Piston/Cylinder assembly mounted on the top of the rigid frame. Load cell incorporated in the piston.
Mobile frame consisting of four high tensile strength columns, upper crosshead directly connected to the piston, intermediate crosshead with compression platen and lower crosshead housing the upper grippers.
Hydraulically operated gripping system and vertical adjustment by two independent auxiliary cylinders controlled by the push button panel. This unique feature make the machine extremely easy to operate enabling one man to perform the test in a very short time.
Crosshead displacement controlled by the use of a bi-directional encoder.
The lower section houses the hydraulic assembly which include: Hydraulic pump, Proportional valve, Hydraulic valves, Heat exchanger to control the oil temperature and the Oil tank.
Three sets of standard grippers supplied with the machine, for rounds and flats, plus two sets of grip liners to provide maximum bearing and holding surface for specimens of all sizes.
Pressure switch, which automatically stops the machine when the oil pressure goes over the maximum permitted level.
Max travel limit switch to stop the mobile frame at its upper position.
Software feature which stops the machine when the maximum load is reached.
Tensile tests in general, flexural (transverse) and bending test on steel conforming to ASTM E290, UNI 564.
No. 3 sets of grips for round specimens up to 40 mm dia. and flat specimens up to 70 mm wide and 35 mm thick and two sets of grip liners.
indicated for strands testing. The distances (max and min) are increased of 140mm. To be factory installed.
Hydraulic valve system to control a secon frame. To be specified at time of order. Frame selection via software.


Abercorn was a football club based in Paisley, operating between 1877 and 1920. They were founder members of the Scottish Football League, but eventually relinquished their status, leaving St Mirren as the town's sole representatives. Though they played on many grounds during their existence, their games involving Dundee Hibs were mostly played on either Old or New Ralston Park. They first played against Dundee Hibs as members of the old Division Two in 1910 when the Hibs joined the Scottish League, and continued to do so until they moved to the regional Western League in 1915.


Regardless of your bug problem-- be termites, cockroaches, or rodents-- nobody enjoys the sight of a insect scampering across their walls and corridors. After all, lots of bugs are understood to provide a range of health threats, with some even known for bring illness into your home. Sadly, when you spot one pest that frequently suggests there are numerous them concealing somewhere and all set to more infest your home. When this occurs, you'll want to deal with the very best pest control specialist around to protect your home or business.
When the bed bugs remain in your house, they'll be going from 1 place to another. Bed bugs are invasive insects Bedminster, NJ that might be hard to spot. They are best dealt with by an experienced specialist.
Bed bugs are somewhat more resilient and resistant than every other pest, consisting of rats and cockroaches in Bedminster, NJ . Unless a bed bug was found, the first sign of an infestation is normally the existence of skin irritation. When bed bugs have attacked your home, they can be somewhat difficult to obtain rid of. They can be more difficult to treat than other home pest insects for a few factors. Because they are hard to see, utilize a magnifying glass. They don't remain on the human that they are eating and so they will generally conceal in clothes and try to get to a brand-new location. They are not able to endure heats, making heat a extremely reliable treatment approach .
Charge of bed bugs can spend some time and several follow-up treatments. Overall control takes a thoroughly prepared and created integrated technique. Bed bug control does not need to be expensive Bedminster, NJ .
You will need to understand the signals of a insect problem. An invasion is something which can take place to anybody Bedminster, NJ . As an example, be specific to look at where you have to look after your bed bug problem. If you're concerned you own a bed bug invasion, start searching for physical indications of their existence. Bed bug infestations can affect a single bed in 1 room or a entire home or home. Chemical methods aren't the only method to manage bed bug infestations.
The Best Ways To Start with Bed Bugs Exterminator or Pest Control Expert Bedminster, NJ ?
Usually, skilled pest control company will use a couple of choices of treatment methods like vacuuming, cleansing, steaming and other mechanical approaches to completely eliminate the bed bug problem. There are a number of pest control companies that may help you for the exact same. Normally speaking, many good pest control companies will initially send out down a specialist to run a visual examination of your home before estimating you a cost Bedminster, NJ .
If you're not required to leave your home for more than a few hours throughout the extermination, or in case you are preparing for the exterminator, there are a variety of approaches to prevent taking bed bugs with you. Once you have the ability to observe that bed bugs have certainly entered your home, it's never too early to attempt and locate an excellent bed bugs pest control expert Bedminster, NJ Bedminster, NJ . During a fumigation, your home will be handled in tarpaulin, which is generally anchored utilizing huge containers of water you need to supply. Whether a full house is infested, it might be required to spray listed below the house, which would increase the total price tag of services. Your home doesn't have to be plagued that you pick up bed bugs. Aim to remember that Do It Yourself insecticides alone are not likely to rid your house of bed bugs, especially items which do not keep killing after they dry.


text:
Satan () — британская хеви-метал-группа, происходящая из Ньюкасла. Известна с 1979 года как часть Новой волны британского хеви-метала.. Хотя в течение их карьеры не достигла мирового успеха, группу считают влиятельной. Satan — неформальные основатели треша, а музыка, которую они играли, справедливо считается самой продвинутой среди коллективов 1980-х.
Группа воссоединилась в 2005 году.

Группа подвергалась многим изменениям, менялось и название группы; какое-то время группа называлась Blind Fury, альбом 1985 года «Out of Reach» вышел когда группа называлась Blind Fury, прежде чем вернуться назад к названию Satan. В 1988 году группа изменила своё название на The Kindred и затем на Pariah и выпустила два альбома под тем названием, прежде чем распасться в начале 90-х. В 1997 году Pariah выпустила ещё один альбом. Группа включала членов многих других хеви-метал-групп таких как Blitzkrieg, Atomkraft, Avenger, Persian Risk, Cronos и Battleaxe. В 1990 году Стив Рамси (гитара) и Грэм Инглиш (бас) вместе с вокалистом Мартином Уолкиером из Sabbat основали группу Skyclad, которая стала одной из первых фолк-металлических групп.

Дискография

Альбомы
 Court in the Act (1983)
 Suspended Sentence (1987)
 Life Sentence (2013)
 Atom by Atom (2015)
 Cruel Magic (2018)

Синглы
 Kiss of Death (Guardian 1982)

Демозаписи
 The first demo (1981)
 Into the Fire (1982)
 Dirt Demo '86 (1986)

Мини-альбомы
 Into the Future (Steamhammer 1986)

Сборники / Концертные альбомы
 Blitzkrieg in Holland (Metal Nation 2000)
 Live in the Act (Metal Nation 2004)

Примечания

Хеви-метал-группы Великобритании
Музыкальные коллективы, появившиеся в 1979 году
Культурные организации Ньюкасл-апон-Тайна
Музыка в Ньюкасл-апон-Тайне

redpajama_set_name: RedPajamaWikipedia


Teltech Exchange has the SUN Microsystems-3001883-01 Delta DPS-1500AB-1 1500W Power Supply in stock and available for shipment. Please contact us by email or by filling out a quote form to receive a firm price quote on SUN Microsystems-3001883-01 Delta DPS-1500AB-1 1500W Power Supply within 24 business hours. In addition to this item, Teltech has many more parts in stock from Sun Microsystems that can help ensure your network's reliability.


The Isle of Man Government Department of Economic Development, rights holder for the Isle of Man TT Races fuelled by Monster Energy, has signed a major new two-year agreement with RST to be the Official Safety Partner.
The deal includes title sponsorship of the Superbike Race on Saturday 6th June, which becomes the RST Superbike Race as well as a series of start-line, podium and track-side branding opportunities. The company will also receive programme, race guide and official iomtt.com advertising as part of their inventory of benefits.
RST will be producing a bespoke TT flash motorcycle clothing collection, featuring the official TT logo, which will be exclusively available in the TT RST concept store located behind the main grandstand on the Isle of Man. The company is planning to release details of the range next year.
The travelling marshals will be fully kitted out in RST kit, including a special version of the Alpha III jacket, helping them to perform their important role in comfort and safety.
Jonny Towers, RST Sales and Marketing Director, commented: "RST has already been working closely with the Isle Man TT team as the safety partner for the 2013 and 2014 Classic TT Races. We are tremendously excited to take this relationship to the next level now being safety partner for both the TT and the Classic TT.
"As RST goes from strength to strength, being the UK's number 1 motorcycle clothing brand, and with huge growth around the world, the Isle Of Man TT is the perfect showcase to raise the brands profile on a global scale.
The Isle of Man TT Races fuelled by Monster Energy takes place from the 30th May to 12th June 2015.


Tile setters, tile layers prepare, conduct and finish tiling work on walls, floors etc.
Organize the materials and the work area, e.g. gather and check required tools and materials.
Check and prepare the surface, e.g. by cleaning, smoothing and applying adhesives.
Set out tile distribution, and measure and mark the surface to be tiled.
Mix mortar, glue or other substances to the right consistency taking into account e.g. tile weight and thickness.
Make tiles the right size, e.g. using (electric) cutting tools.
Prepare surfaces, e.g. apply mortar on floors and strickle off smoothly.
Set or lay tiles straight and evenly on the surface with mortar or glue, and press or tap the tile.
Finish off the tiling work, e.g. finish and dress the joints and clean the work area.
Carry out specialized tiling work (e.g. stairs, mosaic, patterns).
Conduct repair and renovation work on tiling.


text:
Love Island is a 2014 film directed by Jasmila Žbanić.

Cast
Ariane Labed - Liliane
Ermin Bravo - Grebo
Ada Condeescu - Flora
Franco Nero - Marquis Polesini
Leon Lučev - Stipica
Branka Petrić - Madame Henzl

Reception
Stephen Dalton gave a negative review in The Hollywood Reporter: "Love Island is not a subtle movie. The dialogue, written and spoken by people for whom English is plainly a second or third language, is full of corny lines and clunky twists. The camera work also feels oddly jittery, with lots of sudden crash zooms that would make more sense in a mockumentary than in this kind of sweet comic trifle. The characters are cartoons, their romantic crisis deeply implausible and their final reconciliation risibly silly."

References

External links

Croatian romantic comedy films
Films directed by Jasmila Žbanić
Films set on islands
Films shot in Croatia


There's a problem with some magic tricks, and deception in general. If you want to fool someone, you've got to make sure you know what they're already familiar with.
Here's a video of a magic trick you can buy from Tenyo Magic in Japan. (Turn off the sound if you'd prefer not to hear the EXCITED TONES of the announcer.) The trick itself may or may not fool you, but afterward, if you aren't familiar with dumplings, you're left asking, "What the heck is that?" The dumpling ignorant are not fooled or astonished or tricked, but puzzled with the final result, so they don't say "Cool!" They say "Huh?"


text:
\section{Introduction}
Multi-view stereo (MVS) has been a hot research topic in computer vision for decades. It aims to recover the 3D geometry of a scene from a set of images with known camera parameters. Nowadays, multi-view stereo reconstruction is typically decomposed into two separate steps: depth map estimation and fusion~\cite{Galliani2015Massively,Schonberger2016Pixelwise,Xu2019Multi,Xu2020Learning,Xu2020Planar,Yao2018MVSNet,Gu2019Cas}.
For these two steps, accurate depth map estimation is often challenging due to a variety of real-world problems, \emph{e.g.}, low-textured areas, thin structures, occlusions and reflective surfaces.

\begin{figure}[t]
	\centering
	\includegraphics[width=\columnwidth]{teaser.pdf}
	\caption{Illustration of different regularization methods. 3D filtering methods require cubic memory complexity while recurrent methods only have quadratic complexity.
	Our non-local recurrent regularization can explicitly establish interactions for non-adjacent depth values to capture more global context along the depth dimension which previous recurrent regularization methods cannot achieve.}
	\label{fig:teaser}
\end{figure}

Recently, many learning-based MVS methods~\cite{Yao2018MVSNet,Yao2019RMVSNet,Xu2020Learning,Gu2019Cas} have been presented to achieve competitive or better results than traditional methods that rely on optimization of photometric consistency~\cite{Furukawa2010Accurate,Schonberger2016Pixelwise}.
Inspired by plane-sweeping stereo~\cite{Collins1996Space}, most learning-based methods first construct multi-channel cost volumes based on extracted deep image features, then perform deep cost volume processing to predict depth maps.
The core of the successful learning-based MVS methods is the deep regularization of cost volumes to encode local and global information of a scene.
The deep regularization converts the cost volume into a latent probability volume, directly influencing the final depth inference.

At present, the deep regularization of cost volumes can be categorized into two types: 3D cost volume filtering and recurrent 2D cost map regularization. The former one regularizes the entire 3D cost volume in one go, thus 3D convolutions can be seamlessly utilized to incorporate the context of a scene~\cite{Yao2018MVSNet,Xu2020Learning,Luo2019P}.
In general, this kind of method is memory-consuming since its memory requirement is cubic to the model resolution (cf. Fig~\ref{fig:teaser}(b)).
Although many follow-up works~\cite{Chen2019Point,Gu2019Cas,Cheng2020UCSNet,Yang2020CVPMVSNet} have been proposed to alleviate this problem, the scalability of 3D cost volume filtering is still limited when tackling high-resolution and large-scale 3D reconstruction.
As another line of research, the latter one~\cite{Yao2019RMVSNet,Yan2020Dense} sequentially regularizes 2D cost maps along the depth direction.
Therefore, this kind of method reduces the memory requirement from cubic to quadratic to the model resolution.
Moreover, such methods can adaptively sample sufficient depth hypotheses for scenes of different scales.
In order to aggregate spatial as well as temporal context information in the depth direction, recurrent networks, \emph{e.g.}, gated recurrent units (GRU) and long short-term memory (LSTM), are adopted in such methods.
However, these methods only explicitly consider the information interaction between adjacent depth values, hence the long-range dependencies along the depth dimension cannot be fully captured (cf. Fig~\ref{fig:teaser}(c)).

To tackle the above limitation of recurrent 2D cost map regularization, we propose a novel non-local recurrent regularization network for multi-view stereo, namely NR2-Net.
Built upon the regular recurrent neural networks, we divide the depth sampling space into different blocks to model the non-local interactions for non-adjacent depth planes.
Within each block, we design a depth attention module to distill latent high-level features.
This module samples cost map features at every other depth planes to enable large receptive fields like dilated convolution~\cite{Chen2018DeepLab}.
Based on these cost map features, the latent high-level features are distilled via attention mechanism.
Then the high-level features between blocks are further interacted in a gated recurrent manner to capture global scene context, which is used to regularize the bottom-level cost map features in the next block.
In this way, the long-range dependencies along the depth dimension are modeled and the global scene context is perceived to help the long-range cost map regularization (cf. Fig~\ref{fig:teaser}(d)).
At last, in order to fuse depth maps into point clouds, existing learning-based methods usually first predefine a constant depth probability threshold to filter out unreliable depth estimates. However, due to the uncertainty of depth prediction networks~\cite{Kendall2017Uncertainties}, this constant threshold will discard many credible depth values, resulting in point clouds of low completeness.
Thus, we adaptively associate the depth probability threshold with depth consistency to generate more complete point clouds.

Our contributions can be summarized as follows:
\textbf{1)} We present a \textbf{novel non-local recurrent regularization} framework for multi-view stereo. This allows to perceive more global context information in the depth direction to assist cost volume regularization.
\textbf{2)} We propose a \textbf{depth attention module} to distill latent high-level features along the depth dimension. This helps to model non-local interactions for non-adjacent depth hypotheses in the gated recurrent manner.
\textbf{3)} We develop a \textbf{dynamic depth map fusion} strategy to reconstruct point clouds. This strategy jointly considers the depth probability and depth consistency in a dynamic way, which is robust for different scenes. Our method, NR2-Net, achieves state-of-the-art performance on both DTU and Tanks and Temples datasets.

\section{Related Work}
\boldparagraph{Traditional multi-view stereo.} In order to estimate depth maps for all input images, multi-view stereo needs to search correspondences across different images. Plane-sweeping methods~\cite{Collins1996Space,Gallup2007Real} sample depth plane hypotheses in the 3D scene. Then, they employ hand-crafted similarity metrics, \emph{e.g.}, sum of absolute differences (SAD) and normalized cross correlation (NCC), to construct cost volumes and extract the final depth maps via the winner-take-all strategy. Since these similarity metrics are ambiguous to some challenging areas, \emph{e.g.}, low-textured areas and reflective surfaces, some methods adopt engineered regularization technologies, such as graph-cuts~\cite{Kolmogorov2002Multi} and cost filtering~\cite{Hosni2013Fast}, to alleviate this problem. On the other hand, PatchMatch MVS methods~\cite{Zheng2014PatchMatch,Galliani2015Massively,Schonberger2016Pixelwise,Xu2019Multi,Xu2020Planar} adopt the sampling and propagation strategy~\cite{Barnes2009PatchMatch} to efficiently search continuous depth plane hypotheses from the whole depth interval. These methods impose implicit smoothness constraints based on the plane propagation in the 3D scene. Although they have greatly improved the performance of traditional methods, how to handle the ambiguity in the challenging areas is still an open problem.

\begin{figure*}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{NR2-Net.pdf}
	\caption{The NR2-Net architecture. Our network first extracts high-resolution deep features for all input images. Then cost maps at different depth planes are computed by differentiable homography warping and regularized by convolutional LSTM cells. To do non-local recurrent cost map regularization, the whole depth planes are divided into different blocks. The non-local interactions within each block are modeled by a depth attention module to distill latent high-level features. These high-level features are further interacted in a gated recurrent manner to capture global context information, which is used to help regularize the hidden states of LSTM cells in the next depth block.}
	\label{fig:NR2}
\end{figure*}

\boldparagraph{Learning-based multi-view stereo.}
Recently, some works have leveraged deep neural networks to learn deep similarity features and deep regularization for multi-view stereo~\cite{Hartmann2017Learned,Ji2017Surfacenet,Kar2017Learning}.
MVSNet~\cite{Yao2018MVSNet} and DeepMVS~\cite{Huang2018DeepMVS} propose to construct cost volumes to learn depth maps for each input image.
This makes learning-based MVS methods more scalable and applicable to scene reconstruction. To infer depth maps, MVSNet and many following works~\cite{Xue2019MVSCRF,Luo2019P,Xu2020Learning} utilize multi-scale 3D convolutions to regularize cost volumes. However, due to the limitation of cost volume resolution, these methods still cannot tackle high-resolution images.
To predict high-resolution depth maps, cascade methods~\cite{Cheng2020UCSNet,Gu2019Cas,Yang2020CVPMVSNet} employ the coarse-to-fine framework to infer high-resolution estimation via thin cost volumes.
However, these cascade methods heavily rely on coarse-scale estimation which often lacks important detail information.
Consequently, this may result in blurred effects in high-resolution estimation.
To achieve high-resolution prediction, R-MVSNet~\cite{Yao2019RMVSNet} recurrently regularizes cost maps through GRU cells.
This dramatically reduces memory consumption.
However, the generic GRU cells do not allow the regularization to consider enough context information.
$\text{D}^2$HC-RMVSNet~\cite{Yan2020Dense} absorbs both the merits of LSTM and 2D U-Nets to present a 2D U-Net architecture with convolutional LSTM cells.
This architecture considers more context information in image domain and can directly operate on high-resolution image features.
In contrast to our work, existing recurrent methods only explicitly consider interactions between adjacent depth planes and thus lack global context along the depth dimension.

\section{Method}
The network architecture of our method, NR2-Net, is depicted in Fig.~\ref{fig:NR2}.
The input to our network is a reference image $\boldsymbol{I}_0$ and $N-1$ source images $\{\boldsymbol{I}_i\}_{i=1}^{N-1}$, where $N$ is the total number of input images.
Their camera intrinsic parameters $\{{\bf K}_i\}_{i=0}^{N-1}$ and relative extrinsic parameters $\{{\bf R}_i,{\bf t}_i\}_{i=0}^{N-1}$ are known.
Our NR2-Net first extracts high-resolution deep features for all input images.
The cost maps at different depth planes are computed by homography warping.
Our non-local recurrent regularization framework is built upon convolutional LSTM cells to regularize 2D cost maps.
To achieve non-local interactions for non-adjacent depth planes, we divide the whole depth interval into different blocks.
The different depth planes within each block are interacted by a depth attention module to distill latent high-level features.
The global context information is captured by further interacting the high-level features in a gated recurrent manner.
Then, the global context information is in turn used to regularize the hidden states of different LSTM cells in the next block.
This helps each LSTM cell perceive more global context information in the depth direction.
Finally, we convert these hidden states into regularized cost maps to predict the depth map of the reference image.

\subsection{Cost Map Construction}
Similar to previous works, we use the plane-sweeping algorithm~\cite{Collins1996Space} to construct cost maps at different depth planes.
Specifically, after extracting high-resolution deep image features $\{\boldsymbol{F}_{i}\}_{i=0}^{N-1}$ for all input images by stacked dilated convolutions~\cite{Yan2020Dense}, we compute the warped image features $\{\tilde{\boldsymbol F}_i(d)\}_{i=1}^{N-1}$ for $N-1$ source images at depth value $d$ by differentiable homography warping.
Then the 2D cost map at depth value $d$ is calculated by:
\begin{equation}
\boldsymbol{C}(d)=\frac{\sum_{i=1}^{N-1}\big(1+{\boldsymbol w}_i(d)\big)\odot\big(\tilde{\boldsymbol F}_i(d)-\boldsymbol{F}_0\big)^2}{N-1},
\end{equation}
where ${\boldsymbol w}_i(d)$ is the view weight of the $i$-th source image and `$\odot$' represents element-wise multiplication.
Herein, ${\boldsymbol w}_i(d)$ is computed by applying one convolution layer followed by group normalization and rectified linear units, a residual block~\cite{He2016Deep} and one convolution layer followed by a sigmoid function on the feature difference, $\tilde{\boldsymbol F}_i(d)-\boldsymbol{F}_0$.
In this way, a cost volume $\{\boldsymbol{C}(d)\}_{d=0}^{D-1}$ can be obtained by concatenating 2D cost maps of all depth planes in the depth direction, where $D$ is the total number of depth planes.

\subsection{Non-local Recurrent Regularization}
As the core of learning-based MVS methods, cost volume regularization is important to aggregate context information in both spatial and depth domain.
Existing recurrent 2D cost map regularization methods cast the whole cost volume as a series of 2D cost maps in the depth direction and sequentially regularize 2D cost maps via stacked recurrent neural networks.
Although such methods are memory-friendly, they only explicitly consider the local interactions between adjacent depth values.

Inspired by non-local methods~\cite{Wang2018Non,Fu2019Non}, we design a non-local recurrent regularization network to model long-range dependencies along the depth dimension, which helps to regularize cost volumes by aggregating more context information.
As illustrated in Fig.~\ref{fig:NR2}, we divide the whole cost volume into $T$ blocks, each block contains $s$ cost maps.
Each cost map is regularized by stacked convolutional LSTM cells to incorporate context information in both the spatial and the depth domain.
As depicted in Fig.~\ref{fig:U-Net-LSTM}(a), our stacked convolutional LSTM cells are built upon 2D U-Net architecture to aggregate multi-scale context information in the spatial domain.
Moreover, these LSTM cells contain one non-local LSTM cell and four vanilla LSTM cells.
This non-local LSTM cell is beneficial to incorporate more context information in the depth direction.

Next we elaborate on the mechanism of our non-local LSTM cell.
As illustrated in Fig.~\ref{fig:U-Net-LSTM}(b), besides using the current cost map $\boldsymbol{C}(d)$, the previous regularized cost map $\boldsymbol{C}_r(d-1)$ and the previous cell state map $\boldsymbol{\mathcal C}(d-1)$ as input like the vanilla LSTM cells, the non-local LSTM cell also incorporates the cell state map of the previous block, $\boldsymbol{B}(t-1)$, to consider long-range dependencies in the depth direction.
How to obtain $\boldsymbol{B}(t-1)$ through the depth attention module will be detailed in the next section.
With these inputs, the forget gate map ${\bf F}(d)$, candidate state map $\hat{\boldsymbol{\mathcal C}}(d)$, input gate map ${\bf I}(d)$ and output gate map ${\bf O}(d)$ are respectively modeled as:
\begin{align}
{\bf F}(d)&=&\sigmoid&({\bf W}_{f}\ast[\boldsymbol{C}(d),\boldsymbol{C}_r(d-1)]+{\bf b}_f),\\
{\bf I}(d)&=&\sigmoid&({\bf W}_{i}\ast[\boldsymbol{C}(d),\boldsymbol{C}_r(d-1)]+{\bf b}_i),\\
\hat{\boldsymbol{\mathcal C}}(d)&=&\tanh&({\bf W}_{c}\ast[\boldsymbol{C}(d),\boldsymbol{C}_r(d-1)]+{\bf b}_c),\\
{\bf O}(d)&=&\sigmoid&({\bf W}_{o}\ast[\boldsymbol{C}(d),\boldsymbol{C}_r(d-1)]+{\bf b}_o),
\end{align}
where `$\ast$' means convolution, `$[]$' means concatenation, ${\bf W}$ is a transformation matrix and ${\bf b}$ is a bias term. Then, the current cell state map $\boldsymbol{\mathcal C}(d)$ and the current regularized cost map $\boldsymbol{C}_r(d)$ are computed as,
\begin{equation}\label{Eq:nl}
\boldsymbol{\mathcal C}(d)={\bf F}(d)\odot\boldsymbol{\mathcal C}(d-1)+{\bf I}(d)\odot\hat{\boldsymbol{\mathcal C}}(d)+{\bf A}(d)\odot\boldsymbol{B}(t-1),
\end{equation}
\begin{equation}
\boldsymbol{C}_r(d)={\bf O}(d)\odot\tanh(\boldsymbol{\mathcal C}(d)),
\end{equation}
where ${\bf A}(d)$ is the depth attention gate map given by
\begin{equation}
{\bf A}(d)=\sigmoid({\bf W}_{a}\ast[\boldsymbol{C}(d),\boldsymbol{B}(t-1)]+{\bf b}_a).
\end{equation}
Note that, the last term in the right side of Eq.~\eqref{Eq:nl} reflects the non-local interactions in the depth direction. This is the difference between our non-local LSTM cell and the vanilla LSTM cell.
Finally, the regularized cost maps $\{\boldsymbol{C}_r(d)\}_{d=0}^{D-1}$ pass a softmax layer to produce the probability volume $\boldsymbol{P}$. In this way, our regularization not only incorporates multi-scale context information in the spatial domain, but also aggregates long-range dependencies in the depth direction.

\begin{figure}[t]
	\centering
	\includegraphics[width=\columnwidth]{lstm-nl.pdf}
	\caption{Illustrations of (a) structure of 2D U-Net with convolutional LSTM and (b) structure of non-local LSTM cell.}
	\label{fig:U-Net-LSTM}
\end{figure}

\subsection{Depth Attention Module}
In the previous section, the depth attention module plays an important role in distilling latent high-level features within each block.
It captures discriminative depth context information for each block.
Then, the high-level features between blocks are interacted in a gated recurrent manner to capture global depth context information.
Finally, we can use this information to impose explicit long-range regularization constraints for non-adjacent depth values as described above.

\begin{figure}[t]
	\centering
	\includegraphics[width=0.95\linewidth]{Depth_Attention.pdf}
	\caption{Structure of the depth attention module.}
	\label{fig:DA}
\end{figure}

The structure of the depth attention module is illustrated in Fig.~\ref{fig:DA}.
For each block, we only use every other depth value in the block to sample raw cost features $\boldsymbol{F}_\text{raw}\in\mathbb{R}^{32\times \frac{s}{2}\times H \times W}$ and regularized cost features $\boldsymbol{F}_\text{reg}\in\mathbb{R}^{8\times \frac{s}{2}\times H \times W}$, where $H$ and $W$ denote the image height and width respectively.
This enables the larger receptive fields in the depth direction.
We first concatenate these two kinds of cost features into complex cost features $\boldsymbol{F}_\text{comp}\in\mathbb{R}^{40\times \frac{s}{2}\times H \times W}$.
Such dense connection is good to explore their potential interactions and learn non-local information~\cite{Huang2017Densely}.
Then we use max-pooling and average-pooling along the depth dimension to aggregate discriminative depth information, generating two different depth context features, $\boldsymbol{F}_\text{max}$ and $\boldsymbol{F}_\text{avg}$.
As pointed out in \cite{Woo2018Cbam}, we think that max-pooling is good to gather distinctive clues while average-pooling is beneficial to learn the extent of depth space.
Using both can highlight the informative depth values as much as possible.
These two features are further concatenated and forwarded into one convolution layer and a residual block to produce attention features $\boldsymbol{F}_\text{att}\in\mathbb{R}^{16\times H \times W}$.

To further model the global context information in the depth direction, the attention features between blocks are interacted by another recurrent neural network.
Specifically, we reshape raw cost features $\boldsymbol{F}_\text{raw}\in\mathbb{R}^{32\times \frac{s}{2}\times H \times W}$ to $\mathbb{R}^{C \times H \times W}$, where $C=32\times \frac{s}{2}$.
The input gate map ${\bf G}_i(t)$ and the forget gate map ${\bf G}_f(t)$ are modeled as
\begin{align}
\!\!\!\!\!{\bf G}_i(t)&=\sigmoid({\bf W}_{ia}\!\ast\![\boldsymbol{F}_\text{raw}(t),\boldsymbol{B}(t-1)]\!+{\bf b}_{ia}),\!\!\\
\!\!\!\!\!{\bf G}_f(t)&=\sigmoid({\bf W}_{fa}\!\ast\![\boldsymbol{F}_\text{raw}(t),\boldsymbol{B}(t-1)]\!+{\bf b}_{fa}),\!\!
\end{align}
where ${\bf W}$ is a transformation matrix and ${\bf b}$ is a bias term.
Finally, we update the cell state map of the current block
\begin{equation}
{\boldsymbol B}(t)={\bf G}_{i}(t)\odot\tanh(\boldsymbol{F}_\text{att}(t))+{\bf G}_{f}(t)\odot\boldsymbol{B}(t-1).
\end{equation}
By updating the cell states of different blocks in a gated recurrent manner, our method is able to capture global context information in the depth direction.
These cell states of blocks are in turn used to update the cell states of the LSTM cells.

\subsection{Loss Function}
Following previous practices~\cite{Yao2019RMVSNet,Yan2020Dense}, we cast the depth inference task as a multi-class classification problem.
We use the following cross-entropy loss to train our network:
\begin{equation}
{\cal{L}}=\sum_{\boldsymbol{p}\in\Phi}\sum_{d=0}^{D-1}-\boldsymbol{G}(d,\boldsymbol{p})\cdot\log(\boldsymbol{P}(d,\boldsymbol{p})),
\end{equation}
where $\Phi$ denotes the set of valid ground truth pixels, $\boldsymbol{G}(d,{\bf p})$ is the one-hot vector generated according to the ground truth depth map at pixel ${\bf p}$, and $\boldsymbol{P}(d,{\bf p})$ is the predicted depth probability at pixel ${\bf p}$.
Note that, we do not need to store the whole classification probability volume during testing.
Moreover, since the cell states of blocks are also sequentially updated, our designed depth attention module will not occupy too much memory.

\subsection{Dynamic Depth Map Fusion}
After estimating depth maps for all input images, it is necessary to filter out wrong depth estimates for each depth map and then fuse them into a consistent point cloud representation.
There are two critical factors for filtering wrong depth predictions: depth probability and depth consistency.
The depth probability is usually measured by the corresponding probability of the selected depth, \emph{i.e.}, $\theta$.
The depth consistency is usually measured by the geometric constraint: reprojection errors and relative depth errors~\cite{Yao2018MVSNet}.
For a pixel ${\bf p}$ in the reference image $\boldsymbol{I}_0$, we denote its reprojection error w.r.t the source image $\boldsymbol{I}_i$ by $\psi_i$ and its relative depth error w.r.t. $\boldsymbol{I}_i$ by $\phi_i$.
Then, its consistency view set is defined as:
\begin{equation}
\mathcal{S}=\{\boldsymbol{I}_i|\psi_i<\epsilon,\phi_i<\eta\},
\end{equation}
where $\epsilon$ and $\eta$ are related thresholds.
If $|\mathcal{S}|>\mu$ and $\theta>\tau$, the depth estimate of ${\bf p}$ is reliable, where $\mu$ is the threshold of consistent view number and $\tau$ is the probability threshold.
Previous methods~\cite{Yao2018MVSNet,Yao2019RMVSNet,Gu2019Cas} usually set fixed threshold parameters to remove unreliable depth estimates.
However, these intuitively preset parameters make their fusion methods not robust for different scenes.
Thus, $\text{D}^2$HC-RMVSNet~\cite{Yan2020Dense} proposes a dynamic consistency checking algorithm to measure depth consistency.
Its dynamic consistency view set is defined as:
\begin{equation}
\mathcal{S}_d=\{\boldsymbol{I}_i|\psi_i<\epsilon(\mu),\phi_i<\eta(\mu),\epsilon(\mu)=\frac{\mu}{4},\eta(\mu)\!=\!\frac{\mu}{1300}\},
\end{equation}
The above definition makes the thresholds of geometric constraint be adaptively adjusted based on the consistent view number $\mu$.
This means that the estimated depth value is accurate and reliable when satisfying strict depth consistency in a small number of views, or relaxed depth consistency in the majority of views.
However, this method still adopts a fixed depth probability threshold to filter depths with different depth consistency.
Due to the uncertainty of depth prediction networks~\cite{Kendall2017Uncertainties}, the depth probability will sometimes filter credible depth values that meet the strict depth consistency, making reconstructed point clouds less complete.
Thus, the depth probability threshold should also be adjusted according to the level of depth consistency.
Based on these observations and our experiments, we define the dynamic probability threshold as:
\begin{equation}\label{Eq:dp}
\tau(\mu)=0.6\cdot \exp\left[{\frac{(\mu-10)}{8}}\right].
\end{equation}
By traversing different $\mu$ values, the estimated depth of ${\bf p}$ will be deemed accurate and reliable if $|\mathcal{S}_d|>\mu$ and $\theta>\tau(\mu)$.
That is, when the reprojection error and relative depth error are strict, the number of consistent view and depth probability threshold are relaxed to judge if the depth prediction is reliable.
Finally, the reliable depth estimates will be projected into 3D space to generate 3D point clouds.

\begin{table}[t]
	\centering
	\footnotesize
	\begin{tabular}{clccc}
		\toprule
		& {Method} & Acc.$\downarrow$ & Comp.$\downarrow$ & Overall$\downarrow$ \\
		\midrule
		\multirow{4}{0.04cm}{\rotatebox{90}{Geometric}}
		& Furu & 0.613 & 0.941 & 0.777 \\
		& Tola & 0.342 & 1.190 & 0.766 \\
		& Gipuma & {\bf 0.283} & 0.873 & 0.578 \\
		& COLMAP & 0.400 & 0.664 & 0.532 \\
		\midrule
		\multirow{9}{0.04cm}{\rotatebox{90}{Learning-based}} & SurfaceNet & 0.450 & 1.040 & 0.745 \\
		& MVSNet & 0.396 & 0.527 & 0.462 \\
		& R-MVSNet & 0.383 & 0.452 & 0.417 \\
		& CasMVSNet & 0.325 & 0.385 & 0.355 \\
		& CVP-MVSNet & \underline{0.296} & 0.406 & \underline{0.351} \\
		& UCSNet & 0.338 & 0.349 & {\bf 0.344} \\
		& AttMVS & 0.391 & \underline{0.345} & 0.368 \\
		& $\text{D}^2$HC-RMVSNet & 0.395 & 0.378 & 0.386 \\
		& Ours & 0.370 & {\bf 0.332} & \underline{0.351} \\
		\bottomrule
	\end{tabular}
	\caption{Quantitative results on the DTU evaluation set using the distance metric [$mm$] (lower is better). Our method achieves the best completeness and the second best overall score. The best and second best results are in \textbf{bold} and \underline{underlined}, respectively.}
	\label{tab:dtuevaluation}
\end{table}

\begin{figure}[t]
	\centering
	\includegraphics[width=\linewidth]{dtu.pdf}
	\caption{Qualitative results of reconstructed point clouds for the model scan 9 from the DTU evaluation set.}
	\label{fig:dtu}
\end{figure}

\section{Experiments}
We first describe datasets and implementation details.
Then, we show benchmark results on the DTU dataset~\cite{Aanes2016Large} as well as the Tanks and Temples dataset~\cite{Knapitsch2017TTB}.
Finally, we conduct ablation studies to analyze our proposed core components.

\begin{table}[t]
	\centering
	\footnotesize
	\begin{tabular}{clccc}
		\toprule
		& Method & Acc.$[\%]\uparrow$ & Comp.$[\%]\uparrow$ & $F_1 [\%]\uparrow$ \\
		\midrule
		\multirow{3}{0.04cm}{\rotatebox{90}{Geo.}}  & COLMAP & 43.16 & 44.48 & 42.14 \\

		& ACMM & 49.19 & 70.85 & 57.27 \\
		& ACMP & 49.06 & \underline{73.58} & 58.41 \\
		\midrule
		\multirow{8}{0.04cm}{\rotatebox{90}{Learning-based}} & MVSNet & 40.23 & 49.70 & 43.48 \\
		& R-MVSNet & 43.74 & 57.90 & 48.40 \\
		& CVP-MVSNet & 51.41 & 60.19 & 54.03 \\
		& UCSNet & 46.66 & 70.34  & 54.83 \\
		& CasMVSNet & 53.71 & 63.88 & 56.84 \\
		& $\text{D}^2$HC-RMVSNet & 49.88 & \bf 74.08 & 59.20 \\
		& AttMVS & \bf 61.89 & 58.93 & \underline{60.05} \\
		& Ours & \underline{55.72} & 70.28 & \bf 60.49 \\
		\bottomrule
	\end{tabular}
	\caption{Quantitative results on the Tanks and Temples Intermediate set. Our method achieves the best $F_1$-score. The best and second best results are in \textbf{bold} and \underline{underlined}.}
	\label{tab:t2evaluation}
\end{table}

\begin{figure}[t]
	\centering
	\includegraphics[width=0.99\linewidth]{t2.pdf}
	\caption{Qualitative results of reconstructed point clouds on Tanks and Temples dataset.}
	\label{fig:t2}
\end{figure}

\subsection{Datasets and Evaluation Metrics}
\boldparagraph{DTU dataset.} This dataset is captured from $49$ or $64$ views under a controlled indoor environment.
$124$ scenes are included with $7$ different lighting conditions.
The ground truth camera parameters and 3D point clouds are both provided.
Following \cite{Ji2017Surfacenet,Yao2018MVSNet}, we divide this dataset into training, validation and evaluation sets.

\boldparagraph{Tanks and Temples dataset.} This dataset contains both indoor and outdoor scenes, which are captured in the realistic environment.
Moreover, unlike the object-centric scenes in DTU, these scenes are larger and more complex.
This dataset is divided into Intermediate set and Advanced set.
The latter one is more challenging due to scale, textureless regions and complicated visibility.

\boldparagraph{Evaluation metrics.} To evaluate the performance of our method on different datasets, distance metric and percentage metric are used for the DTU and Tanks and Temples dataset, respectively.
For the distance metric, the overall score defined as the mean of accuracy and completeness measures the overall performance of reconstructed point clouds.
For the percentage metric, the $F_1$ score defined as the harmonic mean of accuracy and completeness is adopted.

\subsection{Implementation Details}\label{Sec:Id}
\boldparagraph{Training.} We train our network with ground truth depth maps on the DTU training set.
The ground truth depth maps are generated through Poisson Surface Reconstruction~\cite{Kazhdan2013SPS}.
Due to the limitation of GPU memory, the input images are resized to $H\times W=128\times 160$.
The input image number is $N=7$.
The depth values are sampled from $425mm$ to $905mm$ with $D=192$ in an inverse depth manner.
The block size $s$ is set to $8$.
Our network is implemented using Pytorch~\cite{NIPS2019PyTorch} and trained with Adam~\cite{Kingma2015Adam} optimizer end-to-end for $10$ epochs on two NVIDIA RTX 2080Ti GPU cards.
The initial learning rate is set to $0.001$ and the batch size is set to $2$.
Note that, to prevent estimated depth maps from being biased on the recurrent regularization order, we randomly use forward or backward pass to train samples.

\boldparagraph{Evaluation.} During the evaluation, we only use the forward pass to infer depth maps.
We sample $D=512$ depth planes in an inverse depth manner~\cite{Yao2019RMVSNet,Xu2020Learning} and set the input image number $N$ as 7.
For the DTU dataset, the input image resolution is set to $H\times W=600\times 800$.
For the Tanks and Temples dataset, we use the camera parameters provided by R-MVSNet~\cite{Yao2019RMVSNet} to evaluate our method with the image resolution $H\times W=544\times 960$.
Following previous practices~\cite{Yao2019RMVSNet,Yan2020Dense}, we first use the maximum classification probability as the depth probability of estimated depths.
Then, we apply the designed dynamic depth map fusion method to generate the final point clouds.

\subsection{Benchmarking Results}
In this section, we directly use our model trained on the DTU training set without any fine-tuning for benchmarking evaluations.
We compare our method with other state-of-the-art MVS methods, including geometric and learning-based methods.
The geometric methods include Furu~\cite{Furukawa2010Accurate}, Tola~\cite{Tola2012Efficient}, Gipuma~\cite{Galliani2015Massively}, COLMAP~\cite{Schonberger2016Pixelwise}, ACMM~\cite{Xu2019Multi} and ACMP~\cite{Xu2020Planar}. For the learning-based methods, SurfaceNet~\cite{Ji2017Surfacenet}, MVSNet~\cite{Yao2018MVSNet}, R-MVSNet~\cite{Yao2019RMVSNet}, CasMVSNet~\cite{Gu2019Cas}, CVP-MVSNet~\cite{Yang2020CVPMVSNet}, UCSNet~\cite{Cheng2020UCSNet}, AttMVS~\cite{Luo2020Attention} and $\text{D}^2$HC-RMVSNet~\cite{Yan2020Dense} are compared.

\boldparagraph{Results on DTU.} We evaluate our method on the DTU evaluation set.
The comparison results are shown in Table~\ref{tab:dtuevaluation}.
Our method achieves the best completeness and competitive overall performance among compared methods.
In particular, our method outperforms the previous recurrent regularization methods, including R-MVSNet and $\text{D}^2$HC-RMVSNet.
Moreover, our method is very competitive with the 3D filtering methods, \emph{e.g.}, CVP-MVSNet and UCSNet.
The qualitative comparisons in Fig.~\ref{fig:dtu} show that our reconstructed point clouds are more complete than UCSNet and $\text{D}^2$HC-RMVSNet.

\boldparagraph{Results on Tanks and Temples.} We evaluate the generalization ability of NR2-Net on the Tanks and Temples dataset.
Table~\ref{tab:t2evaluation} summarizes the evaluation results.
As can be seen, our method yields the best mean $F_1$ score, $60.49\%$, over all published methods, demonstrating that our method generalizes better than other methods.
In addition, our method achieves much better performance than other MVS methods based on 3D cost volume filtering on this dataset.
We believe this is because the scenes in Tanks and Temples are larger than the scenes in DTU, while the 3D cost volume filtering methods can only sample limited depth planes due to the GPU memory limitation.
In contrast, our method can sample sufficient depth values and better considers the long-range dependencies in the depth direction, making it more feasible in practice.
Fig.~\ref{fig:t2} shows the qualitative results of our reconstructed point clouds.

\begin{table}[t]
	\centering

	\footnotesize
	\begin{tabular}{l|cc|ccc}
		\toprule
		Model & NLR & DF  & Acc.$\downarrow$ &  Comp.$\downarrow$ & Overall$\downarrow$ \\
		\midrule
		Baseline & & & 0.350 & 0.473 & 0.411 \\
		Model-A & & $\checkmark$ & 0.378 & 0.405 & 0.391 \\
		Model-B & $\checkmark$ & & \bf 0.344 & 0.364 & 0.354 \\
		Full & $\checkmark$ & $\checkmark$ & 0.370 & \bf 0.332 & \bf 0.351 \\
		\bottomrule
	\end{tabular}
	\caption{Ablation study of different components in our NR2-Net. NLR and DF mean non-local recurrent regularization and dynamic depth map fusion respectively.}
	\label{tab:ablation}
\end{table}

\begin{figure}[t]
	\centering
	\includegraphics[width=\columnwidth]{AVis.pdf}
	\caption{Visual comparison of predicted probability distribution at one pixel between Baseline (a) and Full model (b). The cell states of depth blocks indicate the circle region belongs to the background and constrain the probability distribution of cost map regularization to produce true estimates.}
	\label{fig:vis}
\end{figure}

\begin{table}[t]
	\centering
	\footnotesize
	\begin{tabular}{lcccc}
		\toprule
		Method & Input Size & Output Size & Mem. & Time \\
		\midrule
		MVSNet & 800$\times$576 & 200$\times$144 & 8.9 & 0.78 \\
		R-MVSNet & 800$\times$576 & 200$\times$144 & 1.2 & 0.89 \\
		D$^2$HC-RMVSNet & 200$\times$144 & 200$\times$144 & 0.9 & 2.67  \\
		Ours & 200$\times$144 & 200$\times$144 & 1.0 & 1.90 \\
		\midrule
		CVP-MVSNet & 800$\times$576 & 800$\times$576 & 2.2 & 0.49 \\
		D$^2$HC-RMVSNet & 800$\times$576 & 800$\times$576 & 2.4 & 20.94 \\
		Ours & 800$\times$576 & 800$\times$576 & 3.4 & 16.65 \\
		\bottomrule
	\end{tabular}
	\caption{Evaluation of GPU memory [GB] and runtime [s] for different methods.}
	\label{tab:TM}
\end{table}

\subsection{Ablation Study}
\boldparagraph{Effect of different components.} In this section, we conduct an ablation study on the DTU evaluation set to verify the effectiveness of non-local recurrent regularization and dynamic depth map fusion module in the proposed NR2-Net.
The baseline model is created by removing the non-local recurrent regularization module and replacing the dynamic depth map fusion with dynamic consistency checking, which means that Eq.~\eqref{Eq:dp} becomes $\tau(\mu)=0.35$ as \cite{Yan2020Dense} suggested.
The baseline model is also trained with the procedure as described in the previous section.
Based on the baseline model, we add the non-local recurrent regularization module and use the dynamic depth map fusion step by step to construct model-A, Model-B and Full model. The comparison results are listed in Table~\ref{tab:ablation}.
This clearly demonstrates the effectiveness of our proposed two core modules in NR2-Net. In particular, by comparing Baseline and Model-B, Model-A and Full model, we observe that our proposed non-local regularization improves accuracy and greatly boosts completeness, leading to significant overall performance improvement.
By comparing Baseline and Model-A, Model-B and Full model, we see that our proposed dynamic fusion can produce more complete point clouds, resulting in better overall performance.
This also demonstrates the generalization of our proposed fusion for different depth inference networks.

To further demonstrate the effect of the proposed non-local recurrent regularization, we try to visualize the cell states of different depth blocks.
In fact, it is hard to give comprehensive visualization about these cell states. Instead, we show the probability distribution of these cell states to see whether they indicate the global scene context.
To this end, we average the features of $\boldsymbol{B}(t)$ along the channel dimension and apply the softmax operation along the depth dimension for all depth blocks to obtain the probability distribution.
Then, this probability distribution is aligned with the probability distribution of all depth planes by copying the value of each block to its covering $s$ depth planes.
The visualization results are shown in Fig.~\ref{fig:vis}.
The circle region in Fig.~\ref{fig:vis} is challenging as it is close to the central object in the spatial domain but in fact it belongs to the background areas.
Without non-local recurrent regularization, it is easy for the baseline model to propagate the depth information of the central object to the circle region, resulting in incorrect estimation for this region (cf. Fig.~\ref{fig:vis}(a)).
With our proposed non-local recurrent regularization, the cell states of depth blocks indicate the circle region belongs to the background areas and constrain the regularization of LSTM cells (cf. Fig.~\ref{fig:vis}(b)).
This demonstrates to some extent that the cell states of depth blocks capture the global scene context to help cost map regularization.

\boldparagraph{Runtime and GPU memory.} We evaluate the runtime and GPU memory usage of our method to generate each depth map on the DTU evaluation set.
Table~\ref{tab:TM} shows the comparison results with other methods.
For pair comparison with MVSNet and R-MVSNet, we run different methods with the same depth sample number $D=256$.
We see that 3D filtering methods take up a lot of memory and cannot tackle high-res input.
Recurrent regularization methods greatly reduce memory consumption at the cost of runtime and can tackle high-res input.
Although cascade 3D filtering methods such as CVP-MVSNet employ the coarse-to-fine strategy to fit in high-res input, they can only sample limited depth planes in each stage.
This prevents them from perceiving full-space global context information in the depth direction, hindering their performance in practical large-scale scenes, \emph{e.g.}, Tanks and Temples dataset.

\section{Conclusion}
We presented a novel non-local recurrent regularization network for multi-view stereo.
A novel depth attention module is proposed to model non-local interactions within depth blocks.
These non-local interactions are updated in a gated recurrent way to model global scene context.
In this way, long-range dependencies along the depth direction are captured and in turn utilized to help regularize cost maps.
In addition, we design a dynamic depth map fusion strategy to adaptively take both the depth probability and depth consistency into account.
This further improves the robustness of the point cloud reconstruction.
Ablation studies demonstrate the effectiveness of our proposed core components.
Experimental results show that our method achieves competitive results on the indoor DTU dataset and exhibits excellent performance on the large-scale Tanks and Temples dataset.

redpajama_set_name: RedPajamaArXiv


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text:
The Arab Spring: hello or goodbye to democracy?
by Alan Hart on 28 Nov 2011 2 Comments
Israeli democracy fades to black (the black of the blank screen at the end of a film). That was the headline over a recent article by Lawrence Davidson, an American professor of Middle East history. He argued that the suppression of the democratic rights of non-Jews in Israel is coming full circle with Prime Minister Netanyahu's Likudniks and settlers now targeting the rights of Jews as well.
Events in Cairo provoked this question: Are we witnessing the fading to black of the prospects for freedom and democracy in Egypt, or, is resurgent people power going to make it impossible for the military to maintain its controlling grip? (Presumably there would be limits to how many Egyptian civilians Egyptian soldiers were prepared to kill even if the generals, desperate to protect their wealth and privileges, ordered the suppression by all means of protests and demands for real democracy).
Events still to unfold will determine the answer but in advance of them, and before Field Marshall Mohamed Hussein Tantawi demonstrated a degree of panic by announcing that the election of a civilian president would be brought forward, the assessment of many informed observers was in tune with that of Marina Ottaway, a senior associate at the Carnegie Endowment for International Peace. She wrote: "In the early days of the Egyptian uprising, when violence threatened to engulf the country, the military did an admirable job of maintaining order without violence and easing Hosni Mubarak out of office. Ten months later, it has emerged as the most serious threat in the transition to democracy. Recent announcements leave no doubt that the military indeed rules Egypt and intends to maintain its control indefinitely."
The best indicator of whether or not Egypt's generals will eventually bow to people power and let democracy have its way will be in their final decision about dropping or not their proposal that new constitutional principles should preserve special powers for the military after the handover to civilian rule. These special powers as originally proposed would give the military a veto over a new constitution and prevent scrutiny of its vast budget. In other words, these "supra-constitutional" principles would enshrine the military's right to intervene in civilian politics at any time of its choosing.
If Egypt's generals do seek to control the democratic process by (among other things) fixing elections as Mubarak did, they will back their actions with the assertion that they must do whatever is necessary to prevent radical Islam taking over the country. That would put them on the same page as Zionism's propaganda maestros. In a recent article for Ha'aretz, Moshe Arens, a former Israeli Minister of Defence and Foreign Minister, wrote the following:
"A wave of Islamic rule, with all it entails, is sweeping across the Arab world. It will replace secular dictatorships with Islamic ones. We should have expected nothing else… Observers may fool themselves into believing that the Islamic parties contesting the elections in the Arab countries are 'mildly' Islamic, or 'moderate' Islamists, but their leaders are neither mild nor moderate."
The unstated but implicit Zionist message Arens is conveying is that the Arab Spring will create more and more states that will become safe havens for Islamic terrorists, and that Israel and the West, America especially, will have to pursue the "war against terrorism" on many more fronts, with even greater vigour and escalating expense.
What the overwhelming majority of all Arabs want is an end to corrupt, repressive, autocratic rule. In reality there is no prospect of Muslims who preach the need for violence and practise it calling the shots if democracy is allowed to take root and grow in the Arab world. In Egypt for example, and whatever it may or may not have been in the past, the Muslim Brotherhood is in the process of transforming itself, now in the guise of the Freedom and Justice Party, into a modern and progressive political force which truly wants to see Egypt governed by democratic means for the benefit of all and not just a privileged elite. The only thing that could drive a significant number of Egyptians into supporting violent Islamic fundamentalism is never-ending military suppression of their demands for freedom and democracy. (If this were to happen one could say that like "Dubya" Bush and Tony Blair, Egypt's generals had become recruiting sergeants for violent Islamic fundamentalism).
In my analysis Arens' prediction of what will happen in the Arab world is a cover for the real fear of Zionism's in-Israel and in-America leaders. It is that democracy could or even will take root in the Arab world or at least major chunks of it. Why such a prospect alarms Zionism is not complicated.
Democratically elected Arab governments would have to reflect the will of their masses, the voters. On the matter of the conflict in and over Palestine that became Israel, what is the will of the Arab masses? In their heads if not always their hearts, it is not for military confrontation with Israel. It is that their governments be united enough use the leverage they have on America, to cause it to use the leverage it has on Israel, to cause or try to cause enough Israeli Jews to face reality and insist that their leaders make peace on terms which would satisfy the demands and needs of the Palestinians for justice, while at the same time guaranteeing the security and wellbeing of Jews now resident in Palestine that became Israel.
The leverage the Arab world has is in the form of oil, money and diplomatic relations.
For an example of how this Arab leverage could have been used to good effect in the past I'll turn the clock back to 1967. Now let us suppose that in the weeks following the Six Days War the Arab leaders put their act together and sent one of their number secretly to Washington to deliver this message to President Johnson: "If you don't get the Israelis back to the pre-war borders, we'll turn off the oil taps." (That is how Zionism's in-Israel leaders would have played the oil card if the boot had been on the other foot, if they had been in the Arab position).
How would Johnson (or any other occupant of the White House) have responded?
If he believed the Arab leaders were united and serious, not bluffing, he would have said something very like the following: "I can't promise quick action on East Jerusalem but otherwise give me three weeks and I'll do it."
In short, the Arabs would not have had to turn off the oil taps. A credible threat to do so would have been enough to motivate Johnson (or any other American president) to use all necessary leverage to bring Israel's occupation to a quick end.
That's how the game of political leverage is played.
A real hello to democracy in the Arab world or at least significant chunks of it, and Egypt especially, would be very bad news for Zionism.
Netanyahu is fully aware of this and is escalating his anti Arab Spring rhetoric. In his latest speech to the Knesset he blasted Israeli and world politicians who support the demands for change in the Arab world and accused it of "moving not forward, but backward." He asserted that his original forecast that the Arab Spring would turn into an "Islamic, anti-Western, anti-liberal, anti-Israeli and anti-democratic wave" had turned out to be true.
In his report for Ha'aretz, Barak Ravid wrote: "The speech showed an expressed lack of trust in Arab nations' ability to maintain a democratic regime; a yearning to go back to the days of ousted Egyptian President Hosni Mubarak; a fear of the collapse of the Hashemite royal house in Jordan, and an utter lack of willingness to make any concessions to the Palestinians."
Netanyahu also slammed those Western leaders, Obama especially, who had pressed Egypt's generals to tell Mubarak to go. At the time that was happening, Ravid revealed, Netanyahu said in closed talks that the American administration and many European leaders "don't understand reality". In his last speech he called them "naive".
I used to wonder if Netanyahu really believes the nonsense he talks. I am now convinced that he does.
The latest developments in Cairo – the apology by two of the generals on the Supreme Council of the Armed Forces (SCAF) – are making me wonder if the coming days will see the removal of Field Marshall Tantawi, which is what the protestors in Tahrir Square are demanding. The two generals not only apologized for the deaths of protestors, they said, according to the BBC report I heard, "We do not aspire to power and we do not want to continue in power."
If those words can be taken at face value, they suggest to me that a majority of Tantawi's SCAF colleagues have realised that continuing in power, even behind the scenes, would require them at a point to give orders to the army to shoot-to-kill large numbers of Egyptians, orders which would not be obeyed by the lower ranks and foot soldiers.
If that is the case – Tantawi's departure would indicate that it is – the prospects for a real hello to democracy in Egypt are improving. And if something approaching real democracy can take root and grow in Egypt, the days of Arab autocrats and despots almost everywhere (probably not Saudi Arabia) may well be numbered.
[Courtesy Alan Hart, www.alanhart.net]

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The American left has a lot on its plate -- universal health care, climate change, stagnant wages, wealth inequality and more. But there's one more issue that needs to be added to the list: environmental justice. Poor Americans, especially minorities, are exposed to too many toxins and environmental hazards, destroying their health and harming their opportunities for advancement.
One of the worst hazards is lead. There is now good evidence that banning leaded gasoline contributed to significant drops in crime and improvement in cognitive performance. But lead pipes and paint still are prevalent in aging urban housing, posing a serious threat to kids -- who happen to be particularly vulnerable to its ill-effects. Even small amounts of lead can impair mental performance, and cause other health problems.
But there are plenty of other toxins afflicting Americans' health. Pesticides from agriculture afflict rural communities. Cancer-causing industrial chemicals leak into waterways. Coal mine waste contains mercury, another toxic heavy metal. Gases released by hydraulic fracturing can cause all sorts of health problems. Nuclear waste can leak, causing cancer, brain and lung damage. Arsenic can contaminate groundwater. Landfills hold a huge variety of toxic chemicals. Mining, septic systems, industrial plants, chemical storage, irrigation systems and many other features of an advanced industrial economy all pose toxic risks for people who live nearby.
There's little doubt these toxins drive up the country's health care bills. But the true cost may go far beyond the health issues. Many of these toxins, especially heavy metals and pesticides, have significant effects on brain development. They are associated with known problems like attention deficit disorder. But they have also been shown to hurt mental functioning in ways not classified as official disorders -- reducing learning ability, self-control, emotional well-being and other crucial mental functions. Neurologist David Bellinger estimated in 2012 that heavy metals and pesticides have robbed Americans of a staggering total of 41 million IQ points.
Everyone in America is exposed to these toxins, but some are at much more risk than others. There is voluminous academic literature across several disciplines, including economics, showing that disadvantaged minorities -- in particular, black Americans -- are more likely than others to come into close contact with toxic chemicals. The iconic images of brown lead-filled water gushing from the water pipes in majority-black Flint, Michigan, are an extreme example, but they are emblematic of the environmental hazards faced by similar communities across the country.
This environmental injustice is due to several factors. Black and Hispanic Americans tend to be poorer than other groups, and thus have to rent or buy homes where housing is cheaper -- sometimes because of proximity to toxic waste. Poor and minority communities also probably have less political power, and so are less able to prevent toxic chemical plants, mines, landfills or other polluting facilities from locating near them. They're also have less power to compel government to spend money on cleaning up pollution in their neighborhoods.
And finally, some of the difference is due to plain old racial discrimination. A new paper by economists Peter Christensen and Christopher Timmins examined the results of audit studies, in which actors of different races pretend to be prospective house buyers. They found that all else equal, real estate agents systematically steer black buyers toward more polluted neighborhoods. Fair housing laws have been unable to prevent this pervasive form of housing discrimination, which may be covert or even subconscious. But the authors estimate that the difference is great enough to account for 100 percent of African-American mothers' greater likelihood of living near Superfund toxic waste sites.
This environmental injustice almost certainly contributes to many of the social problems that afflict minority communities, from higher crime rates to lower test scores. It also indirectly and directly affects the generally poorer health of black Americans, raising their medical bills and bankruptcy risk. And it reduces mobility for poor minorities, helping maintain the income and wealth gap over generations.
The threat that toxic waste poses to poor and minority Americans should be fought with a multipronged approach. A focused and well-funded national initiative to replace lead pipes and clean up lead paint should be the first order of business. More money for Superfund and other toxic waste cleanup programs should be a priority, and environmental regulations covering factories, mines, landfills and other dangerous facilities should be enforced more strictly. Needless to say, the current administration is going in precisely the wrong direction on these issues, but environmental cleanup should be a top priority for the next one.
Discrimination also needs to be addressed. The government should crack down on steering of poor minorities toward polluted neighborhoods. And it should more protect poor minority communities from companies and municipal authorities that seek to place toxic facilities in their midst.
Environmental justice might not sound as bold or transformative as universal health care or other big programs now being proposed by the left. But its impact could be no less important for the quality of life enjoyed by disadvantaged minorities and the poor.
Noah Smith is a Bloomberg Opinion columnist. -- Ed.


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Chief executives need to know how to unwrap Earth's new powerhouse.
From small roads great economies grow. In the early 1990s, China was a relatively small economy that had just built its very first highway. Benefiting from IMF's help, India had barely managed to escape bankruptcy. How the structure of the global economy has changed over the last 25 years! China is now the world's second-largest economy, and competing with the US on all fronts – economic, technological, and geopolitical. India is today the world's sixth-largest economy. By 2022, it will surpass Germany to become the fourth-largest and, by 2025, may even overtake Japan into third place.
Southeast Asia is another powerhouse. Home to 600 million people, the ten countries comprising the Association of South East Asian Nations (ASEAN) account for US$2 trillion in GDP, just a tad smaller than India's and, as a region, make up the third fastest-growing economic block in the world, after China and India. By almost all measures, it can be said that this will be the Asian Century.
Other than ongoing digital disruption, nothing should be more important for Western chief executives than emerging Asian nations – because it is these economies that will play central roles in shaping Asia's destiny over the next decade and beyond.
Figure 1 tracks the global GDP share of the world's three economic legs – the US, Europe and Asia. As recently as 2000, all three legs were about equal in size, each accounting for almost 30% of the world's GDP. In fact, at that time, the US accounted for a slightly bigger share than the other two regions. It is a very different story today. Asia is now 50% larger than the US or Europe. By 2025, Asia's GDP will be larger than that of the US and Europe combined. In short, in a mere 25 years (2000-2025), the structure of the world economy will have changed dramatically.
Figure 2 helps us make sense of why Asia is growing at such breakneck speed. The answer lies in the three big legs of emerging Asia – China, India, and ASEAN.
All three are growing much faster than the other big emerging markets of the world – Latin America and Africa, whose economies are growing at around 4% a year.
Asia's advantage relative to Latin America and Africa? First, massive scale. As a result, on purely economic grounds, most of the world's multinationals feel compelled to invest and manufacture within China, India and ASEAN rather than export to these markets. This trend brings not only capital, but also knowhow.
Second, a deep love for science, technology, mathematics and engineering (STEM). Just look at Asia's disproportionate share of PhD students in the STEM fields in US universities. As a result, Asian economies are able to absorb foreign technology at a faster pace than in Latin America and Africa. And in terms of patents granted, China and India are the world's two fastest-rising technology powers.
Third, political stability. Country risk is lower in Asia than in much of Latin America and Africa. Lower country risk makes both foreign and domestic companies more willing to invest for the longer-run in Asia than elsewhere.
Fourth, very high rates of saving. As a result, Asian economies have more domestic capital available for investment in infrastructure, a foundational requirement for economic growth.
What could hold Asia back?
The biggest risk is a middle-income trap, where a country attains a certain income and gets stuck at that level. China's per capita income has to reach only one quarter of that of the US for it to become the world's largest economy. India's per capita income needs to reach only one tenth of that of the US for it to become the world's third-largest economy. In short, given their vast populations, even if China and India were to keep growing as middle-income economies, they would become economic powerhouses. Over the last 100 years, very few countries have managed to avoid a middle-income trap, the exceptions being South Korea, Singapore and Taiwan. Will China, India and Indonesia manage to break through? As in the case of South Korea, this will require massive structural reforms.
Asia's fortunes also depend on how geopolitical tensions play out. North Korea, the East China Sea, the South China Sea, the China–India border, the India-Pakistan border and the Middle East are some of the world's major geopolitical hotspots, all based in Asia. If any of these tensions morph into full-scale war, the consequences could be catastrophic.
Asia is becoming more internally integrated. Rapid growth means that opportunities for exports and imports among the Asian economies are growing at a faster pace than trade with the US, Europe or elsewhere. They also benefit from geographic proximity and shorter supply chains. Figure 3 aptly captures the growing intra-Asia integration via trade.
The prospects for further trade integration within Asia look promising. China, India, ASEAN, Japan, South Korea, Australia and New Zealand are currently negotiating an intra-Asia free trade agreement (Regional Comprehensive Economic Partnership, or RCEP). As and when RCEP becomes a reality, it will provide another fillip to rapid growth in intra-Asia trade.
Asian economies are also coming closer via foreign direct investment. When Chinese and Japanese giants look at big, fast-growing markets for investment, India and ASEAN loom large. Thus, it is not surprising that Chinese technology companies such as Alibaba Group and Tencent have made some of their largest – multibillion dollar – outbound investments in India and Southeast Asia. This is true also of Japanese and South Korean companies. Japanese car companies enjoy over 50% market share in India. Hyundai from South Korea is the number two carmaker in India. Samsung and LG are the two largest companies in India's home appliance market. And Japan just launched India's first bullet train project – the capital investment of around US$20 billion is being financed almost solely by Japanese banks.
As China's economic growth slows from the average of 10.5% during 1980-2010, to around 6% today, its economic structure is also changing. Sectors such as steel, cement, glass and construction machinery – whose fortunes are tied to growth in infrastructure and real-estate development – suffer from slow growth and massive overcapacity. These are no longer viable market opportunities for foreign multinationals.
The automobile sector has also slowed considerably. The days of 30+% annual growth are gone. Car sales are now growing at around 5% a year. With the ubiquitous spread of ride sharing, even this level of growth will shortly taper off. Selected opportunities do exist, however. As Chinese buyers trade up, demand is shifting from mass-market cars to luxury brands, including Tesla in electric vehicles. The real growth opportunities lie in industries that cater to new needs and wants.
Industrial automation The driver here is a shrinking labour pool.
Solutions for energy efficiency driven by leaders' desire to reduce China's massive dependence on imported oil.
Solutions for pollution control and abatement Along with India, China is home to many of the most polluted cities on earth.
Solutions for agricultural productivity and water scarcity Even though China is a vast country, only one eighth of its land area is arable. China also suffers from acute scarcity of fresh water.
Business-to-business services As Chinese companies become bigger, get listed, go global and make acquisitions: they need leading-edge professional services of all types – accounting, legal, investment banking, and so on.
Higher-quality goods and travel Given the ferocious clampdown on corruption, conspicuous consumption by government officials has come down dramatically. Yet, with rapidly growing affluence, millions of Chinese households are trading up in the quality of what they eat, what they wear, how they live, what type of car they drive and how they spend their free time. This may not mean more Ferraris and Porsches, but it does mean more Mercedes-Benz, BMW, Audi and Land Rover cars. It also means more imported food and dining out. Perhaps, most important of all, it means much more foreign travel.
India is running about 13-15 years behind China, the time gap between 1978 and 1991, when China and India respectively launched their economic reforms. Thus, market opportunities in the India of 2015-2020 are, in many ways, similar to those in China during 2000-2005.
India's biggest drive today is a rapid buildup of infrastructure. Thus, demand for all types of products and services connected to the construction sector is growing faster than the economy as a whole. This includes not just industrial products, such as construction machinery, but also consumer goods, such as home appliances.
Similar to the China of 15 years back, the car market is growing rapidly. India today is the fastest-growing car market in the world.
There are, of course, some important differences in today's India and the China of 2000-2005. Today's technologies are far more advanced than those of yesteryear. As a result, as India becomes increasingly urban, the government is pushing for the creation of smart cities. As solar power becomes price competitive with fossil power, coal demand is rising much more slowly than anticipated. Instead, demand for solar and wind power equipment has skyrocketed.
Similarly, on the consumer side, some of the biggest growth stories are private schools and after-school tutors for children, financial services to help people manage their savings, smartphones, social media, e-commerce, ride sharing, ready-to-cook food, dining out and travel. After China, outbound travel from India is the second fastest-growing in the world.
In 2013, President Xi Jinping launched the Belt and Road Initiative (BRI) with the goal of boosting China's road and maritime connectivity with the rest of the world, most notably Asia and Eastern Europe. By some estimates, BRI could mean a capital investment of around US$1 trillion over 20 years. Companies such as GE and Honeywell are looking at the BRI as a potential opportunity to leverage their strengths in China and partnerships with Chinese companies to grow their business in the BRI countries.
India is playing a different kind of platform role. Unlike China, the Indian government makes the country far more open to foreign multinationals and does not pursue a my-market-for-your-technology policy. As a direct result, most of the world's technology giants prefer to do leading-edge R&D work in their Indian, rather than Chinese, labs. Google has its second largest R&D lab in the world – after Silicon Valley – in India. This is true also of other technology giants, such as IBM, SAP, Adobe and Texas Instruments. Google and Facebook look at India as the ideal platform for the development of internet services that would work well with low-end smartphones, slow bandwidth speeds, and be extremely affordable. Once developed for India, these solutions can then be rolled out to other emerging markets.
Something similar is also playing out in the automobile sector. Companies such as Ford, Hyundai, and the Renault-Nissan-Mitsubishi Alliance now use India as the launchpad for the development and marketing of cars aimed specifically at emerging markets. The logic is that success in India can be cloned easily in other emerging markets.
Asia's GDP today is 50% larger than either the US or Europe. By 2025, it could be bigger than the US and Europe combined. Asia will still be growing faster than any other region of the world. Asia is also becoming internally more integrated – by trade as well as investment linkages. Thus, becoming smart and serious about Asia is a strategic imperative, not an option.
There exist massive and growing opportunities to benefit from the large markets for almost everything in Asia. In addition, China and India offer the potential to serve as global platforms to enter and succeed in markets outside Asia.
Two of the key requirements for success in Asia include making Asia your second home, and learning to make sense of the world, not just through Western, but also Asian, eyes.
— Anil K Gupta is the Michael D Dingman chair in strategy and entrepreneurship at the Smith School of Business, the University of Maryland and a visiting professor at INSEAD. Haiyan Wang is managing partner of the China India Institute.