text:
Preparation for college and career is critical for both individual and societal economic prosperity. Yet, educational attainment is not sufficient to meet economic demand and educational opportunities are not equally distributed. State policymakers and education leaders across the state are working to increase opportunity and improve student outcomes.
At the heart of these efforts is better alignment of California's K–12 education system with higher education systems and the labor market to ensure successful transitions for young adults between high school and postsecondary pursuits.
In this topic area, PACE researchers investigate students' educational trajectories and the state and local endeavors to decrease disparities in access to educational opportunities and improve student success in college and career.
Is Online Learning the Silver Bullet for Men of Color?
Angelica M.G. Palacios
Nexi Delgado
J. Luke Wood
Using Math Diagnostics to Inform Course Placement in Community Colleges
Tatiana Melguizo
Federick Ngo
Missed Signals
Andrew Foote
Lisa Schulkind
Teny M. Shapiro
Key Decisions Await Next California Community Colleges Chancellor as Search Winds Down
January 26, 2023 | EdSource
California University Faculty Discuss Recent Drops in Community College Enrollment
December 9, 2022 | Daily Bruin
Growing Numbers of California High Schoolers Dual Enroll in College Courses—But Access Uneven Statewide
November 10, 2022 | EdSource
What are the recent state-level policies intended to address college and career readiness?
In the last decade, California implemented major reforms to improve outcomes for all students. As detailed in a primer on K12 education, curricular changes include the Common Core State Standards adopted in 2010 and the aligned Smarter Balanced Assessments implemented in 2014-15. In addition, California revamped its school funding and accountability mechanisms with the implementation of the Local Control Funding Formula and Local Control and Accountability Plans in 2013-14 to align fiscal activities with local needs in order to improve student outcomes. Finally, the California School Dashboard, with multiple measures of school quality, was launched in 2017. Recent survey results indicate that California superintendents, principals, and voters support these reforms despite implementation challenges.
How is college and career readiness defined and measured in California?
Recent reports illustrate the multiple ways in which college and career readiness is defined in California. The State Board of Education adopted an integrated definition with the College/Career Indicator, which includes 8 pathways through which students demonstrate their preparedness for college and career. District leaders across the state suggest college eligibility metrics (i.e., A-G course completion, SAT participation, college application) are critical components for defining and measuring college readiness. Importantly, standardized assessments and high school GPA predict college outcomes, serving as important measures of college readiness. Finally, many educators submit that the true measure of college readiness is college enrollment.
How are California public high school students faring in K-12 and postsecondary schooling?
In general, student outcomes are improving in California. According to recent research, the high school graduation rate improved from 79 percent in 2012 to 82 percent in 2015. Total enrollment in public colleges increased by 13 percent since 2006,with 63 percent of public high school graduates enrolling in college. Despite this steady progress, California lags other states on most measures. Moreover, California's college readiness exam indicates that just 30 percent of 11th grade students met the college readiness standards in English language arts and mathematics. Importantly, substantial disparities exist across all measures of academic performance and educational attainment by student race/ethnicity, socioeconomic status and geographic location.
What factors matter most in college readiness and how can high schools prepare students for college and career?
As described in a comprehensive literature review, the key factors in college readiness and educational attainment coalesce in four key areas: educational aspirations and beliefs; academic preparation for college; college information; and college navigation and resilience skills. Among these, academic preparation, especially in the area of math, is of critical importance. Yet, research demonstrates the disparities in students' math course-taking, likely the result of student choice and structural constraints. Recognizing the important work of preparing students for college and career, districts across the state are working across all these domains to support the development of students' dispositions and skills for successfully navigating postsecondary pursuits.
How do intersegmental (or cross-sector) efforts work to improve educational and workforce outcomes in California?
Due to fragmented and misaligned segments of public education, many students lack access to opportunities that ensure their success in college and career. This problem may be solved through better coordination between high school and college, between systems of higher education, and between education and economic development sectors. When effectively implemented, intersegmental collaboration is a key lever for change. Local partnerships, including supporting dual enrollment in high school and college, strengthen alignment in expectations between K-12 and post-secondary education and accelerate students' progress. Advocates submit that intersegmental efforts offer opportunities to bridge research, policy, and practice to improve outcomes.
How do higher education systems contribute to improved education and workforce outcomes?
Many of the efforts to improve educational attainment and workforce outcomes are focused on K-12 systems. However, substantial research, as well as complex theoretical models, indicate that multiple factors predict and influence success in college and career. Success in college depends on both individual disposition and skills as well as structural or environmental factors. These factors include, but are not limited to, academic preparation, basic needs, social-emotional competencies, college knowledge and navigation skills, and institutional structures. Moreover, an oft-discussed aspect of college knowledge and institutional structures is the area of college affordability and financial aid.
What can be done to foster greater post-secondary success for students with disabilities?
Post-secondary outcomes for students with disabilities lag behind those for nondisabled peers. Students with disabilities are less likely to graduate from high school, more likely to enroll in community colleges or short-term vocational programs, less likely to enroll in 4-year colleges or university, and are often employed in low-wage, poverty-level jobs
There are a number of predictors that will improve these outcomes. Four key predictors include the following:
Family involvement: Students are more likely to complete high school and go on to some kind of employment or post-secondary education if parents are involved as role models and advocates for students' lifelong goals.
Work based learning: If a young person with a disability has the opportunity to work while they're in high school, they are much more likely to be employed as an adult, and this work could be through a range of different kinds of work experiences.
Inclusion: The inclusion of students with disabilities in general education settings is predictive of better outcomes. Inclusion has been shown to impact all sorts of different outcomes from absences to better on-time graduation rates to higher rates of college attendance and employment.
Interagency Collaboration: When local organizations get together and provide coordinated planning and formalize agreements to support students and families, outcomes improve for students.
Read more in Promoting Successful Transitions for Students with Disabilities and Work-based Learning for Students with Disabilities for more on preparing students for post-school success. These briefs were produced as part of the PACE Policy Research Panel on Special Education.
Assessing Voters' and Parents' Perspectives on Current Threats to Public Education
The 2022 PACE/USC Rossier Poll
The 2021–22 academic year was profoundly challenging for California schools. Eight critical issues emerged as serious threats to student learning, the operation of schools, and even the very institution of public education: (1) gun violence, (2)…
English Learners' Pathways in California's Community Colleges Under AB 705
California's Assembly Bill 705 has affected the way that English learners (ELs) access English and English as a second language (ESL) coursework, requiring that degree- or transfer-seeking students have the right to enroll in English or ESL courses…
Californians' Attitudes About College Access, Affordability, and Equity in the Time of COVID-19
Evidence from the 2021 PACE/USC Rossier Annual Poll
The 2021 PACE/USC Rossier poll provides key insights into Californians' perceptions of higher education issues during the COVID-19 pandemic, specifically equity and affordability. A large percentage of Californians acknowledge that college…
Dual Enrollment is Growing Among California High School Students
Research tells us that high school students who take college courses while they are still in high school benefit from the experience in both systems. To capitalize on the benefits of this dual enrollment, California and other states have moved to…


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Steamboats and sailors of the Great Lakes
The Great Lakes shipping industry can trace its lineage to 1679 with the launching on Lake Erie of the Griffon, a sixty-foot galley weighing nearly fifty tons. Built by LaSalle, a French explorer who had been commissioned to search for a passage through North America to China, it was the first sailing ship to operate on the upper lakes, signaling the dawn of the Great Lakes shipping industry as we know it today.
Steamboats and Sailors of the Great Lakes is the most thorough and factual …
Queen of the Lakes
This book is an account of the ships that have borne the name "Queen of the Lakes," an honorary title indicating that, at the time of its launching, a ship is the longest on the Great Lakes. In one of the most comprehensive books ever written on the maritime history of the lakes, Mark L. Thompson presents a vignette of each of the dozens of ships that have held the title, chronicling the dates the ship sailed, its dimensions, the derivation of its name, its role in the economic development of…
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\section{Introduction}

	With the popularity of 3D sensors, it's relatively easy for us to obtain more raw 3D data, e.g., RGB-D data, LiDAR data, and MEMS data~\cite{PointConv}. Considering point clouds as the fundamental representative of 3D data, the understanding of point clouds has attracted extensive attention for various applications, e.g., autonomous driving~\cite{shi2019pointrcnn}, robotics~\cite{wang2015voting}, and place recognition~\cite{liu2019lpd}.  Here, a point cloud has two components: the points $\mathcal{P} \in \mathbb{R}^{N\times	3}$ and the features $\mathcal{F} \in \mathbb{R}^{N\times	D}$. Unlike 2D images, the sparsity and disorder proprieties make robust point clouds processing a challenging task. Furthermore, the raw data obtained from those 3D sensors or reconstruction algorithms inevitably contain outliers or noise in real-world situations.

	\begin{figure}[t]
		\begin{center}
			\includegraphics[width=\columnwidth]{picture/fig1.pdf}
		\end{center}
		\caption{{\bf PointASNL for robust point clouds processing}. The adaptive sampling module adaptively adjusts the sampled point from point clouds with noise. Besides, the local-nonlocal module not only combines the local features in Euclidean space, but also considers the long-range dependency in feature space.}
		\label{fig:fig1}
		\vspace{-0.5cm}
	\end{figure}

	In this work, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise or outliers effectively. Our proposed PointASNL mainly consists of two general modules: adaptive sampling (AS) module and local-nonlocal (L-NL) module. The AS module is used to adjust the coordinates and features of the sampled points, whereas the L-NL module is used to capture the neighbor and long-range dependencies of the sampled points.

	Unlike the cases in 2D images, traditional convolution operations cannot directly work on unstructured point cloud data. Thus, most of the current methods usually use sampling approaches to select points from the original point clouds for conducting local feature learning. Among these sampling algorithms, farthest point sampling (FPS)~\cite{pointnet2}, Poisson disk sampling (PDS)~\cite{Monte}, and Gumbel subset sampling (GSS)~\cite{Gumbel} are proposed in previous works. However, as the most representative one, FPS is rooted in Euclidean distance, which is task-dependent and outliers sensitive. PDS, a predefined uniformly sampling method, also cannot solve the problem above in a data-driven way. GSS only performs sampling from a high-dimension embedding space and ignores the spatial distribution of points. Furthermore, the shared key issue in these approaches is that the sampled points are limited to a subset of the original point clouds. Therefore, as shown in the left part of Fig.~\ref{fig:fig1}, suppose an outlier point is sampled, it will influence the downstream process inevitably.

	To overcome the issues mentioned above, we propose a differentiable adaptive sampling (AS) module to adjust the coordinates of the initial sampled points (e.g., from FPS) via a data-driven way. Such coordinate adjusting facilitates to fit the intrinsic geometry submanifold and further shifts to correct points beyond original point clouds without the influence of outliers. Thus, the AS module can not only benefit point feature learning, but also improve the model robustness to noise.

	To further enhance the performance as well enables the learning process insensitive to noise, we proposed a local-nonlocal (L-NL) module for capturing neighbor and long-range dependencies of the sampled points. The underlying reason is that, currently, most appealing methods for feature learning is to query a local group around the each sampled point, and then they construct the graph-based learning~\cite{ecc,DGCNN,PointWeb, HPEIN} or define convolution-like operations~\cite{Pointwise,Spidercnn,groh2018flex,PCNN,PointConv,KPCONV} (we denote them as \textit{Point Local Cell}). Nonetheless, such point local cell only considers local information interaction in the neighbor area and then acquires the global context through a hierarchical structure, which usually leads to bottom-up feature learning. Inspired by the success of the Nonlocal network~\cite{Nonlocal}, we innovatively design this L-NL module, in which the key component is the \textit{Point Nonlocal Cell}. In particular, the point nonlocal cell allows the computation of the response of a sampled point as a weighted sum of the influences of the entire point clouds, instead of just within a limited neighbor range. With the learned long-dependency correlation, the L-NL module can provide more precise information for robust point clouds processing. As shown in the right part of Fig.~\ref{fig:fig1}, although the sampled points within the lower engine are covered with noise, our L-NL module can still learn the features from the other engine with a different noise distribution.

	Our main contribution can be summarized as follows: 1) We propose an end-to-end model for robust point clouds processing, PointASNL, which can effectively ease the influence of outliers or noise; 2) With the proposed adaptive sampling (AS) module, PointASNL can adaptively adjust the coordinates of the initial sampled points, making them more suitable for feature learning with intrinsic geometry and more robust for noisy outliers; and 3) We further design a point nonlocal cell in the proposed local-nonlocal (L-NL) module, which enhances the feature learning in point local cells. Extensive experiments on classification and segmentation tasks verify the robustness of our approach.

	\section{Related Work}

	\begin{figure*}
		\begin{center}
			\begin{center}
				\includegraphics[width=0.95\linewidth]{picture/figure3.pdf}
			\end{center}
		\end{center}
		\caption{Part (a) shows adaptive sampling (AS) module, which firstly updates features of grouping point by reasoning group relationship, then normalized weighs re-weight initial sampled points to achieve new sampled points. Part (b) illustrates the construction of local-nonlocal (L-NL) module, which consists of point local cell and point nonlocal cell. $N_s$ stands for sampled point number, $N$ stands for point number of entire point clouds, $D_l, D_{mid}$, and $D_{l+1}$ stand for channel numbers.}
		\label{fig:ASModule}
		\vspace{-0.3cm}
	\end{figure*}
	{\noindent\bf Volumetric-based and Projection-based Methods.}
	Considering the sparsity of point clouds and memory consumption, it is not very effective to directly voxelized point clouds and then use 3D convolution for feature learning. Various subsequent improvement methods have been proposed, e.g., efficient spatial-temporal convolution MinkowskiNet~\cite{choy20194d}, computational effective Submanifold sparse convolution~\cite{SparseConv}, and Oc-tree based neural networks O-CNN~\cite{wang2017cnn} and OctNet~\cite{riegler2017octnet}. Such methods greatly improve the computational efficiency, thus leading to the entire point clouds as input without sampling and superior capacity. There are also other grid-based methods using traditional convolution operations, e.g., projecting 3D data to multi-view 2D images~\cite{su2015multi} and lattice space~\cite{su2018splatnet}. Yet, the convolution operation of these methods lacks the ability to capture nonlocally geometric features.

	{\noindent\bf Point-based Learning Methods.}
	PointNet~\cite{pointnet} is the pioneering work directly on sparse and unstructured point clouds, which summarizes global information by using pointwise multi-layer perceptions (MLPs) followed by the max-pooling operation.  PointNet++~\cite{pointnet2} further applies a hierarchical structure with k-NN grouping followed by max-pooling to capture regional information. Since it aggregates local features simply to the largest activation, regional information is not yet fully utilized. Recently, much effort has been made for effective local feature aggregation. PointCNN~\cite{PointCNN} transforms neighboring points to the canonical order, which enables traditional convolution to play a normal role. Point2Sequence~\cite{Point2Sequence} uses the attention mechanism to aggregate the information of different local regions. Methods~\cite{Spidercnn,PointConv, Monte,rscnn,ecc, PCCN} directly use the relationship between neighborhoods and local centers to learn a dynamic weight for convolution, where ECC~\cite{ecc} and RS-CNN~\cite{rscnn} use ad-hoc defined 6-D and 10-D vectors as edge relationship, PCCN~\cite{PCCN} and PointConv~\cite{PointConv} project the relative position of two points to a convolution weight. A-CNN~\cite{acnn} uses ring convolution to encode features that have different distances from the local center points, and PointWeb~\cite{PointWeb} further connects every point pairs in a local region to obtain more representative region features. Still, these methods only focus on local feature aggregation and acquire global context from local features through a hierarchical structure. On the other hand, there are various works for learning the global context from the local features. A-SCN~\cite{ASCN} uses a global attention mechanism to aggregate global features but lacks the support of local information, which does not achieve good results. DGCNN~\cite{DGCNN} proposes the EdgeConv module to generate edge features and search neighbors in features space. LPD-Net~\cite{liu2019lpd} further extends DGCNN on both spatial neighbors and features neighbors aggregation. Nonetheless, the neighbors in the feature space are not representative of the global features, and spatial receptive fields of the network gradually become confused without a hierarchical structure.

	{\noindent\bf Outlier Removal and Sampling Strategy.} Outliers and noise usually exist in raw point clouds data. Previous robust statistics methods~\cite{aggarwal2015outlier} for outlier removal suffer from non-trivial parameter tuning or require additional information~\cite{wolff2016point}. Various data-driven methods~\cite{guerrero2018pcpnet,rakotosaona2019pointcleannet} are proposed for outlier removal, which first discard some outliers and then projects noisy points to clean surfaces. Yet, such methods cannot inherently merge the robust point cloud feature learning with outlier removal in a joint learning manner. On the other hand, deep learning based point cloud processing methods usually sample points to decrease computational consumption. Though, most sampling methods are limited by noise sensitivity and not driven by data~\cite{pointnet2,Monte}, or without the consideration of spatial distribution ~\cite{Gumbel}. SO-Net~\cite{So-net} uses an unsupervised neural network, say self-organizing map (SOM), to utilize spatial distribution of point clouds. It then employs PointNet++~\cite{pointnet2} to multiple smaller sampled 'nodes'. However, SO-Net does not belong to online adaptive sampling. Under the assumption of local label consistency, some works use the geometric centers of voxel grids to uniformly represent sampled points~\cite{KPCONV,ecc}, which ignores the difference of point distribution influence. Still, these methods are extremely sensitive to noise and cannot learn the spatial distribution of sampled points at the same time.

	\section{Our Method}
	In this paper, we propose two modules in PointASNL, namely adaptive sampling (AS) module in Sec.~\ref{AS} and local-nonlocal (L-NL) module in Sec.~\ref{NL}. In Sec.\ref{PNL}, we combine AS and L-NL modules in a hierarchical manner to form our proposed PointASNL model.

	\subsection{Adaptive Sampling (AS) Module}
	\label{AS}
	Farthest point sampling (FPS) is widely used in many point cloud framework, as it can generate a relatively uniform sampled points. Therefore, their neighbors can cover all input point clouds as much as possible. Nevertheless, there are two main issues in FPS: (1) It is very sensitive to the outlier points, making it highly unstable for dealing with real-world point clouds data. (2) Sampled points from FPS must be a subset of original point clouds, which makes it challenging to infer the original geometric information if occlusion and missing errors occur during acquisition.

	To overcome the above-mentioned issues, we first use FPS to gain the relatively uniform points as original sampled points. Then our proposed AS module adaptively learns shifts for each sampled point. Compared with the similar process widely used in mesh generation~\cite{wang2018pixel2mesh}, the downsampling operation must be taken into account both in spatial and feature space when the number of points is reduced.  For the AS module, let $\mathcal{P}_{s} \in \mathbb{R}^{N_s\times 3}$ as the sampled $N_s$ points from  $N$ input points of certain layer, $x_i$ from $\mathcal{P}_{s}$ and $f_i$ from $\mathcal{F}_s \in \mathbb{R}^{N_s\times D_l}$ as  a sampled point and its features. We first search neighbors of sampled points as groups via k-NN query, then use general self-attention mechanism~\cite{vaswani2017attention} for group features updating.

	As shown in Fig.~\ref{fig:ASModule}~(a), we update group features by using attention within all group members. For $x_{i,1},..., x_{i,K} \in \mathcal{N}(x_i)$ and their corresponding features $f_{i,1},..., f_{i,K}$, where $\mathcal{N}(x_i)$ is $K$ nearest neighbors of sampled point $x_i$, feature updating of group member $x_{i,k}$ can be written as
	\begin{equation}
	f_{i,k} =  \mathcal{A}(\mathcal{R}(x_{i,k} ,x_{i,j})\gamma(x_{i,j}),~~ \forall x_{i,j} \in \mathcal{N}(x_i)),
	\label{eqattention}
	\end{equation}
	where a pairwise function $\mathcal{R}$ computes a high level relationship between group members $x_{i,k}, x_{i,j} \in \mathcal{N}(x_i)$. The unary function $\gamma$ change the each group feature $f_{i,j}$ from dimension $D_l$ to another hidden dimension $D'$ and $\mathcal{A}$ is a aggregation function.

	For less computation, we consider $\gamma$ in the form of a linear transformation of point features  $\gamma(x_{i,j}) =W_{\gamma} f_{i,j} $, and relationship function $\mathcal{R}$ is dot-product similarity of two points as follows,
	\begin{equation}
	\mathcal{R}(x_{i,k} ,x_{i,j}) = {\text{Softmax}}(\phi(f_{i,k})^T \theta(f_{i,j}) /\sqrt{D'}),
	\end{equation}
	where $\phi$ and $\theta$ are independent two linear transformations and can be easily implemented by independent 1D convolution ~~$Conv:\mathbb{R}^{D_{l}} \mapsto \mathbb{R}^{D'} $, where $D_l$ and $D^{'}$ are input and output channel, respectively.

	After that, point-wise MLPs, i.e., $\sigma_p$ and $\sigma_f$ with softmax activation function on $K$ group members are used to obtain the corresponding intensity of each point in a group, which can be represented as normalized weights for each coordinate axis and features channel.
	\begin{equation}
	\begin{split}
	&{F_p} = \{\sigma_{p}(f_{i,k})\}_{k=1}^K, ~~~{W}_p =  \text{Softmax}({F_p}),\\
	&{F_f} = \{\sigma_{f}(f_{i,k})\}_{k=1}^K, ~~~{W}_f =  \text{Softmax}({F_f}),
	\end{split}
	\end{equation}
	where ${F_p}, {F_f}, {W}_p, {W}_f \in \mathbb{R}^{K\times 1}$ are outputs of point-wise MLPs and normalized weights after softmax function. Finally, a adaptive shifting on both $K$ neighbors' coordinates from ${X}\in \mathbb{R}^{K\times 3}$ and their features from ${F}\in \mathbb{R}^{K\times D'}$ are implemented by the weighted sum operation. We obtain a new coordinate of the sampled point $x^*_i$ and its features $f^*_i$ by following operations,
	\begin{equation}
	\begin{split}
	&x^*_i =  {W}_p^T {X}, ~~~{X}=\{x_{i,k}\}_{k=1}^K,\\
	&f^*_i =  {W}_f^T {F}, ~~~{F}=\{f_{i,k}\}_{k=1}^K.
	\end{split}
	\end{equation}

	\subsection{Local-Nonlocal (L-NL) Module}
	\label{NL}
	Within our L-NL module, there are two cells: point local (PL) cell and point nonlocal (PNL) cell. Specifically, the PL cell can be any appealing algorithms (e.g., PointNet++~\cite{pointnet2}, PointConv~\cite{PointConv}), and the PNL cell innovatively considers the correlations between sampled points and the entire point cloud in multi-scale. Consequently, the contextual learning of the point cloud is enhanced by combining the local and global information (See Fig.~\ref{fig:ASModule}(b)).

	\subsubsection{Point Local Cell}

	The local features mining of point clouds often exploits the local-to-global strategy~\cite{pointnet2}, which aggregates local features in each group and gradually increases the receptive field by hierarchical architectures. We adopt such methods in point local (PL) cell. Similar to the previous definition for a local sampled point $x_i$, corresponding feature $f_i$ and neighborhoods $\mathcal{N}(x_i)$, a generalized local aggregation function used in PL can be formulated as

	\begin{equation}
	f_{i}^l =  \mathcal{A}(\mathcal{L}({f_n}), \forall x_n \in \mathcal{N}(x_i)),
	\label{eq1}
	\end{equation}
	where $f^{l}_{i}$ is updated features of local center $x_i$, which is updated by local feature transformation function $\mathcal{L}$ and aggregation function $\mathcal{A}$. For PointNet++~\cite{pointnet2}, $\mathcal{L}$ is multi-layer perceptions (MLPs) and $\mathcal{A}$ is max-pooling. Recently, more and more works directly design convolution operators on the local regions, which mainly change $\mathcal{L}$ to be a learnable weighted multiply obtained by neighbor relationships. Considering the efficiency and effectiveness of the operation in a compromise, we implement the convolution operation by adaptively projecting the relative position of two points to a convolution weight~\cite{PCCN,PointConv}, and aggregate local features,
	\begin{equation}
	\mathcal{L}(f_n) := g(x_n-x_i) f_n,
	\end{equation}
	where $g$ is chosen as MLPs: $\mathbb{R}^3 \mapsto \mathbb{R}^{D_{l}\times D_{mid}}$, which transfers 3 dimension relative position to $D_{l}\times D_{mid}$ transformation matrix. $D_{l}$ represents the channel of the input features in certain layer and $D_{mid}$ is the channel of the updated features by PL cell.

	\begin{figure}
		\begin{center}
			\includegraphics[width=0.86 \columnwidth, height=5.2cm]{picture/fig3.pdf}
		\end{center}
		\caption{\textbf{Inner structure of point nonlocal (PNL) cell.} For the notations $N_s, N, D_l, D_{mid}$ please refer to the caption of Fig.~\ref{fig:ASModule}, $D'$ is the intermediate channel numbers.}
		\label{fig:fig3NLC}
		\vspace{-0.3cm}
	\end{figure}
	\subsubsection{Point Nonlocal Cell}
	Inspired by nonlocal neural networks~\cite{Nonlocal} in 2D images for long-range dependencies learning, we design a specific point nonlocal (PNL) cell for global context aggregation (Fig.~\ref{fig:fig3NLC}). There are two main differences between our point nonlocal cell and component proposed in~\cite{Nonlocal}: (1) We use our sampled points as query points to calculate similarity with entire points in certain layers (say, key points $\mathcal{P}_{\text{k}}$). Furthermore, our query points are not limited within a subset of input point clouds, as each sampled point adaptively updates its coordinate and features by the AS module (Sec.~\ref{AS}). (2) Our output channel is gradually increased with the down-sampling operation in each layer, which avoids information loss in the down-sampling encoder. Specifically, similar with Eq.~\ref{eqattention}, given query point $x_i$ and key point from $\mathcal{P}_{\text{k}}$, the nonlocal operation $\mathcal{NL}$ is defined as:
	\begin{equation}
	\mathcal{NL}(x_i, \mathcal{P}_{\text{k}}) :=  \mathcal{A}(\mathcal{R}(f_i ,f_j)\gamma(f_j), \forall x_j \in \mathcal{P}_{\text{k}}),
	\label{Nonlocal}
	\end{equation}
	where $\mathcal{P}_{k}\in \mathbb{R}^{N\times3}$ stands for the entire $N$ key points in a certain layer. Finally, a single nonlinear convolution layer  $\sigma$ fuse the global context and adjust the channel of each point to the same dimension with the output of PL $D_{l+1}$ (Eq.~\ref{eq1}). Hence, for a sampled point $x_i$,  its updated feature is computed by PNL with function
	\begin{equation}
	f_i^{nl} =  \sigma(\mathcal{NL}(x_i, \mathcal{P}_{k})).
	\end{equation}

	\subsubsection{Local-Nonlocal (L-NL) Fusion}

	By combining PL and PNL, we construct a local-nonlocal module to encode local and global features simultaneously. As shown in Fig.~\ref{fig:ASModule} (b), it uses query points and key points as inputs, and exploit k-NN grouping for neighborhoods searching for each query point. Then, the group coordinates and features of each local region are sent through PL for local context encoding. For PNL, it uses whole key points  to integrate global information for each query point via an attention mechanism.  Finally, for each updated point, a channel-wise sum with a nonlinear convolution $\sigma$ is used to fuse local and global information.

	\subsection{PointASNL}
	\label{PNL}
	By combining the two components proposed in Sec.\ref{AS} and Sec \ref{NL} in each layer, we can implement a hierarchical architecture for both classification and segmentation tasks.

	For the {classification}, we designed a three-layer network and down-sample input points at two levels. In particular,  the first two layers sample 512 and 124 points. The third layer concatenate global features of former two layers with max pooling, where new features are processed by fully connected layers, dropout, and softmax layer, respectively. The batch normalization layers and the ReLU function are used in each layer. Furthermore, skip connections~\cite{resnet} are used in the first two layers.

	For the {segmentation} (see Fig.~\ref{fig:figure4}), each encoder layer is similar with the setting in classification, but network has a deeper structure (1024-256-64-16). In the decoder part, we use 3-nearest interpolation~\cite{pointnet2} to get the up-sampled features and also use the L-NL Block for better feature learning. Furthermore, skip connections are used to pass the features between intermediate layers of the encoder and the decoder.

	\begin{figure}[t]
		\begin{center}
			\includegraphics[width=\columnwidth]{picture/figure4.pdf}
		\end{center}
		\caption{{Architecture of our PointASNL for point cloud semantic segmentation.} The L-NL modules are used in both encoder and decoder.}
		\label{fig:figure4}
		\vspace{-0.3cm}
	\end{figure}

	\section{Experiment}
	\label{exp}

	We evaluate our PointASNL on various tasks, including synthetic dataset, large-scale indoor and outdoor scene segmentation dataset. In all experiments, we implement the models with Tensorflow on one GTX 1080Ti GPU.

	\subsection{Classification}
	We evaluate our model on synthetic dataset \textit{ModelNet10} and \textit{ModelNet40}~\cite{modelnet} for classification, where \textit {ModelNet40} is composed of 9843 train models and 2468 test models in 40 classes and  \textit {ModelNet10} is a subset of \textit{ModelNet40} that consists of 10 classes with 3991 training and 908 testing objects.

	{\noindent\bf Shape Classification.}  Training and testing data in classification are provided by \cite{pointnet}. For training, we select 1024 points as the input. The augmentation strategy includes the following components: random anisotropic scaling in range $[-0.8, 1.25]$, translation in the range $[-0.1, 0.1]$, and random dropout $20\%$ points. For testing, similar to \cite{pointnet,pointnet2}, we apply voting test using random scaling and then average the predictions. 	In Tab.~\ref{tab:tab1}, our method outperforms almost all state-of-the-art methods in 1024 input points except RS-CNN. Note that RS-CNN~\cite{rscnn} can achieve 93.6\% from 92.9\% on uniformly sampling with tricky voting strategy (the best of 300 repeated tests), which is different from normal random sampling and once voting setting.

	\begin{table}
		\small
		\caption{Overall accuracy on ModelNet10 (M10) and ModelNet40 (M40) datasets. ``pnt'' stands for coordinates of point and ``nor'' stands for normal vector.}
		\begin{center}
			\begin{tabular}{lcccc}
				\hline
				Method &input&\#points &\textit {M10}  &\textit {M40} \\
				\hline
				\hline

				O-CNN~\cite{wang2017cnn} &pnt, nor&-&-  &90.6 \\
				SO-Net~\cite{So-net} &pnt, nor&2k&94.1  &90.9 \\
				Kd-Net~\cite{KD-Tree} &pnt&32k&  94.0& 91.8 \\
				PointNet++~\cite{pointnet2} &pnt, nor&5k& - & 91.9  \\
				SpiderCNN~\cite{Spidercnn} &pnt, nor &5k& -&  92.4 \\
				KPConv~\cite{KPCONV}  &pnt&7k& -&  92.9 \\

				SO-Net~\cite{So-net} &pnt, nor&5k&\textbf{95.7}  &\textbf{93.4} \\

				\hline
				Pointwise CNN~\cite{Pointwise}  &pnt&1k& -  &86.1\\
				ECC~\cite{ecc} &graphs&1k&  90.8&  87.4 \\
				PointNet~\cite{pointnet} &pnt&1k&-  &89.2 \\
				PAT~\cite{Gumbel} &pnt, nor&1k&-  &91.7 \\
				Spec-GCN~\cite{SpecGCN} &pnt&1k&-  &91.8 \\
				PointGrid~\cite{pointgrid} &pnt&1k& - &92.0 \\
				PointCNN~\cite{PointCNN} &pnt&1k&  - &92.2 \\
				DGCNN~\cite{DGCNN} &pnt&1k&  -&  92.2 \\
				PCNN~\cite{PCNN} &pnt&1k& 94.9&  92.3 \\
				PointConv~\cite{PointConv} &pnt, nor&1k& - &92.5 \\
				A-CNN~\cite{acnn} &pnt, nor&1k& 95.5 &92.6 \\
				Point2Sequence~\cite{Point2Sequence} &pnt&1k& 95.3& 92.6 \\
				RS-CNN~\cite{rscnn} &pnt&1k& - & 93.6 \\
				\hline
				PointASNL &pnt&1k&  {95.7}&{92.9} \\
				PointASNL &pnt, nor&1k&  \bf{95.9}&\bf{93.2} \\
				\hline
			\end{tabular}
		\end{center}

		\label{tab:tab1}
		\vspace{-0.6cm}
	\end{table}

	{\noindent\bf Shape Classification with Noise.}
	Most of the methods can achieve decent performance on synthetic datasets, as they have stable distribution and do not contain any noise. Though, such a good performance often leads to a lack of robustness of the model. To further verify the robustness of our model, we did the experiments like KC-Net~\cite{KC-Net} to replace a certain number of randomly picked points with random noise ranging $[-1.0, 1.0]$ during testing. The comparisons with PointNet~\cite{pointnet}, PointConv~\cite{PointConv} and KC-Net~\cite{KC-Net} are shown in Fig.~\ref{fig:figure8} (b). As shown in this figure, our model is very robust to noise, especially after adding the AS module. It can be seen from (c) and (d) that the adaptive sampling guarantees the proper shape of the sampled point clouds, making the model more robust.

	\subsection{Segmentation}
	{\noindent\bf Indoor Scene Segmentation.}\footnote {Supplementary material shows that with more sampled points and deeper structure, our PointASNL can still achieve further improvement to 66.6\% on \textit{ScanNet} benchmark.} Unlike classification on synthetic datasets~\cite{modelnet,shapenet}, indoor 3D scene segmentation is a more difficult task, because it is real-world point clouds and contains lots of outliers and noise. We use \textit{Stanford 3D Large-Scale Indoor Spaces (S3DIS)}~\cite{s3dis} and \textit{ScanNet v2} (\textit{ScanNet})~\cite{scannet} datasets to evaluate our model.

	\textit{S3DIS} dataset is sampled from 3 different buildings, which includes 6 large-scale indoor areas with 271 rooms.  Each point in this dataset has a semantic label that belongs to one of the 13 categories. We compare mean per-class IoU (mIoU) on both 6-fold cross-validation over all six areas and Area 5 (see supplementary material). \textit{ScanNet} dataset contains 1513 scanned indoor point clouds for training and 100 test scans with all semantic labels unavailable. Each point has been labeled with one of the 21 categories. We submitted our results to the official evaluation server to compare against other state-of-the-art methods on the benchmark.

	\begin{figure}[!bp]
		\begin{center}
			\includegraphics[width=1 \linewidth]{picture/figure8.pdf}
		\end{center}
		\caption{(a) Point cloud with some points being replaced with random noise. (b) Classification results of different models with noisy points, where PL, PNL, AS mean point local cell, point nonlocal cell and adaptive sampling, respectively. (c) Farthest point sampling on noisy data. (d) Adaptive sampling on noisy data, which maintain the distribution of the point cloud.}
		\label{fig:figure8}

	\end{figure}

	\begin{table}[!tbp]
		\centering
		\small

		\caption{Segmentation results on indoor S3DIS and ScanNet datasets in mean per-class IoU (mIoU,\%).}\label{tab:tabindoor}
		\vspace{0.2cm}
		\setlength{\tabcolsep}{5mm}{
			\begin{tabular}{l|c|c}
				\hline
				{Method}    & {S3DIS}         & {ScanNet}  \\       \hline \hline
				\multicolumn{3}{c}{\textit {methods use unspecific number of points as input}}\\\hline
				{TangentConv~\cite{TangentConv}} & {52.8}          & 40.9                 \\
				{SPGraph~\cite{SPGraph}}     & {62.1}          & -                              \\
				{KPConv~\cite{KPCONV}}      & {\textbf{70.6}} & \textbf{68.4}                           \\ \hline

				\multicolumn{3}{c}{\textit {methods use fixed number of points as input}}\\\hline
				{PointNet++~\cite{pointnet2}}  & {53.4}             & 33.9      \\
				{DGCNN~\cite{DGCNN}}       & {56.1}          & -       \\

				{RSNet~\cite{RSNet}}       & {56.5}          & -         \\
				{PAT~\cite{Gumbel}}         & {64.3}          & -       \\
				{PointCNN~\cite{PointCNN}}    & {65.4}          & 45.8     \\
				{PointWeb~\cite{PointWeb}}    & {66.7}          & -      \\

				{PointConv~\cite{PointConv}}   & {-}             & 55.6  \\
				{HPEIN~\cite{HPEIN}}       & {67.8}          & 61.8    \\  \hline
				{PointASNL}        & {\textbf{68.7}} & \textbf{63.0}  \\ \hline
				\multicolumn{3}{c}{ }
		\end{tabular}}
		\vspace{-0.6cm}
	\end{table}

	\begin{figure*}
		\begin{center}
			\includegraphics[width=0.95 \linewidth, height=5.6cm]{picture/figure6.pdf}%
		\end{center}
		\vspace{-0.5cm}
		\caption{Examples of indoor semantic segmentation on \textit{S3DIS} and \textit{ScanNet} datasets.}
		\label{fig:figindoor}

	\end{figure*}

	During the training process, we generate training data by randomly sample $1.5m\times1.5m\times3m$ cubes with 8192 points from the indoor rooms. $0.1m$ padding of sampled cubes is used to increase the stability of the cube edge prediction, which is not considered in the loss calculation. On both datasets, we use points position and RGB information as features. We did not use the relative position in PointNet~\cite{pointnet} as a feature to train the model in \textit{S3DIS}, because our model already learns relative position information well. During the evaluation process, we use a sliding window over the entire rooms with $0.5m$ stride to complement $5$ voting test.

	In Tab.~\ref{tab:tabindoor}, we compare our PointASNL with other state-of-the-art methods under the same training and testing strategy (randomly chopping cubes with a fixed number of points), e.g., PointNet++~\cite{pointnet2}, PointCNN~\cite{PointCNN},  PointConv~\cite{PointConv}, PointWeb~\cite{PointWeb} and HPEIN~\cite{HPEIN}. We also list results of another kind of methods (using points of unfixed number or entire scene as input),  e.g., {TangentConv~\cite{TangentConv}} and {KPconv~\cite{KPCONV}}. All methods use only point clouds as input without voxelization.

	As shown in Tab.~\ref{tab:tabindoor}, PointASNL outperforms all methods with same the training strategy in both \textit{S3DIS} and \textit{ScanNet}. In particular, our result is 8\% higher than previous state-of-the-art PointConv~\cite{PointConv} on the ScanNet with the same experiment setting, in which the convolution design is similar to our PL cell. Nevertheless, without proper sampling and global information support, it cannot achieve such results with the same network architecture.

	On the other hand, training using more points as input can obtain more information. Instead of learning from randomly selected cubes with fixed number,  KP-Conv~\cite{KPCONV} performs grid sampling based on the assumption of local label consistency so that larger shape of the point cloud can be included as input.

	The qualitative results are visualized in Fig.~\ref{fig:figindoor}. Our method can correctly segments objects even in complex scenes.

	{\noindent\bf Outdoor Scene Segmentation.} Compared with its indoor counterpart, an outdoor point cloud covers a wider area and has a relatively sparser point distribution with noise. For this reason, it is more challenging to inference from outdoor scenes.

	\begin{table}[]
		\centering
		\small

		\setlength{\tabcolsep}{4mm}{
			\caption{{Semantic Segmentation results on SemanticKITTI, where "pnt" stands for coordinates of point and SPGraph~\cite{SPGraph} uses all of points as input.}}
			\vspace{0.2cm}
			\label{tab:tabkitti}
			\begin{tabular}{l|c|c}
				\hline
				{Method}&  input  & mIoU(\%)  \\
				\hline \hline

				{PointNet~\cite{pointnet}} &50k pnt     & 14.6    \\
				{SPGraph~\cite{SPGraph}}  &-     & 17.4  \\
				{SPLATNet~\cite{su2018splatnet}} &50k pnt      & 18.4     \\
				{PointNet++~\cite{pointnet2}{}} &45k pnt    & 20.1     \\
				{TangentConv~\cite{TangentConv}} &120k pnt   & {40.9}   \\
				\hline
				{PointASNL} &8k pnt& \textbf{46.8}          \\ \hline
		\end{tabular}}
		\vspace{-0.5cm}
	\end{table}

	\begin{figure}[!hbp]
		\vspace{-0.5cm}
		\begin{center}
			\includegraphics[width=1 \linewidth]{picture/figure7.pdf}
		\end{center}
		\caption{ {Example of outdoor SemanticKITTI datasets}. }
		\label{fig:SemanticKITTI}
	\end{figure}

	We evaluated our model on SemanticKITTI~\cite{behley2019iccv}, which is a large-scale outdoor scene dataset, including 43,552 scans captured in the wild. The dataset consists of 22 sequences (00 to 10 as the training set, and 11 to 21 as the test set), each of which contains a series of sequential laser-scans. Each individual scan is a point clouds generated with a commonly used automotive LiDAR. The whole sequence can be generated by aggregating multiple consecutive scans.

	In our experiments, we only evaluated our model under a single scan semantic segmentation. In the single scan experiment~\cite{behley2019iccv}, sequential relations among scans in the same sequence are not considered. The total number of 19 classes is used for training and evaluation. Specifically, the input data generated from the scan is a list of coordinates of the three-dimensional points along with their remissions.

	During training and testing, we use a similar sliding windows based strategy as indoor segmentation. Since point clouds in the outdoor scene are more sparse, we set the size of the cube with $10m\times10m\times6m$  and $1m$ padding. In Tab.~\ref{tab:tabkitti}, we compare PointASNL with other state-of-the-art methods. Our approach outperforms others by a large margin. Supplementary material shows that our method achieves the best result in 13 of 19 categories. Furthermore, Fig.~\ref{fig:SemanticKITTI} illustrates our qualitative visualization of two samples, even if the scene is covered with a lot of noise caused by unmanned collection, our model can still predict perfectly.

	\subsection{Ablation Study}
	To further illustrate the effectiveness of proposed AS and L-NL module, we designed an ablation study on both the shape classification and the semantic segmentation. The results of the ablation study are summarized in Tab.~\ref{tab:tab3}.

	We set two baselines: A and B. Model A only encodes global features by PNL, and model B only encodes local features. The baseline model A gets a low accuracy of 90.1\% and 45.7\% IoU on segmentation, and model B gets 92.0\% and 56.1\%, respectively. When we combine local and global information (models C), there is a notable improvement in both classification and segmentation. Finally, when we add the AS module, the model will have a significant improvement in the segmentation task (93.2\% and 63.5\% in model D).

	\begin{table}
		\small
		\renewcommand\tabcolsep{3.5pt}
		\caption{Ablation study on \textit {ModelNet40} and \textit{ScanNet v2} validation set. PL, PNL and AS mean point local cell, point nonlocal cell and adaptive sampling.}
		\begin{center}
			\begin{tabular}{cc|cc}

				Model& Ablation  & \textit {ModelNet40}& \textit{ScanNet} \\
				\hline
				\hline
				A& PNL only & {90.1}& {45.7} \\
				B& PL only  & {92.0}& {56.1} \\

				C& PL+PNL & {93.2}& {60.8} \\
				D&PL+PNL+AS & {\bf{93.2}}& {\bf{63.5}} \\
				\hline
				E& PointNet2~\cite{pointnet} & {90.9}& {48.9} \\
				F& PointNet2+PNL & {93.0}& {54.6} \\
				G& PointNet2+PNL+AS & {92.8}& {55.4} \\\hline

				H&DGCNN~\cite{DGCNN}& {92.2}& {52.7} \\
				I&DGCNN+PNL& {92.9}& {56.7} \\
				J&DGCNN+PNL+AS & {93.1}& {58.3} \\

				\multicolumn{3}{c}{ }\\

			\end{tabular}
			\label{tab:tab3}
		\end{center}
		\vspace{-1cm}
	\end{table}
	Furthermore, our proposed components L-NL module and AS module can directly improve the performance of other architecture. When we use PointNet++~\cite{pointnet2} in our PL cell (model F), it will reduce the error of classification and segmentation tasks by 23.1\% and 12.6\%, respectively, with its original model (model E). It should be noted that the AS module does not increase the accuracy of the classification task, even reduced the accuracy of classification when adding on PointNet++ (model F). This is because the synthetic dataset does not have a lot of noise like scene segmentation, for some simpler local aggregation (e.g., max pool), it may make them unable to adapt the uneven point cloud distribution after using AS.  Furthermore, we also use DGCNN~\cite{DGCNN} as our local aggregation baseline (model H), and fused architecture (model I and J) can largely improve the performance on two datasets.

	\subsection{Robustness for Sparser Point Clouds}
	\begin{figure}[!tbp]
		\begin{center}
			\includegraphics[width=\linewidth]{picture/sp_fig1.pdf}
		\end{center}
		\caption{ { (a) A sample of input point cloud. (b)  A sample of input point cloud after randomly select 64 points (c) Results of testing with sparser points}. }
		\label{fig:sp_fig1}
		\vspace{-0.4cm}
	\end{figure}
	To further verify the robustness of the PointASNL model, we take sparser points (i.e., 1024, 512, 256, 128 and 64) as the input to various models trained with 1024 points. Then we compare our method with PointNet~\cite{pointnet}, PointNet++~\cite{pointnet2}, SO-Net~\cite{So-net} and the recent state-of-the-art RS-CNN~\cite{rscnn}. We follow these methods to apply random input dropout during the training.

	As can be seen from the Fig~.\ref{fig:sp_fig1} (c), PNL can greatly improve the robustness of our model with different density inputs. In particular, when the input contains only 64 points, PNL can even help to improve the accuracy of our model, from 73.9\% to 85.2\%, which largely exceeds the current state-of-the-art RS-CNN~\cite{rscnn} (about 75\%). The experimental results fully demonstrate that the use of local and global learning methods can greatly improve the robustness of the model.  As shown in Fig~.\ref{fig:sp_fig1} (a) and (b), when the input points reduce to 64, even humans can hardly recognize the airplane, but our model can classify it correctly. Such superior robustness makes our proposed PointASNL model suitable for raw noisy point clouds with limited sampling points, especially for large scale outdoor scenario.

	\section{Conclusion}

	We have presented the adaptive sampling (AS) and the local-nonlocal (L-NL) module to construct the architecture of PointASNL for robust 3D point cloud processing. By combining local neighbors and global context interaction, we improve traditional methods dramatically on several benchmarks. Furthermore, adaptive sampling is a differentiable sampling strategy to fine-tune the spatial distribution of sampled points, largely improve the robustness of the network. Experiments with our state-of-the-art results on competitive datasets and further analysis illustrate the effectiveness and rationality of our PointASNL.\\

	{\noindent{\bf Acknowledgment.} The work was supported by grants No. 2018YFB1800800, NSFC-61902335, No. 2019E0012, No. ZDSYS201707251409055, No. 2017ZT07X152, No. 2018B030338001, and CCF-Tencent Open Fund.}

	{\footnotesize
		\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


(18/P060) TRENTON – – In the interest of public safety, the New Jersey Department of Environmental Protection reminds visitors to swim only at beaches where lifeguards are on duty, Commissioner Catherine R. McCabe announced today.
At least five people have drowned this month in New Jersey waterways due to issues such as exhaustion, riptides and swimming at areas where there were no lifeguards present.
With risks at waterways ranging from ponds to the ocean, the public must be extra cautious around all bodies of water, no matter how inviting and refreshing they may appear. There may be unseen hazards such as rip currents in the ocean, a deceptively swift current in a river, or unexpected cold water just below the surface in a lake or pond. These dangers could lead to exhaustion, fatigue or shock, and ultimately result in drowning.
Ensure that everyone in the family becomes water competent and learns to swim well, knows limitations, recognizes and avoids water hazards, and understands how to prevent and respond to water emergencies.
Swim only at lifeguarded beaches.
Use U.S. Coast Guard-approved life jackets when on a boat or in a water situation beyond your skill level.
Swim with a partner near a lifeguard's chair. Obey all instructions and orders from lifeguards, and ask them about local conditions.
Pay close attention to children, seniors and new swimmers at the beach. Even in shallow water, wave action can cause anyone to lose her or his footing.
Swim sober and know your limitations. Make sure you have enough energy to swim back to shore.
Leave the water immediately if a storm approaches. Get off the beach and as far away from the water as possible.
Be cautious around natural bodies of water including ocean shorelines, rivers and lakes, even if you will not be swimming. Cold temperatures, currents and underwater hazards can make a fall into these bodies of water dangerous.
Wear personal flotation devices when you boat, fish or wade into water with strong currents. Wear protective water suits when you kayak or canoe in areas where cold water may be expected.
Protect your neck and don't dive headfirst. Walk carefully into open water. Watch for and avoid aquatic life.
Swimmers should also take extra caution to recognize rip currents, the leading hazard at ocean beaches, and swim only at designated areas with lifeguards. These currents can become more prevalent when strong storms are passing off the Atlantic coast. The National Weather Service issues advisories when rip currents are expected to increase.
Rip currents can form in any large open water area and tend to form at low spots and breaks in sandbars, or near structures such as jetties and piers. They are often visible as ribbons of choppy water that flow perpendicular to the beach.
Rip currents pull people away from shore; they do not pull people under the water. Drowning occurs when people pulled offshore are unable to stay afloat and swim to shore due to any combination of fear, panic, exhaustion, or lack of swimming skills.
Stay calm and swim parallel to the beach until out of the current.
Turn and swim toward shore once you are free of the current. If you can't swim to shore, float or tread water until you are free of the rip current and then head toward shore.
Draw attention to yourself by waving and calling for help if you feel you can't make it to the shore.
Get help from a lifeguard if you see someone in trouble in a rip current. If a lifeguard is not available, have someone call 9-1-1.
Avoid becoming a victim yourself and throw the swimmer something that floats, such as a lifejacket, cooler, raft, or inflatable ball.


Personalized with 2 letters (one atop the other), these initial stickers come in a huge selection of colors. Whatever color you select will be used for the actual initials and then a lighter shade will be used for the sticker border. For instance, the photo shows the sticker in Fuchsia. Add these custom stickers to favor boxes, envelopes or other packaging for a unique look. Min order is 36 stickers. Product dimensions - 1 1/2 x 1 1/2" H.


LOS ANGELES (AFP) – Big-grossing films "Jurassic World: Fallen Kingdom" and "Incredibles 2" again dominated the North American box office this weekend, but two new entrants beat expectations to place third and fourth, according to industry estimates.
Universal's latest earth-stomping dinosaur movie took in $60 million for the three-day weekend, industry watcher Exhibitor Relations reported.
The superhero-family-next-door of "Incredibles 2," a Pixar/Disney animation, earned $45.5 million.
Together they helped estimated industry receipts to the end of June rise 15 percent against the same month last year.
But Variety said that two new films, Sony's "Sicario: Day of the Saldado" and Lionsgate's "Uncle Drew," had easily outperformed industry expectations to place third and fourth.
The "Sicario" sequel, an action thriller starring Benicio Del Toro and Josh Brolin, took in $19 million, outperforming the well-reviewed original film by $7 million. Its highly-topical premise has CIA agent Brolin teaming up with hitman Del Toro to fight drug cartels smuggling "terrorists" across the Mexican border.
On a lighter note, "Uncle Drew" draws on the talents of basketball stars present and past — Kyrie Irving, Shaquille O'Neal, Chris Webber, Lisa Leslie and Reggie Miller, with a court-full of others in cameo roles.
Hard-luck streetball team manager Uncle Drew (Irving, in a role that originated in a Pepsi ad) rounds up his old crew ahead of a big tournament in Brooklyn. The film took in $15.5 million.
"Ocean's 8" from Warner Bros. slipped two spots to fifth place, taking in $8 million in its fourth week out. The female-centric heist film stars Sandra Bullock, Cate Blanchett and Anne Hathaway.


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text:
Yoo Seung Ho Gets Completely Ignored in His Own Fan Cafe
His reaction though…
Yoo Seung Ho recently uploaded a photo on his official Instagram account along with the caption, "That's right…. No one cares…" and fans can't help but laugh and feel sorry for the actor at the same time.
The photo he shared was a post he made on the fan cafe that he just opened, and it reads, "Hello. It's nice to meet you. I'm Yoo Myung Wol."
But according to a screenshot that was taken 4 minutes afterward, no one read or commented on his post.
After anxiously waiting for his fans' responses, he expressed his sadness in being disregarded by posting about it on his Instagram account.
It appears that Yoo Seung Ho's agency, BS Company officially opened his fan cafe today and even posted a link to it along with the caption, "Yoo Seung Ho's fan cafe is now open!"
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text:
Justice Stevens: The First Three Terms
George C. Lamb, III
Charles L. Schlumberger
D. J. Simonetti
James D. Spratt Jr.
Joel R. Tew
Douglas W. Ey, Jr. Special Projects Editor
This Special Project undertakes an examination of Justice Stevens' Supreme Court opinions in an effort to identify his philosophical orientations, to evaluate the consistency of his views, and to determine the extent to which he has developed workable analytical methods. To achieve these goals, Justice Stevens' opinions are examined in three contexts: first, the area of federal-state relations,including commerce clause and supremacy clause questions; second, the individual rights area, emphasizing criminal constitutional and first amendment issues, and problems of fifth and fourteenth amendment analysis; and third, questions concerning the proper role of the Supreme Court in the constitutional scheme. Even though any vote cast by a Supreme Court Justice may illustrate a significant aspect of his ideology, this Special Project limits attention to opinions authored by Justice Stevens on the assumption that they most accurately represent his views.'
George C. Lamb, III; Charles L. Schlumberger; D. J. Simonetti; James D. Spratt Jr.; Joel R. Tew; and Douglas W. Ey, Jr. Special Projects Editor, Justice Stevens: The First Three Terms, 32 Vanderbilt Law Review 671 (1979)
Commercial Law Commons, Constitutional Law Commons, Supreme Court of the United States Commons


text:
Annie Haslam/Renaissance TheYestival-Interview w/classical guitarist David Cohen
Annie Haslam Renaissance – Grandine il Vento
Interview with classical guitarist David Cohen
Michael Dunford (1944 -2012) guitarist and principle composer for Renaissance passed away on November 20, 2012 from a cerebral hemorrhage. Annie Haslam stated that the band will continue touring in the future, despite losing "our 'guiding light' Michael Dunford". In February 2013, it was announced that Ryche Chlanda would be the guitarist on their 2013 tour.
Yestival
Susquehanna Bank Center Camden NJ
Annie Haslam is a singer-songwriter with credits worthy of some of the most well known entertainers in the world. Primarily known for her role as the lead singer with the English classical rock band 'Renaissance' she later became equally heralded as a solo artist. Annie has traveled the world delighting audiences, most recently as far off as Japan and Brazil with her five octave voice and warm personality. Despite such a busy and varied life in music, Annie recently has been bestowed with an entirely new gift; she has embarked on a whole new journey in the form of oil painting. This came as a complete surprise and wondrous discovery, both to her and her fans. On somewhat of a mission, Annie has been painting fervently for since 2002, with colors and movement that make you want to walk right into the canvas. She has been described as a 'Dream Expressionist'. Her art is organic, yet with a dream-like feel, it is contemporary but does not seem to be influenced by anything other than her own feelings and thoughts.
David Cohen: The first time we spoke you said "I was also one of those people that was a little afraid of change. The older I get the easier it is to change. It's a hard thing to do, change something you know." that has always stuck in my head.
Annie Haslam: If you can't confront those things you can never change and move on.
David: When we first spoke you stopped singing and had no intention of singing again. What changed your mind?
Annie: Well, I woke up one morning and decided that I wanted do more singing.
David: What brought Renaissance back together for this CD?
Annie: In 2000 we recorded Tuscany and a short tour of Japan in 2001. I called Micky on that one to see if he wanted to do something but not with all the original band. We just wanted to work with John Tout and Terry Sullivan. Roy Wood filled in on bass and some backing vocals. After we did that album we didn't intend to go any further with it. Micky and I kept in touch because we were friends and also in charge of re-releases and such. A few times after that he called me to see if I was interested in getting the band back together and I didn't want to do it. Then in 2009 he called me and said, "Listen I know what you're going to say", and I said I didn't want to do it. But then I said yes but only if he got John Scher involved (former band manager). So we started getting all the arrangements together and then there was a conflict with the dates with Jon Camp. So he pulled out and then Terry and John followed. At that point we didn't know if we could pull it off with just MD and myself, but John Scher had the confidence and so the rest is history. John knew I had kept the band name alive over the years and also had a fairly strong fan base, whilst touring and recording with my own solo band. It was decided to go forward, but we didn't make that decision lightly, but this was our only chance or it wasn't going to happen at all.
I contacted Rave Tesar and David J Keyes who were in my band and then we contacted Frank Pagano and then found Tom Brislin who was on the Yes Symphonic Tour. We did the tour – it was named our 40th anniversary tour.
David: The first show without Michael must have been difficult.
Annie: The first show was very difficult for us, we were almost all in tears. It was very difficult for me. I knew Micky for forty-one years. Micky was right next to me onstage and I felt that gap. What we did was put Ryche (who was now playing guitar) next to Jason with Dave in the middle. This worked better for me and for Dave as we sang together and the closeness helped the healing process we all needed. That's how it has always been anyway, bass in the middle.
I was consumed by a lot of emotion, sadness and doubt that I may be doing the wrong thing carrying on without Micky.
Having someone else on stage playing his parts was quite unreal, but Ryche did a good job.
David: There's so much interaction with fans now with the Internet and Facebook. How did the fans react that you were going on with Renaissance?
Annie: Very positive – supportive. There were a few people who wrote and said it's time to let it go, without Micky it's not Renaissance. That's ridiculous because Renaissance is the songs and my voice as well. It was very powerful with the both of us. I was thinking of not doing it. But then I thought of the fans and the music that needed to be heard. The songs were so powerful and emotional, how could you not go on? It's the joy of going out and singing and making people happy that is the most important thing to me.
Part II …….coming Part III……coming
Lili Añel-I Can See Bliss From Here, Interview w/Classical Guitarist David Cohen
Sanders Bohlke in Philly-Interview w/ David Cohen Classical Guitarist Part 1
Lili Añel-I Can See Bliss From Here, PT II w/Classical Guitarist David Cohen
GashouseRadio.com-Give Piece a Chance by David Cohen
TN Governor Haslam vetoes Ag Gag bill
Classical recording artist David Garrett set to release new album
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text:
Q: Check type of parameter in haskell I have a function (a->a) which can take in different types but is there a way to determine what datatype the parameter is?
Like
foo :: a -> a
foo a == int =..
foo a == char =..

Is there anyway to do a check like this in haskell?

A: No.
Use a datatype instead:
data MyType = MyInt Int | MyChar Char

foo :: MyType -> MyType
foo (MyInt i) = MyInt (i + 1) -- Or whatever
foo (MyChar c) = case c of {'a' -> MyChar 'A'; k -> MyChar k}

Though this of course restricts what types you can use.
Alternatively, use a typeclass:
class MyClass a where
    foo :: a -> a

instance MyClass Int where
    foo i = i + 1

instance MyClass Char where
    foo c = case c of {'a' -> 'A'; k -> k}

-- N.B. foo :: MyClass a => a -> a

Though once again this restricts what types you can use.

A: A way to do this, which however I do not recommend, is to add a Typeable constraint.
foo :: forall a . Typeable a => a -> a
foo x = case eqT :: a :~: Int of -- check if a = Int
   Just Refl -> x + 1            -- here we can use x as an Int
   Nothing   -> x                -- here we can not use x as an Int

For this to work you need a number of extensions, such as ScopedTypeVariables and GADTs.
This is usually not a good idea, in general. Before adopting this, you should first understand if you really need it: Typeable is very powerful and it should be used as a last resort, when simpler alternatives are not enough. Perhaps a custom type class could be a better solution for your tasks, as AJFarmar showed.
Also note that, without a type class constraint, a function foo :: a -> a, according to its "free theorem" (AKA parametricity property) can only be the identity function, or be undefined (e.g. infinite recursion, or a runtime exception).

redpajama_set_name: RedPajamaStackExchange


We've long been fans of Aspesi, the cool, sporty Italian line that specializes in outerwear and — even after all these years — still flies fairly under the radar. Turns out that it's just as wearable in warmer weather, because their daytime stuff is as simple and unfussy as their winter gear. Like this little belled-out striped top, for example... as cute as it gets.


text:
// Styles
import './VBanner.sass'

// Extensions
import VSheet from '../VSheet'

// Components
import VAvatar from '../VAvatar'
import VIcon from '../VIcon'
import { VExpandTransition } from '../transitions'

// Mixins
import Mobile from '../../mixins/mobile'
import Toggleable from '../../mixins/toggleable'

// Utilities
import mixins from '../../util/mixins'
import {
  convertToUnit,
  getSlot,
} from '../../util/helpers'

// Typeslint
import { VNode } from 'vue'

/* @vue/component */
export default mixins(
  VSheet,
  Mobile,
  Toggleable
).extend({
  name: 'v-banner',

  inheritAttrs: false,

  props: {
    app: Boolean,
    icon: String,
    iconColor: String,
    singleLine: Boolean,
    sticky: Boolean,
    value: {
      type: Boolean,
      default: true,
    },
  },

  computed: {
    classes (): object {
      return {
        ...VSheet.options.computed.classes.call(this),
        'v-banner--has-icon': this.hasIcon,
        'v-banner--is-mobile': this.isMobile,
        'v-banner--single-line': this.singleLine,
        'v-banner--sticky': this.isSticky,
      }
    },
    hasIcon (): boolean {
      return Boolean(this.icon || this.$slots.icon)
    },
    isSticky (): boolean {
      return this.sticky || this.app
    },
    styles (): object {
      const styles: Record<string, any> = { ...VSheet.options.computed.styles.call(this) }

      if (this.isSticky) {
        const top = !this.app
          ? 0
          : (this.$vuetify.application.bar + this.$vuetify.application.top)

        styles.top = convertToUnit(top)
        styles.position = 'sticky'
        styles.zIndex = 1
      }

      return styles
    },
  },

  methods: {
    /** @public */
    toggle () {
      this.isActive = !this.isActive
    },
    iconClick (e: MouseEvent) {
      this.$emit('click:icon', e)
    },
    genIcon () {
      if (!this.hasIcon) return undefined

      let content

      if (this.icon) {
        content = this.$createElement(VIcon, {
          props: {
            color: this.iconColor,
            size: 28,
          },
        }, [this.icon])
      } else {
        content = this.$slots.icon
      }

      return this.$createElement(VAvatar, {
        staticClass: 'v-banner__icon',
        props: {
          color: this.color,
          size: 40,
        },
        on: {
          click: this.iconClick,
        },
      }, [content])
    },
    genText () {
      return this.$createElement('div', {
        staticClass: 'v-banner__text',
      }, this.$slots.default)
    },
    genActions () {
      const children = getSlot(this, 'actions', {
        dismiss: () => this.isActive = false,
      })

      if (!children) return undefined

      return this.$createElement('div', {
        staticClass: 'v-banner__actions',
      }, children)
    },
    genContent () {
      return this.$createElement('div', {
        staticClass: 'v-banner__content',
      }, [
        this.genIcon(),
        this.genText(),
      ])
    },
    genWrapper () {
      return this.$createElement('div', {
        staticClass: 'v-banner__wrapper',
      }, [
        this.genContent(),
        this.genActions(),
      ])
    },
  },

  render (h): VNode {
    const data = {
      staticClass: 'v-banner',
      attrs: this.attrs$,
      class: this.classes,
      style: this.styles,
      directives: [{
        name: 'show',
        value: this.isActive,
      }],
    }

    return h(VExpandTransition, [
      h(
        'div',
        this.outlined ? data : this.setBackgroundColor(this.color, data),
        [this.genWrapper()],
      ),
    ])
  },
})

redpajama_set_name: RedPajamaGithub


Methylfolate Plus™ supplies folate, in the active form of (6S)-5-Methyltetrahydrofolic acid, glucosamine salt. Methylfolate is a highly absorbable, activated form of folate, which does not require enzymatic conversion to 5-methylfolate in the body. The glucosamine salt form utilized in Methylfolate Plus™ is more stable than other forms of methylfolate often used.
We are dedicated to providing the purest, most effective nutraceutical products, bringing about health and overall balance to the body.