text:
Cole Hauser Says His Daughter Gets "Uncomfortable" Watching Beth & Rip's Steamy Scenes On 'Yellowstone'
· January 12, 2023
You know, I've always wondered this.
How does an actor's kids feel when they watch their dad or mom on screen kissing, or fake doing the dirty with somebody who isn't their mom or dad? Or a spouse for that matter?
Of course, there's nothing truly intimate about it at all, as that's what actors and actresses sign up for from day one.
However, I could definitely imagine the kids feel a bit uncomfortable and confused while watching scenes like that. Then again, Yellowstone probably isn't really for kids in the first place… we know Kevin Costner doesn't let his kids watch it.
We all know Rip Wheeler and Beth Dutton as the lovers who were simply made to be together in Yellowstone. But, of course, Cole Hauser (Rip) and Kelly Reilly (Beth) are married to different people, and Cole has three kids of his own.
So, how does Cole's kids react when they watch Rip getting intimate with Beth on screen?
According to FOX News, Hauser and his wife Cynthia Daniel admitted on the red carpet at the Golden Globes that it definitely makes their 10-year-old daughter, Steely Rose, a bit uncomfortable.
Daniel said:
"It makes her really uncomfortable."
Hauser added:
"Especially at first. She was like, 'What the hell was that?'"
He added that she:
"She almost ran out of the room pissed off."
However, the two admitted that she has since gotten used to it, and definitely understands that the two are simply playing characters on a TV show.
Daniel also admitted that it can be uncomfortable for herself as well:
"I mean, I have to say the first episode was Cole slamming Beth against the wall, having sex and making out, so it was a little intense, but like I said Kelly is such a cool girl, and she makes it very comfortable."
Daniel, a former actress herself, also added that she loves Beth and Rip's characters together, and Hauser said Reilly is really like a sister to him.
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The two also noted that their family has gotten really close with Reilly and her husband Kyle Baugher, and they've basically become family.
But, as far as Yellowstone goes, it truly is sad that we're going to have to wait until summer to watch the second half of season five premier.
However, we still have the prequel series 1923 to look forward to, as things are just now starting to heat up big time, although it doesn't kickstart again until February.
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The Dutton Family Tree
James Dutton
The original patriarch of the Dutton Family, James (Tim McGraw) was a farmer from Tennessee who took his family out west in search of a new life. The husband of Margaret Dutton and father of Elsa, John and Spencer, he chose to establish the modern day Dutton Ranch where his daughter Elsa passed away.
Margaret Dutton
The original matriarch of the Dutton Family, Margaret (Faith Hill) is married to James Dutton and the mother of Elsa, John and Spencer Dutton.
Elsa Dutton
Elsa (Isabel May) was the eldest child of James and Margaret Dutton. She also serves as the narrator of both 1883 and 1923. She was going to marry a cowboy named Ennis, however, he was killed on the journey West. She vowed to return to a Comanche warrior named Sam once her family reached its destination, however she was killed in an attack on the wagon train. The Dutton Ranch property was established where she died, and she was the first to be buried there.
John Dutton (Audie Rick in 1883, James Badge Dale in 1923) is the son of James and Margaret Dutton, brother to Elsa and Spencer. He was just a young boy during the events of 1883, however in 1923, he works alongside his uncle Jacob on the Dutton Ranch. He is married to Emma Dutton, and they have at least one son, Jack.
Claire Dutton
Claire (Dawn Olivieri) is James Dutton's sister, who joins the journey west with her daughter Mary Abel. A widow, she kills herself once her daughter is killed on the trail.
Jacob Dutton
Jacob (Harrison Ford) is the brother of James Dutton, who becomes the leader of the family once James passes away. He and his wife, Cara Dutton, work to run the ranch with their nephews, John and Spencer, and John's son Jack.
Cara Dutton
Cara (Helen Mirren) is the wife of Jacob Dutton. A hard-nosed Irish immigrant, she becomes the matriarch of the Dutton family in 1923, following the passing of Margaret and James.
Spencer Dutton
Spencer (Brandon Sklenar) is the second son of James and Margaret Dutton. Born after the Duttons settled in Montana (and the death of his sister, Elsa), Spencer is a WWI vet who spends time hunting in Africa before returning back to Montana to work the ranch. It's possible that Spencer could be the link between the modern day Dutton family.
Emma Dutton
Emma (Marley Shelton) is the wife of John Dutton Sr. and the mother of Jack Dutton.
Jack Dutton
Jack (Darren Mann) works on the Dutton Ranch alongside his father, John Dutton Sr. and great-uncle, Jacob Dutton. He is engaged to Elizabeth Strafford, the daughter of another notable rancher.
With the hints that Elizabeth may already be pregnant, my suspicion is that Jack may be the father of John Dutton II since the "II" title is generally used when the child is named after another relative, not the father.
This is just a guess, but my hunch is that John Dutton won't survive 1923, and Jack will name his first born son after his late father… John Dutton II. The other option is that Spencer has a son who then goes on to father John II.
Elizabeth Strafford-Dutton
Elizabeth (Michelle Randolph) is Jack's fiancée. The two end up postponing their wedding briefly, but are planning on marrying very soon. There is also a suspicion that she might already be pregnant.
John Dutton III
The modern day leader of the Dutton family and the current Governor of Montana. He is the father of Lee Dutton, Beth Dutton and Kayce Dutton, and the adoptive father of Jamie Dutton.
Evelyn Dutton
Evelyn (Gretchen Mol) is the wife of John Dutton III who passed away in a horseback riding accident when her children were young. She is the mother of Lee, Beth and Kayce Dutton.
Lee Dutton
Lee (Dave Annable) is the oldest son of John and Evelyn Dutton. The heir to the Dutton Ranch, Lee was killed by Robert Long (Monica's brother) after an argument broke out over stolen cattle.
Beth (Kelly Reilly) is the daughter of John Dutton III and wife of Rip Wheeler, her father's ranch foreman and right-hand man. Unable to have children of her own, her and Rip have taken in teenage Carter.
Rip Wheeler
Rip (Cole Hauser) is the Yellowstone foreman and right-hand man of John Dutton III. He came to the Dutton Ranch as an orphaned teenager after he killed his own father (who killed his mother and brother). Teenage sweethearts, he eventually marries John's daughter, Beth Dutton.
Kayce Dutton
Kayce (Luke Grimes) is the youngest of the Dutton siblings, and a former Navy SEAL. He's married to Monica Dutton and the father of Tate Dutton and John Dutton IV (deceased).
Monica Dutton
Monica (Kelsea Asbille) was raised on the Broken Rock Reservation. She is married to Kayce Dutton and the mother of Tate Dutton and John Dutton IV.
Tate Dutton
Tate (Brecken Merrill) is the oldest son of Kayce and Monica Dutton, the eldest of the 7th generation of Duttons.
John Dutton IV
John Dutton IV is the second son of Kayce and Monica Dutton, who tragically only lived a few hours following complications from an early birth and a car accident.
Jamie Dutton
Jamie (Wes Bentley) is the adopted son of John Dutton III and the black sheep of the Dutton family.
Christina is Jamie's ex assistant and the mother of his child.
James Dutton Jr.
The son of Jamie Dutton and his ex-girlfriend Christina, Jamie finds out he has a son after the birth.
1923 is available to stream… NOW.
In that 1893 flashback from Yellowstone Season 4, we see that James has two sons with him, John Sr. (who will be a major character in 1923) and Spencer… Generation Two.
They should be in their forties or so by the time 1923 rolls around and will have children of their own (Generation Three), and John Sr. and Spencer might even have young grandchildren (people got married pretty young back then), so that's Generation Four.
Now, here's where it get's interesting… John Dutton III (born sometime in the 1950s) of Yellowstone would be Generation Five, which means Kayce, Jamie (adopted), the late Lee Dutton, and Beth Dutton would all be Generation Six, and young Tate would be Generation 7.
And that's exactly what Tim McGraw himself has confirmed.
Tim says he plays the great-great-grandfather of John Dutton in the bonus Blu-ray content:
"I play John Dutton's great-great-grandfather. Our family is the first to discover Yellowstone and settle it. And in doing that, we are the first to really defend it and fight people off, and try to establish it and then survive.
J.D.'s the patriarch of the family. He's the guy that had the balls, I guess, to set out and take his family across the country and head up to Montana, and sorta settle this unknown land, this untamed land up there, and turn it into something."
Of course, which generation of Dutton has implications for the show because in the Season Finale of 1883, Elsa Dutton (Isabel May) is on the verge of death and her father, James Dutton (Tim McGraw) needs to find a place to bury her because wherever they bury her body is where the Dutton family is going to settle.
So Spotted Eagle, a Crow elder, recommends a spot called the Paradise Valley.
But, there's a catch… the Duttons can't have it forever.
"Yes, Paradise. Good name. But you know this: that in seven generations, my people will rise up and take it back from you."
To which James says:
"In seven generations, you can have it."
Doesn't bode well for the Dutton family in Yellowstone Season 5, does it?
If you're looking for the entire Yellowstone soundtrack, complete through every episode of Season 5 so far, then look no further.
Our Yellowstone: The Soundtrack Playlist, featuring every song from every episode, updated in real time, every Sunday night.
Beth DuttonCole HauserFeaturedLifeRip WheelerVideoYellowstone


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text:
Restoration of the Oast House
The Oast was built around 1840 and for the following 130 years it was the kingpin on the farm when hops were Hare Farm's main crop and income.
The hops were dried and stored within the Oast's three round kilns. A lucrative crop in their heyday, hops gradually lost their viability in the 60s. The 1970 season was the Oast's last. From then, the Oast wasn a redundant building, no longer useful for modern farming methods. The Oast's restoration started in March 2010, and its full conversion into a self catering holiday home completed in June 2011.
After fifteen years' duty as the roof over Mum and Dad's garden tools and bits and bobs, holes in the roof and ever mounting instability, meant it was a "do or die" situation. The building needed to be saved. Our brief throughout was to restore the building, and to sensitively repair the original design, even to retain its interesting mix of window and door shapes and styles.
The oast had been built into a bank. Damp was a major problem. Tonnes of soil were dug out to reveal the full height of the beautiful roundels. Then the oast house could be fully underpinned. We kept all we could by way of original materials, and only replaced what we really had to. Today, as much of the brickwork and timber is exposed internally as possible. The roof repair included at least half of the original tiles, with new coming from the only remaining Kentish kent peg tile maker, Babylon Tile Works.
Danny Butler and his team from down the road in Westfield led the build project and tirelessly nurtured the building back to its original state. Every roundel's roof was rebuilt by hand, a tremendous technical challenge when the building is not square. And when there's three roundels to do (not many oasts have three roundels for sure). After many years naked of cowls, the oast could stand very tall and proud again. Two of the cowls were airlifted in and the largest one, complete with boxing hares on its weather vanes, was carried up by human hands on a very cold day in November.
Restored again, the sleeping beauty has finally awoken and can be enjoyed by all who stay in this large, spacious and stylish self-catering holiday home. We can be sure now she'll live for many generations to come.
The Shepherd's Hut is Ten
Ten years ago from now, on Sunday 4th November in 2011, around about lunchtime we caught the first g..
What is an Oast House?
An oast, oast house or hop kiln is an agricultural building designed for kilning (drying) hops as pa..
Preparing to Open Again
What extraordinary times we are all going through. Our last guests were here mid March and since the..

redpajama_set_name: RedPajamaCommonCrawl


High Quality The Use Of Stucco As A Common Substrate Also Lends Itself To Bright Colors, But Painted Wood Is Also In Evidence.
Amazing Phenomenal Beautiful Tropical Home Paint Color Schemes Pool Ideas Beautiful Tropical Home Paint Color Schemes Ideas .
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Feature Design Ideas Lavish Exterior Color Schemes For Modern Excerpt Homes. Exterior Design House.


text:
CV Grad Helps UAlbany Win AE Softball Championship
WNBF News/Roger Neel Photo
The Albany Great Danes defeated the UMass Lowell River Hawks 4-1 Sunday afternoon at the Bearcats Sports Complex to capture the America East softball championship. The Great Danes won the regular season championship and came into the tournament as the top seed. The River Hawks were the second seed.
In the double elimination format and with Albany falling to UMass Lowell earlier in the tournament the Great Danes were facing elimination when they met the River Hawks in the first game on Sunday. Albany came from behind to defeat UMass Lowell 4-3 to send the title contest to a second game on Sunday afternoon.
In that second game the Great Danes used a pair of two-run innings to clinch the championship by defeating UMass Lowell 4-1 and take home their second consecutive conference championship.
One of the keys for Albany's success is catcher and former Chenango Valley standout catcher Chelsea Henige. Henige went 9-15 in the tournament with a pair of home runs. Henige has had an outstanding season for the Great Danes. She is currently hitting .366 with four home runs and 27 runs batted in while in the clean-up position. She has shined defensively as well making just two errors.
Henige played before a number of family members and friends. She earned Second Team All-Conference honors and was named to the All Championship Team for her outstanding play this past weekend. Her season is far from over. Albany earned an automatic bid to play in the NCAA Tournament with the America East title. The Great Danes will travel to play in the Eugene, Oregon Regional and on Thursday they meet the top-seeded Oregon Ducks on Thursday night.


United National Independence Party (UNIP) acting secretary general Alfred Banda has resigned.
Reverend Banda says there is a lot of intimidation in the party by party president Tilyenji Kaunda and vice Njekwa Anamela.
Rev Banda has since called for a national congress to elect new office holders.
He said he won't join any other party.


Different classes, of antiretroviral drugs target different stages of the HIV life-cycle. Two of the approved types of drugs – nucleoside reverse transcriptase inhibitors (NRTIs) and nucleotide reverse transcriptase inhibitors (NtRTIs) (usually considered as a single class) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) – interfere with the action of HIV's reverse transcriptase enzyme. Drugs in the protease inhibitor class block the action of the protease enzyme.
HIV entry inhibitors work in various ways. There is one approved HIV fusion inhibitor and one approved CCR5 antagonist, but other types of entry inhibitors are under development. The first integrase inhibitor was licensed in 2007 and agents that target other steps of the HIV life-cycle are further back in the drug development pipeline.
For a summary of currently approved antiretroviral drugs licensed in the European Union, see the antiretroviral drug chart.


text:
\section{Introduction}
\label{sec:introduction}
Data arises from many applications in science and engineering is commonly represented in non-Euclidean structures, such as meshes, point clouds and graphs. Such data includes 3D shapes in computer graphics, scanned point clouds in remote sensing, social networks in social science, functional networks in neuroscience \cite{henaff2015deep,koren2009matrix,Wu_2015_CVPR,morgan1965generation}, etc. These unstructured data often need to be properly analyzed first before they can be effectively utilized in various downstream tasks, such as classification, segmentation, registration, prediction, etc.

Recently, deep learning has enabled major breakthroughs in many fields in science and engineering. This is largely due to the capability of deep neural networks in extracting features, analyzes features and make high level decisions and predictions in an end-to-end fashion. Among different types of deep neural networks, the convolutional neural networks (CNNs) is particularly effective, especially for analyzing Euclidean data such as audios, images, and videos \cite {lecun2015deep, goodfellow2016deep}. Due to the success of CNNs in analyzing Euclidean data, much effort has recently been made to extend CNNs to non-Euclidean data, which is one of the main objectives in geometric deep learning \cite{Masci_2015_ICCV_Workshops, Monti_2017_CVPR, 7974879, cao2020comprehensive}. However, generalizing CNNs to non-Euclidean data is not straightforward. A typical CNN is a composition of simple operators such as convolution operator, down-sampling/pooling operators, batch normalization, etc. Notice that convolution operator, which is the crucial operator in any CNNs, does not admit a simple extension to non-Euclidean data.

This article focuses on generalizing the convolution operator on (manifold structured) point clouds so that it inherits desirable properties of the planar convolution. This in turn enables us to design CNNs on point clouds. In the remaining part of this introduction, we shall first briefly review the applications of point cloud data, and provide a unified view on the existing extensions of convolutions on non-Euclidean data and discuss their relations with our proposed convolution which is called the narrow-band parallel transport convolution.

\subsection{Applications of point clouds data}

Point cloud data can be acquired by 3D laser scanners, such as Light Detection and Ranging (LIDAR) and RGB-D cameras. It can also be obtained by 3D scene reconstruction from 2D images, such as reconstruction earth's landscape through aerial photography. Furthermore, one can obtain point clouds through sampling of CAD models. Formally, a point cloud $\mathcal{P}=\{x_i\in\mathbb{R}^3: i=1,\ldots,N\}\subset\mathbb{R}^3$ consists of coordinates of points in a $3$-dimensional Euclidean space. Unlike meshes and graphs, point clouds have no connectivity information.

Point clouds provide a direct and convenient way of representing geometric data. For example, in autonomous driving, accurate environment perception is needed to realize reliable navigation and decision in a complex dynamic environment \cite{li2020deep}. Traditionally, image data can provide 2D semantic and texture information with low cost and high efficiency. However, image data lacks 3D geographic information. Therefore, dense and accurate point cloud data with 3D geographic information collected by LIDAR is commonly used by modern autonomous vehicles. In addition, LIDAR is insensitive to the change of lighting conditions and can work both in the daytime nighttime. Other than autonomous driving, many applications prefer to use point cloud data, such as face recognition in e-commerce, urban planning and agricultural production, animation and virtual reality, etc. \cite{liu2019deep}. Due to the vast importance of point cloud data, we would like to extend the convolution operator and design CNNs on point clouds.

\subsection{Related Work of Convolutions on Non-Euclidean Domains}
Convolution is one of the most widely used operators in applied mathematics, computer science and engineering. It is also the most important building block of Convolutional Neural Netowrks (CNNs) which are the main driven force in the recent success of deep learning.

In the Euclidean space $\mathbb{R}^n$, the convolution of function $f$ with a kernel (or filter) $k$ is defined as
\begin{equation} \label{convR}
(f \ast k)(x):=\int_{\mathbb{R}^n} k(x-y)f(y) dy.
\end{equation}
This operation can be easily calculated in Euclidean spaces due to the shift-invariance of the space so that the translates of the filter $k$, i.e. $k(x-y)$ is naturally defined.

One of the main challenges of proposing geometric meaningful convolution on manifolds and point clouds (a discrete form of manifolds) is to define an analogy of the Euclidean translation $x-y$ on the non-Euclidean domain.
Multiple types of generalized convolutions on manifolds, graphs and point clouds have been proposed in recent years. We shall review some of them and discuss the relation between existing definitions of convolutions and the proposed narrow-band parallel transport convolution.

\textbf{Spectral methods} avoid the direct definition of translation $x-y$ by utilizing the convolution theorem \cite{katznelson2004introduction}: given any two functions $f$ and $g$, $\widehat{f\ast g} = \hat{f} \cdot \hat{g}$. Therefore, we have $f\ast g = (\hat{f} \cdot \hat{g})^{\vee}$, where $\wedge$ and $\vee$ represent generalized Fourier transform and inverse Fourier transform provided through the associated Laplace-Beltrami (LB) eigensystem on manifolds. To avoid computing convolution through full eigenvalue decomposition of the LB operator, polynomial approximation has been proposed and yields convolution as action of polynomials of the LB operator~\cite{HAMMOND2011129,DONG2017452}. Thus, convolutional neural networks can be designed \cite{bruna2014spectral,NIPS2016_6081,8521593}. Spectral methods, however, suffer two major drawbacks. First, these methods define convolution in the frequency domain. As a result, the learned filters are not spatially localized. Second, spectral convolutions are domain-dependent as deformation of the ground manifold will change the corresponding LB eigensystem. This obstructs the use of learning networks from one training domain to a different testing domain~\cite{DBLP:journals/corr/abs-1805-07857}.

\textbf{Spatial mesh-based methods} are more intuitive and similar to the Euclidean case, and this is one of the reasons why most of the existing works fall into this category \cite{Masci_2015_ICCV_Workshops,NIPS2016_6045,DBLP:journals/corr/abs-1805-07857}. The philosophy behind these methods is that the tangent plane $\mathcal{T}_{x}\mathcal{M}$ of a $2$-dimensional manifold $\mathcal{M}$ is embedded to a $2$-dimensional Euclidean domain where convolution can be easily defined. In this paper, we make the first attempt to interpret some of the existing mesh-based methods in a unified framework. We claim that most of the spatial mesh-based methods can be formulated as
\begin{equation} \label{convM}
(f \ast k)(x):= \int_{\mathcal{T}_{x,\epsilon}\mathcal{M}}k(\phi(x,v))f(v){\rm d}v,\quad x\in\mathcal{M}. \end{equation}
Here, $k: \mathbb{R}^2\to \mathbb{R}$ is a convolution kernel and $\mathcal{T}_{x,\epsilon}\mathcal{M} =\{v \in \mathcal{T}_x \mathcal{M}: \left<v,v\right>_{g_x} \le\epsilon^2\}$ with $\epsilon>0$ being the size of the kernel. Given vector fields $\vec{u}^j\in \Gamma(\mathcal{T}\mathcal{M})$, $j=1,2$,  the mapping $\phi(x,\cdot): \mathcal{T}_x\mathcal{M}\to\mathbb{R}^2$ is defined as
\begin{equation}\label{ufunction}
\phi(x,v)  = \left(\left<v,\vec{u}(x)^1\right>_{g_x},\left<v,\vec{u}(x)^2\right>_{g_x}\right),
\end{equation}
Most of the designs of the existing manifold convolutions focused on the designs of $\vec{u}^j$. We remark that possible singularities will lead to no convolution operation at those points. These are isolated points on a closed manifold and do not affect experiment results. In addition, singularities from a given vector field can be overcome using several pairs of vector fields and pooling~\cite{DBLP:journals/corr/abs-1805-07857}.

For example, GCNN \cite{Masci_2015_ICCV_Workshops} and ACNN \cite{NIPS2016_6045} construct a local geodesic polar coordinate system on a manifold, formulating the convolution as $$(f\ast k)(x) = \int k\left((\phi\circ \psi)(\theta,r)\right) (\mathcal{Q}_xf)(r,\theta){\rm d r}{\rm d\theta},$$ where $\mathcal{Q}$ is a local interpolation function with interpolation domain an isotropic disc for GCNN and an anisotropic ellipse for ACNN. A local geodesic polar coordinate system on a manifold can also be transformed to a 2-dimensional planar coordinate system on its tangent plane. Such transformation is the mapping $\psi$ which is defined by the inverse exponential map:
$v = exp^{-1}(z(\theta,r))$ with $z(\theta,r)$ being a point in the local geodesic polar coordinate system at $x\in\mathcal{M}$ with coordinates $(\theta,r)$. With this, we can easily interpret ACNN within the framework of (\ref{convM}). Indeed, ACNN essentially chooses $\vec{u}^j(x)$ as the directions of the principal curvature at point $x$. For GCNN, on the other hand, it avoids choosing a specific vector field on the manifold by taking max-pooling among all possible directions of $\vec{u}(x)^1$ at each point. Such definition of convolution, however, ignores the correspondence of the convolution kernels at different locations.

The newly proposed PTC \cite{DBLP:journals/corr/abs-1805-07857} defines convolution directly on the manifold, while using tangent planes to transport kernels by a properly chosen parallel transport. PTC can be equivalently cast into the form of (\ref{convM}) using the inverse exponential map, and implementation of the proposed parallel transported is realized through choosing specific vector fields $\{\vec{u}^j\}_{j=1,2}$ guided by a Eikonal equation for transforming vectors along geodesic curves on manifolds. Similarly, some definitions of convolution depend on particular frames. PFCNN \cite{yang2020pfcnn} uses the optimized frame as $\{\vec{u}^j\}_{j=1,2}$. The objective function is based on the angles between adjacent frames. CGCNN \cite{yang2021continuous} uses two different data-driven local reference frames as $\{\vec{u}^j\}_{j=1,2}$.

\textbf{Spatial point-based methods} have wider applications due to their weaker assumptions on the data structure.

There are mainly two types of point-based convolution. The first type is to combine the information of points directly. These methods can be formulated as
\begin{equation} \label{convP}
(f \ast k)(x_i):=\sum_{x_j\in \mathcal{N}(x_i)} k(x_i,x_j)f(x_j),
\end{equation}
where $\mathcal{N}(x_i)\subset\mathcal{P}$ is a neighborhood of $x_i$ and kernel $k$ takes different forms in different methods. PointNet \cite{Qi_2017_CVPR} is an early attempt to extract features on point cloud. PointNet is a network structure without convolution, or alternatively we can interpret the convolution defined by PointNet has the simplest kernel $k(x_i,x_j)=\delta(x_i,x_j)$ where $\delta$ is the Kronecker-Delta. Various later works attempt to improve PointNet by choosing different forms of the kernel $k$. For example, PointNet++ \cite{NIPS2017_7095} introduces a max pooling among local points, i.e. choosing kernel $k$ as an indicator function: $k(x_i,x_j) = I_{x_j=\mathop{\arg\max}_{z\in \mathcal{N}(x_i)} f(z)}$. Pointcnn \cite{NIPS2018_7362} chooses $k(x_i,\cdot)=\beta'A_{x_i}$ where $\beta\in\mathbb{R}^K$ and $A_{x_i}\in\mathbb{R}^{K\times K}$ are trainable variables with $K=|\mathcal{N}(x_i)|$. DGCNN \cite{DBLP:journals/corr/abs-1801-07829} proposes an "edge convolution" that can be viewed as fixing $f(x_j)\equiv1$ in (\ref{convP}) and $k(x_i,x_j)=$MLP$(f(x_i),f(x_i)-f(x_j))$, where MLP means the Multi-Layer Perceptron \cite{pal1992multilayer}.

The second type of convolution is defined by first projecting the point cloud locally on an Euclidean domain and then employ regular convolution. This type of methods can also be formulate as (\ref{convM}). For example, Tangent convolutions \cite{Tatarchenko_2018_CVPR} define kernels on the tangent plane, and use 2 principal directions of a local PCA as $\vec{u}_x$. Pointconv \cite{DBLP:journals/corr/abs-1811-07246} constructs local kernels by interpolation in $\mathbb{R}^3$, i.e. letting $\phi(x,v) = x-v $ which is essentially a local Euclidean convolution. Similarly, \cite{wang2018deep,thomas2019kpconv,mao2019interpolated} are also based on local Euclidean convolution. Their main difference are various forms of $k$ and interpolation methods.

\subsection{The Proposed Convolution: NPTC}

We propose a Narrow-Band Parallel Transport Convolution (\textbf{NPTC}) in this paper. It is a geometric convolution based on point cloud discretization of a manifold parallel transport defined in a specific way. As we discussed in the previous section, convolutions in many methods can be written in the form of (\ref{convM}) and (\ref{ufunction}), while the differences mostly lie in the choices of the vector field $\{\vec{u}^j\}_{j}$. As observed by \cite{DBLP:journals/corr/abs-1805-07857} that choosing the vector field properly, the associated convolution can be interpreted as transporting the kernels using the parallel transport associated with the prescribed vector field.

\begin{wrapfigure}{r}{0.5\textwidth}
  \centering

  \includegraphics[width=.8\linewidth]{main.jpg}
  \caption{Narrow-band parallel transport convolution on point cloud: black and blue points are sample points of the surface (blue). Each kernel (colored dots) is defined on the tangent plane (yellow). The vectors on the tangent planes that are of the same color are defined by parallel transport.}
\end{wrapfigure}

We attempt to define geometric convolutions that can be viewed as translating kernels on the point cloud in a parallel fashion. One naive approach to extend mesh based methods to point cloud is to generate a triangulated surface based on the point cloud. However, this is not as convenient as working directly with the point cloud since in practice not every point cloud corresponds to a legitimate parameterized surface and  pooling is not as easy to implement on triangulated surfaces as on point clouds. In addition, it is time-consuming to construct meshes on point clouds. When applied in practice, mesh construction time may be much longer than inference time of some methods directly applied on the point cloud. To avoid mesh constuction and to handle point clouds data directly, we propose to define convolution on point clouds by combining voxelization and geometric convolution.

Now, we describe how NPTC is computed on point clouds. Firstly, point clouds are approximated by voxel-based narrow-band with appropriate resolution. For a manifold $\mathcal{M}$ or point cloud $\mathcal{P}$, its narrow-band is defined as the region with distance from $\mathcal{M}$  (or $\mathcal{P}$) less than $\epsilon$: $\mathcal{NB}(\mathcal{M}):=\{x\in \mathbb{R}^3|dist(x,\mathcal{M})<\epsilon\}$, where $dist$ represents a distance function. For calculation efficiency and robustness to noise, we choose voxel-based narrow-band approximation. It should be noted that the result of the traditional voxelization method \cite{Wu_2015_CVPR} is a solid 3-d structure and the voxel-based narrow-band can be understood as the "shell" with a certain thickness of the former (if the point cloud is sampled from the surface of the object). It is very efficient to calculate the distance function in voxel approximation with appropriate resolution. The vector field $\{\vec{u}_x\}_{x\in \mathcal{M}}$ is defined as the projections of the gradient field of a narrow-band-based distance function on the approximated tangent plane of the point cloud. Such definition of the vector field is robust to noise, since if the distortion of the coordinates of the points by noise does not exceed the width of the narrow-band, the computed gradient field of the distance function in the narrow-band remains unchanged. After the vector field $\{\vec{u}_x\}_{x\in \mathcal{M}}$, we use local PCA to estimate normal vectors on the point cloud following \cite{Xu_2018_ECCV}. Finally, the convolution kernel can be constructed on the tangent plane and the corresponding $f(v)$ can be obtained by interpolating the value $f(x)$ on the point cloud.

Note that we prefer to use geometric convolution in NPTC because compared with methods that translate kernels in the ambient space of the manifold, NPTC translates kernels on the tangent planes which effectively avoids having convolution kernels defined away from the underlying manifold of the point cloud. In other words, NPTC can well reflect point cloud geometry and is a natural generalization of planar convolution in the sense that when the point cloud reduces to planar grids, the NPTC reduces to the planar convolution.

\subsection{Contributions}

\begin{itemize}
\item We introduce a new point cloud convolution, NPTC, based on parallel transport defined by a narrow-band approximation of the point cloud. The proposed NPTC is a natural generalization of planar convolution.

\item The proposed NPTC combines voxelization and geometric convolution. Voxelization with appropriate resolution brings robustness and geometric convolution can better reflect point cloud's geometry.

\item Based on NPTC, we designed convolutional neural networks, called NPTC-net, for point clouds classification and segmentation with state-of-the-art performance.
\end{itemize}

The rest of this paper is organized as follows. In section \ref{sec:background}, we will discuss the mathematical background of parallel transports on manifolds and the Eikonal equation for computing distance functions. In section \ref{sec:NPTC}, we propose the narrow-banded parallel transport convolution and it is associated with convolutional neural networks on point cloud represented manifolds. After that, we report our intensive numerical experiments of point clouds classification and segmentation on benchmark data sets in section \ref{sec:experiment}. We conclude the paper in \ref{sec:conclusion}.

\section{Background}
\label{sec:background}
\subsection{Manifold and Parallel Transport}

Let $\mathcal{M}$ be a two-dimensional differential manifold embedded in $\mathbb{R}^3$. We write  $\mathcal{T}_{x} \mathcal{M}$ as the two-dimensional tangent plane at point $x\in\mathcal{M}$. The disjoint union of the tangent planes at each point on the manifold defines the tangent bundle $\mathcal{T}\mathcal{M}$. A vector field $V$ is a smooth assignment: $\mathcal{M} \rightarrow \mathcal{T}\mathcal{M}$ such that $V(x)\in \mathcal{T}_x\mathcal{M}, \forall x \in \mathcal{M}$. The collection of all smooth vector fields is denoted as $\Gamma( \mathcal{T} \mathcal{M})$.

An affine connection is a bilinear mapping $\nabla$: $\Gamma( \mathcal{T} \mathcal{M}) \times \Gamma( \mathcal{T} \mathcal{M}) \rightarrow \Gamma( \mathcal{T} \mathcal{M})$, such that for all smooth functions $f$ and $g$ in $C^{\infty}(\mathcal{M})$ and all vector fields $U,V$ and $W$ on $M$:
\begin{equation}
\left\{
\begin{array}{l}
\nabla_{fU + gV} W = f \nabla_U W + g \nabla_V W, \\
\nabla_{U} (aV + bW) = a \nabla_U V + b \nabla_U W, \quad a, b \in \mathbb{R}, \\
\nabla_U (fV) = df(U)V+f\nabla_U V. \\
\end{array}
\right.
\end{equation}
A vector field $U$ is called \textbf{parallel} along a curve $\gamma:I\rightarrow \mathcal{M}$ if $\nabla_{\dot \gamma} U = 0$. Given an vector $\vec{e} \in \mathcal{T}_{x_0}\mathcal{M}$ at $x_0 = \gamma(0) \in \mathcal{M}$, the \textbf{parallel transport} of $\vec{e}$ along $\gamma$ is the extension of $\vec{e}$ to a parallel section $U$ on $\gamma$. More precisely, $U$ is the unique section of $\Gamma( \mathcal{T} \mathcal{M})$ along $\gamma$ satisfying the ordinary differential equation $\nabla _{\dot{\gamma}(t)}U(t) = 0$ with the initial value $U(0) = \vec{e}$.

In differential geometry, a geodesic on a smooth manifold $\mathcal{M}$ with an affine connection $\nabla$ is a curve $\gamma(t)$ such that parallel transport along the curve preserves the tangent vector to the curve. It is a generalization of the notion of a "straight line". Formally, a geodesic is $\gamma:[0,l]\rightarrow\mathcal{M}$ if $\nabla _{\dot{\gamma}(t)}\dot{\gamma}(t) = 0$. For any two points $x_0$ and $x_1$ on a closed manifold $\mathcal{M}$, there will be a geodesic connecting $x_0$ and $x_1$. More details on aforementioned concepts can be referred in \cite{kobayashi1969foundations}.

Alternatively, an affine connection can be defined by an assignment $\Xi$ as a family of linear transformations on tangent spaces along any smooth curve on $\mathcal{M}$. Consider $\gamma^y_x$ a smooth arc joining two, not necessarily distinct, points from $x$ to $y$, define $\Xi(\gamma^y_x):\mathcal{T}_x\mathcal{M} \rightarrow \mathcal{T}_y\mathcal{M}$.  If $\Xi(\gamma^y_x)$ satisfies the following properties:
\begin{itemize}
  \item [1)]
  $\Xi(\gamma^y_x)$ is non-singular,
  \item [2)]
  $\lim_{y\rightarrow x}\Xi(\gamma^y_x)=Id$,
  \item [3)]
  $\Xi(\gamma^z_x)=\Xi(\gamma^z_y)\Xi(\gamma^y_x)$,
  \item [4)]
  $\Xi$ is Frech\'{e}t differentiable in terms of $\gamma$, $x$ and $y$.
\end{itemize}
Then, the vector $\vec{v}_y$ is obtained by parallel displacement of $\vec{v}_x$ along $\gamma^y_x$ is provided as $\vec{v}_y=\Xi(\gamma^y_x)\vec{v}_x$. Condier $V$ is a tangent vector field along $\gamma$, the associated infinitesimal connection $\nabla_{\dot{\gamma}}V = \lim_{h\rightarrow 0}\frac{1}{h} (\Xi(\gamma^{\gamma(0)}_{\gamma(h)})V_{\gamma(h)} - V_{\gamma(0)})$ can be induced from $\Xi$ \cite{knebelman1951spaces}.

For convenience, instead of transporting the kernel on the manifold, we can locally construct the parallel transported kernel at every point $x$ by formulating $k$ as $k(\phi(x,\cdot))$, where $\phi(x,v)$ is defined in (\ref{ufunction}). It is known in differential geometry that transporting the kernel to every point on the manifold is the same as locally reconstructing the kernel in the aforementioned way.

\subsection{Solving Eikonal Eqution on points clouds}

As mentioned before, the proposed NPTC relies on the computation of a distance function on a voxel-based narrow-band approximation of the given point cloud. Therefore, we briefly review what a narrow-band approximation is and how the distance function is calculated.

Geometric attributes calculation of large unstructured point-based data sets is a challenging task. Hence, the most common way of
dealing with unstructured point-based data is to re-sample to a structured grid \cite{franke1991scattered}. A large variety of well-known methods like isosurface extraction \cite{lorensen1987marching},
region-growing methods \cite{hojjatoleslami1998region}, and level-set methods \cite{osher1988fronts,osher2006level} can be applied to gridded data. The original idea of level sets is to implicitly represent a surface as the solution of an equation concerning an underlying scalar field. To improve computation efficiency of level-set methods, local level-set method was introduced \cite{adalsteinsson1995fast} which essentially suggests conduct computations only within a narrow-band of the zero level set.

Different methods are proposed to obtain narrow-band representation of shapes in 2D \cite{jiang2012improve, bindu2014fast} or 3D \cite{rosenthal2008smooth, rosenthal2011narrow}. In this work, the narrow-band of a point cloud $\mathcal{P}$ is defined as the region with distance from $\mathcal{P}$ less than $\epsilon$: $\mathcal{NB}(\mathcal{P}):=\{x\in \mathbb{R}^3|dist(x,\mathcal{P})<\epsilon\}$, where $dist$ represents the standard Euclidean distance function.

We follow the previous work and consider the gridded narrow-band. Consider the point cloud with $N$ points in the unit cube and divide the unit cube into $M^3$ small cubes. The side length of each small cube is $\frac{1}{m}$. The set of all small cubes whose distance from the point cloud is less than $\epsilon$ form the voxel-based narrow-band approximation of the point cloud. The number of grid points in traditional voxelization is $M^3$, and the number of grid points in the voxel-based narrow-band for a smooth surface is $O(kM^2)$, and $k$ depends on the thickness of the narrow-band.

Distance functions can be easily computed by solving the Eikonal equation \cite{eldar1997farthest}. The Eikonal equation is a non-linear partial differential equation describing wave propagation:
\begin{equation} \label{Eikonal}
|\nabla \rho| = 1/h(x), \quad x\in\Omega,\quad \rho|_{\Lambda} = 0,
\end{equation}
where $\Lambda\subset\overline{\Omega}\subset\mathbb{R}^n$ and $h(x)$ is a strictly positive function. The solution $\rho(x)$ of (\ref{Eikonal}) can be viewed as the shortest time needed to travel from $x$ to $\Lambda$ with $h(x)$ being the speed of the wave at $x$. For the special case when $h=1$, the solution $\rho(x)$ represents the distance from $x$ to $\Lambda$ limited in the $\Omega$. The Eikonal equation can be solved by the fast marching method \cite{sethian1996fast} or the fast sweeping method \cite{zhao2005fast}. Both methods have an optimal computation complexity of $O(L)$ with $L$ being the number of the grid points. Naturally, when $N\gg M^2$, solving the equation in the voxel-based narrow-band will be faster than calculating directly on the point cloud or the classical voxelization. In fact, in practical applications, such as LiDAR data, the number of points in a point cloud is often far greater than the resolution required to adequately represent the shape of interest.

\section{Narrow-band Parallel Transport Convolution (NPTC) and Network Design}
\label{sec:NPTC}
Generalization of convolution defined by parallel transport on triangulated surfaces has already been proposed in \cite{DBLP:journals/corr/abs-1805-07857}. In this section, we discuss how to transport kernels on point clouds in a similar fashion.

\subsection{Narrow-band Parallel Transport Convolution (NPTC)}

For a given function $f:\mathcal{P}\to\mathbb{R}$, the NPTC of $f$ with kernel $k$ takes the same form as (\ref{convM}). Under such formulation, the key to design a convolution is to design vector fields $\{\vec{u}^j\}_{j=1,2}$. In this subsection, we discuss the general idea of NPTC and the interpretation of it in terms of parallel transport.

\subsubsection{General Idea of NPTC}

To select a suitable vector field, we first recall the choice of the vector fields of PTC which defines convolution on triangulated surfaces via parallel transport with respect to the Levi-Civita connection \cite{DBLP:journals/corr/abs-1805-07857}. Geodesic curve represents the shortest path between two points on a Riemannian manifold. Given a geodesic connecting two points $x$ and $y$, the tangential direction at $x$ corresponds to the ascent direction of geodesic distance from $y$. PTC chooses such direction as $\vec{u}_x^1$ and defines $\vec{u}_x^2=\vec{u}_x^1\times \vec{n}_x$ where $\vec{n}_x$ is the normal vector at $x$.

To construct a vector field on a point cloud, we also consider to use gradient of a distance function on the point cloud. However, unlike triangulated surfaces, distance function is not easily defined on point clouds due to the lack of connectivity. It is then natural to approximate the point cloud with another data structure with connectivity, so that distance function can be easily calculated. For convenience and efficiency, we use voxelization \cite{Wu_2015_CVPR} to approximate the point cloud in a narrow-band in $\mathbb{R}^3$ covering the point cloud. We denote such distance function as $\rho:\mathbb{R}^3\to\mathbb{R}^+$. We will elaborate how $\rho$ can be calculated in later parts of this subsection.

Note that if the point cloud is sampled from a plane, the narrow-band is flat as well. Then, by a proper choice of the distance function, the vector fields $\{\vec{u}^j\}_{j=1,2}$, can be reduced to the global coordinate $\{\vec{e}_j\}, j=1,2$ on the plane. This means that NPTC is reduced to the traditional planar convolution.

Once the distance function $\rho$ is computed, we choose $\vec{u}_x^1=\nabla_{\mathcal{P}}\rho(x)$, where $\nabla_{\mathcal{P}}\rho(x)$ is a projection of $\nabla \rho(x)$ on an approximated tangent plane at $x$. Then, $\vec{u}_x^2$ can be calculated by the outer product $\vec{u}_x^2=\vec{u}_x^1\times \vec{n}_x$ with $\vec{n}_x$ the normal vector at $x$. The value $f(v)$ is computed by nearest-neighbor interpolation \cite{grevera1998objective}, i.e., $f(v)=f(z)$ where $z\in\mathcal{P}$ is the closest point to $v$. Note that one may use a more sophisticated method to compute $f(v)$ rather than using nearest-neighbor interpolation. We choose nearest-neighbor interpolation because of its simplicity.

\begin{figure}[h!]
\begin{center}
\includegraphics[width=360pt]{demo.jpg}
\end{center}
\caption{Illustration of a point cloud $\mathcal{P}$ sampled from the unit sphere. (A) shows the narrow-band approximation (blue boxes) of part of $\mathcal{P}$ (in red). (B) is a cross section of (A). (C), (D) show the distance function $\rho$ and vector field $\{\vec{u}_x^1\}$ ($\{ \nabla_{\mathcal{P}}\rho(x) \}$) on the point cloud. We can see that distance propagates from the bottom center to the top center reflecting the geometry of the sphere.}\label{demo}
\end{figure}

\subsubsection{Computing Distance Function on Point Clouds}

A point cloud is entirely discrete without inherent connectivity. Therefore, it is not straightforward to compute the distance function on point clouds although the local mesh method~\cite{lai2013local} can be applied to solve the Eikonal equation. For simplicity, we use voxels to approximate point clouds and to compute distance functions on the voxels using the well-known fast marching method based on regular grid provided by the voxelization~\cite{sethian1996fast}. Note that, using voxels to compute distance functions is fast and robust to noise and local deformations.

The solution $\rho(x)$ of the Eikonal equation $|\nabla \rho(x)| = 1$ presents the distance form $\Lambda$ to $x$ limited inside the narrow-band. Here $\Lambda$ is chosen as a certain point on the point cloud. We note that the starting point will become a singularity, but on non-parallelizable manifolds such as the sphere, it is not possible to construct a smooth vector field \cite{helgason1979differential}. So that the vector field must have at least one a singularity. Although generating multiple vector fields by selecting different starting points is helpful to eliminate singularities, experiments show that directly selecting one point already provides satisfactory results. Also, the ensemble result of several vector fields has almost no improvement compared with each of a single vector field.

Finally, we interpolate the distance function from the voxels to the point cloud.

\subsubsection{Computing the Vector Fields on Point Clouds}

We first compute the tangent plane on each point. Tangent planes are important features of manifolds and have been well-studied in the literature \cite{lai2013local}. In this paper, we use local principal component analysis (LPCA) to estimate the tangent plane. We estimate the local linear structure near $x\in\mathcal{P}$ using the covariance matrix $$\sum_{x_k \in \mathcal{N}(x)} (x_k-c)^\top (x_k-c),\quad c=\frac{1}{k}\sum_{x_k \in \mathcal{N}(x)} x_k,$$ where $\mathcal{N}(x)$ is the set of neighboring points of $x$. The eigenvectors of the covariance matrix form an orthogonal basis. If the point cloud is sampled from a two dimensional manifold, and the local sampling is dense enough to resolve local features, the eigenvectors corresponding to the largest two eigenvalues provide the two orthogonal directions of the tangent plane, and the remaining vector represents the normal direction at $x\in\mathcal{P}$. Here, we denote the space spanned by the two eigenvectors of the covariance matrix at $x$ as $\mathcal{T}_{x}\mathcal{P}\subset\mathbb{R}^3$.

With the computed distance function $\rho(x)$, it is nature to define the vector field by projecting $\nabla  \rho$ on the approximated tangent planes of the point cloud. Given a point $x_k\in\mathcal{P}$ close enough to $x\in\mathcal{P}$, we have $$\left< \nabla \rho , x_k-x \right> \approx \rho(x_k) - \rho(x),$$ where $\rho(x_k)$ and $\rho(x)$ are known. If we consider $k$-nearest neighbors of $x$, we have $k-1$ equations with 3 unknowns that are the three components of $\nabla \rho$. We can use least squares to find $\nabla \rho$. We then project the vector $\nabla \rho(x)$ onto the tangent plane at $x$. We denote the projected vector $\nabla_{\mathcal{P}}\rho$, which is the vector we eventually need to define NPTC as described in Section 3.1.1. Denote the calculated vector field as $\{\vec{u}^j\}_{j=1,2}$, and the starting point of the distance function as $x_0$. For any point $x$, let $\gamma_{x_0}^x$ be the integral curve of $\nabla\rho$  connecting $x$ and $x_0$. Define a linear transformation $\Xi(\gamma_{x_0}^x)$ satisfying $\vec{u}^j_x=\Xi(C_{x_0}^x)\vec{u}^j_{x_0},j=1,2$. As mentioned in the section \ref{sec:background}, defining linear transformations on any arc satisfying specific properties means defining connection and parallel transport. In applications, we do not need linear transformations along all arcs. NPTC is defining linear transformations on integral curves of $\nabla\rho$ (as approximated geodesic curves).

However, the quality of vector fields is affected by the quality of the estimated normal directions. On natural concerns is how such estimation is affected by noise. We remark that LPCA is in fact quite robust to noise. In the section of experiments, we will observe that our method can still achieve satisfactory results on the real-world scanned dataset with noise.

\subsection{NPTC-net: Architecture Design for Classification and Segmentation}

This section, we present how to use NPTC to design convolutional neural networks on point clouds for classification and segmentation tasks. For that, other than the NPTC, we need to define some other operations that are frequently used in neural networks.

\begin{figure}[h!]
  \centering

  \includegraphics[width=.8\linewidth]{net3.JPG}
  \caption{Architecture of NPTC-net. The network for segmentation tasks is at the top right with
encoding and the network for classification tasks is at the bottom right with encoding.}
  \label{arch}
\end{figure}

\textbf{Down-sampling:} Given the input point cloud $\mathcal{P}$, we use farthest point sampling \cite{eldar1997farthest} to generate a sequence $\{\mathcal{P}^{(i)}\}$, where $\mathcal{P}^{(0)}=\mathcal{P}$ and $\mathcal{P}^{(i)} \subset \mathcal{P}^{(i-1)}$ represents the $i$-th hierarchical structure of $\mathcal{P}$. The down-sampled set of $\mathcal{P}^{(i)}$ is $\mathcal{P}^{(i+1)}$ and up-sampled set is $\mathcal{P}^{(i-1)}$ during convolution layer. In $k$-th layer, feature maps is denoted as $\mathcal{F}^{k}\in \mathbb{R}^{N^k\times c^k}$, where $N^k$ is the number of the points and $c^k$ is the number of channels at layer $k$.

\textbf{Multi-Layer Perceptron(MLP):} Given the feature maps $\mathcal{F}$, $\text{MLP}(\mathcal{F})$ is defined as $\sigma(...\sigma(\mathcal{F}W^1)W^2...)W^L)$, where $\sigma$ is a non-linear function and $W^i$ is a matrix.

\textbf{Concatenate:} Given the feature maps $\mathcal{F}_1\in \mathbb{R}^{N\times c_1}$ and $\mathcal{F}_2\in \mathbb{R}^{N\times c_1}$, concatenate of $\mathcal{F}_1$ and $\mathcal{F}_2$ is defined as $\mathcal{F}_3=[\mathcal{F}_1;\mathcal{F}_2]\in\mathbb{R}^{N\times (c_1+c_2)}$.

\textbf{Global max pooling:} Given the feature maps $\mathcal{F}\in \mathbb{R}^{N\times c}$, $\hat{f} \in \mathbb{R}^c$ is the global max pooling of $\mathcal{F}$ satisfying $\hat{f}_j=\max\limits_{i}{\mathcal{F}_{ij}}$.

\textbf{Convolution layer:} Our $k$-th convolution layer takes points $\mathcal{P}^k \in \mathbb{R}^{N^k\times 3}$ and their corresponding feature maps $\mathcal{F}^{k}\in \mathbb{R}^{N^k\times c^k}$ as input. The corresponding output is $\mathcal{F}^{k+1}\in \mathbb{R}^{N^{k+1}\times c^{k+1}}$ living on the points $\mathcal{P}^{k+1} \in \mathbb{R}^{N^{k+1}\times 3}$. The NPTC-net have encoding and decoding stages. Normally, $N^{k+1} \leq N^k$ during encoding and $N^k \geq N^{k+1}$ during decoding. Convolution at the $k$-th layer during encoding is only performed on the point set $\mathcal{P}^{k+1}$, which resembles convolution with stride $\geq 1$ for planar convolutions.

\textbf{Residual block:} One residual block takes the feature maps $\mathcal{F}\in \mathbb{R}^{N\times c}$ on the point set $\mathcal{P} \in \mathbb{R}^{N\times 3}$ as input and same number of points and same number of channels of features as output. One residual layer consists of three components: MLP from $c$ channels to $\frac{c}{2}$ channels, convolution layer from $\frac{c}{2}$ channels to $\frac{c}{2}$ channels, MLP from $\frac{c}{2}$ channels to $c$ channels plus the feature maps from the bypass connection. A residual block consists of several residual layers.

\textbf{NPTC-net} consists of the aforementioned operations and its architecture is given by Figure \ref{arch}. The left half of the NPTC-net is the encoder part of the network for feature extraction. For classification, features at the bottom of the network are directly attached to a classification network; while for segmentation, features are decoded using the right half of the NPTC-net (decoder part of the network) to output the segmentation map. Each rectangle represents a feature map, the height of rectangle represents the number of points, and the width represents the number of channels.

\section{Experiments}
\label{sec:experiment}
In order to evaluate our new NPTC-nets, we conduct experiments on three widely used 3D datasets, ModelNet \cite{Wu_2015_CVPR}, ShapeNet Part \cite{Yi:2016:SAF:2980179.2980238}, S3DIS \cite{7780539}. ModelNet40 and ShapeNet Part contain point clouds generated from CAD models, S3DIS is a real-world scanned dataset. ModelNet40 is for 3D shape classification, and ShapeNet Part and S3DIS are for segmentation (or partition) of 3D shapes.

\subsection{Implementation Details}

We implement the model with Tensorflow\cite{abadi2016tensorflow}. The neural network is denoted as $f_\theta$, where $\theta$ corresponds to the trainable parameters of the network. Given labeled data pair $\{P_i,y_i\}$, where $P_i$ corresponds to the point cloud data (i.e. the collection of 3D points of a given geometric object) and $y_i$ is its associated label, the learning tasks is formulated as the optimization problem $\min\limits_{\theta} \sum_i L(f_\theta(P_i),y_i)$ for some loss function $L$. For classification, $L$ is the cross-entropy loss: $L(f_i, y_i)=-\sum_{i=1}^{c} y_{ij}log(f_{ij})$, where $y_{ij}$ corresponds to the $j$-th element of one-hot encoded label of $y_i$ and $f_{ij}$ denotes the $j$-th element of $f_i$. Segmentation task can be viewed as point-wise classification task with cross-entropy loss.

We using SGD optimizer \cite{robbins1951stochastic} with an initial learning rate $0.1$ for ModelNet40 and ADAM optimizer \cite{DBLP:journals/corr/KingmaB14} with an initial learning rate $0.002$ for ShapeNet Part and S3DIS on a GTX TITAN Xp GPU.

To avoid over-fitting, data augmentation \cite{shorten2019survey} is used during training. Data augmentation is to generate more data pairs $\{\mathcal{A}_j(P_i),y_i\}$, where $\mathcal{A}_j$ corresponds to some transformation without changing the label. For the classification task, the data is augmented by random rotation, scaling and Gaussian perturbation on the coordinates of the points. For segmentation, the data is augmented by scaling and Gaussian perturbation on the coordinates of the points.

We do inferences on the augmented data during testing and aggregate the results by voting following \cite{NIPS2017_7095}. Let $\hat{y}_{ij} = f_\theta(\mathcal{A}_j(P_i))$ denote the probability distribution of the predict on $\mathcal{A}_j(P_i)$. The aggregated inference result of $P_i$ is $\hat{y}_i = \frac{1}{n_a}\sum_{j=1}^{n_a} \hat{y}_{ij}$.

Following \cite{NIPS2018_7362}, the data of S3DIS is firstly split by room, and then the rooms are sliced into $1.5m$ by $1.5m$ blocks, with $0.3m$ padding on each side. The points in the padding areas serve as context of the internal points, and themselves are not linked to loss in the training phase, nor used for prediction in the testing phase. Each block will be viewed as a point cloud during training and testing.

\subsection{3D Shape Classification and Segmentation}

We test the NPTC-net on ModelNet40 for classification tasks. ModelNet40 contains 12,311 CAD models from 40 categories with 9,842 samples for training and 2,468 samples for testing. For comparison, we use the data provided by \cite{Qi_2017_CVPR} sampling 2,048 points uniformly and computing the normal vectors from the mesh. As shown on Tabel \ref{all}, our networks outperform other state-of-art methods. (If a compared method has results on both 2048 (or 1024) and 5000 points, we only compare with the former.).

We also evaluate the NPTC-net on ShapeNet Part for segmentation tasks. It contains 16,680 models from 16 shape categories with 14,006 for training and 2,874 for testing, each annotated with 2 to 6 parts and there are 50 different parts in total. We follow the experiment setup of previous works, putting object category into networks as known information. We use point intersection-over-union (IoU) to evaluate our NPTC-net. Table \ref{all} shows that our model ranks second on this dataset and is fairly close to the best known result.

\begin{table}[h]
  \caption{Comparisons of overall accuracy (OA) and mean per-class accuracy (mA) on ModelNet40 as well as comparisons in instance average IoU (mIoU) and class average IoU (mcIoU) on ShapeNet Part. Models ranking first is colored in red and second in blue.}
  \label{all}
  \centering
  \begin{tabular}{ccccc}
\hline
   &\multicolumn{2}{c}{Modelnet40} &\multicolumn{2}{c}{ShapeNet part}\\
\hline

    Method  & OA(\%)    &mA(\%)    &  mIoU     & mcIoU  \\
    \hline
kd-net \cite{8237361} & 91.8& \textcolor{blue}{88.5} & 82.3 & 77.4 \\

pointnet \cite{Qi_2017_CVPR} & 89.2& 86.2& 83.7 & 80.4\\
SO-Net \cite{Li_2018_CVPR} & 90.9& 87.3& 84.9 & 81.0\\
pointnet++ \cite{NIPS2017_7095} & 90.7&  -& 85.1 &81.9\\

SpecGCN \cite{Wang_2018_ECCV} & 92.1 &- & 85.4& -\\
SpiderCNN \cite{Xu_2018_ECCV} & \textcolor{blue}{92.4}& -& 85.3 & 81.7\\
pointcnn \cite{NIPS2018_7362} & 92.2& 88.1&\textcolor{red}{ 86.1 }& \textcolor{red}{84.6}\\
Ours & \textcolor{red}{92.7}  & \textcolor{red}{ 90.2} & \textcolor{blue}{85.8} & \textcolor{blue}{83.3} \\
    \hline
  \end{tabular}
\end{table}

\subsection{3D scene semantics segmentation}
As pointed out earlier, using voxelization can bring rubustness to the definition of geometric convolution.
In order to show the robustness of our model for real data, we tested scene semantics segmentation on the "Stanford Large-Scale 3D Indoor Spaces Dataset" (S3DIS). S3DIS covers six large-scale indoor areas from 3 different buildings for a total of 273 million points annotated with 13 classes. This is a real-world scanned dataset without normal and with noise. Following \cite{8374608}, we advocate the use of Area-5 as test scene to better measure the generalization ability of our method. Table \ref{S3DIS} shows that geometric convolution methods are close to the methods which do not need normal estimation. NPTC-net outperforms the best known geometric convolution method TangentConv\cite{Xu_2018_ECCV}.

\begin{table}[h]
  \caption{Comparisons of overall accuracy (OA) and mean per-class IoU (mIoU) on S3DIS. Models ranking first is colored in red and second in blue.}
  \label{S3DIS}
  \centering
  \begin{tabular}{cccc}
    \hline
Convolution Type & Method   &  OA(\%)  &   mIoU(\%) \\
   \hline
no convolution & pointnet \cite{Qi_2017_CVPR} &78.8 & 41.3 \\
   \hline
\multirow{3}{1.2in}{\ $3$-d convolution} & SegCloud \cite{8374608}&- & 48.9 \\
 & Eff3DConv\cite{zhang2018efficient} & 69.3 & 51.8 \\

 &ParamConv\cite{wang2018deep} & - & \textcolor{red}{58.3} \\
 \hline
\multirow{2}{1.6in}{geometric convolution}  & TangentConv \cite{Xu_2018_ECCV}&\textcolor{blue}{82.5} & 52.8  \\
 & Ours& \textcolor{red} {83.7} & \textcolor{blue}{54.0}\\
\hline
  \end{tabular}
\end{table}

\subsection{Visualization of Segmentation}
To better demonstrate the advantages of our model, we visualize the segmentation results of the test data in the ShapeNet Part. We compare NPTC-net with the popular model Pointnet++ \cite{NIPS2017_7095} and select several representative examples. In Figures \ref{seg_out} and \ref{seg_tiny}, three columns from left to right represent the ground truth, the prediction by pointnet++, and the prediction by NPTC-net. Each blue box of Figure \ref{seg_out} contains objects within the same class (handgun, chair and skateboard). In each blue box, the first row contains objects with standard structures while the second row contains objects with certain unusual structures which is highlighted by red boxes. In each blue box of Figure \ref{seg_tiny}, the first row contains the overviews of the objects while second row contains the corresponding zoom-in views.

As shown in Figure \ref{seg_out}, we found that both models can make good predictions on objects with standard structures. However, if the object contains some unusual structures, such as abnormal shape, tilt, or asymmetry, NPTC-net is able to generate smoother results that respect geometric information.

\begin{figure}[h!]
  \centering

  \includegraphics[width=360pt]{seg_out.JPG}
  \caption{Comparison of segmentation results of unusual structures: three columns from left to right represent the label, the prediction of pointnet++, and the prediction of NPTC-net.}
\label{seg_out}
\end{figure}

\begin{figure}[h!]
  \centering

  \includegraphics[width=360pt]{seg_tiny.JPG}
  \caption{Comparison of segmentation results of small structures: three columns from left to right represent the label, the prediction of pointnet++, and the prediction of NPTC-net.}
\label{seg_tiny}
\end{figure}

\subsection{Running Statistics}

\begin{table}[h!]
  \caption{Comparisons of number of parameters and FLOPs for classification.}
  \label{par}
  \centering
  \begin{tabular}{ccc}
    \hline
method  &  Parameters  &  FLOPs(Inference)\\
\hline
pointnet \cite{Qi_2017_CVPR}& 3.48M& 14.70B\\
pointnet++ \cite{NIPS2017_7095}&1.48M & 26.94B\\
pointcnn \cite{NIPS2018_7362}& \textbf{0.6M}& 25.30B\\
 Ours & 1.29M & \textbf{11.7B}\\
    \hline
  \end{tabular}
\end{table}

\begin{table}[h!]
  \caption{Comparisons of training time of networks on ModelNet40}
  \label{time}
  \centering
  \begin{tabular}{ccc}
\hline
Method & Settings & \makecell[c]{Accuracy and Training \\(+Pre-processing) time} \\
\hline
\multirow{2}{2.5cm}{PointNet++ \cite{NIPS2017_7095}} & adam, 1024 points & 90.7\%, 6 hours \\
                   & adam, 5000 points & 91.9\%, 20 hours \\
\hline
\multirow{2}{2.5cm}{ours} & adam, 2048 points & 92.4\%, 6h (+1.5h) \\
                   & SGD, 2048 points & 92.7\%, 12h (+1.5h) \\
\hline
  \end{tabular}
\end{table}

As shown in Table \ref{par}, we summarize our running statistics based with model for ModelNet40 with batch size 16. In comparison with several other methods, although we use ResNet structure, the fewer channels, smaller kernels and simpler interpolation (nearest neighboring) make NPTC use similiar parameters and even fewer FLOPs.

Total pre-processing time mainly depends on the grid's density. For most cases, the resolution of $100^3$ is enough to describe the shape of the point cloud, which is what we chose for our experiments. We also remark that the whole computation cost of constructing convolution is linearly depending on the resolution of voxel and the size of data. On a PC with Core i7-7700 CPU, the pre-processing takes about \textbf{0.5} seconds per point cloud with Matlab, and the whole dataset of ModelNet40 (12,311 shapes with 10,000 points each) takes only about 1.5 hours. It is negligible compared to the training of the deep neural networks and acceptable to do inference in practice.

\subsection{Feature Visualization}

To visualize the effects of the proposed NPTC in the NPTC-net, we trained the network on ShapeNet Part and visualize learned features by coloring the points according to their level of activation. In Figure \ref{vist}, filters from the the first Convolution layer in the the first Residual block and final Convolution layer in the second Residual block are chosen. In order to easily compare the features at different levels, we interpolate them on the input point cloud. Observe that low-level features mostly represent simple structures like edges (top of (A)) and planes (bottom of (A)) with low variation in their magnitudes. In deeper layers, features are richer and more distinct from each other, like bottleneck (upper left of (B)), "big-head"(upper right of (B)), plane base (lower left of (B)), bulge (lower right of (B)).

\begin{figure}[h!]
  \centering

  \includegraphics[width=360pt]{feature.jpg}
  \caption{Feature Visualization: each feature from low (A) or high (B) level is displayed on 2 point clouds from different categories. High-level activations are in yellow and low-level activations are in blue.}
\label{vist}
\end{figure}

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

This paper proposed a new way of defining convolution on point clouds, called the narrow-band parallel transport convolution (NPTC), based on a point cloud discretization of a manifold parallel transport. The parallel transport was defined specifically by a vector field generated by the gradient field of a distance function on a narrow-band approximation of the point cloud. The NPTC was used to design a convolutional neural network (NPTC-net) for point cloud classification and segmentation. Comparisons with state-of-the-art methods indicated that the proposed NPTC-net is competitive with the best existing methods.

\bibliographystyle{siamplain}

redpajama_set_name: RedPajamaArXiv


Toniic is a angel network for global impact investment headquartered in San Francisco. This 32 page pdf outlines usage of standardized impact measurement metrics (i.e. traditional financial performance plus additional measures of positive "impact"), such as those published by Impact Reporting and Investment Standards (IRIS) or Global Impact Investing Rating System (GIIRS).


Relatively easy access to credit and rising costs make most students extremely susceptible to overspending. This lighthearted, yet cautionary tale is the story of a young shopaholic whose spending habits are out of control due to her twelve "magic" credit cards. A sample sale mob scene for discounted high-end merchandise serves as a useful introduction into the basics of supply and demand as well as establishing the economic concepts of shortages, surpluses and a consumer's reservation price. Brand-conscious students will also be able to distinguish between the film's variety of normal, inferior and luxury goods and note the diversity in consumers' tastes and preferences. Meanwhile, savvier economic students will pick up on the marginal analysis that occurs with each purchasing decision. Note: This is a great discussion starter on student debt or financial literacy education (resources at mymoney.gov).


Easy Lasagna Soup is an easy cheesy and comforting soup that tastes like lasagna in a bowl, and can be on the table in 30 minutes.
You are going to be so happy today! Why? Because you are going to love this comforting Easy Lasagna Soup. It does not have to simmer for hours. You do not have to dirty a dozen pans to make it. You just get to sit down and enjoy a big bowl of comforting cheesy Easy Lasagna Soup.
You can tell that I love easy versions of lasagna, like my Cheesy Tortellini Skillet Lasagna and my Jalapeño Popper Lasagna Rolls and my Creamy Cajun Chicken Lasagna. However, my favorite is this Easy Lasagna Soup.
Granted, it does not get nearly as cold down here in Southeast Texas as it does up north.
However, our winters can still get cold and rainy and chill you to the bone. OK. I'm a winter wimp to you Canooks and Yankees. When the temperature drops down to the 40's with a cold rain, that is cold to me. In this type of weather nothing is better than a bowl of Lasagna Soup.
You think it would take a long time to prepare and cook, but I made this Easy Lasagna Soup in just 30 minutes. I was amazed at how quickly it came together and how amazing it tasted. It required no layering, no baking in the oven, just cook everything in one pot and then enjoy.
Use your favorite homemade or store-bought marinara sauce to add flavor to this soup and good quality pasta and cheeses.
Watch the video to see how easy it is to make this Easy Lasagna Soup.
Before printing out the Easy Lasagna Soup recipe below, check out these other incredible Italian inspired recipes.
If you like this recipe try some of my other Italian recipes, such as my Italiano Chicken, which is like a lasagna on top of chicken.
If you like this Easy Lasagna Soup, be sure to try the Tortellini Lasagna that can be ready in 30 minutes.
An easy cheesy and comforting Italian lasagna soup that tastes like your favorite lasagna.
Boil the broken lasagna noodles per package directions.
In a dutch oven cook the ground beef until browned and no longer pink.
Stir in the minced garlic and cook for about 1 minute.
Pour in the diced tomatoes and their liquid. Simmer for about 2 minutes.
Pour in the marinara sauce and beef broth.
Stir in the Italian seasoning.
Stir in the drained cooked pasta.
Let the soup simmer for about 5 minutes.
About 5 minutes before serving spoon in the ricotta cheese, and top with shredded Italian style cheese.
Cover for about 3 - 5 minutes to let cheese melt. Serve hot.
If you like this recipe, you may also like these recipes.
From experience, this is the best soup I have ever made..
Instead of marinara, I used sauce with sausage in and would not do that again. I added an onion, crushed red pepper and frozen meatballs- yum! Next time I'll add green pepper and mushroom. Served with garlic bread, wow.
Hi, thanks for coming back and letting me know what worked and what didn't. I think the addition of green pepper and mushrooms sounds delicious.


A pre-prohibition classic given a Scottish spin, with our indulgent Raspberry Liqueur, made exclusively with raspberries from Perthshire.
Shake all ingredients with ice, then dry shake.
Double-strain into a coupe glass.


ah valentine's day. definitely not my favorite. my lukewarm feelings about this holiday go wayyyy back — i'm just not a big fan of the lovey-dovey, rose & chocolate filled mush nor the pressure and expense of a forced declaration of love. however, when i was growing up, my mother always gave the three of us a little "treat" the morning of February 14th before we ran off to school, and the tradition is certainly one I can get behind. this little heart shaped bag could prove to be my perfect v-day treat. its cheeky and cheery, and the perfect accent piece for my mid winter "all black outfit" schlump.
other little valentines day pick me up ideas? i love the idea of….
something simple. a simple cuff or bracelet, especially engraved with something sweet inside! we gave my boyfriends mother this one for Christmas and i've been secretly eyeing it for myself.
something practical. monogrammed pouches. these ear bud organizers indulge my every OCD whim, but it's the kind of thing i wouldn't usually buy for myself (thus making it the perfect treat). i also love this similar looking smythson makeup bag.
something feminine. a classic scent in a beautiful bottle for your dresser is the ultimate ladylike gift. i discovered Jo Malone's peony & blush suede perfume as a gift last Christmas, but a new bottle never hurts ;).
something easy. pajamas are one of my go to favorite gifts, regardless of the season. i love sleeping in something new and cute and this romper is not only that, but also easy to throw on and pair with a robe during chillier months.
something sweet. but not to eat! with dry winter lips, this agave lip scrub is calling out for an invite to my next pampering sesh.
something furry. given that it is February after all, a cuddly scarf is just the thing to fend of winter chill. i love this rainbow version (in sort of Fendi-ish stripes) as a fun way to spice up your same ole boring coat.
something yummy! if you are going to indulge in an edible treat, these cookies (from one of my favorite bakeries in the city) are too good to pass up. and now, new yorker or not, momofuku milk bar is now coming to you, with a new line of baking mixes all available at target- including the classic crack pie, birthday truffles and always delicious compost cookies.


This art piece addresses the ultimate futility of it all… the ennui that lurks beneath the surface of our everyday lives. If you know what we're talking about, you should purchase this sticker and seek help… in that order. Submitted by Stephen Whalen.


The Tata Tiago is the latest entry level mid-range compact hatchback by Tata Motors which competes with Maruti Suzuki Celerio, Hyundai Grand i10 and Honda Brio.
Tata has introduced a new, innovative & fresh design language with cutting edge driving dynamics in their latest hatchback.
The Tiago comes with the next generation IMPACT design language, which not only looks elegant but also sporty, confident and has an agile stance.
Tata is using terms like "Exciting", "Expressive" and "Extraordinary" to refer to the Exterior and terms like "Inviting", "Intelligent" and "Intouch" for the Interiors, under the new philosophy.
signature hexagon grille leaving a lasting IMPACT effortlessly.
The next generation sharp and sculpted design language makes Tiago stand-out in the crowd.
A layered design theme coupled with a dual tone interior, customizable air vents and premium graphics on the fabric.
The height adjustable driver?s seat and headrest gives a perfect driving position for a comfortable journey.
The body hugging seat bolster is crafted with utmost precision for a premium feel and exemplified elegance.
The geometric texture accented with chrome handles and premium knitted fabrics add to the high quality look and feel.
Multi storage spaces intelligently designed to maximize utility and provide easy accessibility. All controls are within reach.
The Tiago is engineered to give you a power-packed performance under all conditions. The powerful Revotron 1.2L (petrol) engine delivers a power and torque of 85 PS and 114 Nm at 3500 RPM. The contemporary 3-cylinder engine comes with two different driving modes - Eco & City; giving you the power of two engines in one! And the Revotorq 1.3L (diesel) engine delivers optimum power of 70 PS with the highest torque in the segment - 140 Nm at 3000 RPM, delivering an exceptional driving experience. The Tiago is equipped with an advanced dual path suspension that improves its handling and delivers a comfortable and smooth driving experience. The Electric Power Assist Steering (EPAS) system in the Tiago eases your efforts in negotiating tight spots and enables you to maintain control at all times. The Tata Tiago is also equipped with a gear shift recommendation feature that enables you to experience a smooth and efficient drive.
The Tiago comes loaded with advanced safety features that empower you to drive confidently. It is equipped with dual front air bags, advanced ABS with EBD and Corner Stability Control, and rear parking sensors. The infotainment system?s display even offer's a visual image off the car's proximity to any object. The body structure of the Tiago is very strong and can absorb impact energy, thus offering superior safety.
As far as comfort and convenience is concerned, the Tiago offers unparalleled comfort with 22 intelligently designed utility spaces that take care of all your needs, height adjustable driver's seat, the scooped out front seats provide more leg room, thus maximizing comfort and the Multi-information Display instrument cluster offers access to a host of useful information about the Tiago. It also empowers you to control a variety of in-car features.
But the NVH is poorly built, rear headroom is tight and Tata's service quality is also not up to the mark.
The Tiago is an excellent option for car buyers in the "compact hatchback" segment.
It is the combination of Good Looks, Quality Interiors and a Respectable Ride and Handling.
The Tiago competes with Maruti Celerio, Hyundai Grand i10 and Honda Brio in the compact hatchback segment. The Tiago overtakes the Brio and the Grand i10 with ease but is still in close competition with the Celerio.
With a total domestic sales of 8286 units , Tiago made its entry for first time since January 2018.
Home-grown automaker, Tata Motors may enter mini car segment in the near future to compete with cars like Maruti Suzuki Alto, Renault Kwid and Datsun Redi-Go.
The cumulative sales for the domestic market (April 2018 -February 2019) grew by 18% with 610,108 units as compared to 517,198 units over the same period last year.
The first EV for personal buyers will hit the Indian roads in 2020, which may be a Tigor EV.
Tata Motors Friday said its hatchback Tiago has crossed cumulative sales milestone of two lakh units in the domestic market.
In a year's time, BSNL's alliance will enable Tata Motors to convert its models such as Tiago, Hexa and the newly unveiled SUV Harrier into smart cars.
It will be available in all authorized Tata Motors dealerships from December 12, the company said in a statement.
Tata Motors, which is on a comeback trail, is betting big on its upcoming SUV Harrier to place itself among the top three passenger vehicle manufacturers on a sustainable basis, a top company executive said.
Tata Motors is launching Tiago JTP and Tigor JTP on October 26. The most important update is the 1.2-litre turbocharged petrol engine that makes 114PS of power and 150Nm of torque.


The word "Racism" is thrown around a lot these days… okay, over the last few hundred years and it's usually aimed at White folks – understandably. Racism however, is a human condition; it is not regulated to just "White folk". White people do not own the monopoly on racism no matter what some people might think.
Every race believes it's the best. Every ethnicity believes theirs to be the best. Every country believes they are the best. Before we even get started, we all think we're the best. No shortage of egos here on planet earth.
Some countries go out of their way to brag about how grand they are to the point where they piss off other countries who throw things at them.
Racism and discrimination go hand in hand, and in some way, shape or form, everyone experiences the feeling of being looked upon by our human brethren as a worthless piece of scum. Welcome to the human race ya'll.
Can We Afford FORD More Years?
This forum was part of the Greater Toronto Workers' Assembly GMM.
- Stefan Kipfer is Professor of Environmental Studies, York University.
- Israt Ahmed is a Community Planner at Social Planning Council in Toronto.
- Michael Laxer is a candidate in the upcoming municipal election in Toronto, and active with the Socialist Party of Ontario.
- David Kidd is a CUPE and GTWA activist.
- Erin Pope is active with Toronto West End Against Line 9.
- Elizabeth Rowley is a community activist and with the Communist Party of Canada.
- Herman Rosenfeld is a GTWA and Socialist Project activist.
Before we left and went on our journey to reinvent the GFP, we posted this very cool video of people from around the world groovin' to Pharrell William's "Happy" song. This silly yet contagious song has somehow inspired people to get off their collectives asses and jump around their neighbourhoods wearing big happy smiles.
We've decided to put it back on – for eternity, or until the internet breaks.
Besides Pharrell's 'official' version, hundreds of people in dozens of countries and cities across the planet have indulged us all with their own home-grown versions. People from Argentina, China, Russia, Kuwait... to places I didn't know existed with names I can't pronounce are all up and bouncing around the hood!
Every colour imaginable, every religion possible, young and old, straight, gay and in-between, rich and poor, in virtually every video – together – dancing and smiling… wow…who knew?
Discussion with Director Reena Kukreja - Tied in a Knot: Narratives from Bride Seeking Regions of India examines the newly emergent phenomenon of bride buying and commoditization of the female body in India, and the attendant gender based violence that the sourced, bought, or trafficked brides undergo in their marital homes.
Years of repressive social morés that promoted girl dispreference is now seeing men from these regions breach customary rules and travel to distant poorer regions of India to find wives.
This one hour film was two years in making, while Director Reena Kukreja travelled to 226 villages in both the source and receiving regions of India, interviewing brides, their parents, husbands, conjugal family members and villagers.
This internationally acclaimed documentary demonstrates how the intersection of patriarchy and poverty with the current regime of neoliberalism in India have created an oppressive environment for poor rural women.
9 Out Of 10 Americans Are Completely Wrong About This Mind-Blowing Fact!
At 1:05 minutes I get a rude awakening. At 1:41, he starts talking about you. At 2:24, he says a "bad" word. At 3:50, he kind of breaks my brain.
At 4:50, he lets you know how broke you really are. At 5:20, he rubs it in.
And at 5:50 minutes, he points out that reality isn't close to what we think it is.
Rachael Johnstone is a feminist scholar and activist, who recently completed her Ph.D. in Political Studies at Queen's University, with a specialization in Gender and Politics.
She is currently an Adjunct Professor of Gender Studies at Queen's University in Kingston, Ontario.
Her thesis, titled The Politics of Abortion in Canada After Morgentaler: Women's Rights as Citizenship Rights, is based on extensive interviews and documentary analysis of abortion rights at the federal and provincial levels, with a focus on New Brunswick, Ontario and Quebec.


Electric KTM Long Way Down. Colossal achievement or failure?
To travel the length of the Americas is on many rider's short lists. To most riders, doing the 25,000 miles on an electric bike doesn't come to mind as a viable option.
That is the challenge facing three riders who are setting out to prove it can be done on electrically modified KTM950's while riding some very challenging terrain!
That looks like a serious adventure!?
First thought that comes to my mind reading their website, if you call yourself a hero... You probably aren't.


text:
Q: php: simple breadcrumb solution? hey guys,
i'm working on a very simple breadcrumb-path solution, however i have one little thing that kind of bugs me.
PATH is e.g. folder/subfolder/subsubfolder
i'm simply splitting the PATH and i'm creating links to it. really simpel.
// breadcrumb path
$crumb = explode("/", PATH);
if (PATH != 'root' && realpath(PATH)) {
    print "
";
    $newpath = '';
    foreach($crumb as $value) {
        $newpath .= $value;
        print "$value > ";
        $newpath .= '/';
    }
    print "
";
}

however the only thing that bugs me is that the breadcrumb path looks like this:
folder > subfolder > subsubfolder >
can you see the > at the end. even though there is not another subsubsubfolder there i'm getting this > arrow. of course it's currently set that way, however i cannot think of an easy solution to get rid of the last arrow.
thank you for your help

A: Here you go:
// breadcrumb path
$crumb = explode("/", PATH);
if (PATH != 'root' && realpath(PATH)) {
    print "
";
    $newpath = '';
    foreach($crumb as $index => $value) {
        $newpath .= $value;
        // is not last item //
        if($index < count($crumb)-1)
            print "$value > ";
        // it is last item //
        else
            print $value;
        $newpath .= '/';
    }
    print "
";
}

Also try to use more suggestive names for your variables.

A: Change your code to (not tested!):
// breadcrumb path
$crumb = explode("/", PATH);
if (PATH != 'root' && realpath(PATH)) {
    print "
";
    $newpath = '';
    foreach($crumb as $key=>$value) {
        $newpath .= $value;
        print "$value";
        if($key!= (count($crumb)-1) )print "> "
        $newpath .= '/';
    }
    print "
";
}

redpajama_set_name: RedPajamaStackExchange


Click link below to read all about the game sponsored by The Rotary Club of Amherstburg.
At our meeting January 3/18 Dan Hunt was inducted by Hazen Price our longest sustaining member Dan is Interim President until June 30th, 2018. His message was of Membership Development, open communication, and meeting structure. Please join us in welcoming Dan as our new President.


text:
Jonathan Lindsay Penner (* 5. März 1962 in New York City, New York) ist ein US-amerikanischer Schauspieler und Drehbuchautor.

Leben
Penner begann seine Karriere 1989 mit einer Gastrolle in der Fernsehserie Hawk. Im Jahr darauf spielte er neben Susan Sarandon und James Spader im Filmdrama Frühstück bei ihr. 1992 erhielt er eine der Hauptrollen in der Sitcom Grapevine, die jedoch bereits nach sechs Episoden eingestellt wurde.

1993 erhielt er eine Oscarnominierung für seinen Kurzfilm Down on the Waterfront, den er zusammen mit seiner Ehefrau Stacy Title schrieb und produzierte, und in dem er auch als Schauspieler mitwirkte. Im selben Jahr war er im Pilotfilm der Sitcom Die Nanny zu sehen. Er spielte den Verlobten der Hauptdarstellerin Fran Fine, der diese sitzen ließ und damit zwang, als Nanny zu arbeiten. Zwischen 1995 und 1997 stellte Penner in 21 Folgen die Rolle des Nick Columbus in der Serie The Naked Truth dar; zwischen 1998 und 2001 spielte er eine der Hauptrollen in der Sitcom Rude Awakening – Nur für Erwachsene!.

In den Vereinigten Staaten ist Penner dem Fernsehpublikum hauptsächlich durch die Reality-Show Survivor bekannt. Er nahm 2006, 2008 und 2012 als Kandidat an der Show teil.

Filmografie (Auswahl)

Als Schauspieler
 1989: Hawk (A Man Called Hawk, Fernsehserie, Episode 1x08)
 1990: Frühstück bei ihr (White Palace)
 1991: California Clan (Santa Barbara, Seifenoper)
 1993: Down on the Waterfront (Kurzfilm)
 1993: Dragon – Die Bruce Lee Story (Dragon: The Bruce Lee Story)
 1993: Die Coneheads (Coneheads)
 1993: Melrose Place (Fernsehserie, Episode 2x09)
 1995–1997: The Naked Truth (Fernsehserie, 23 Episoden)
 1996: Die Stunde der Teufelinnen (Wedding Bell Blues)
 1996: Mission: Rohr frei! (Down Periscope)
 1998: Seinfeld (Fernsehserie, Episode 9x17)
 1998–2001: Rude Awakening – Nur für Erwachsene! (Rude Awakening, Fernsehserie, 55 Episoden)
 1999: Black Devil
 2004: Arrested Development (Fernsehserie, Episode 1x21)
 2004: CSI: Vegas (Fernsehserie, Episode 5x01)
 2006: CSI: NY (Fernsehserie, Episode 2x20)
 2006: Hood of Horror
 2017: The Bye Bye Man

Als Drehbuchautor
 1993: Down on the Waterfront (Kurzfilm)
 1999: Black Devil
 2003: The Lone Ranger (Fernsehfilm)
 2017: The Bye Bye Man

Auszeichnungen
 1994: Oscar-Nominierung in der Kategorie Oscar/Bester Kurzfilm für Down on the Waterfront

Weblinks

Filmschauspieler
Drehbuchautor
US-Amerikaner
Geboren 1962
Mann

redpajama_set_name: RedPajamaWikipedia


text:
Entertainment Bollywood 24 Aug 2016 Work on SLB's P ...
Entertainment, Bollywood
Work on SLB's Padmavati sets in full wing, shoot begins in September
Published Aug 24, 2016, 10:10 am IST
Updated Aug 24, 2016, 10:11 am IST
Known for portraying tragic love stories on celluloid, Bhansali this time has tried his hands on an anti-hero project.
Film is based on ruthless ruler Alauddin Khilji's obsession for Mewar queen Padmavati.
Mumbai: Director Sanjay Leela Bhansali has begun work on building a set for his next magnum opus 'Padmavati' at Mehboob Studios here, even as official announcement about the cast is still awaited.
"The set is being made for 'Padmavati'. The work started few days ago and it is likely to be completed in another two-three weeks. The shoot is likely to begin from next month so the set is getting ready at the earliest," sources close to the film, said.
However, they refused to divulge details on what kind of set up is coming up stating that "it's too early to say anything."
There is a board outside the studio that reads Bhansali Productions. Workers at the studio were seen carrying big wooden planks and fixing it in vertical ways.
The National award-winning director is known for grand and lavish sets for his films.
"The work for 'Padmavati' will begin from here (set at Mehboob studios). We were looking at having a set in Filmcity but it was booked. Later we might move in there (Filmcity) depending upon the availability," sources said. Both 'Ram Leela' and 'Bajirao Mastani' had huge sets erected at Filmcity in suburban Goregaon.
Known for portraying tragic love stories on celluloid, Bhansali this time has tried his hands on an anti-hero project as it is based on ruthless ruler Alauddin Khilji's obsession for Mewar queen Padmavati.
Ranveer Singh is rumoured to be doing the film but the 'Bajirao Mastani' actor is yet to confirm the same.
Reportedly 'Padmavati' will bring Deepika Padukone and Ranveer together again for the third time with Shahid Kapoor also playing a prominent role in the film.
Tags: padmavati, sanjay leela bhansali, deepika padukone, ranveer singh
Location: India, Maharashtra, Mumbai (Bombay)
■Have Ranveer Singh and SLB made peace over Padmavati?
■No, Hrithik Roshan has not replaced Ranveer Singh in Padmavati
■Fawad Khan wasn't offered Deepika Padukone starrer Rani Padmavati
Latest From Entertainment
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Karan Johar unveils first look of Puri Jagannadh's 'Liger' starring Vijay Deverakonda
More From Bollywood
No comparison between Govinda and Varun...
Aakanksha Singh joins team Mayday
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Ranbir-Shraddha film gets rolling
Bebo's packed calendar


text:
#include <stdlib.h>
#include <curses.h>
#include <menu.h>

#define ARRAY_SIZE(a) (sizeof(a) / sizeof(a[0]))
#define CTRLD 	4

char *choices[] = {
                        "Choice 1", "Choice 2", "Choice 3", "Choice 4", "Choice 5",
			"Choice 6", "Choice 7", "Choice 8", "Choice 9", "Choice 10",
			"Choice 11", "Choice 12", "Choice 13", "Choice 14", "Choice 15",
			"Choice 16", "Choice 17", "Choice 18", "Choice 19", "Choice 20",
                        "Exit",
                        (char *)NULL,
                  };

int main()
{	ITEM **my_items;
	int c;
	MENU *my_menu;
        WINDOW *my_menu_win;
        int n_choices, i;

	/* Initialize curses */
	initscr();
	start_color();
        cbreak();
        noecho();
	keypad(stdscr, TRUE);
	init_pair(1, COLOR_RED, COLOR_BLACK);
	init_pair(2, COLOR_CYAN, COLOR_BLACK);

	/* Create items */
        n_choices = ARRAY_SIZE(choices);
        my_items = (ITEM **)calloc(n_choices, sizeof(ITEM *));
        for(i = 0; i < n_choices; ++i)
                my_items[i] = new_item(choices[i], choices[i]);

	/* Crate menu */
	my_menu = new_menu((ITEM **)my_items);

	/* Set menu option not to show the description */
	menu_opts_off(my_menu, O_SHOWDESC);

	/* Create the window to be associated with the menu */
        my_menu_win = newwin(10, 70, 4, 4);
        keypad(my_menu_win, TRUE);

	/* Set main window and sub window */
        set_menu_win(my_menu, my_menu_win);
        set_menu_sub(my_menu, derwin(my_menu_win, 6, 68, 3, 1));
	set_menu_format(my_menu, 5, 3);
	set_menu_mark(my_menu, " * ");

	/* Print a border around the main window and print a title */
        box(my_menu_win, 0, 0);

	attron(COLOR_PAIR(2));
	mvprintw(LINES - 3, 0, "Use PageUp and PageDown to scroll");
	mvprintw(LINES - 2, 0, "Use Arrow Keys to navigate (F1 to Exit)");
	attroff(COLOR_PAIR(2));
	refresh();

	/* Post the menu */
	post_menu(my_menu);
	wrefresh(my_menu_win);

	while((c = wgetch(my_menu_win)) != KEY_F(1))
	{       switch(c)
	        {	case KEY_DOWN:
				menu_driver(my_menu, REQ_DOWN_ITEM);
				break;
			case KEY_UP:
				menu_driver(my_menu, REQ_UP_ITEM);
				break;
			case KEY_LEFT:
				menu_driver(my_menu, REQ_LEFT_ITEM);
				break;
			case KEY_RIGHT:
				menu_driver(my_menu, REQ_RIGHT_ITEM);
				break;
			case KEY_NPAGE:
				menu_driver(my_menu, REQ_SCR_DPAGE);
				break;
			case KEY_PPAGE:
				menu_driver(my_menu, REQ_SCR_UPAGE);
				break;
		}
                wrefresh(my_menu_win);
	}

	/* Unpost and free all the memory taken up */
        unpost_menu(my_menu);
        free_menu(my_menu);
        for(i = 0; i < n_choices; ++i)
                free_item(my_items[i]);
	endwin();
}

/*

       O_ONEVALUE
            Only one item can be selected for this menu.

       O_SHOWDESC
            Display  the  item  descriptions  when  the  menu  is
            posted.

       O_ROWMAJOR
            Display the menu in row-major order.

       O_IGNORECASE
            Ignore the case when pattern-matching.

       O_SHOWMATCH
            Move the cursor to within the item  name  while  pat
            tern-matching.

       O_NONCYCLIC
            Don't   wrap   around  next-item  and  previous-item,
            requests to the other end of the menu.
*/

redpajama_set_name: RedPajamaGithub


text:
Home » Website » National » Rohtang Tunnel Leaves Filth In Its Wake
Rohtang Tunnel Leaves Filth In Its Wake
Locals rattled as lack of facilities, irresponsible tourists lead to littering, open defecation, and even eve teasing and theft
Ashwani Sharma	16 October 2020
(Left) A beautiful valley side of Sissu in Lahual, (Right) Liquor bottles dump somewhere near Chander-Bagga River.
Ashwani Sharma
Ashwani Sharma	2020-10-16T19:54:38+05:30 Rohtang Tunnel Leaves Filth In Its Wake
The worst fears of the strong-32,000 population at Lahaul-Spiti have come true. The heavy tourist influx following the opening of the Rohtang Tunnel has exposed the three ecologically fragile and culturally distinct Lahaul valleys – Chandra, Bagga, and Chenab -- to random littering, garbage dumps, pollution, open defecation, eve-teasing, and theft by the seasonal guests.
The theft of potato bags from the road-side stocks-- earlier an unheard thing in the Lahaul valley, mass destruction of a local apple orchard (the entire production was plucked and taken away) and cases of eve-teasing of Lahaul women are a few of unsavory incidents reported over the past 12 days. This has left locals completely baffled and visibly shaken over the impact of allowing mass tourist arrivals across the Rohtang Pass.
"We have conveyed all our apprehensions of things turning a bit ugly if not stopped and checked appropriately. Lahaul society is a very gentle and hospitable section. We want tourists to visit but don't want nuisance or breaking of the laws, and especially Swacchta must not be compromised," said Prem Katoch, president of Save Lahaul Society, a social action group formed recently to check the side-effects of reckless tourist influx.
The local administration is partly to blame. The strategic Rohtang Tunnel, which provides all-weather connectivity to the people of Lahaul-Spiti, was opened on October 3 by Prime Minister Narendra Modi. But there are no public toilets along the Sissu (North-portal hamlet of the tunnel) to Keylong – the district headquarters, and Udaipur. Nor are there any signs warning against littering or open defecation. So tourists dump empty packets of chips, noodles, empty water, and liquor bottles wherever they want. And of course, the lack of toilets means people go wherever they can.
"The situation, I can say in lighter terms, is like you have fixed the wedding date, printed and issued invitation cards, the marriage party has arrived, but there is no clue about the bride and groom," quips Prof (Dr) Chander Mohan Parsheera, Director, Institute of Vocational (Tourism ) and Tribal studies, HP university, Shimla.
Dr Parsheera, who has been vociferous about appropriate preparedness in Lahaul-Spiti, says that "our response now should be how to deal with the situation and ensure that locals are not in conflict with tourists. We are, thus, trying to involve the local community, the panchayats, Mahila mandals, and youths to host the tourists."
And to spread awareness, Save Lahaul Society has decided to immediately put up hoardings across the region. One right at North portal will say: "You are entering a cleanest valley. Be responsible tourists /citizens. Don't destroy the ecology .Take your garbage back ".
The people of Lahaul and Spiti want responsible tourists and visitors.
But the lack of garbage bins or scientific disposal system means the garbage rolls down to the Chandra River and its tributary Bagga –which meets at Tandi, note activists. The littering at other places including Trilokinath temple at Udaipur is already a cause of serious concern.
Yet the task in hand for the local administration is not just to regulate tourists and create amenities. There is also the need to take steps to prevent the spread of the COVID-19 pandemic in the tribal belt.
Rajneesh, Secretary (Environment) and Chairman HP Pollution Control Board, said that "necessary directions have been issued to the local SDMs. The administration has a duty to ensure that no damage is caused to the environment since there will be increased tourist arrivals."
The state tourism secretary Divesh Kumar supports the view that no littering or roadside disposal of garbage should be permitted. The government will put up hoardings, and those violating the norms will be penalised, he said. "We are in the process of creating proper amenities for the tourists. All facilities like proper eating places and toilets etc. will be built very soon," he said.
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Ashwani Sharma	Shimla	Pollution	Plastic Pollution	Plastic ban	National