---
abstract: 'Motivated by the previous success of Two-Dimensional Convolutional Neural Network (2D CNN) on image recognition, researchers endeavor to leverage it to characterize videos. However, one limitation of applying 2D CNN to analyze videos is that different frames of a video share the same 2D CNN kernels, which may result in repeated and redundant information utilization, especially in the spatial semantics extraction process, hence neglecting the critical variations among frames. In this paper, we attempt to tackle this issue through two ways. 1) Design a sequential channel filtering mechanism, i.e., Progressive Enhancement Module (PEM), to excite the discriminative channels of features from different frames step by step, and thus avoid repeated information extraction. 2) Create a Temporal Diversity Loss (TD Loss) to force the kernels to concentrate on and capture the variations among frames rather than the image regions with similar appearance. Our method is evaluated on benchmark temporal reasoning datasets Something-Something V1 and V2, and it achieves visible improvements over the best competitor by $2.4\%$ and $1.3\%$, respectively. Besides, performance improvements over the 2D-CNN-based state-of-the-arts on the large-scale dataset Kinetics are also witnessed.'
author:
- 'Junwu Weng[^1]'
- 'Donghao Luo[ ^fnsymbol[1]{}^]{}'
- Yabiao Wang
- Ying Tai
- Chengjie Wang
- Jilin Li
- Feiyue Huang
- Xudong Jiang
- Junsong Yuan
bibliography:
- 'egbib.bib'
title: |
    Temporal Distinct Representation Learning\
    for Action Recognition
---

Introduction {#sec:intro}
============

Owing to the computer vision applications in many areas like intelligent surveillance and behavior analysis, how to characterize and understand videos becomes an intriguing topic in the computer vision community. To date, a large number of deep learning models [@c3d; @tsn; @tsm; @trn; @nonlocal; @convlstm; @lu2018deep; @lu2019see; @lu2020learning] have been proposed to analyze videos. The RNN-based models [@convlstm; @valstm] are common tools for sequence modeling for its sequential nature of visual representation processing, by which the order of a sequence can be realized. However, in these models the spatial appearance and temporal information are learned separately. Motivated by the success in image recognition, Convolutional Neural Network (CNN) becomes popular for video analysis. 3D CNNs [@nonlocal; @i3d; @slowfast; @c3d] are widely used in video analysis as they can jointly learn spatial and temporal features from videos. However, their large computational complexities impede them from being applied in real scenarios. In contrast, 2D CNNs are light-weight, but do not bear the ability for temporal modeling. To bridge the gap between image recognition and video recognition, considerable 2D-CNN-based researches [@tsn; @tbn; @tsm; @trn; @abm] recently attempt to equip the conventional 2D CNNs with a temporal modeling ability, and some improvements are witnessed.

However, another direction seems to be less explored for 2D-CNN-based video analysis, namely diversifying visual representations among video frames. Although the 2D CNN takes multiple frames of a video at once as input, the frames captured from the same scene share the same convolution kernels. A fact about CNN is that each feature channel generated by the kernel convolution from the high-level layers highly reacts to a specific semantic pattern. Hence, with 2D CNN, the yielded features from different frames may share multiple similar channels, which thereafter results in repeated and redundant information extraction for video analysis. If the majority part of frames is background, these repeated redundant channels tend to describe the background scene rather than the regions of interest. This tendency may lead to the ignorance of the motion information which can be more critical than the scene information for action understanding [@jiang2012trajectory; @wang2016trans; @wang2018context; @choi2019mall]. Besides, the customary strategy that features from different frames of a video are learned under the same label of supervision will make this issue even more severe. We observe that for one temporal reasoning dataset like Something-Something [@goyal2017something], video samples under the same category are from various scenes and the actions therein are performed with various objects. The scene and object information may not be directly useful for the recognition task. Thus, a 2D-CNN-based method like TSN [@tsn] is easy to overfit as the model learns many scene features and meanwhile neglects the variations among frames, [*e.g.*]{} the motion information. We state that due to this redundant information extraction, the previously proposed temporal modeling method cannot fully play its role. In this paper, we propose two ways to tackle the issue.

We first introduce an information filtering module, i.e., Progressive Enhancement Module (PEM), to adaptively and sequentially enhance the discriminative channels and meanwhile suppress the repeated ones of each frame’s feature with the help of motion historical information. Specifically, the PEM progressively determines the enhancements for the current frame’s feature maps based on the motion observation in previous time steps. This sequential way of enhancement learning explicitly takes the temporal order of frames into consideration, which enables the network itself to effectively avoid gathering similar channels and fully utilize the information from different temporal frames. After PEM, we set a temporal modeling module that temporally fuses the enhanced features to help the discriminative information from different frames interact with each other.

Furthermore, the convolution kernels are calibrated by the Temporal Diversity Loss (TD Loss) so that they are forced to concentrate on and capture the variations among frames. We locate a loss right after the temporal modeling module. By minimizing the pair-wise cosine similarity of the same channels between different frames, the kernels can be well adjusted to diversify the representations across frames. As the TD Loss acts as a regularization enforced to the network training, it does not add an extra complexity to the model and keeps a decent accuracy-speed tradeoff.

We evaluate our method on three benchmark datasets. The proposed model outperforms the best state-of-the-arts by $2.4\%$, $1.3\%$ and $0.8\%$ under the $8f$ setting on the Something-SomethingV1, V2 and the Kinetics400 datasets, respectively, as shown in Table \[tbl:sth\] and Table \[tbl:k400\]. The proposed PEM and TD Loss outperform the baseline by $2.6\%$ and $2.3\%$ on Something-Something V1, respectively. The experimental results demonstrate the effectiveness of our proposed 2D-CNN-based model on video analysis.\
\
Our contributions can be summarized as follows:

-   We propose a Progressive Enhancement Module for channel-level information filtering, which effectively excites the discriminative channels of different frames and meanwhile avoids repeated information extraction.\

-   We propose a Temporal Diversity Loss to train the network. The loss calibrates the convolution kernels so that the network can concentrate on and capture the variations among frames. The loss also improves the recognition accuracy without adding an extra network complexity.

Related Work {#sec:works}
============

### 2D-CNNs for Video Analysis

Due to the previous great success in classifying images of objects, scenes, and complex events [@imagenet; @verydeep; @goingdeep; @resnet], convolutional neural networks have been introduced to solve the problem of video understanding. Using two-dimensional convolutional network is a straightforward way to characterize videos. In Temporal Segment Network [@tsn], 2D CNN is utilized to individually extract a visual representation for each sampled frame of a video, and an average pooling aggregation scheme is applied for temporal modeling. To further tackle the temporal reasoning of 2D CNNs, Zhou [*et.al.*]{} proposed a Temporal Relational Network [@trn] to hierarchically construct the temporal relationship among video frames. Ji [*et.al.*]{} introduced a simple but effective shift operation between frames into 2D CNN, and proposed the Temporal Shift Module [@tsm]. Following the same direction, the Temporal Enhancement Interaction Network (TEINet) [@tei] introduces a depth-wise temporal convolution for light-weight temporal modeling. Similar methods include Temporal Bilinear Network [@tbn] and Approximate Bilinear Module [@abm], which re-design the bilinear pooling for temporal modeling. These methods attempt to equip the 2D CNN with an ability of temporal modeling. However, one neglected limitation of the 2D CNN is the redundant feature extraction among frames or the lack of temporal representation diversity. This is the battle field to which our proposed method is engaged. We first propose a Progressive Enhancement Module before temporal modeling to enhance the discriminative channels and meanwhile suppress the redundant channels of different frames sequentially. Furthermore, after the temporal modeling module, we create a Temporal Diversity loss to force the convolution kernels to capture the variations among frames.

### Channel Enhancement

The idea of enhancing the discriminative channels for recognition first appears in image recognition. In Squeeze-and-Excitation Network (SENet) [@hu2018squeeze], an attention sub-branch in the convolutional layer is involved to excite the discriminative channels of frame’s features. Inheriting from the SENet, to emphasize the motion cues in videos, TEINet uses the difference between feature maps of two consecutive frames for channel-level enhancement learning. In our method, we expand the receptive field of this channel enhancement module. At each time step, the enhancement module is able to be aware of the motion conducted in previous frames, therefore avoiding activating the channels emphasized previously.

### Diversity Regularization

In fine-grained image recognition, to adaptively localize discriminative parts, attention models are widely used. However, the previously proposed attention models perform poorly in classifying fine-grained objects as the learned attentions tend to be similar to each other. In [@zheng2017learning; @zhao2017diversified], attention maps are regularized to be diverse in the spatial domain to capture the discriminative parts. In this paper, we take the temporal diversity of the feature maps into consideration and propose the Temporal Diversity Loss. The TD Loss directly sets the regularization on the visual representation of each frame to obtain the discriminative and dynamic features for video analysis.

Proposed Method {#sec:method}
===============

In this section, we elaborate on the two contributions of this work. We first give the framework of the proposed method in Sec. \[sec:framework\]. The Progressive Enhancement Module is introduced in Sec. \[sec:progressatt\]. In Sec. \[sec:tdloss\], the Temporal Diversity Loss for diverse representation modeling is described. The illustration of the whole framework is shown in Fig. \[fig:framework\].

![An illustration of the proposed framework. In PEM, 1) features of each frame are GAP-ed and down-sampled to get a vector. 2) The differencing operation is performed on the vectors of each two consecutive frames. 3) The [*memory*]{} vector (in the red box) accumulates historical difference information. 4) With the Sigmoid function, the channel-level enhancement is obtained to excite discriminative channels of each frame. To compress the model complexity, the $1\times1$ convolution operation in $f(\cdot)$ reduces the vector dimensionality and the one before $\sigma(\cdot)$ recovers it back to $C$. In TD regularization, the same channels of each frame pair are regularized to be distinguished from each other.[]{data-label="fig:framework"}](framework.pdf){width="1.0\linewidth"}

Framework {#sec:framework}
---------

In our model, each video frame is represented by a tensor $\bm{X}^b_t \in \mathbb{R}^{C_b \times W_b \times H_b}$, which bears $C_b$ stacked feature maps with width $W_b$ and height $H_b$, and $b$ indicates the block index. In the following, we use $C$, $W$ and $H$ instead to simplify the notations. Given a video sequence/clip with $T$ sampled frames $V=\{\bm{X}_t\}^{T}_{t=1}$, the goal of our established deep network is to extract a discriminative visual representation of $V$ and predict its class label $k \in \{1, 2, ..., K \}$, where $K$ is the number of categories. Each block of the network takes the $T$ frames as input and outputs the feature maps for the next block, which is formulated as follows:

$$(\bm{X}^b_1, \dotsm, \bm{X}^b_T) = \mathcal{F}(\bm{X}^{b-1}_1, \dotsm, \bm{X}^{b-1}_T; \bm{\theta}^b), \label{eq:formulation}$$

where $\mathcal{F}$ is the feature mapping function, which involves the Progressive Attention for information filtering (Sec. \[sec:progressatt\]) and temporal information interaction. $\bm{\theta}$ is the block parameters to be optimized. The input and output of the network are denoted as $\bm{X}^0$ and $\bm{X}^B$, and $B$ is the total number of blocks.

The output feature maps $\{\bm{X}^B_t\}^{T}_{t=1}$ are gathered by average pooling, and are further fed into the Softmax function for category prediction. This mapping is defined as $\bm{\hat{y}} = \mathcal{G}(\bm{X}^B_1, \dotsm, \bm{X}^B_T)$, where $\bm{\hat{y}} \in [0,1]^K$ contains the prediction scores of $K$ categories. Therefore, the loss function is defined as

$$\mathcal{L} = \mathcal{L}_c + \lambda\mathcal{L}_r = -\sum^{K}_{i=1} y_i \cdot log\,\hat{y}_i + \lambda\mathcal{L}_r, \label{eq:loss}$$

where $\mathcal{L}_c$ is the Cross Entropy Loss for category supervision. $y_i$ is the groundtruth label concerning class $i$, and it is an element of the one-hot vector $\bm{y} \in \{0,1\}^K$. $\mathcal{L}_r$ is the regularization term for network training, and $\lambda$ balances the importance between category supervision and network regularization. To enhance the temporal diversity of feature maps from different frames and thereafter to model the crucial motion information, the regularization term is defined as the Temporal Diversity Loss as depicted in Sec. \[sec:tdloss\].

Progressive Enhancement Module {#sec:progressatt}
------------------------------

As discussed in Sec. \[sec:intro\], one drawback of using 2D CNN for video analysis is that most of the kernels in one convolutional network are inclined to focus on repeated information, like scenes, across the features from different time steps, which cannot easily take full advantage of information from the video. The Progressive Enhancement Module (PEM) can sequentially determine which channels of each frame’s features to focus on, and therefore effectively extract action related information. In each block, the feature maps $\{\bm{X}^{b-1}_t\}^{T}_{t=1}$ from the preceding block are first fed into the Progressive Enhancement Module for information filtering, as illustrated in Fig. \[fig:framework\]. Let $\bm{a}^b_t \in \mathbb{R}^{C}$ denote the enhancement vector to excite the discriminative channels of each frame. This operation is defined as

$$\bm{U}^b_t = \bm{X}^{b-1}_t \odot \bm{a}^{b}_t \,, \label{eq:excite}$$

where $\bm{U}^b_t$ is the $t$-th frame output of PEM in the $b$-th block, and $\odot$ is a channel-wise multiplication. For notational simplicity, we remove the block-index notation $b$ in the following description.

The input feature maps $\{\bm{X}^{b-1}_t\}^{T}_{t=1}$ are first aggregated across the spatial dimensions by using Global Average Pooling (GAP), and the channel-wise statistics $\{\bm{x}_t\}^{T}_{t=1}$, $\bm{x} \in \mathbb{R}^C$ are then obtained. Each pair of neighboring frames in $\{\bm{x}_t\}^{T}_{t=1}$ is then fed into two individual $1 \times 1$ convolution operations $f_1$ and $f_2$ with ReLU activation, respectively, for feature selection. As discussed in [@tei], taking the difference of channel statistics between two consecutive frames as input for channel-level enhancement learning is more effective for video analysis than the original channel-wise statistics $\{\bm{x}_t\}^{T}_{t=1}$ proposed in Squeeze-and-Excitation Network [@hu2018squeeze], which is especially designed for image recognition. We choose to use the difference of channel statistics between two consecutive frames as the input of PEM. With the differencing operation, we obtain the difference of channel-wise statistics $\{\bm{d}_t\}^{T}_{t=1}$. The differencing operation is defined as

$$\bm{d}_t = f_2(\bm{x}_{t+1}) - f_1(\bm{x}_t) \,, \label{eq:diff}$$

and the difference of the last frame, $\bm{d}_T$, is set as a vector with ones to maintain the magnitude of the memory vector .

To extend the receptive field of enhancement learning, we here introduce an accumulation operation into the learning of channel-level enhancement for each frame. By the accumulation, the enhancement module of each current frame can be aware of the vital motion information in the previous timings, and not be trapped into the local temporal window as in [@tei]. The accumulated vector $\bm{m}$, named as [*memory*]{}, accumulates $\bm{d}$ at each time step, and the accumulation operation is controlled by $\gamma \in [0,1]$, as defined in Eq. (\[eq:accumulate\]):

$$\bm{m}_t = (1-\gamma)\cdot\bm{m}_{t-1} + \gamma\cdot\bm{d}_{t}\,,~~~~~\gamma = \sigma(\bm{W}_g(\bm{m}_{t-1} \mathbin\Vert \bm{d}_{t}))\,.\label{eq:accumulate}$$

The factor $\gamma$ is determined by the accumulated vector $\bm{m}_{t-1}$ and the difference information $\bm{d}_t$, where $\mathbin\Vert$ denotes a concatenation operation, $\bm{W}_g$ is a projection matrix for linear transformation, and $\sigma(\cdot)$ is a Sigmoid activation function. The final enhancement vector $\bm{a}$ is then generated by

$$\bm{a}_t = \sigma(\bm{W}_a\bm{m}_t)\,, \label{eq:biga}$$

where $\bm{W}_a$ is a matrix linearly projecting $\bm{m}$ into a new vector space. With PEM, the network is able to progressively select the motion-related channels in each frame, and adaptively filter the discriminative information for video analysis. The enhanced feature maps $\{\bm{U}_t\}^{T}_{t=1}$ are then fed into a temporal modeling module for temporal information fusion, and we write the output as $\{\bm{Z}_t\}^{T}_{t=1}, \bm{Z} \in \mathbb{R}^{C \times W \times H}$.

Temporal Diversity Loss {#sec:tdloss}
-----------------------

It is well-known that feature maps from high-level layers tend to have responses to specific semantic patterns. Convolution kernels that focus on the background of a video may generate similar semantic patterns for the same channels of features from different frames, which may lead to redundant visual feature extraction for video analysis. To calibrate the kernels in 2D CNN and force the network to focus on and capture the variations among frames of a video sequence, we propose the Temporal Diversity Loss to regularize the network toward learning distinguished visual features for different frames. For the feature map $\bm{Z}_t$ from each frame, its $C$ vectorized channel features are denoted as $\{\bm{z}^c_t\}^C_{c=1}$, $\bm{z} \in \mathbb{R}^{WH}$. We use the Cosine Similarity to measure the similarities of a specific channel between two frames of each video frame pair, and then define the loss as:

$$\mathcal{L}_\mu = \sum_c \frac{1}{|\mathds{I}|} \sum_{(i,j) \in \mathds{I}} \eta(\bm{z}^c_i,\bm{z}^c_j)\,, \label{eq:tdloss}$$

where $\mathds{I}=\{(i,j)\,|\,i\neq j,\,1 \leqslant i,j \leqslant T\}$, $\vert\cdot\vert$ indicates the total number of elements in a set, and $\eta(\cdot)$ defines the Cosine Similarity measure, namely $\eta(\bm{x},\bm{y}) = \frac{\bm{x}^\intercal\bm{y}}{{\|\bm{x}\|}_2\cdot{\|\bm{y}\|}_2}$. Considering that the static information among frames is also beneficial to recognition, we only use $C_{\mu}$ ($C_{\mu} < C$) channels for temporal diversity regularization. A further analysis will be discussed in Sec. \[sec:ratio\]. With the proposed Temporal Diversity $\mathcal{L}_{\mu}$, the regularization term $\mathcal{L}_r$ is then defined as $\mathcal{L}_r = \sum^{B_{\mu}}_{b=1} \mathcal{L}^b_{\mu}$, where $B_{\mu}$ is the number of blocks with temporal diversity regularization.

Experiments {#sec:experiment}
===========

In this section, the proposed method is evaluated on three benchmark datasets, the Something-Something V1 dataset [@goyal2017something], Something-Something V2 dataset [@mahdisoltani2018effectiveness], and the Kinetics400 dataset [@carreira2017quo]. We first briefly introduce these three datasets and the experiment settings in Sec. \[sec:dataset\] and Sec. \[sec:setting\], respectively. Then, our method is compared with the state-of-the-arts in Sec. \[sec:sotas\]. The ablation study is conducted in Sec. \[sec:ablation\] to evaluate the performance of each individual module of our proposed method. In Sec. \[sec:analysis\], we evaluate the proposed method in detail, including parameter, position sensitivity analysis and visualization.

Datasets {#sec:dataset}
--------

### Something-Something V1&V2 {#sec:something}

are crowd-sourced datasets focusing on temporal modeling. In these two datasets, the scenes and objects in each single action category are various, which strongly requires the considered model to focus on the temporal variations among video frames. The V1 & V2 datasets include 108,499/220,847 videos, respectively, containing 174 action categories in both versions.

### Kinetics400 {#sec:kinetics}

is a large-scale YouTube-like dataset, which contains 400 human action classes, with at least 400 video clips for each category. The average duration of video clips in this dataset is around 10s. Unlike Something-Something datasets, Kinetics is less sensitive to temporal relationships, so the scene information is of importance in its recognition.

[r]{}[0.5]{}

![image](blocknew-eps-converted-to.pdf){width="50.00000%"}

\[fig:block\]

Experimental Setup {#sec:setting}
------------------

In all the conducted experiments, we use the ResNet-50 [@resnet] as our backbone considering the tradeoff between performance and efficiency, and our model is pre-trained by ImageNet [@imagenet]. We set the Progressive Enhancement Module (PEM) in front of all the blocks of the ResNet backbone. Given that the early stages of the Convolutional Network focus more on spatial appearance modeling and the later ones focus on temporal modeling [@r(2+1)d; @gst] and for better convergence, we regularize the temporal diversity of feature maps in the last blocks of each of the last three layers. The Temporal Diversity Loss (TD Loss) is located right after the temporal modeling module. What follows the temporal modeling module is the convolution operation (ResConv) taken from the ResNet block, which includes one $1\times1$, one $3\times3$, and one $1\times1$ 2D convolutions. The position of the PEM and the temporal diversity regularization are illustrated in Fig. \[fig:block\]. We define the TDRL-A as the block without the TD Loss, and TDRL-B as the one with the TD Loss, where TDRL stands for Temporal Distinct Representation Learning. The ratio of channels regularized by Temporal Diversity Loss in each feature is 50%. Without loss of generality, we use the Temporal Interaction Module proposed in [@tei] as our temporal modeling module (TM). $\lambda$ for loss balancing is set as $2\times 10^{-4}$.

[p[2.8cm]{}< p[2.1cm]{}< p[1.4cm]{}< p[1.4cm]{}< p[0.9cm]{}< p[0.9cm]{}< p[0.9cm]{}< p[0.9cm]{}<]{} **Method** & **Backbone** & **Frames** & **FLOPs** & **Val$_1$** & **Test$_1$** & **Val$_2$** & **Test$_2$**\
I3D [@i3d] & \*

  ----------
   Res3D-50
     (IK)
  ----------

  : Comparison with the state-of-the-arts on Something-Something V1&V2 (Top1 Accuracy %). The notation ‘I’ or ‘K’ in the backbone column indicates that the model is pre-trained with ImageNet or Kinetics400 dataset. The subscripts of ‘Val’ and ‘Test’ indicate the version of the Something-Something dataset. ’2S’ indicates two streams.[]{data-label="tbl:sth"}

& \*[$32f\times2$]{} & 306G & 41.6 & – & – & –\
NL I3D [@i3d] & & & 334G & 44.4 & – & – & –\
NL I3D+GCN [@i3d]& & & 606G & 46.1 & 45.0 & – & –\
\*[TSM [@tsm]]{} & \*

  ----------
   Res2D-50
     (IK)
  ----------

  : Comparison with the state-of-the-arts on Something-Something V1&V2 (Top1 Accuracy %). The notation ‘I’ or ‘K’ in the backbone column indicates that the model is pre-trained with ImageNet or Kinetics400 dataset. The subscripts of ‘Val’ and ‘Test’ indicate the version of the Something-Something dataset. ’2S’ indicates two streams.[]{data-label="tbl:sth"}

& $8f$ & 33G & 45.6 & – & – & –\
& & $8f\times 2$ & 65G & 47.3 & – & 61.7 & –\
& & $16f$ & 65G & 47.2 & 46.0 & – & –\
TSM$_{En}$ [@tsm] & & $16f+8f$ & 98G & 49.7 & – & – & –\
TSM-2S [@tsm] & & $16f+16f$ & – & 52.6 & 50.7 & 64.0 & 64.3\
\*[TEINet [@tei]]{} & \*[Res2D-50(I)]{} & $8f$ & 33G & 47.4 & – & 61.3 & 60.6\
&& $8f\times 10$ & 990G & 48.8 & – & 64.0 & 62.7\
& & $16f$ & 66G & 49.9 & – & 62.1 & 60.8\
&& $16f\times 10$ & 1980G & 51.0 & 44.7 & 64.7 & 63.0\
TEINet$_{En}$ [@tei] & & $8f+16f$ & 99G & 52.5 & 46.1 & 66.5 & 64.6\
\*[GST [@gst]]{} & \*[Res2D-50(I)]{} & $8f$ & 29.5G & 47.0 & – & 61.6 & 60.0\
& & $16f$ & 59G & 48.6 & – & 62.6 & 61.2\
\*[Ours]{} & \*[Res2D-50(I)]{} & $8f$ & 33G & **49.8** & **42.7** & **62.6** & **61.4**\
& & $8f\times 2$ & 198G & **50.4** & **–** & **63.5** & –\
& & $16f$ & 66G & **50.9** & **44.7** & **63.8** & **62.5**\
& & $16f\times 2$ & 396G & **52.0** & **–** & **65.0** & –\
& & $8f+16f$ & 99G & **54.3** & **48.3** & **67.0** & **65.1**\

### Pre-processing

We follow a similar pre-processing strategy to that described in [@nonlocal]. To be specific, we first resize the shorter side of RGB images to 256, and center crop a patch followed by scale-jittering. The image patches are then resized to $224\times 224$ before being fed into the network. Owing to the various lengths of video sequences, we adopt different temporal sampling strategies for different datasets. The network takes a clip of a video as input. Each clip consists of 8 or 16 frames. For the Kinetics dataset, we uniformly sample 8 or 16 frames from the consecutive 64 frames randomly sampled in each video. For the Something-Something dataset, due to the limited duration of video samples, we uniformly sample 8 or 16 frames from the whole video.

\[t\]

[p[3.0cm]{}< p[3.4cm]{}< p[2.8cm]{}< p[1.2cm]{}< p[1.2cm]{}<]{} **Method** & **Backbone** & **GFLOPs$\times$views** & **Top-1** & **Top-5**\
I3D$_{64f}$ [@i3d] & Inception V1(I) & 108$\times$N/A & 72.1 & 90.3\
I3D$_{64f}$ [@i3d] & Inception V1(Z) & 108$\times$N/A & 67.5 & 87.2\
NL+I3D$_{32f}$ [@nonlocal] & Res3D-50(I) & 70.5$\times$30 & 74.9 & 91.6\
NL+I3D$_{128f}$ [@nonlocal] & Res3D-50(I) & 282$\times$30 & 76.5 & 92.6\
NL+I3D$_{128f}$ [@nonlocal] & Res3D-101(I) & 359$\times$30 & 77.7 & 93.3\
Slowfast [@slowfast] & Res3D-50(Z) & 36.1$\times$30 & 75.6 & 92.1\
Slowfast [@slowfast] & Res3D-101(Z) & 106$\times$30 & 77.9 & 93.2\
NL+Slowfast [@slowfast] & Res3D-101(Z) & 234$\times$30 & **79.8** & **93.9**\
LGD-3D$_{128f}$ [@lgdnet] & Res3D-101(I) & N/A$\times$N/A & 79.4 & 94.4\
R(2+1)D$_{32f}$ [@r(2+1)d] & Res2D-34(Z) & 152$\times$10 & 72.0 & 90.0\
R(2+1)D$_{32f}$ [@r(2+1)d] & Res2D-34(S) & 152$\times$10 & 74.3 & 91.4\
ARTNet$_{16f}$+TSN [@artnet] & Res2D-18(Z) & 23.5$\times$250 & 70.7 & 89.3\
S3D-G$_{64f}$ [@s3d] & Inception V1(I) & 71.4$\times$30 & 74.7 & 93.4\
TSM$_{16f}$ [@tsm] & Res2D-50(I) & 65$\times$30 & 74.7 & 91.4\
TEINet$_{8f}$ [@tei] & Res2D-50(I) & 33$\times$30 & 74.9 & 91.8\
TEINet$_{16f}$ [@tei] & Res2D-50(I) & 66$\times$30 & 76.2 & 92.5\
Ours$_{8f}$ & \*[Res2D-50(I)]{} & $33\times 30$ & **75.7** & **92.2**\
Ours$_{16f}$ & & $66\times 30$ & **76.9** & **93.0**\

### Training

For the Kinetics dataset, we train our model for 100 epochs. The initial learning rate is set as $0.01$, and is scaled with $0.1$ at $50$, $75$, and $90$ epochs. For the Something-Something dataset, the model is trained with 50 epochs in total. The initial learning rate is set as $0.01$ and reduced by a factor of $10$ at $30$, $40$, and $45$ epochs. In the training, Stochastic Gradient Decent (SGD) is utilized with momentum $0.9$ and weight decay of $1\times {10}^{-4}$. The experiments are conducted on [*Tesla M40*]{} GPUs, and the batch size is set as $64$. The memory vector can be initialized by zeros, or the difference vector $\bm{d}_t$ from the first or last frame, and we experimentally find that the last frame difference $\bm{d}_{T-1}$ can achieve slightly better performance. We therefore use $\bm{d}_{T-1}$ as the memory initialization in the experiments.

### Inference

For fair comparison with the state-of-the-arts, we follow two different data processing settings to evaluate our model. In single-clip ($8$ or $16$ frames) comparison, namely model trained with $8$ frames only ($8f$), or with $16$ frames only ($16f$), we use center cropping for input image processing. The analysis experiments are under the $8f$ setting. In multi-clip comparison, we follow the widely applied settings in [@nonlocal; @tsm] to resize the shorter side of images to 256 and take 3 crops (left, middle, right) in each frame. Then we uniformly sample $N$ clips ($8f\times N$ or $16f\times N$) in each video and obtain the classification scores for each clip individually, and the final prediction is based on the average classification score of the $N$ clips.

Comparison with State-of-the-Arts {#sec:sotas}
---------------------------------

We compare the proposed method with the state-of-the-arts on the Something-SomethingV1&V2 and the Kinetics400 datasets under different settings for fair comparison. The results are shown in Table \[tbl:sth\] and Table \[tbl:k400\], respectively. Table \[tbl:sth\] shows that on the Something-Something V1 dataset, our proposed method outperforms the so far best model, TEINet [@tei], by $2.4\%$, $1.3\%$, and $1.8\%$ under the $8f$, $16f$, and $8f+16f$ settings on the validation set, respectively. Our performance under the two-clips setting is even better than TEINet’s performance under the ten-clips setting. On the Something-Something V2 dataset, the performance of our model under the $8f$ setting is even better or the same as the TEINet and GST under the $16f$ setting, which indicates that we can use only half of the inputs of these two models to achieve the same or better accuracy. These results verify the effectiveness of the temporal representation diversity learning. On the Kinetics dataset, the results are reported under the ten-clips-three-crops setting. As can be seen from Table \[tbl:k400\], our proposed model outperforms all the 2D-CNN-based models under different settings, and it even performs better than the 3D-CNN-based nonlocal [@nonlocal] and slowfast [@slowfast] networks with less frames input. We can also witness consistent improvement on the test set of V2, and our model beats TEI by $1.2\%$ under the $8f+16f$ setting.

\[t\]

[l p[1.2cm]{}< p[1.2cm]{}<]{} **Method** &**Top-1** &**Top-5**\
baseline [@tsm] & 45.6 & 74.2\
$+$PEM & 48.1 & 77.4\
$+$TDLoss & 47.5 & 76.8\
$+$PEM+TDLoss & 48.4 & 77.4\

[l p[1.2cm]{}< p[1.2cm]{}<]{} **Method** &**Top-1** &**Top-5**\
baseline [@tei] & 46.1 & 74.7\
$+$MEM [@tei] & 47.4 & 76.6\
$+$PEM & 48.7 & 77.8\
$+$TDLoss & 48.4 & 77.3\
$+$PEM+TDLoss & 49.8 & 78.1\

Ablation Study {#sec:ablation}
--------------

In this section, we evaluate the performances of different modules in our model. We use the single-clip $8f$ setting for the experiment conducted in this section. We use a temporal shift module (TSM) [@tsm] and a temporal interaction module (TIM) [@tei], respectively, as the baseline in our experiment to show the generality of our model cooperating with different temporal modeling modules. As can be seen from Table \[tbl:ablationtsm\], with the PEM and the TD Loss, there are $2.5\%$ and $1.9\%$ Top-1 accuracy improvements over the TSM, respectively. With both the PEM and TD Loss, the improvement reaches $2.8\%$. Similarly, as shown in Table \[tbl:ablationtim\], PEM gives $2.6\%$ Top-1 accuracy improvement over the baseline, and it outperforms MEM [@tei] by $1.3\%$, which also involves a channel enhancement module. With the TD Loss, there is $2.3\%$ improvement over the baseline. We can see from Table \[tbl:ablationtim\] that there is $3.7\%$ improvement over the baseline when both the PEM and TD Loss are applied. One more thing we need to point out is that, after 50 epochs training, the TIM baseline’s training accuracy reaches $79.03\%$, while with the PEM and the TD Loss, the training accuracies are down to $77.98\%$ and $74.45\%$, respectively. This training accuracy decline shows that our proposed method can avoid overfitting, and force the model to learn the essential motion cues.

\[t\]

[l p[1.2cm]{}< ]{} **Method** &**Top-1**\
baseline & 46.1\
$+25\%$ TDLoss & 47.7\
$+50\%$ TDLoss & 48.4\
$+75\%$ TDLoss & 47.8\
$+100\%$ TDLoss & 47.3\

[l p[1.2cm]{}< ]{} **Order** &**Top-1**\
TM & 46.1\
PEM B. TM & 48.7\
PEM A. TM & 49.0\
TDLoss B. TM & 46.9\
TDLoss A. TM & 48.4\

[p[1.5cm]{}< p[1.2cm]{}< ]{} **$\bm{\lambda}$** &**Top-1**\
$0\times 10^{-4}$ & 46.1\
$1\times 10^{-4}$ & 47.9\
$2\times 10^{-4}$ & 48.4\
$3\times 10^{-4}$ & 47.8\
$4\times 10^{-4}$ & 48.0\

Detailed Analysis {#sec:analysis}
-----------------

### Ratio of Channel Regularization {#sec:ratio}

The ratio of channel regularization indicates that how many channels are involved for diversity regularization. We set the ratio from $0\%$ (baseline) to $100\%$ to evaluate the impact of the TD loss in this section. The results are shown in Table \[tbl:ratio\]. From this table we can see that when half of the channels are regularized, the model achieves the best performance, $48.4\%$. If all the channels are set under the regularization, the model reaches the lowest accuracy $47.3\%$. This comparison shows that not all the channels require the regularization, and channels without regularization are still useful for the recognition task. However, no matter how many channels are involved in the loss, good improvement is still witnessed over the baseline, TIM.

### Position of Blocks {#sec:position}

In this part, we discuss where to insert the PEM and TD Loss in each block. The two modules are located before (B.) or after (A.) the temporal module (TM) individually to see the impact of position on accuracy. We follow the position of MEM in TEINet [@tei] for fair comparison. The results are shown in Table \[tbl:position\]. As can be seen, for PEM, there is no much difference between the two locations. It can fairly provide stable improvement on two different positions. For the TD loss, we discover that when it is located before the TM, the improvement over the baseline is limited. Because TM is inclined to make the representation similar to each other, the following ResConv cannot well extract the video representation. While when the TD regularization is after TM, there is $2.3\%$ improvement over the baseline. The TD loss effectively diversifies the representations among different frames after TM.

![Visualization of the features excited and suppressed by PEM. The features are organized in the temporal order, and they are from the first block of the first layer. **R1**: the images in this row are the input images of the network. **R2**: the features in this row are those before PEM. The channels of each of these features are divided by three groups, and the channels in each group are gathered by average pooling to generate a 3-channels feature, presented as an RGB image. **R3-4**: Each of the feature map in the these two rows is the average of channels picked from the features before PEM. Each feature map in the third row is gathering of ten channels with the highest enhancement, and each one in the fourth row is gathered with the lowest enhancement.[]{data-label="fig:visPEM"}](visPEM-eps-converted-to.pdf){width="1.0\linewidth"}

### Loss Balancing Factor $\lambda$ {#sec:lambda}

We analyze the impact of the loss balancing factor $\lambda$ on the accuracy in this section. We set $\lambda$ from $1\times 10^{-4}$ to $4\times 10^{-4}$. The result comparisons are shown in Table \[tbl:lambda\]. As can be seen, the fluctuation is in the range of $0.6\%$, which shows that the proposed TD Loss is not very sensitive to $\lambda$ when this factor is in an appropriate range. No matter which $\lambda$ we set, the involvement of the TD Loss can still help improve the accuracy over the baseline, which shows the effectiveness of the proposed temporal diversity loss.

### Visualization {#sec:visualization}

We visualize feature maps from different blocks to show the effect of the PEM and TD Loss. The experiment is conducted under the Kinetics dataset. We show the feature maps filtered by the PEM in Fig. \[fig:visPEM\]. There are two video samples shown in the figure. The input images are uniformly sampled from a video. From Fig. \[fig:visPEM\] we can see that the top ten enhanced channels mainly focus on the motion, while the top ten suppressed channels highly respond to the static background. This visualization shows that the proposed PEM can well discover which are the motion-related channels and which are the repeated static background channels. By enhancing the motion-related ones and suppress the repeated ones, the redundant information can be filtered out and the discriminative one can be well kept. As can be seen from Fig. \[fig:visTD\], with the TD Loss, the feature maps after TM can well encode the information from the current frame and its neighboring frames, while the motion encoded in the features after TM without the TD regularization is very limited. The figures indicate that the TD loss can calibrate the temporal convolution kernels and also enhance the temporal interactions among them.

![Visualization of feature maps before and after TM w/ or w/o the TD loss. These feature maps are uniformly sampled from one video and are organized following the temporal order. They are from the last block of the second layer. **R1**: The images are the input to the network. The purple dashed rectangles mark and illustrate the difference between feature maps with and without TD Loss. **w/ TD Loss**, the feature maps can well encode action from neighboring frames, and emphasize the variations among them, as marked by red rectangles in the last row. **w/o TD loss**, the features cannot enhance those variations, as marked by red rectangles in the third row.[]{data-label="fig:visTD"}](visTD-eps-converted-to.pdf){width="1.0\linewidth"}

Conclusions {#sec:conclusion}
===========

In this work, we proposed two ways to tackle the issue that the 2D CNN cannot well capture large variations among frames of videos. We first introduced the Progressive Enhancement Module to sequentially excite the discriminative channels of frames. The learned enhancement can be aware of the frame variations in the past time and effectively avoid the redundant feature extraction process. Furthermore, the Temporal Diversity Loss was proposed to diversify the representations after temporal modeling. With this loss, the convolutional kernels are effectively calibrated to capture the variations among frames. The experiments were conducted on three datasets to validate our contributions, showing the effectiveness of the proposed PEM and TD loss.

### Acknowledgement.

We thank Dr. Wei Liu from Tencent AI Lab for his valuable advice.

[^1]: Equal contribution. This work is done when Junwu Weng is an intern at Youtu Lab.


The present invention relates to an image file storing and retrieving method and apparatus and, more particularly, to an image storing and retrieving method suitably adapted to an image information storing and retrieving apparatus for retrieving images by use of layout information about document images.
Recently introduced image filing devices are being progressively introduced into automated offices following the introduction of the word processor, personal computer and facsimile machine. The filing device is attracting much attention for its potential for promoting paperless work in clerical and design offices. The image filing apparatus is capable of storing large amounts of image information on optical disks from which necessary information is instantaneously retrieved and printed out. This feature makes the apparatus particularly attractive to--and purchased by--those departments that handle huge quantities of documents. A big challenge for the image filing apparatus is how to efficiently retrieve any desired document from the massive storage inside.
One way to implement effective document retrieval is to use keywords. This method involves adding a keyword to each image, the keyword representing the contents of each image when the data of that image is stored. Later, a desired image is retrieved using the corresponding keyword. Generally, a keyword to be added to an image is an important word that represents the concept of that image. One disadvantage of this method is that because keywords are linguistic by definition, they cannot fully represent pattern characteristics intrinsic to images.
One solution to the above problem is the apparatus disclosed in Japanese Patent Laid-open No. 60-83179. This prior art apparatus achieves image retrieval in accordance with human intuition using line drawings of original images in place of keywords and works as follows. An image to be stored is first read by an input device, the read image being shown on a display unit. The contour of the displayed image is traced manually using a pointing device (e.g., mouse, tablet, etc.). The traced figure is contracted and used as the equivalent of a keyword. Because each contracted figure represents only the extracted characteristic of the corresponding image, it is easy-to distinguish the desired image from other images. And because the traced contours are contracted, a large number of "keywords" may be collectively displayed on the display screen. When one of the contracted figures on the display screen is designated with the pointing device, the corresponding image is read from optical disk for output.
As described, the prior art apparatus defines line drawings or figures as keywords. This allows the operator to use conceptual patterns conforming to his or her intuition as virtual keywords representing images. Thus any desired image is retrieved with relative ease from optical disk storage of large capacity. The devices associated with the above prior art apparatus are also disclosed in Japanese Patent Laid-open Nos. 60-19275, 62-72067 and 62-93757.
The prior art apparatus, as described, allows a line drawing to be extracted upon image entry, the extracted drawing or figure being used as a virtual keyword. Unlike linguistic keywords, these line drawing "keywords" provide image retrieval more in accordance with human intuition.
One disadvantage of the above described prior art is that it takes huge amounts of time to trace manually the contour of each and every image in order to create the corresponding "keyword".
Another disadvantage is that there are collectively displayed all stored line drawing patterns to choose from and the greater number of images stored, the greater amount of time is required to select a desired pattern from the increasingly numerous patterns representing the stored images.
A further disadvantage of the prior art is that because one stored line drawing pattern corresponds only to one original image, other images similar to one selected image cannot be retrieved without selecting the patterns specifically corresponding thereto.


Prevalence and Prognostic Impact of Wilms' Tumor 1 (WT1) Gene, Including SNP rs16754 in Cytogenetically Normal Acute Myeloblastic Leukemia (CN-AML): An Iranian Experience.
The aim of this study was to evaluate the effect of Wilms' tumor 1 (WT1) gene mutations in adult cytogenetically normal acute myeloblastic leukemia (CN-AML) patients on survival and clinical outcome. A total of 88 untreated Iranian adult patients with CN-AML were selected as a study group. Exons 7 (including the SNP rs16754), 8, and 9 as a WT1 gene hotspot region were evaluated by polymerase chain reaction and direct sequencing for detection of mutations. Response to treatment and clinical outcome including overall survival (OS) and disease-free survival (DFS) were evaluated according to WT1 gene mutational status. WT1 gene mutations were found in 12.5% of patients, most of which were found in exon 7. Complete remission was lower and relapse was higher in patients with WT1 gene mutation compared with WT1 gene wild type patients. OS and DFS was significantly lower in patients with WT1 gene mutation compared with patients with WT1 gene wild type (P < .001). Also, we did not find any significant effects of SNP rs16754 in exon 7 on clinical outcome and survival in patients with CN-AML. WT1 gene mutations are a predictor indicator of a poor prognosis factor in CN-AML patients. It is recommended that WT1 gene mutations be included in the molecular testing panel in order to better diagnose and confirm their prognostic significance for better management and treatment strategy.


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2002 Cinema Rex

Raiders of the Lost Ark

(widescreen) PG 1981 115 min.dir. Steven Spielberg. w/Harrison Ford, Karen Allen, Denholm Elliot, Full-Sized John-Rhys Davies.
Obligatory. Since it is against Rex¹s policy to ever show an Indy film in pan and scan, we proudly present the only widescreen version currently available  the letterboxed LaserDisc.

The Specials

(widescreen) R 2000 82 min.dir. Craig Mazin. Prod. Mark Altman. Assoc. Prod. Robert Meyer Burnett. w/Rob Lowe.
Guest of Honor Mark Altman introduces this zany superhero studded drama. Often described as similar to Mystery Men, but with a better script, this film, co-produced by Altman, was meant to be shown at conventions. You won¹t want to miss this one. This might be your only chance to see Rob Lowe in Cinema Rex, ever!

Planeta Burg

NR 1962 82 min.dir Pavel Klushantsev.
While we were headed towards “The Forbidden Planet,” the other side of the Iron Curtain was headed for Venus in this rare Soviet science fiction classic that follows a group of intrepid Cosmonauts, and of course their faithful robot companion, to the Planet of Storms (that¹s Venus, you decadent western capitalists!)

Stargate

(Director¹s Cut  widescreen) PG13 1994 129 min.dir. Roland Emmerich. w/Kurt Russell, James Spader, Jaye Davidson.
Opening Ceremony blues? Sick of all those crappy jokes the board members tell? And those skits . . . borrrrrrrring. Leave all that bunk behind and head down to Cinema Rex for some REAL entertainment with the adventurous Kurt Russell as he travels to Egypt and beyond. Check out the extra nine minutes and other subtle differences in the director¹s cut. (NOTE FROM THE BOARD: Wait until they see their budget next year!)

Seconds

(widescreen) R 1966 108 min.dir. John Frankenheimer. w/Rock Hudson, Salome Jens.
Overlooked by most of fandom, this rarely seen film is essentially a feature length, high budget, very dark, Twilight Zone episode. It is also an excellent opportunity for Deep Space Nine fans to play “Find the Founder.”

Conan the Barbarian

(European Version  widescreen) R 1982 130 min.dir. John Milius. w/Arnold Schwarzenegger, Sandahl Bergman, James Earl Jones, Mako.
We are very pleased to present the recently released European version of this splendid fantasy film. Often unappreciated in its televised, edited, pan-and-scan format, this film¹s brilliant score, flavorful supporting cast, and excellent directing make it one of the best fantasy films ever made. It is also an opportunity to see Arnold Schwartzenegger before he became a parody of himself. Please, if you haven¹t seen this movie in the last 10 years, you have to give it another shake!!!!

The Lord of the Rings: The Fellowship of the Ring

(widescreen): PG13 2001 178 min.dir. Peter Jackson. w/Elijah Wood, Ian McKellen, Christopher Lee, Half-Sized John Rhys-Davies.
One Film to rule them all. One Film to find them, One Film to bring them all (to Cinema Rex), And in the darkness bind them. Special thanks to our Guest of Honor, Robert Meyer Burnett, who is working on the upcoming DVD release, for making this event possible.

Snow White and the Seven Dwarfs

G 1937 80 min.prod. Walt Disney.
See the movie that started it all. The first Disney feature film. No animated deer were harmed during the making of this film.

Free Enterprise

(widescreen) R 1998 116min.dir. Robert Meyer Burnett. Prod. Mark Altman. Written by Mark Altman, Robert Meyer Burnett. w/William Shatner.
Rex is very pleased to present Guests of Honor Mark Altman and Robert Meyer Burnett as they introduce this year¹s installment of Star Trek Actor Theatre made by, for and about, well, the sort of people who have living rooms full of action figures. This hilarious geek-fest is an absolute must-see for fans of Star Trek, Logan¹s Run, and William Shatner. By the way, William Shatner is in this film.

The Adventures of Robin Hood

NR 1938 106 min.dir. Michael Curtiz, William Keighley. w/Errol Flynn, Olivia de Havilland, Basil Rathbone, Claude Rains.
Nominated for Best Picture in 1938, this beautiful Technicolor epic is quite possibly the greatest adventure film ever made. With this year¹s theme, Rex just couldn¹t resist bringing you this Costner-free classic that long ago inspired some of us to become fantasy gamers.

Creature from the Black Lagoon

NR 1954 79 min.dir. Jack Arnold.
Adventure finds itself deep in the Amazon jungle in this classic monster movie that defined a genre and Ospawned¹ two sequels. A recent, unofficial Rex poll indicated many of you have never seen this historic film, so here¹s your chance to see it in style.

Trailer Park with Rob and Mark

NR 2002 60 min.
Our guests are back (they can¹t resist Rex¹s charm) to share a bounty of trailers, clips, and excerpts they¹ve acquired through their torrid, geeky lives, fraught with years of producing, directing, and editing films and special edition DVDs such as Tron and The Fellowship of the Ring. The only way to discover what tasty morsels they are bringing is to attend this event. Even Rex himself cannot fathom exactly what lies in store for us.

Pitch Black

(Director¹s Cut  widescreen) NR 2000 112 min.dir. David Twohy. w/Vin Diesel, Radha Mitchell.
A family, fleeing from political oppression, travels to Earth unknowingly carrying a deadly disease that spells DOOM for the human race!!! If you¹ve seen this film, then you know that none of that had anything to do with Pitch Black at all, but what can you say about Pitch Black without giving everything away? Take Rex¹s word for it, this overlooked movie is an exceptional sci-fi film, and is his sleeper pick for the year. This may be your last chance to see it on the big screen.

Dawn of the Dead

(Director¹s Cut  widescreen) NR 1979 142 min.dir. George Romero. w/ David Emge, Ken Foree, Tom Savini
When there¹s no more room in Hell, the dead will walk the Earth, and the geeks will go to Cinema Rex. Come join a small army of your zombified buddies for the ultimate late night convention movie. We proudly present the hard to find full length George Romero cut, with 15 minutes of restored footage, in its (LaserDisc only!) original aspect ratio of 1.85:1.

Free Enterprise w/live commentary

(widescreen) R 1998 116min.dir. Robert Meyer Burnett. Prod. Mark Altman. Written by Mark Altman, Robert Meyer Burnett. w/William Shatner.
Mark and Robert are back and this time, instead of letting us watch their film in peace and quiet, they will stick around and provide live commentary. Who better to spoof a film than its geeky creators? Come early for a good seat lest you find yourself watching this movie from just outside of a completely packed film room.


Former Head Of Landbank Caught In Dallas

Runnells Gone For 2 Years

Charged With Massive Fraud

March 30, 1990|By DAVID CHERNICKY Staff Writer

The former head of Landbank Equity Corp. was caught in Dallas Thursday morning, ending a two-year hunt by authorities who had charged him with bilking the company's investors out of millions of dollars while living an expensive lifestyle.

William R. Runnells Jr., 49, and his wife, Marika, 47, were arrested Thursday morning at an apartment where they had been living, said Bob Webster, assistant United States attorney in Dallas.

The two had been fugitives since April 1988, when they and three other former officials of Landbank were named in a 73-count indictment that included charges of wire fraud and racketeering.

A month before he disappeared from Hampton Roads, Mr. Runnells had been indicted on 24 counts that included making false tax returns, 10 counts of bankruptcy fraud, seven counts of contempt of court and conspiracy, according to court records.

The Runnellses had been living in the apartment in North Dallas since October under an assumed name, said Special Agent Woody Specht of the FBI's Dallas office Thursday. They were arrested at 7:10 a.m. when agents went to the apartment and Mr. Runnells met officers at the door, he said.

He said the couple had set up a business to offer hypno-therapy to people who needed to lose weight and quit smoking, and were making money at it.

After a hearing in U.S. District Court in Dallas, a federal magistrate denied bail and ordered the Runnellses placed in the custody of federal marshals for return to Norfolk, said Assistant U.S. Assistant Attorney James A. Metcalfe of the Eastern District's Norfolk office said.

Mr. Runnells, a man with less than a ninth-grade education, founded Landbank Equity Corp. in Virginia Beach in 1980, principally to orginate and sell mortgage-backed loans, federal authorities said.

On Sept. 17, 1985, Landbank filed for a reorganization in U.S. Bankruptcy Court in Norfolk. By the time Landbank went out of business, it had sold more than 10,000 second mortgages to about 40 banks and lending instutitions nationwide.

The loans amounted to about $200 million, according to a statement from the U.S. Attorney's Office in Virginia.

In a trial of other former Landbank officers in 1988, there was evidence that banks and savings and loans that had done business with Landbank lost more than $50 million after Landbank went bankrupt.

Mr. Runnells was indicted March 24, 1988 on a 24-count indictment charging him with five counts of making false tax returns; 10 counts of bankruptcy fraud; seven counts of contempt; one count of obstruction of justice and one count of conspiracy, according to court records.

He never showed up for his initial appearance and bond hearing April 6, 1988, Metcalfe said.

Federal authorities believe that Runnells and his wife fled Virginia soon after the indictments were issued. At the time, Mrs. Runnells had not been indicted, Metcalfe said.

After the couple fled, a federal grand jury on April 21, 1988, returned a 73-count indictment against Mr. and Mrs. Runnells, Ross E. Schumann Jr., a former senior vice president of Landbank, Owen R. Thornton, a certified public accountant from Virginia Beach, and Frank E. Butler III, a Virginia Beach lawyer.

The five were charged with racketeering and 36 counts of wire fraud involving Landbank's activities.

The same indictment also charged Mr. Runnells and Butler with 10 counts of bankruptcy fraud.

Mr. Runnells and his wife were charged together in 19 counts of making false statements and fraudulently overvaluing property to influence federally-insured institutions, according to the indictment.

If convicted of all counts in the second indictment, Mr. Runnells would face up to 293 years in prison and Mrs. Runnells would face up to 272 years, Metcalfe said.

Schumann, Butler and Thornton were prosecuted in Norfolk in September 1988. During a recess in the trial, Schumann killed himself.

In October 1987, William R.Runnells' mother, Lucille P. Runnells, 74, his adopted son Steven Z. Runnells, 22, and a brother, John Runnells were indicted on 65 counts with conspir acy, various counts of fraud and contempt.


386 F.2d 754
Julius E. KERN, Appellant,v.TRI-STATE INSURANCE COMPANY, Appellee.
No. 18685.
United States Court of Appeals Eighth Circuit.
Dec. 12, 1967, Rehearing Denied Jan. 22, 1968.

Donald L. Schmidt, of McMahon, Kieffer & Schmidt, Clayton, Mo., for appellant and filed brief.
William A. Moffitt, Jr., St. Louis, Mo., for appellee; John L. Harlan, Jr., of Harlan & Harlan, St. Louis, Mo., with him on the brief.
Before MEHAFFY and GIBSON, Circuit Judges, and STEPHENSON, District judge.
MEHAFFY, Circuit Judge.

1
Julius E. Kern, plaintiff-appellant, brought suit against Tri-State Insurance Company, defendant-appellee, in Missouri state court, alleging that in 1953 Tri-State wrongfully terminated an insurance agency contract with him by failure to furnish notice of termination or consideration as required by the contract.  Kern, in an effort to avoid the statute of limitations, alleged in his petition that he became insane sometime late in 1952 and that this mental condition existed continuously until after June, 1962.  This suit was instituted in the state court in August of 1965 and was timely and properly removed to the United States District Court for the Eastern District of Missouri, Eastern Division, on diversity grounds.  Tri-State first filed a motion to dismiss which was overruled, and then filed its motion for summary judgment on the ground that the action was barred by the Missouri five-year statute of limitations, which the trial court sustained because of lack of material factual dispute and because of clear record evidence that Kern was sane during the period alleged in his petition and therefore the statute was not tolled.  Chief Judge Harper's extensive and wellconsidered opinion is reported at 260 F.Supp. 378 (E.D.Mo.1966).  We affirm.

2
Tri-State supported its motion for summary judgment by an affidavit and supporting documentary evidence setting forth that Kern had never been adjudged incompetent; that he originally filed this same cause of action in the Missouri state court and that it was removed to the United States District Court and there dismissed by the late Judge George H. Moore because of failure to state a claim upon which relief could be granted; that Kern filed a claim under the Missouri Workmen's Compensation Act for an injury alleged to have occurred in December, 1952, that his deposition in that case was taken on June 6, 1958 and a compromise settlement was effectuated; that subsequently Kern filed suit against The Prudential Insurance Company of America and that his physician testified on April 30, 1959 that there was no question about Kern's competency in 1954 (extracts of this testimony are included in the affidavit); and that additionally Kern took part in bankruptcy proceedings and at all times had access to legal advice and attorneys.

3
An affidavit of a doctor was filed in opposition to the motion for summary judgment and this doctor gave as his opinion that Kern was of unsound mind continuously from December, 1952 until after June 29, 1962.  The doctor, however, did not personally see Kern until June 29, 1962, and his opinion was based on letters written by other doctors and summaries of hospital records.  Chief Judge Harper took judicial notice of other proceedings in his court which were instituted by Kern, noting that in 1958 Kern filed a number of suits in state court against more than thirty insurance companies.  Of these cases, nineteen were removed to the United States District Court for the Eastern District of Missouri, Eastern Division, and six were before Judge Harper.  All six were dismissed without prejudice by Kern's counsel, and the District Court records indicate that a short time thereafter the other cases, including the suit against the present appellee, were dismissed.  In considering the issues, Judge Harper properly took judicial notice of other proceedings in his court.  Meredith v. Van Oosterhout, 286 F.2d 216, 217 (8th Cir. 1960); Ellis v. Cates, 178 F.2d 791 (4th Cir. 1949), cert. denied, 342 U.S. 870, 72 S.Ct. 113, 96 L.Ed. 655 (1951); United States v. City of Philadelphia, 140 F.2d 406 (3rd Cir. 1944).  Chief Judge Harper's opinion makes particular reference to two suits filed by the plaintiff against the Prudential Insurance Company which were ultimately consolidated for trial, appealed to this court, and reported as Kern v. Prudential Ins. Co., 293 F.2d 251 (8th Cir. 1961).  In this case, Kern testified at length about his illness and that he want to work in October, 1954 for an insurance company and continued this work until March 15, 1956; that during the same period of time he worked part time in a law office where he filed papers, did legal research, handled the bank account, investigated cases, prepared petitions and drafted instructions.  Kern's doctor who treated him for his nervous condition and who was his general physician also testified as did a doctor who examined Kern upon order of the court.  There was no indication in any of this evidence that Kern was insane and this court said in its opinion:

4
'Plaintiff does not plead that he was incompetent to contract in 1956 when the conversion option expired, nor is there proof to support such a contention.  The court in its fact findings expressly found that plaintiff was under no disability which would prevent him from timely requesting the policy conversion.  Such finding is supported by substantial evidence and is not clearly erroneous.'  293 F.2d at 259.

5
The material upon which Kern's doctor based his expert opinion is a matter of court record with which Chief Judge Harper was thoroughly apprised, and was not a matter of personal knowledge of the docter whose affidavit purported to establish Kern's insanity.  Fed.R.Cir.P. 56(e), 28 U.S.C.A., specifically provides that such affidavits shall be made on personal knowledge and shall set forth such facts as would be admissible in evidence.  These mandatory provisions must be complied with.  Maddox v. Aetna Cas. & Sur. Co.,259 F.2d 51 (5th Cir. 1958); Roucher v. Traders & Geneeral Ins. Co., 235 F.2d 423 (5th Cir. 1956); Zampos v. United States Smelting, Refining & Mining Co.,  206 F.2d 171 (10th Cir. 1953); Person v. United States, 112 F.2d 1 (8th Cir. 1940), cert. denied, 311 U.S. 672, 61 S.Ct. 35, 85 L.Ed. 432 (1940).  Not only was the expert's opinion defective in failing to have a basis of personal knowledge, but the admissibility of such evidence was clearly a matter of the trial court's discretion.  Jones v. Goodlove, 334 F.2d 90 (8th Cir. 1964).  In view of the judicial records in the trial court, said court would have been within its rights in rejecting the affidavit as being pure speculation and not substantial evidence.  Compare United States v. Thornburgh, 111 F.2d 278, 280 (8th Cir. 1940), and cases there cited.

6
In considering motins for summary judgment, a trial court may and should pierce formal allegations where there obviously are no genuine issues presented for trial.  The record here clearly indicates that the only evidence in opposition to the motion for summary judgment was the affidavit based on hearsay evidence and which conclusion, as indicated by the trial court, was a clear refutation of prior judicial findings.

7
We have heretofore stated the purpose of the summary judgment rule as being one to expeditiously determine cases without necessity for formal trial where there is no substantial issue of fact and have held that such motions should be granted when a trial judge would be bound to direct a verdict in movant's favor.  Chambers v. United States, 357 F.2d 224 (8th Cir. 1966).  Much liberality is allowed in cases where there is a possible question of fact involved in summary judgment dispositions, but the records of the District Court clearly indicate that there is no genuine issue of fact involved here.  The court properly entered summary judgment since a trial would have been futile on account of lack of competent evidence.  Compare Yoder v. Nutrena Mills, Inc., 294 F.2d 505, 512, 513 (8th Cir. 1961).

8
Fed.R.Civ.P. 1, 28 U.S.C.A., provides that rules 'shall be construed to secure the just, speedy and inexpensive determination of every action.'  In our view, Kern should not be permitted to again force defendant into a protracted and expensive trial merely be filing an affidavit of a doctor based on matters not within his personal knowledge and completely contrary to the opinion of the doctors who had personally examined Kern during the period involved.  Kern had ample time and facilities to bring his suit and, in fact, did bring it once but did not pursue it to a conclusion and should not be permitted to continually burden the courts with frivolous litigation clearly barred by the Missouri statute of limitations.

9
The order granting Tri-State's motion for summary judgment is affirmed.


Q:

AVD Manager loses user-created device definitions after closing and reopening itself

When I create a new device definition in AVD Manager, I can create a new AVD with this device definition.
If I close and reopen AVD Manager, the device definition is lost and the AVD created gets "repairable" status.

A:

I resolved this issue by opening my devices.xml, where device definition are stored, and replacing every comma ',' with a dot '.'
<d:screen-size>normal</d:screen-size>
<d:diagonal-length>3,70</d:diagonal-length>
<d:pixel-density>hdpi</d:pixel-density>
<d:screen-ratio>long</d:screen-ratio>
<d:dimensions>
    <d:x-dimension>480</d:x-dimension>
    <d:y-dimension>720</d:y-dimension>
</d:dimensions>
<d:xdpi>254,00</d:xdpi>
<d:ydpi>254,00</d:ydpi>

>
<d:screen-size>normal</d:screen-size>
<d:diagonal-length>3.70</d:diagonal-length>
<d:pixel-density>hdpi</d:pixel-density>
<d:screen-ratio>long</d:screen-ratio>
<d:dimensions>
    <d:x-dimension>480</d:x-dimension>
    <d:y-dimension>720</d:y-dimension>
</d:dimensions>
<d:xdpi>254.00</d:xdpi>
<d:ydpi>254.00</d:ydpi>

devices.xml is located in .android\devices.xml
I realized this solution comparing a user-created device definition and a built-in Google device definition.
No idea of why AVD Manager saves user-created device definition XML this weird way.

A:

This seems to be a locale specific bug. We are discussing it on the mailing list: https://groups.google.com/forum/?fromgroups=#!topic/adt-dev/nfJvhxO8iJk
As you pointed out, a temporary fix is to create the device definition, then edit ~/android/devices.xml and replace commas with dots.


Pages

Monday, July 28, 2008

One compliment per day

“Hey, that’s a really pretty dress. You look great in it.”“I’ll kill to have hair like yours. How do you keep it looking so good.”‘That colour really suits you – I do think peacock blue is a most beautiful colour, don’t you?”“Where did you get that handbag – I have been looking for something just like that for ages.”

There are better places to be in than the early morning local train on Mumbai’s Western line. In fact, there are better places to be in than Mumbai.Get woken up by the ringing of the alarm clock at 7 am, brush your teeth before you are fully awake, stumble into the kitchen, nearly burn your tongue gulping down a scalding cup of tea, shower, change, check that you have your purse, mobile and keys in your hand-bag, pick up the daily paper and tuck it unread under your arm, bang the front door shut, hail three autos before the fourth agrees to take you to Bandra station, fret and fume at the traffic lights, silently curse the pedestrians who prefer walking in front of your auto to being on the pavement, rush onto the over-bridge, grab the last remaining seat on the 8:57 local, catch your breath, open the newspaper to check if the government has fallen or India has lost yet another cricket match.No wonder then, the only expression you see on people’s faces is a vacant one. Surviving in this City is so tough, who has the time to Live?

Which is where the compliments come in.The unwritten rules of Mumbai local trains are clear. You avoid eye contact and NEVER speak to someone you do not know. But one day, I found myself standing next to a lady wearing such a pretty dress, I couldn’t but comment on it. She was clearly shocked that a total stranger had complimented her, and barely managed to stammer out a thanks, but five minutes later, I found that a secret smile was still playing around her lips.Without intending to, I had introduced some colour into an otherwise grey existence, and I was sure that in her current state, she would inadvertently pass her cheer along – a seat given up on the connecting bus, a receptionist greeted with more than a polite nod, perhaps another compliment that made someone else’s day.I decided then and there to pass one genuine compliment every morning. It had to be sincere – if your nose is red from sneezing, you know the person doesn’t mean it when you are told you are looking great – and it had to be something the person herself believed could be true. To make one person happy per day, everyday.What is one person in a city of over twenty million? Does one person even matter among the six ten million that uses local trains everyday. Maybe not, but if even some of those people pass the happiness along, and some of the people they pass the message onto pass the message along further… well, you do the maths!And even if they do not, making one person happier for a couple of minutes is better than nothing at all.