cross to access this training.


Alice In Wonderland – Family Adventure Movie Night – We're all MAD around here! Especially over Disney DonnaKay's newest movie night adventure, Alice in Wonderland!
Cast of Marvel's "AVENGERS: AGE OF ULTRON" Appear on Jimmy Kimmel Live 4/13/15 – "Jimmy Kimmel Live" welcomed Robert Downey Jr., Chris Hemsworth, Mark Ruffalo, Chris Evans, Scarlett Johansson and Jeremy Renner from the cast of Marvel's highly anticipated new film, "Avengers: Age of Ultron," for a special episode, "Jimmy Kimmel Live: The Avengers Assembled," MONDAY, APRIL 13 at 11:35 p.m., ET/PT on ABC.
New Featurette from TOMORROWLAND – A new TOMORROWLAND featurette is now available including interviews with George Clooney, Director Brad Bird and more. TOMORROWLAND is in theaters May 22!
TOMORROWLAND / "Vision of Tomorrow" Featurette – Learn about Walt Disney's vision for the future in this TOMORROWLAND featurette starring George Clooney, producer/writer Damon Lindelof, and director Brad Bird!
#DWTS: Disney Night – Week 5 Recap – NDM Tamela shares her Disney Side recap with her favorite show, Dancing with the Stars on Disney Night!
Disney Joins "The BFG" – Steven Spielberg-Directed Roald Dahl Adaptation Filming In Vancouver. Bill Hader, Jemaine Clement, Rebecca Hall, Penelope Wilton and others join cast.
Breaking News: Disney and Dr. Who – Worlds Collide – Get ready! Dr. Who is coming to Disney XD! Seasons 2-4 will air on DIsney XD starting in May!
These Are A Few of My Favorite Things – Wordless Wednesday – A journey into Once Upon a Toy at Downtown Disney has NDM 130 finding a few of her favorite characters in plush form!
My Easter Minnie – NDM MaryBeth is surprised by a thoughtful Disney gift from someone very unsuspecting!
Disney Store Celebrates Earth Day with Monkey Kingdom Tote – This Earth Day, Disney Store invites guests to celebrate and protect our planet with a free reusable bag giveaway. Guests who visit a local Disney Store on April 22nd will receive a free reusable "Monkey Kingdom" shopping tote with any purchase.
America on Parade – Throwback Thursday – Lucinda shares her 1975 vacation memories with this vintage 1975 photo of America on Parade in Magic Kingdom, Florida!
STAR WARS Emojis on Twitter – #C3PO #Stormtrooper #BB8 – Starting today, Twitter users will have even more ways to express their excitement and celebrate their favorite pop-culture phenomenon with exclusive new Twitter #StarWarsEmojis.
STAR WARS: THE FORCE AWAKENS – New Trailer and Images – Yesterday in Anaheim, California, J.J. Abrams and Kathleen Kennedy kicked off Star Wars Celebration with an exclusive new look at STAR WARS: THE FORCE AWAKENS.
Star Wars Celebration 2015 – Images – New images from STAR WARS: THE FORCE AWAKENS panel and activities at Star Wars Celebration are here!
Monkey Kingdom – Disneynature's Newest Hit – As a homeschooling family, Disneynature films provide us with the unique opportunity to learn about nature in a way that only Disney can tell the story!


HCAT lab held another writing retreat October 12th – 16th, 2016 in Park City, UT. The writing retreat was held at a 5 bedroom house located just above main street in old town (you can rent it here). Fourteen people attended the retreat, including folks from Texas, Illinois, and Maryland. We had two research groups at the retreat: a skin cancer research group and a psychological reactance group. Both groups wrote manuscripts, analyzed data, and designed future studies.
The 2016 retreat also saw two different trips to the emergency room. Jake was in the emergency room when one of his sons developed a nasty case of parainfluenza. Kevin John ended up in the emergency room with appendicitis!
Despite these obstacles, the retreat was a huge success. We wrote from the moment the house opened on the 12th until 2 am in the morning the night of the 15th. Lisa Guntzviller led a workshop on building tables in Excel, and Jake led a workshop on the Johnson-Neyman (JN) technique.
The tattoo sleeves made a second appearance too. As folks finished papers, they were rewarded with the awesome sleeves. Nick Carcioppolo and Rob Yale weren't able to make it this year, so we made Nick and Rob paper cutouts – and enjoyed taking crazy photos with them.
It was another wonderful writing retreat!
The Golden Anniversary Monograph Awards were created to mark NCA's 50th Anniversary in 1964. Originally, there were awards given to monographs and to books. The book award was deleted and later reinstated as the Diamond Anniversary Book Award. The Golden Anniversary Monograph Awards are presented to the most outstanding scholarly monograph(s) published during the previous calendar year.
My randomized controlled trial published in Social Science & Medicine was honored with the 2015 Golden Anniversary Monograph Award from NCA. You can read more about it here. You can see a list of past award winners here.
HCAT lab held another writing retreat October 16th – 19th, 2014 in Park City, UT. The writing retreat was held at a 5 bedroom cabin just off the old town roundabout (you can rent it here). Nick Carcioppolo led a workshop on Mplus, I led a workshop on validity in confirmatory factor analysis, and we worked on 15 manuscripts, 8 of which were completed by the end of the retreat. A pool table on the top floor of the house was our relaxation sport of choice. We also enjoyed trying to ride the house elevator, which was wonderfully inconsistent. Finally, we visited Olympic Park during this retreat, and that is quickly becoming a tradition.
HCAT held a writing retreat March 14th – 17th, 2014 in Dallas, TX. Twelve faculty and grads attended the retreat. Jake led a workshop on writing grants, and we also talked a lot about publishing strategies. Jake's consumption of Mountain Dew during the retreat reached epic levels, I think I slept a total of 2 hours. An air hockey table provided us with quite a bit of fun. A pool also occupied our time. The retreat was put on by HCAT members Rob Yale (Assistant Professor, University of Dallas) and Andy King (Assistant Professor, Texas Tech University). A great time was had by all.
HCAT lab held another writing retreat October 11th – 14th, 2013 in Park City, UT. The writing retreat was held at a 5 bedroom cabin in Deer Valley that described itself as the Nordic House (you can rent it here). Eleven faculty and graduate students attended the retreat. I led a workshop on Lisrel and confirmatory factory analysis, Lisa Guntzviller led a workshop on bibliography software, and Rob Yale took us all out to dinner at the Blue Iguana (which would become a writing retreat tradition). We also discovered numerous quirks such as my love of high quality scrap paper and Kevin Coe's need to have lots of gum on hand while writing. Finally, Rob Yale impressed everyone (as he tends to) with an amazing suitcase.
We worked on 12 manuscripts during the writing retreat, 7 of which were finished by end. We played an excessive amount of shuffleboard.
HCAT lab members Andy King, Nick Carcioppolo, and Jake all attended a Grant Writing Workshop hosted by NCA in Chicago. We worked furiously on grant applications, and Nosh, Dale, and Susan took us out drinking.
In 2010, we won a subcontract on a Department of Defense/Telemedicine and Advanced Technology Research Center Grant. Our job was to conduct research to inform the design of an HPV vaccination campaign. To that end, we created experimental stimuli to test threat-to-efficacy ratio hypotheses at the heart of EPPM research. This required the creation of lots and lots and lots of stimuli – one for each ratio combination. Here they are, provided for your use and pleasure. Seriously. Feel free to use them.
Carcioppolo, N.,* Jensen, J. D., Wilson, S. E., Collins, W. B., Carrion, M., & Linnemeier, G. (2013). Examining HPV threat-to-efficacy ratios in the extended parallel process model. Health Communication, 28, 20-28.