At Impact Physiotherapy we provide high quality Physiotherapy, Hydrotherapy and Clinical exercise services.
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Dr. Kari Watkins is a newly tenured Associate Professor in Civil and Environmental Engineering at Georgia Tech and the Frederick Law Olmsted Junior Professor. She returned to her undergraduate alma mater to become a faculty member in 2011 after completing her PhD at the University of Washington. Dr. Watkins focuses her research on the creation of a more livable and sustainable transportation system using the application of information technologies to improve public transit and bikeability. Her work in transportation technology has been lauded with numerous awards, most recently the 2017 CUTC New Faculty Award. She began her work in this field co-developing OneBusAway, a real-time transit rider information system that relies on open data from transit agencies.
Sean Óg Crudden is an independent, open source developer from Ireland with 20 years development and consulting experience. His current project is managing all aspects of the implementation and expansion of the Leap (multi-modal smart card) in Irish Rail. This is fully deployed in Dublin and Sean is currently involved with the scheme expansion to Cork, the second largest city in Ireland. He is an expert in open source transit technologies and an elected member of the board of OneBusAway since 2015. Sean Óg is the main open source contributor to transiTime and a GTFS real-time expert. Sean is currently partnering with Georgia Tech to implement a new holding method for the Atlanta Streetcar and VIA in San Antonio as an extension to transiTime using an algorithm proposed by PhD student, Simon Berrebi. This system manages the headway of the transit vehicles to allow the move from a scheduled based service to a frequency based service resulting in consistent waiting times for passengers.
Is there any way to access the content of this webcast without using Flash and Adobe Connect? We do not support installation of this software due to its security vulnerabilities.
Hi Keith, Currently our webinars are only available using Flash and Adobe Connect.


Today I am going to tell you how to create Facebook Style Tag Selector using jQuery. This kind of tag selectors are used in many popular sites like facebook, twitter, linkedIn & even in gmail also.
In my previous article I had explained how to design css3 tags . If you want to add tags separated by comma, I am sure that this tutorial will help you a lot.
Keycode – 13 is for Enter Key. Once user pressed the enter key, createTag function will called with typed text.
Hi.. In which browser? Is there any error?


text:
Hot Reads: Williams At Home in Huskers' All-You-Can-Eat Offense
"There's really nothing to do there," new Nebraska wide receiver Mike Williams said of Scooba, a small town in Mississippi made famous by Netflix. "There's like two gas stations and a Subway and a Dollar General. That's pretty much it."'
Sounds like the right complement of amenities for a town that listed 732 residents as of the 2010 census. Scooba, located on the Alabama-Mississippi border, is home to East Mississippi Community College, immortalized as "Last Chance U" first in an article for GQ, which was then turned into a Netflix series. The Lions are a football power of late with four national championships this decade, it's last coming last season. Williams was on that team.
It was not a team, however, that was part of the show. The second season of "Last Chance U" told the story of the 2016 season before moving on to a new school for the upcoming season of the show. It's something Williams still gets asked about all the time, however.
"I have a couple of East Mississippi jackets and I wear them sometimes," he said. "People ask me, 'Was I on the show?' I'll be like, 'No.' They'll be like, 'How was Coach Stephens?' and stuff like that."
Coach Stephens is Buddy Stephens, the show's Ralph Kramden-like patriarch with a big heart that sometimes gets eclipsed by an even bigger temper. So, how was Coach Stephens?
"Good coach."
Did you ever feel his wrath?
"Everybody did."
Ever wish you were part of the show?
"I wasn't there for the show," Williams said. "I was there to succeed and help the team win and get where I wanted to go."
Where he wanted to go ended up being Nebraska. Williams recalled that Scott Frost called him on Jan. 7 to offer a scholarship. He thought it over for a few days before committing on Jan. 10. Williams got to Lincoln and was in class the next day.
So far the transition has felt pretty smooth for Williams. He's enjoying the family atmosphere Nebraska's staff works hard to create. Classes are going well, "all As and one B." Practices, from the sound of it, are as good with Williams looking more and more like a player who is going to contribute in a big way in 2018.
At East Mississippi last year he was the big-play option for an offense that had four players catch 30 or more passes. Williams ended up with 30 catches, fourth on the team, but led the Lions with 669 yards on 22.3 yards per catch with seven touchdowns.
That should make for a good fit in the Huskers' new offense. Receivers Stanley Morgan Jr. and JD Spielman are sure to draw a ton of attention from opposing defenses, leaving a a ton of room for some player, or players, to put up big numbers as the keep-you-honest option in the passing game. UCF, like East Mississippi in 2017, also had four guys catch at least 30 balls last season.
That's not just familiar for Williams, it's his favorite part of the offense.
"Everybody eats," Williams said. "You want the next man to eat as bad as you want to eat."
Based on Williams' rapid rise since arriving in Lincoln, apparently you can get pretty hungry during a semester at place with two gas stations, a Subway and a Dollar General.
Northwestern officially opened its gleaming new practice facility this week.
Texas AD Chris Del Conte wants Mack Brown to be around the football program for reasons that aren't entirely clear to me.
The NFL is a longshot for former Husker David Knevel, but he's a virtual lock for the Canadian Football League.
ICYMI: Slew of stuff yesterday – practice report, practice video, a report on the leaders at inside linebacker, a discussion of what the Huskers hope to do with Breon Dixon and a baseball series preview.
Today's Song of Today
Tags: Football, Hot Reads, Mike Williams, nebraska

redpajama_set_name: RedPajamaCommonCrawl


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text:
\section{Introduction}
3D object detection is pivotal in helping intelligent agents perceive the environment in autonomous driving systems and robotics. Sensors like cameras~\cite{monodet,3dbbox,monodet2}, LiDARs~\cite{ku2018joint,pvrcnn,pointpillars}, and RaDARs~\cite{voxelnet,pointrcnn} are broadly used for this task, among which camera-based methods have drawn growing attention due to their lower cost.

According to the view that detection is conducted, camera-based 3D object detectors can be categorized into two major classes: Camera-View (CV) detectors and Bird's-Eye-View (BEV) detectors.
CV detectors, such as FCOS3D~\cite{fcos3d} and PGD~\cite{pgd}, follow the design of 2D detectors, extend the detection head for 3D tasks. These methods often show inferior performance to BEV methods, according to the nuScenes leaderboard. However, since CV detectors directly detect objects on images, they can retain a fully convolutional structure just like 2D detectors. This character makes them deploy-friendly and thus popular in the industry.

In contrast, detecting 3D objects in BEV~\cite{caddn,bevdet,bevdet4d} is a new paradigm designed for 3D scenarios, showing excellent performance in 3D tasks. To conduct detection in BEV space, they use a view transforming stage that transforms features from Camera-View into Bird's-Eye-View. An extended 2D detecting head~\cite{centerpoint} is then applied to the transformed BEV features.
Among existing transforming methods, Lift-Splat~\cite{lss} based methods have drawn growing attention due to their high efficiency and efficacy.
The Lift-Splat-based~\cite{lss} methods transform views in three steps: 1) Projecting 2D image features to 3D space using estimated depth. This step produces a 3D representation that allows one to view features from BEV. 2) Aligning 3D image features to pre-defined BEV grids/anchors. These anchors are often distributed regularly (\emph{i.e.}, uniformly) in space. Since the 3D representation carries perspective distortion derived from perspective images, this step adopts \emph{feature sampling} operation to align features with the BEV anchors and remove the distortion. 3) Collapse the 3D feature along height dimension to obtain the 2D BEV feature.
While other steps in the Lift-Splat transformation show a simple pipeline, the second step adopts the feature sampling operator that can not be deployed or accelerated on many devices, being the major impediment to making BEV detectors feasible in industrial scenarios. This situation makes us wonder:
\emph{Can we enjoy the benefits of the BEV paradigm while retaining a simple pipeline?}

To answer this question, we delve into the details of the alignment between features and anchors. As demonstrated in Fig.~\ref{fig:anchors}, CV detectors lay anchors in the image plain along the height and width axes. These anchors are naturally aligned to the down-sampled feature map extracted from the image. In contrast, the anchors of BEV detectors distribute among the width and depth axes since the semantics of the BEV feature correspond to objects on the horizontal reference plane. Since the Lift-Splat method is initially designed for segmentation tasks, these anchors conventionally distribute uniformly on the reference plane~\cite{lss}.
Such distribution introduces the requirement of feature sampling --- the perspective effect is carried in the projected feature but not in the anchors. In this paper, we propose introducing the perspective effect to the anchors rather than removing it from the features. In that case, the semantic information can be aligned with real-world anchors without feature sampling. Furthermore, since the feature sampling operations in existing methods often accompany by information loss and structural distortion (see Fig.~\ref{fig:sampling}) caused by over-sampling and under-sampling phenomena, the removal of feature sampling results in extra performance gains.

With this improvement, we build a high-performance detector that directly conducts detection on perspective BEV features --- PersDet.
Concretely, we first project image features into the perspective BEV space following a Lift-Splat~\cite{lss} like pattern. Then, instead of sampling the features, we directly collapse this 3D representation and put our PersHead onto the BEV features with perspective distortion. The PersHead deploys anchors for regression in the perspective space, where anchors are sparse in the remote area and dense on the contrary. In that case, the feature produced by outer production is naturally aligned to anchors.

We conclude our contributions as follows:
\begin{enumerate}
    \item We show the drawbacks of feature sampling and propose perspective BEV to avoid this process.
    \item We propose PersDet, which performs object detection on perspective BEV features, validating the effectiveness of perspective BEV features.
    \item The proposed PersDet achieved state-of-the-art performance on the nuScenes benchmark while showing the advantage of being deploy-friendly.
\end{enumerate}

\section{Related Works}
\subsection{Camera-View 3D Object Detection}
Image data is the most convenient and economical to obtain among data forms in autonomous driving and robotics. Image-based 3D object detectors use image data and associated camera parameters as input to detect objects within the image range and predict their 3D bounding boxes.
Camera-View detectors like FCOS3D~\cite{fcos3d} and PGD~\cite{pgd}, inspired by 2D object detector FCOS~\cite{fcos}, extend the FCOS detection head for extra bounding box attributes and depth prediction. These detectors retain a fully convolutional structure and show fair performance on several benchmarks~\cite{kitti,nuscenes} but have the problem of perceiving target translation.
SS3D~\cite{ss3d} proposes a single-stage monocular 3D object detector that produces a redundant representation of each relevant object in the image together with uncertainty estimations. MonoDIS~\cite{monoDis} presents decoupled regression losses to better multi-task training. Our work, using a single image as input, differs from previous work that extracts features on the image view. We project the features onto the BEV space and perform 3D object detection on the BEV space.
PGD propose using geometric constraints and depth probabilities to improve depth estimation accuracy base on FCOS3D. It considerably reduces the depth estimation problem while increasing the compute budget and inference delay.

\subsection{BEV Representation}
The Bird's Eye View (BEV) representation was firstly proposed for segmentation task~\cite{vpn,pon,lss} in 3D scenarios, in which models aim to predict surrounding maps from images. These models use an image encoder to obtain the image feature and then apply view-transformation to get the BEV feature. The transformation can be implemented by data-driven methods or depth-based methods.

Among data-driven methods, VPN~\cite{vpn} uses MLP to transform flatten CV features into BEV features. PON~\cite{pon} uses column-wise (rather than image-wise) MLP to perform transformation on each direction. These methods efficiently implement transformation but perform poorly in detection and segmentation tasks.

For depth-based methods, they often outer product the image feature and categorical depth distribution to obtain BEV features. OFT~\cite{oft} uses a uniformly distributed depth to perform projection.
LSS~\cite{lss} predicts categorical depth distribution in an unsupervised way, using 3D bounding boxes for implicit depth supervision. CaDDN~\cite{caddn} introduces depth supervision from LiDAR to obtain high-quality depth prediction.
These methods efficiently generate high-quality BEV features, but they all apply feature sampling methods like grid sampling~\cite{caddn} or voxel pooling~\cite{bevdepth} on the projected BEV feature to obtain a non-perspective BEV feature. That is because previous works generate BEV features for segmentation tasks, where a canonical feature is required. This operation becomes the major impediment to deploying and accelerating these models.

\subsection{BEV 3D Object Detection}
Based on LSS, BEVDet~\cite{bevdet} introduces the detection head to the generated BEV feature and implements object detection in BEV space. Unlike BEVDet, CaDDN~\cite{caddn} generates a BEV feature map for each camera rather than a single BEV feature for each scene. BEVDepth~\cite{bevdepth} introduces depth supervision based on BEVDet. This work also proposes an individual DepthNet for better depth prediction. BEVDet4D~\cite{bevdet4d} propose concatenating multi-frame BEV features to predict object velocity better.

This work explores the detection capability of the ``uncorrected'' BEV feature. By implementing object detection on the perspective BEV feature, we eliminate any complex operation and propose a fully convolutional BEV detector.

\begin{figure}[]
	\centering
    \includegraphics[width=\columnwidth]{fig_tab/I5_.pdf}
    \caption{Illustration of existing BEV framework (CaDDN) and our improvement.  Lift-Splat-based methods adopt feature sampling before collapsing the 3D feature (red), and we propose to remove this procedure to produce a better BEV representation (green).}
    \label{fig:dloss}
\end{figure}

\section{PersDet}
In this section, we first introduce the pipeline of existing monocular BEV detectors in Sec~\ref{sec:overall}. Then, in Sec.~\ref{sec:oversample} we analyze the disadvantages of feature sampling adopted in existing methods. To overcome these problems, we propose PersDet as our solution in the remaining part, detailed information, including structural design and learning target design, is introduced.
\subsection{BEV Detection Framework}
\label{sec:overall}

Existing BEV detectors represented by CaDDN~\cite{caddn} follow a Lift-splat pattern, in which a feature extractor and a DepthNet are adopted for feature extraction and depth prediction. The depth prediction is individually supervised by the ground truth depth obtained by LiDAR. Note that the LiDAR depth is only utilized during training; the detector takes only images for testing and validation.
As illustrated in Fig.~\ref{fig:dloss}, the BEV detection framework performs uniformed training using both 3D bounding boxes and depth supervision. The overall loss $\mathcal{L}$ of PersDet is defined as:
\begin{equation}
    \mathcal{L} = \mathcal{L}_{det}+w_d \mathcal{L}_{d}
\end{equation}
where $\mathcal{L}_{det}$ is the detection loss, $\mathcal{L}_{obj}$ and $w_d$ is the depth loss and its weight.

With the extracted image features $F\in \mathbb{R}^{C\times H\times W}$ and depths $D\in \mathbb{R}^{D\times H\times W}$, the projection is performed by doing the outer product of these tensors.
Traditionally, the generated 3D features $F_{3D}\in \mathbb{R}^{C\times D\times H\times W}$ is then sampled or pooled to align with regular anchors and remove the perspective effect. A CollapseConv is used after feature sampling to obtain the 2D BEV feature representation $F_{BEV}\in \mathbb{R}^{C\times D\times W}$:
\begin{equation}
    F_{3D} = F \otimes D
\end{equation}
\begin{equation}
    F_{3D}^* = GridSample(F_{3D})
\end{equation}
\begin{equation}
    F_{BEV} = CollapseConv(F_{3D}^*)
\end{equation}

However, in the below section, we show that the feature sampling operation can bring several disadvantages and thus be sub-optimal.

\subsection{Disadvantages of Feature Sampling}
In this section, we analyze the behavior of feature sampling. We take CaDDN as a representative monocular BEV detector, which adopt Grid Sampling to align BEV feature.
\subsubsection{Over-Sampling and Under-Sampling}
\label{sec:oversample}
\begin{figure}[]
	\centering
    \includegraphics[width=0.95\columnwidth]{fig_tab/I7.pdf}
    \caption{Illustration of over-sampling (blue car) and under-sampling (orange car) effect. Inner semantic information of object is destroyed due to feature sampling.}
    \label{fig:sampling}
\end{figure}
The Grid Sampling~\cite{caddn} operation samples features based on spatial coordinate information and pre-defined grids (anchors). The anchors' setting has a decisive impact on the performance and efficiency of this process. As illustrated in Fig.~\ref{fig:sampling}, when giving a dense anchor distribution, the problem of over-sampling could result in duplicated feature representation. The over-sampling phenomenon will cause a waste of memory and deterioration of structural integrity. In contrast, a sparse distribution could cause an under-sampling problem where some source features are not sampled, resulting in information loss.
Worse still, the over-sampling and the under-sampling problem can not be solved entirely since they always occur simultaneously. Because the perspective camera FOV has different widths at different depths (wide in the distance and narrow on the contrary), there is always an under-sampling problem in the near field and an over-sampling problem at a distance. The best performance obtained by adjusting the hyper-parameter is only a trade-off.
Therefore, the deterioration of features is inevitable once feature sampling is adopted.
\begin{figure}[]
	\centering
    \includegraphics[width=0.85\columnwidth]{fig_tab/3.pdf}
    \caption{The impact of increasing (horizontal) anchor density on performance and memory consumption. }
    \label{fig:mem}
\end{figure}

\subsubsection{Memory Inefficiency}
The irregular camera Field Of View (FOV, the green region in Fig.~\ref{fig:sampling}) causes the memory inefficiency of the regular BEV feature. Since computing devices store and calculate data in the form of matrices, and the FOV of the camera is a frustum, there is a large portion of the memory area that does not contain valid information. Such inefficiency leads to a dramatic increase in the size of the 3D tensor, accompanied by significant memory waste.
In Fig.~\ref{fig:mem} we present the performance and memory consumption of the CaDDN detector under different anchor densities on the X-axis (horizontal axis of images). It shows that the memory consumption of the 3D tensor grows quadratically with increasing anchor density and leads to increasing overall memory consumption. When the number of anchors increases to 176, the under-sampling problem is alleviated, allowing an adequate performance. However, the expansion of the 3D tensor has doubled the overall memory consumption of the detector.
In other words, to get a suitable performance, the feature sampling operation could significantly increase the memory consumption of detectors, leading to higher model application costs.

\subsubsection{Troublesome Operator}
As mentioned in the above sections, the feature sampling uses custom operations like Grid Sampling or Voxel Pooling~\cite{bevdepth}. These operations can be deployed and accelerated by employing custom operators on flexible platforms represented by CUDA. However, there are also many other platforms on edge devices where a such complex operator can not be performed or accelerated. Therefore, the advantages of the BEV detector cannot be enjoyed by many low-end scenes due to the feature sampling.

Considering the above shortcomes of feature sampling, we explore performing detection directly on projected BEV features where the perspective effect has not been removed. The PersHead, as the solution to the above problems, is proposed for simple and high-performance object detection.

\subsection{Object Detection in Pespective BEV Space}
\begin{figure}[]
	\centering
    \includegraphics[width=\columnwidth]{fig_tab/I2_b.pdf}
    \caption{Heatmap target of regular BEV detectors, CV detectors and the PersDet.}
    \label{fig:head}
\end{figure}
\subsubsection{Anchors and Targets}
We design the perspective anchors to align with the perspective BEV features. Specifically, we apply inverse projection to the uniformly distributed \emph{frustum} anchors to obtain anchors in real-world coordinates. Giving frustum anchors $A^{frustum}$, the inverse projection is applied as:
\begin{equation}
    A^{world*} = I^{-1} \times A^{frustum}
\end{equation}
where $I$ is the camera intrinsic matrix. After this inverse projection, the distribution of perspective anchors is sparse in the distance and dense on the contrary.
Since these anchor points contain a perspective effect, corresponding changes are required for the heatmap target (\emph{i.e.}, objectness score targets). As shown in Fig.~\ref{fig:head}, the perspective effect is introduced to the target to align with perspective features.

\subsubsection{PersHead}
We design the PersHead following the CenterPoint head~\cite{centerpoint}, using task-specific branches for classification and regression. Giving BEV feature $F^{bev} \in \mathbb{R}^{C\times W\times D}$ produced by the Lift-Splat module, the PersHead performs shared convolution on the BEV feature and makes predictions using task-specific sub-nets in a dense form. For each grid of the BEV feature $F^{bev}_{w,d}$, the PersHead predicts its objectness score $P^{obj}_{w,d}$ and bounding box attributes $P^{attr}_{w,d}$ including center offsets, center height, box size, box local yaw~\cite{fcos3d}, direction category, and velocity. The overall detection loss $\mathcal{L}_{det}$ is calculated as:
\begin{equation}
    \mathcal{L}_{det} = CELoss(P^{obj},T^{obj}) + L1Loss(P^{attr},T^{attr})
\end{equation}
where $T^{obj}_{w,d}$ is the objectness score target and $T^{attr}$ is the attributes of boundingbox on each grid.

Finally, with the predicted box attributes, the real coordinates of the box center can be obtained by adding the center offsets to the coordinates of corresponding anchors.

\begin{table*}[!h]
	\begin{center}
		\begin{tabular}{c|cc|ccccccc}
  \toprule
	Methods  & Image Size & mAP & mATE & mASE & mAOE & mAVE & mAAE & NDS\\
\midrule
CaDDN~\cite{caddn}
    & $704\times256$ &0.294 &0.702 &0.283 &0.579 &0.988 &0.222 &0.370\\
BEVDet§~\cite{bevdet}
    & $704\times256$ &0.298 &0.725 &0.279 &0.589 &0.860 &0.245 &0.379\\
PersDet
    & $704\times256$ &\textbf{0.319} &0.676 &0.284 &0.589 &0.924 &0.229 &\textbf{0.389}\\

\midrule
FCOS3D*~\cite{fcos3d}
    & $1600\times900$&0.288 &0.777 &0.266 &0.544 &1.228 &0.170 &0.368\\
PGD*~\cite{pgd}
    & $1600\times900$&0.320 &0.735 &0.266 &0.492 &1.114 &0.170 &0.394\\
PersDet
    & $1408\times512$&\textbf{0.346} &0.660 &0.279 &0.540 &0.964 &0.207 &\textbf{0.408}\\
\bottomrule
  \end{tabular}
\caption{Experimental results on nuScenes. All experiments use \emph{ResNet-50} as backbone. Detectors with * are trained under settings from original implementation to get best performance. The method with § uses multi-view fusion.}
\label{table:sota}
	\end{center}
\end{table*}

\section{Experiments}

\subsection{Implementation Details}
\subsubsection{Dataset}
We conduct experiments on the nuScenes~\cite{nuscenes} dataset, which is a challenging autonomous driving benchmark.
For the detection task, 1.4M annotated bounding boxes from nuScenes are categorized into six meta-classes (tasks) and ten classes. Following previous works~\cite{bevdet,bevdepth}, we only detect and evaluate objects within 58 meters. We report the evaluation metrics defined in the official nuScenes API, including mAP, ATE, ASE, AOE, AVE, AAE, and the NuScenes Detection Score (NDS). We mainly focus on the mAP and the NDS metrics for monocular detection. The mAP is evaluated as the 2D mAP on the ground plane. The NDS is a reweighted combination of other metrics, indicating an overall detecting capability.

\subsubsection{Training Settings}
Our experiments are implemented on PyTorch~\cite{pytorch} with CUDA acceleration. If not otherwise specified, we use ResNet-50~\cite{resnet} as our backbone, and a SECOND FPN~\cite{second} is adopted to merge multi-level image features. Following BEV detectors that report performances on nuScenes~\cite{bevdet,bevdepth}, most of our experiments were performed under $256\times 704$ resolution with batchsize 8 ($8\times 6$ images for six images per sample). Data augmentation, including RandomFlip, Random Rotate, Random Scaling, and Random Cropping as in BEVDepth~\cite{bevdepth} are used. We train our models on the nuScenes dataset for 24 epochs, using learning rate 2e-4, depth weight 3, and EMA strategy. When comparing with other methods on nuScenes, CBGS~\cite{cbgs} is adopted.

\subsubsection{Reproduction of CaDDN}
In order to conduct a fair comparison, we reproduce CaDDN~\cite{caddn} on our code base. We align the backbone, FPN, task-specific head, image feature size, and the training setting of the reproduced CaDDN and our model.
For the new nuScenes dataset, we use the voxel grid range $[2,58]\times [-40,40] \times [-5,3]$ (m) and voxel grid size $[0.64, 0.64, 0.64]$ (m) since our feature down-sample rate is 16.

\subsection{Comparison with State-of-the-Arts}
\label{sota}
We compare our method with both monocular and multi-ocular detectors since they adopt a similar pipeline. For multi-ocular detectors, BEVDet~\cite{bevdet} is taken as a representative method.
As demonstrated in Tab.~\ref{table:sota}, PersDet outperforms existing monocular detectors with significant advantages.
Besides, thanks to the explicit modeling of depth and depth supervision, PersDet shows a greater performance over the multi-ocular detector --- BEVDet by a margin of 2.2\% mAP.
When giving high-resolution images as input to compare with FCOS3D~\cite{fcos3d} and PGD~\cite{pgd}, PersDet also shows better performance even when taking images of lower resolution (for simplicity, we scale the inputs by an integer multiple).

\begin{table*}[!h]
	\begin{center}
		\scalebox{0.95}{
		    \begin{tabular}{c|c|c|c|ccccccc}
\toprule
View & Depth / Depth Sup. & Feat. Sample & Feat. Size & mAP & mATE & mASE & mAOE & mAVE & mAAE & NDS  \\
\midrule
CV & bboxes & - & $44\times 16$
& 0.189 & 0.925 & 0.293 & 0.636 & 1.056 & 0.269 & 0.282 \\
\midrule
BEV & bboxes \& LiDAR & GS  & $44\times56$
& 0.252 & 0.838 & 0.295 & 0.708 & 1.087 & 0.269 & 0.315 \\
BEV & bboxes \& LiDAR & GS  & $125\times87$
& 0.276 & 0.758 & 0.290 & 0.658 & 1.042 & 0.260 & 0.341 \\
BEV & bboxes \& LiDAR & VP  & $44\times56$
& 0.245 & 0.914 & 0.302 & 0.695 & 0.992 & 0.268 & 0.306 \\
BEV & bboxes \& LiDAR & VP  & $125\times87$
& 0.275 & 0.766 & 0.293 & 0.657 & 1.075 & 0.257 & 0.340 \\
\midrule
BEV & bboxes \& LiDAR & $\times $ & $44\times56$
& \textbf{0.300} & 0.735 & 0.296 & 0.678 & 1.104 & 0.312 & \textbf{0.348} \\
BEV & bboxes & $\times $ & $44\times56$
& 0.290 & 0.795 & 0.289 & 0.685 & 1.068 & 0.293 & 0.339 \\
BEV & static random & $\times $ & $44\times56$
& 0.249 & 0.849 & 0.293 & 0.722 & 1.100 & 0.325 & 0.306 \\
BEV & uniform & $\times $ & $44\times56$
& 0.052 & 0.908 & 0.306 & 0.739 & 1.099 & 0.323 & 0.200 \\

\bottomrule
            \end{tabular}
}
    \caption{Performances of detectors in different views and sample densities. GS stands for GridSampling, and VP stands for VoxelPooling. Models were trained without CBGS.}
    \label{table:fa}
    \end{center}
\end{table*}

\subsection{Boosts of BEV Feature}
\label{analy:anchors}
In order to sort out the boost of PersDet from the BEV paradigm, we conduct a series of experiments to compare detectors with different feature distributions. We use detectors with the same backbone, same FPN, and same head structure to fairly compare the impact of feature distribution.
For the Camera-View detector, we remove the view transformer and let the detector predict object depth using an individual branch.

\subsubsection{Features on Depth Dimension}

\begin{figure}[]
	\centering
    \includegraphics[width=0.95\columnwidth]{fig_tab/I9_.pdf}
    \caption{Visualization of heatmap and depth predictions of different detctors. The heatmap of regular BEV detector contains a large portion of invalid area (gray area).}
    \label{fig:depth}
\end{figure}

To demonstrate the effect of feature distribution, we first replace the categorical depth prediction of PersDet with a random tensor (static during training and evaluation) to remove the boost of any depth information.
Image features are projected into BEV space with random intensity with the static random depth distribution.
In this case, the PersDet decreased 4.1\% mAP, still showing a gap of 5\% mAP with its CV counterpart.
We then set the categorical depth of PersDet to uniformly 1. Since the BEV feature is the very same at any depth, the feature distribution along the depth dimension is counterbalanced. Under this setting, the PersDet shows really poor performance and gets to 5.2\% mAP, even lower than the CV detector. These results show that the BEV detector obtains most of the performance boost by distributing features along the depth dimension. Since the mAP metric in autonomous driving does not require precise height, the locating ability of detectors is dependent on horizontal and depth-wise perception. By modeling depth positions and horizontal positions into a unified heatmap, BEV detectors learn to locate objects in an end-to-end pattern, thus leading to better performance.

\subsubsection{Precise Depth Prediction}
We also note that the differences between the CV and BEV detectors are mainly represented in the ATE (Average Transition Error) and mAP metrics.
Considering the fancy performance of CV detectors in 2D tasks, it is fair to attribute such low performance to imprecise depth.
While the CV detector predicts object depth using an individual branch, the BEV representation transforms the depth prediction from a regression task into a classification task, learning depth in a joint pattern. Such a joint training scheme enables the detector to produce depth fit for the detection task. Furthermore, the experimental results show that the extra depth supervision during training could boost 1\% mAP and 1\% NDS.
The improvement of depth prediction can also be seen in the depth prediction visualization (see right of Fig.~\ref{fig:depth}), in which BEV detectors clearly address the target car in depth prediction, while the CV detector fails to sort out the car from the background.

\subsection{Information Loss of Feature Sampling}
\label{analy:scale}
As stated in Sec.~\ref{sec:oversample}, the performance of CaDDN is affected by voxel-grid resolution due to under-sample and over-sample problems.
In Sec.~\ref{sota}, we follow the original implementation of CaDDN, use fixed grid size and obtains a feature size (width$\times$depth) of $125\times87$. In this section, we scale this feature to a different shape to demonstrate its effect on performance.
It can be seen from Tab.~\ref{table:fa} and Tab.~\ref{table:scale} that as the density of the sample grid increases, the model performance firstly shows an observable boost. However, when the sampling grid is too dense, the detector performance shows a significant drop. This phenomenon validates our under-sampling and over-sampling analysis, indicating that feature sampling methods are sensitive to this hyper-parameter, bringing extra obstacles when transplanting the model to a new area.

Furthermore, even when CaDDN obtains the best performance, there is still a significant performance drop caused by feature sampling.
As seen in Tab.~\ref{table:fa} and Tab.~\ref{table:scale}, the PersDet shows a boost of 1.4\% mAP compared with the best performance of CaDDN. We attribute this gain to the efficient and high-quality perspective BEV features.

\subsection{Latency Improvements}

\begin{figure}[h]
	\centering
	\begin{tabular}{@{}c@{}c@{}}
	\includegraphics[width=0.5\columnwidth]{fig_tab/1.pdf} &  \includegraphics[width=0.5\columnwidth]{fig_tab/2.pdf}
	\end{tabular}
	\caption{Latency of CaDDN and PersDet on different devices.}
	\label{fig:speed}
\end{figure}

By removing the feature sampling, PersDet improves its inference speed by a large margin. We measure the inference speed on GPU with CUDA acceleration and CPU without any acceleration in Fig.~\ref{fig:speed}. The CaDDN takes a sampling density of $44\times 56\times 16$, the same as the source feature. The latency data indicate that PersDet has a significant advantage in speed over methods using Grid Sampling~\cite{caddn}. On CPU platforms without custom operators (like the case of edge devices), the latency of PersDet is reduced by 46\% for a single image (batch 1). Although CUDA accelerates the feature sampling on the GPU platform, the latency still decreased by 14\%.

These results show that the pipeline of PersDet brings notable acceleration for the detector, making it suit for edge devices.

\subsection{Efficient BEV Representation}
\label{analy:eff}

\begin{table}[h]
	\begin{center}
		\scalebox{1}{
		    \begin{tabular}{c|ccc|c}
\toprule
Density & mAP & mATE & NDS & Mem. (MB)  \\
\midrule
$44\times56$
& 0.254 & 0.841 & 0.318 & 61\\
$88\times56$
& 0.281 & 0.768 & 0.336 & 83\\
$176\times56$
& \textbf{0.286} & 0.759 & 0.341 & 137\\
$352\times56$
& 0.277 & \textbf{0.758} & \textbf{0.342} & 247\\
\bottomrule
            \end{tabular}
        }
    \caption{Performance of CaDDN under different sample density.}
    \label{table:scale}
    \end{center}
\end{table}

According to Tab.~\ref{table:scale}, when the CaDDN detector achieves its best performance, it takes twice as much memory as PersDet needs. This colossal memory consumption is caused by inefficient representation.
As shown in Fig.~\ref{fig:depth}, the regular BEV feature leaves a large portion of space as ``invalid''. The existence of these areas limited the space for valuable information. In that case, larger overall memory space is required for the same amount of information.
This observation indicates that the perspective BEV space enjoys the benefit of memory efficiency; thus, the PersDet achieves better performance while requiring less memory space.

\subsection{Ablation Study}

\begin{table}[h]
	\begin{center}
		\scalebox{1}{
		    \begin{tabular}{c|c|ccccccc}
\toprule
L.Y.       & Dir.Cls.     & mAP & mAOE & mAAE & NDS  \\
\midrule
$\times$        & $\times$     &
\textbf{0.309} & 0.809 & 0.337 & 0.339 \\
$\times$        & $\checkmark$ &
0.298 & 0.805 & \textbf{0.282} & 0.336 \\
$\checkmark$    & $\checkmark$ &
0.300 & \textbf{0.678} & 0.312 & \textbf{0.348} \\
\bottomrule
            \end{tabular}
        }
    \caption{Performance boost of PersDet with our improved learning target. L.Y. denotes replace yaw angel with local yaw. Dir.Cls. denotes using direction classification.}
    \label{table:head}
    \end{center}
\end{table}

\subsubsection{Local Yaw and Categorical Direction}
In PersHead, we use local yaw and categorical direction derived from FCOS3D~\cite{fcos3d} to predict the yaw angle of objects.
We show the different performances of the head under several settings in Tab.~\ref{table:head}. As can be seen in the table, the categorical direction reduces the Average Attribute Error (mAAE). This indicates that categorical direction also helps the model to classify objects better. When changing the yaw angle target to local yaw, the Average Orientation Error (mAOE) is further improved since the local yaw exactly corresponds to the image pattern.

\begin{table}[h]
	\begin{center}
		\scalebox{1}{
		    \begin{tabular}{c|ccccc}
\toprule
 DepthBins & mAP & mATE & mAOE & mAAE & NDS  \\
\midrule
14 & 0.272 & 0.823 & 0.645 & \textbf{0.282} & 0.332 \\
 28  & \textbf{0.304} & 0.751 & \textbf{0.637} & 0.286 & \textbf{0.356} \\
 56 & 0.300 & 0.735 & 0.678 & 0.312 & 0.348 \\
 112 & 0.290 & \textbf{0.725} & 0.693 & 0.327 & 0.342 \\
\bottomrule
            \end{tabular}
        }
    \caption{Performances of PersDet under different depth division. Models are trained \emph{without} CBGS.}
    \label{table:depth}
    \end{center}
\end{table}

\subsubsection{Number of Depth Categories}
Since the partition density of depth affects the density of anchors, the granularity of the DepthNet, and the receptive field of a single BEV grid, we conduct experiments on this hyper-parameter. As the experimental results in Tab.~\ref{table:depth} show, the model achieves the best mAP and NDS when dividing the depth into 28 bins. However, the Transition Error (mATE) increased by 1.6\% compared with the 56 bins setting due to inaccurate depth prediction.
When the partition becomes denser, a lower Transition Error is obtained at the cost of lower mAP. In that case, the division granularity of depth is a trade-off between two metrics.

\section{Conclusion}
In this paper, we analyze the BEV paradigm for 3D object detection and point out that the essential difference between BEV and Camera-View detectors is the distribution of features on the depth dimension.
By revisiting the view-transformation procedure that generates BEV features, we propose PersDet, which performs object detection on perspective BEV features without feature sampling. We demonstrate that the PersDet solved problems caused by feature sampling in existing BEV detectors and achieved state-of-the-art performance among monocular 3D detectors. According to detailed results and ablation studies, our approach performs better than existing methods while being simpler and deploy-friendly for edge devices.

\clearpage

redpajama_set_name: RedPajamaArXiv


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The ULTIME is a white Milano sweatshirt with striped Reglan sleeves adorned with embroidered flowers in green and pink. This style features a round neckline, a back invisible zipper closure and fitted long sleeves and hem. The floral embroidery highlights the sporty feminine style inspired by ANNE FONTAINE for her casual line.


Government has appointed new envoys to its embassies, in a move that is seen  as  the country's efforts to continue strengthening its relations with  other countries. The appointed include Counselors and first secretaries.
The appointments according to  Minister in Charge of Cabinet Affairs, Stella Ford MUGABO were approved by an extraordinary Cabinet Meeting chaired by His Excellency the President of the Republic, Paul Kagame, convened in Village Urugwiro.
This comes at the time when the country is also increasing its diplomatic missions in countries where it didn't have its presence and have been seen as vital markets for Rwandan products, something that is likely to boost exports.
The country through bilateral trade relations is looking at opening new markets to  boost its export revenues to balance trade receipts that have been facing deficit. The new missions include Israel, Abu Dhabi Congo-Brazzaville, Angola, and Zambia.
In a statement signed by  Minister Ford MUGABO Yasmin D. Amri-Sued who has been working at Tigo Rwanda as marketing manager has been appointed as Second Counselor, in Abu Dhabi. Others appointed to Abu Dhabi are Moustapha SIBOMANA as Second Counselor and Denis RURANGA as the First Secretary.
While Joel Uwizeye who has been working with Letshego Microfinance Ltd  is going to Ankara, Turkey as First Secretary while Kim KAMASA, who has been working in the office of the Government Spokesperson-OGS has been appointed First Secretary in Nairobi.
Others include Capt. Noel Mucyo, First Counselor, in Kampala, Glory Mbabazi, Second Counselor, in Nairobi, Fidele Munyeshyaka First Secretary in Bujumbura. While Clementine Uwamuguha, Jean Marie Vianney Nkurayija, Grace Nyinawumuntu go to Addis Ababa as second  counselors.
Issa Mugabutsinze goes to  Dar-Es-Salaam as First Secretary, while Lusaka will have Abel Buhungu, Michel Makuza as First Counselors in Lusaka, and Vincent Nsengiyumva as First Secretary.
Olivier Habineza goes to Kinshasa as First Secretary. Also, Yves Muneza, First Secretary, in Paris, Emile Mwepesi, Second Counselor, in New Delhi.
While new York will have Jeanne d'Arc Byaje Karuretwa as Minister Counselor, Sana Maboneza, First Counselor, Emmanuel Nibishaka, First Counselor, Chantal Uwizera, Second Counselor, Aline Mukashyaka, Second Counselor, Jeannette Nzamuye, Administrative Attaché.
James Ngango, First Counselor, in Geneva.
Eliane Nzabahimana, First Secretary, in Geneva.
Fidelis Mironko, First Counselor, in London.
Isaie Bagabo, Second Counselor, in Luanda.
Sam Masengesho, First Secretary, in Luanda.
Claude Ganza Kanyanduga, First Counselor, in Seoul.
Michel Uwizeye, First Secretary, in Seoul, Richard MURARA, First Secretary, in Stockholm.
Robert Kayinamura, First Counselor, in Hague.
Herbert Ndahiro, First Secretary, in Hague.
Niyirora Gashegu, First Secretary, in Tel Aviv.
Eric Rubayita, First Counselor, in Tokyo.
Lawrence Manzi, First Counselor, in Washington, DC.
Frank Kayijuka, First Counselor, in Washington, DC.
Francis Bukuzagara, Second Counselor, in Washington, DC.
Augustin Buregeya, First Secretary, in Beijing.
Benedicto Nshimiyimana, First Counselor, in Berlin.
Claudette Umulisa, First Secretary, in Berlin.
Casimir Nteziryimana, First Counselor, in Brazzaville.
Wilson Rwigamba, Second Counselor, in Brazzaville.
Athena R. Rubayi, Second Counselor, in Brazzaville.
Gustave Ntwaramuheto, First Counselor, in Brussels.
John Bosco Ntibitura, First Secretary, in Brussels.
Yvette Rugasaguhunga, Second Counselor, in Dakar.
Vincent Gacinya, First Secretary, in Dakar.


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U.S. Attorneys » Southern District of New York » News » Press Releases
Former Employee Of Technology Company Charged With Stealing Confidential Data And Extorting Company For Ransom While Posing As Anonymous Attacker
Damian Williams, the United States Attorney for the Southern District of New York, and Michael J. Driscoll, Assistant Director-in-Charge of the New York Office of the Federal Bureau of Investigation ("FBI"), announced the arrest today of NICKOLAS SHARP for secretly stealing gigabytes of confidential files from a New York-based technology company where he was employed ("Company‑1"), and then, while purportedly working to remediate the security breach, extorting the company for nearly $2 million for the return of the files and the identification of a remaining purported vulnerability. SHARP subsequently re-victimized his employer by causing the publication of misleading news articles about the company's handling of the breach that he perpetrated, which were followed by a significant drop in the company's share price associated with the loss of billions of dollars in its market capitalization.
SHARP was arrested earlier today in the District of Oregon and will be presented this afternoon before U.S. Magistrate Judge John V. Acosta. The case was assigned to U.S. District Judge Katherine Polk Failla.
U.S. Attorney Damian Williams said: "As alleged, Nickolas Sharp exploited his access as a trusted insider to steal gigabytes of confidential data from his employer, then, posing as an anonymous hacker, sent the company a nearly $2 million ransom demand. As further alleged, after the FBI searched his home in connection with the theft, Sharp, now posing as an anonymous company whistle-blower, planted damaging news stories falsely claiming the theft had been by a hacker enabled by a vulnerability in the company's computer systems. Now the alleged theft and lies have been exposed, and Sharp is facing serious federal charges."
FBI Assistant Director Michael J. Driscoll said: "We allege Mr. Sharp created a twisted plot to extort the company he worked for by using its technology and data against it. Not only did he allegedly break several federal laws, he orchestrated releasing information to media when his ransom demands weren't met. When confronted, he then lied to FBI agents. Mr. Sharp may have believed he was smart enough to pull off his plan, but a simple technical glitch ended his dreams of striking it rich."
According to the Indictment unsealed today in Manhattan federal court[1]:
At all times relevant to the Indictment, Company-1 was a technology company headquartered in New York that manufactured and sold wireless communications products, and whose shares were traded on the New York Stock Exchange. NICKOLAS SHARP, the defendant, was employed by Company-1 from in or about August 2018 up to and including on or about April 1, 2021. SHARP was a senior developer who had access to credentials for Company-1's Amazon Web Services ("AWS") and GitHub Inc. ("GitHub") servers.
In about December 2020, SHARP repeatedly misused his administrative access to download gigabytes of confidential data from his employer. For the majority of this cybersecurity incident (the "Incident"), SHARP used a virtual private network service that he subscribed to from a company named Surfshark to mask his Internet Protocol ("IP") address when he accessed Company-1's AWS and GitHub infrastructure without authorization. At one point during the exfiltration of Company-1 data, SHARP's home IP address became unmasked following a temporary internet outage at SHARP's home.
During the course of the Incident, SHARP caused damage to Company-1's computer systems by altering log retention policies and other files, to conceal his unauthorized activity on the network. In or about January 2021, while working on a team remediating the effects of the Incident, SHARP sent a ransom note to Company-1, posing as an anonymous attacker who claimed to have obtained unauthorized access to Company-1's computer networks. The ransom note sought 50 Bitcoin, a cryptocurrency – which was the equivalent of approximately $1.9 million, based on the prevailing exchange rate at the time – in exchange for the return of the stolen data and the identification of a purported "backdoor," or vulnerability, to Company-1's computer systems. After Company-1 refused the demand, SHARP published a portion of the stolen files on a publicly accessible online platform.
On or about March 24, 2021, FBI agents executed a search warrant at SHARP's residence in Portland, Oregon, and seized certain electronic devices belonging to SHARP. During the execution of that search, SHARP made numerous false statements to FBI agents, including, among other things, in substance, that he was not the perpetrator of the Incident and that he had not used Surfshark VPN prior to the discovery of the Incident. When confronted with records demonstrating that SHARP purchased the Surfshark VPN service in July 2020, approximately six months prior to the Incident, SHARP falsely stated, in part and substance, that someone else must have used his PayPal account to make the purchase.
Several days after the FBI executed the search warrant at SHARP's residence, SHARP caused false news stories to be published about the Incident and Company-1's response to the Incident and related disclosures. In those stories, SHARP identified himself as an anonymous whistleblower within Company-1 who had worked on remediating the Incident. In particular, SHARP falsely claimed that Company-1 had been hacked by an unidentified perpetrator who maliciously acquired root administrator access to Company-1's AWS accounts. In fact, as SHARP well knew, SHARP had taken Company-1's data using credentials to which he had access in his role as Company‑1's AWS cloud administrator, and SHARP had used that data in a failed attempt to extort Company-1 for millions of dollars.
Following the publication of these articles, between March 30, 2021, and March 31, 2021, Company-1's stock price fell approximately 20%, losing over $4 billion in market capitalization.
SHARP, 36, of Portland, Oregon, is charged in four counts. The first count charges him with transmitting a program to a protected computer that intentionally caused damage, which carries a maximum sentence of 10 years in prison. The second count charges transmission of an interstate threat, which carries a maximum sentence of two years in prison. The third count charges wire fraud, which carries a maximum sentence of 20 years in prison. The fourth count charges the making of false statements to the FBI, which carries a maximum sentence of five years in prison. The maximum potential sentences are prescribed by Congress and are provided here for informational purposes only, as any sentencing of the defendant will be determined by the judge.
Mr. Williams praised the extraordinary work of the FBI.
This case is being handled by the Office's Complex Frauds and Cybercrime Unit. Assistant U.S. Attorney Vladislav Vainberg is in charge of the prosecution.
The charges contained in the Indictment are merely accusations, and the defendant is presumed innocent unless and until proven guilty.
[1] As the introductory phrase signifies, the entirety of the text of the Indictment, and the description of the Indictment set forth herein, constitute only allegations, and every fact described should be treated as an allegation.
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Known as the "most Colombian of Colombian artists," Fernando Botero is well known and loved around the country and the globe. On this tour, you'll adventure in the three main Colombian cities with a creative lens. This isn't your standard art tour, and in fact, isn't a true art tour. Botero's prominence is utilized as a tool to deepen your appreciation for the country.
…in the beautiful city of eternal spring, home to the famous Fernando Botero. Medellín has so much to offer, and we'll see as much as time allows: the largest public Botero exhibits, the Museum of Antioquia (where Botero has donated a good portion of his personal collection), the Metropolitan Cathedral, and some of Medellín's most beautiful street art. We'll also be including our Social Innovation Tour during our stay; this tour focuses on the social change and creativity that has bloomed in Medellín since the times of its violent past. Our time in this resilient city will gift us with a chance to be involved directly in this innovation.
…at the Botero Museum. A blast from the prehistoric past will deepen your appreciation for Colombia's history as you wander through the halls of the Gold Museum, which hosts the world's largest collection of gold pieces. While in Bogotá, we will also venture out to Zipaquirá on our Zipaquirá Tour. Here we can admire the magnificent feat of engineering that is the Salt Cathedral of Zipaquirá and its incredible Stations of the Cross. The hustle and bustle of the nation's capital is sure to spark your energy and interest in getting to know Colombia.
…walking (provided you're comfortable with that) since that is the best way to get up close and personal with this walled city. Like the other two cities on our 11-day adventure, this historical monument of humanity received a generous donation from Botero, a striking sculpture in Plaza Santo Domingo titled "La Gordita" ("The Fat Woman"). The Castle of San Felipe, La Popa Monastery, and many other attractions will make this visit to balmy Cartagena de Indias unforgettable. Of course, no trip to Cartagena is complete without taking our Rosario Islands Tour, in which we cruise across the deep navy waters to this magical archipelago. Our adventure together ends here in Cartagena, but you'll feel as though time has flown by you in this incredible artistic and cultural experience.
We recommend that you fly into Medellín and out of Cartagena for this tour. The Bogotá airport manages a great deal of traffic and can get quite chaotic.
This tour isn't just for lovers of art! While we will spend some time in museums and looking at the art, there is a bit for everyone on this tour. We'll make sure to keep you interested and engaged as we explore Colombia with an artist's magnifying glass.


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Home News Wake County How (to Fail) to Buy a Home In Raleigh: Adventures in Pursuit of the American Dream
How (to Fail) to Buy a Home In Raleigh: Adventures in Pursuit of the American Dream
by Leigh Tauss
Step 1: Have a Dream (But Know the Odds)
Despite an unfortunate paint job, the house on East Hargett Street was the one.
Just under nine hundred square feet, it had everything I wanted: close to downtown Raleigh, a kitchen I could work with, room to grow (but not too much). An hour earlier, I'd spotted the day-old listing on Zillow and emailed my realtor. Now, I was standing in the living room, imagining where I'd put my clawed-up red couch and crammed bookshelves and picturing my cats lazing about in the fenced-in backyard.
Better: It was a seven-minute bike ride from Battery Heights to downtown.
Best: It was within my budget, or close enough.
Two days later, on May 30, I submitted my bid: $206,000, $1,000 over the asking price. Another time and place, that would have been more than enough.
But this being Raleigh in 2019, several other people submitted bids, too. And we all lost to a national development firm.
My dream home is about to be gutted, renovated, and flipped—or, worse, razed and replaced with a chic half-million-dollar box with open floor plans and balconies from which to watch the rest of the neighborhood "improve." Such architectural harbingers of revitalization dot the street with increasing frequency as you head downtown.
Either way, it will no longer be my dream home. And I won't be able to afford it, anyway.
Such is the pursuit of the American Dream for first-time Raleigh homebuyers like me: hard, fast, and heartbreaking.
Before we go further, an acknowledgment: I didn't come to this home-buying adventure from a place of adversity, but one of privilege, generational wealth, and downright luck.
Journalism doesn't pay much, but my parents offered to help with the down payment, as their parents had done for them. And I don't have much debt—about $20,000 in student loans, $2,000 on credit cards, no car payment, and I've managed to avoid any bankrupting medical catastrophes in my twenty-nine years on earth.
But even with all that going for me, trying to buy a house often felt impossibly frustrating, and sometimes simply impossible.
My generation isn't committing to homeownership like our parents and grandparents did. Just 37 percent of millennials between the ages of twenty-five and thirty-four own a home, according to a 2018 Urban Institute study, down from 45 percent of Gen Xers and baby boomers when they were that age. There are lots of factors at play: more student loan debt, which makes it hard to scrape together a down payment; increased diversity, as people of color are less likely to buy a home than whites; delaying marriage, as nuptials (and dual incomes) increase your likelihood of buying a home; and millennials' gravitation toward downtowns in high-skill cities—in other words, we want to live in places where many of us can't afford to buy.
It's that last one that's most relevant to my situation.
The notion that homeownership is the gateway to financial stability—that you should build equity instead of paying off your landlord's mortgage—is a fundamental tenet of the American ethos, grounded in the ethic of rugged individualism and the postwar idealization of picket-fence suburbia.
But for many middle earners in Wake County, that ideal can prove elusive.
It's Econ 101: The housing stock isn't keeping pace with a fast-growing population, so prices are rising. Between 2010 and 2017, Wake County's population grew by about twenty-four thousand residents a year. During that period, the county added between ten thousand and twelve thousand housing units a year. You see the problem.
Wake's median household income was just under $78,000 in 2017, according to the Federal Reserve Bank of St. Louis. In May, the median sales price of a Wake County home was more than $320,000, according to the Triangle MLS. If you apply the common wisdom that you shouldn't pay more than three times your income for a home, that means the county's median household can't afford the county's median house.
And for those earning less than $50,000 a year—schoolteachers, firefighters, police officers, people in the service industry, certain late-twentysomething journalists—shopping for a home usually presents a choice: learn carpentry skills or buckle up.
"If you are a modest-income professional, buying a home either means doing a lot of fixing up yourself, or it means going to the fringe with a long commute," says Duke economics professor Charles Becker.
When I started house-hunting a few months ago, it was, at best, a half-hearted effort. It began with an offhand suggestion from my editor that I try to buy a house and write about the experience; I had no intention of actually buying anything. But the deeper I dug into the process—obsessing over online listings, furiously emailing my realtor whenever something interesting popped up—the more the allure of owning my own house tugged at me.
I'm almost thirty. For so long, I've felt like I've been waiting for my life to start. But lately, I've realized that it's happening—right here, right now. I want to start thinking about starting a family (even if it's not a conventional one). I want to have something to show for myself financially. And maybe, though it had never occurred to me before, I really do want my own piece of the American Dream.
So I decided to give homeownership an honest-to-god shot. This is what happened.
Step 2: Get a Mortgage (and Thank Your Mom)
The first important number: $200,000.
That's how much that broker James Hedges told me I'd prequalified for.
I understood the basics of how a mortgage works—a bank gives you money, you pay it back with interest for thirty years—but I only vaguely grasped how my lender figured out how much I could afford, what type of loan I qualified for, and how much interest I'd have to pay.
That, Hedges told me, comes back to my debt-to-income ratio: how much I owe versus how much I make. Then the lender runs a credit score to assess how reliable I am about making payments.
Which brings us to the second important number: 698.
That's considered good—twenty-three points better than the 2017 VantageScore national average, but six points below the 2018 FICO average.
But it's also misleading. Deep in my credit report is an American Express account opened in 1982—seven years before I was born—with a low balance on a high limit that I've never seen or used. My parents had opened it and added me as a user, in the process making me appear to have a better borrowing history than I actually do.
Score one for Mom and Dad.
The better your credit, the better your options. Scores above 580 qualify you for a Federal Housing Administration loan; above 620, you can get a conventional loan. FHA loans require lower down payments (3.5 percent) but can have higher monthly payments and come with more strings attached. Conventional loans typically require at least a 10 percent down payment—yeah, right—but have a lower interest rate.
Hedges nudged me toward an FHA loan, which allows a higher debt-to-income ratio, meaning I could spend almost half of my paycheck on my mortgage payment. He said I would also qualify for the NC Home Advantage Tax Credit, a program that allows first-time and military veteran homebuyers to claim an income tax credit worth 30 percent of my annual interest payments, up to $2,000 a year. That's $167 a month in savings.
With a 4.5 percent interest rate, I'd pay $995 a month on my $200,000 loan, about $100 less than I'm currently paying to rent a six-hundred-square-foot downtown apartment. Not bad.
But then come property taxes. Including Wake County's recent 10 percent hike, a $200,000 house will set me back about $2,300 a year in city and county taxes. And don't forget insurance and mortgage insurance.
Put it all together, and you get the third important number: $1,358.
That's how much I'll pay every month for a $200,000 FHA loan.
After I pay my student loan, that leaves me with less than $200 a week to live on: food, cat food, gas, utilities, or, god forbid, an emergency. Going out? Unlikely.
At first, that was a depressing thought. But I realized that my disposable income would only be about $75 a week less than it is now. I could keep brown-bagging my lunch and mostly breaking even, and at least I'd own something for my trouble.
Here again, I'm fortunate to pay only $200 a month in student loans. Hedges says he's seen teachers who make $35,000 a year but have more than $200,000 in student loans, which means they're paying well over a grand a month. Banks won't take the risk.
"That's a house payment right there," Hedges says. "If you paid face value for college, it would equal a home loan when you graduate."
Lenders have tightened up since the 2008 crash, says Becker, the Duke professor. The Great Recession came about largely because of unscrupulous lending practices—high-interest loans issued to high-risk borrowers, who defaulted. The wave of defaults led to the fiscal crisis and the stock market crash. That, in turn, depressed the housing market, which left millions of people owing more for their homes than they were worth.
The market, of course, has recovered (and then some) over the last decade. Becker says he expects another crash eventually, though not in the next year or two.
Perhaps the possibility that I was trying to buy near the peak of the housing cycle should have given me more pause than it did. But I had my pre-qual in hand, and I was ready to shop.
"We want to be ready to go," Hedges advises. "Especially in this market, we've got to be ready to move fast. First-time homebuyers are missing out on more homes because by the time you talked to an agent, get pre-qualified, it's three days later and the house is already under contract."
Step 3: Move Fast (and Check for Squatters)
The carport barely hanging over the driveway looks one strong gust away from collapse. The Southeast Raleigh fixer-upper on Sunnybrook Lane is listed for $150,000, well within my budget, but "fixer-upper" is too polite a term. Debris is strewn about the backyard. Everything screams bad vibes.
"Because of the state of the property, I am checking for a squatter before we go in," says realtor Shannon Brien.
Open the front door, and you're hit with the smell of mold and mildew. The wood floors are stained with what Brien believes is animal or human urine. There are unsightly brown stains on the wall and dirt everywhere. A kitchen cabinet is full of green mold. Most problematic, the floor dips in peculiar ways, leading Brien to believe there may be issues with the foundation.
Renovations would probably cost more than $70,000. Even if I wanted to bite that bullet, the FHA would never sign off.
The Sunnybrook nightmare is less than a minute drive away from a house on Cooper Street, a recent flip listed for $200,000. You can tell it's a flip, Brien says, because there are hardly any "signs of life"—minor stains or scratches that betray activity. The walls are painted a brilliant white. The new granite countertops gleam. There are three bedrooms and barbed wire on the fence guarding the backyard, hinting at a past painted over for a premium.
We look at another house in the same neighborhood listed for $210,000. The brick has been painted a soft shade of gray, and the door is bright red.
"It looks like it doesn't belong," Brien says.
The other houses on this street have yet to undergo such a makeover. Another flip, it smells like fresh paint. There are kitschy mugs on the kitchen counter with "Mr. and Mrs." embossed in cursive letters. It's small for a three-bedroom.
It will sell quickly, Brien says.
Homes like this hit the market and receive dozens of offers within days, sometimes hours, sometimes in all cash. The result, especially in historically low-income parts of the city—the last places inside the Beltline where acquiring single-family houses for under $300,000 is even conceivable—is rapid gentrification.
In May, The New York Times took note of a trend that's been obvious to local real estate agents, policy makers, and housing advocates for years: Small homes throughout Southeast Raleigh are being scooped up, renovated, and resold to mostly white buyers, transforming longtime black neighborhoods into hip hamlets where the price of admission edges a half-million dollars.
This dynamic is playing out in cities all over the country: Black renters are being pushed out by rising rents—40 percent of Wake renters are already considered cost-burdened, and Triangle rents rose 4 percent in 2018 alone—or landlords who want to cash out as their once-downtrodden neighborhoods become hot commodities. Black homeowners are facing higher taxes as their property values go up or simply see an opportunity in a booming market.
And as with so many things, the problem of gentrification inexorably traces back generations and has its roots institutionalized white supremacy.
As education and housing policy expert Richard Rothstein explains in his 2017 book The Color of Law: A Forgotten History of How Our Government Segregated America, federal policies enacted during and after the New Deal prevented most African Americans from obtaining the home mortgages they needed to buy into the burgeoning suburbs to which whites flocked after World War II. Instead, they were left to languish in deteriorating and ignored urban neighborhoods.
(Because they couldn't purchase homes, they also couldn't build equity and pass along generational wealth. African Americans still lag whites in homeownership—41 percent to 71 percent, according to The Urban Institute—and the divide is worse among younger people.)
Things began to change around the turn of the century when downtowns started regaining their mojo. Suddenly these "undesirable" neighborhoods became places where you could buy cheap and live close to the downtown action, or renovate older homes and make a mint on the resale.
The story begins a little differently in Raleigh. Before the 1920s, Rothstein writes, African Americans lived throughout the city, especially in thriving sections of North Raleigh. The city's white leaders, however, wanted to concentrate the black population in the southeast, near a dump and a rock quarry full of stagnant water. The problem was that the U.S. Supreme Court had forbidden municipalities from using zoning to create blacks-only neighborhoods.
In stepped Raleigh's school board: Because the Supreme Court allowed school segregation, it simply located the black schools in Southeast Raleigh and refused to bus students in from other areas.
Within a decade, Southeast Raleigh was a black neighborhood, and it remained so for much of the twentieth century. Like many black neighborhoods in the South, it suffered from neglect, with some (but not all) pockets beset by crime and poverty. Then came downtown's renaissance. And as the value of their neighborhoods rose, the African Americans whose families had lived in Southeast Raleigh for decades could no longer afford the houses being sold to their new white neighbors.
"As we continue to grow, where are these people supposed to go?" Brien asks.
Step 4: Prepare for Disappointment (or Learn to Love the Hinterlands)
Olivia Robinette and Evan White are holding hands, walking through the backyard of a one-story ranch-style house in Fuquay-Varina. It boasts tall trees and spans nearly an acre, more than enough room for the couple's chocolate Lab.
After months of looking—and being outbid on several houses closer to Raleigh's Moore Square Middle School, where Robinette works as a middle school teacher—this house sparks optimism.
"We could get married in this backyard," Robinette says.
It would be about a half-hour commute, but other teachers drive farther. Some of her co-workers live in Johnston and Harnett Counties, where you can still buy a big house on an educator's budget. Robinette has toured about fifteen houses so far, some she really liked, but she didn't move fast enough. Besides, closer to Raleigh, houses came with tiny lots and big mortgage payments. In Fuquay-Varina, they could get a big lot and a smaller payment.
She's going to make an offer, she decides.
Other millennials have had luck in Wake's smaller towns, too—though who knows how much longer that will be possible. Jacob Bowen, a twenty-three-year-old sheet metal fabricator, lived with his grandparents for three years while he saved up money to buy a house in Youngsville. He paid $160,000—after being outbid for other houses six times over eight months.
Dave Shay, a twenty-nine-year-old photographer, earns about $85,000 a year working two jobs, but he didn't even bother looking in Raleigh. The day after touring a house in Holly Springs, he offered $270,000 for it.
"The fear was, if we don't buy something soon, we're not going to be able to afford anything in a few years," he says. "The market was going up just about as fast as we were saving."
I looked at places in the suburbs, too: There was the home in Wake Forest with a ton of land and lots of modern fixtures, but I couldn't stomach the thought of spending an hour a day on Capital Boulevard. There was the cute house in Wendell, but that commute would also have been miserable. Then there was the new construction near Fuquay-Varina's blossoming downtown, a modern ranch with an open floor plan on the market for $210,000. It was built by a nonprofit specializing in affordable housing. The paint was barely dry, and the whole place stank like sawdust. Someone else's affordable dream, perhaps.
I could have moved to North Raleigh. I looked at a lime-green two-story home outside the Beltline; the fixtures reminded me of my grandmother's house. I also found a house in a golf community within city limits. I've never played golf, but the high ceilings had some appeal. The dead roach on the carpet did not.
At heart, though, I've always been a city girl. I want to live in Raleigh, as close to downtown as possible. I want to bike to work. I want to live near the retailers and bars and restaurants I like to frequent (even if living there meant I couldn't afford to frequent those places).
But checking all of those boxes presented a moral quandary: Would I become just another gentrifier with a bright red door, contributing to, and eventually profiting from, what seems like an inescapable tide of displacement?
That thought left a bad taste in my mouth.
Affordable housing is the number one issue in this fall's city elections. Everyone agrees it's a problem, though there's no consensus on how to fix it. We know there will probably be a housing bond on next year's ballot. We also know that some incumbent city council members are pushing developers to "voluntarily" add affordable units to get their projects moving—under state law, the city can't compel them to do so—while challengers are pushing for more density, betting that having supply match demand will keep prices from rising too much too fast.
But no matter what, houses aren't going to get cheaper.
In the end, I didn't buy a house. Or rather, I haven't bought one yet.
I looked at a dozen houses, was sort of interested in three, and put in an offer on one. Despite my longsuffering realtor and patient mortgage broker doing the heavy lifting, I still found the whole exercise emotionally exhausting: the high of hope on Hargett Street, the low of losing it, and the Goldilocks-like slog of seeing home after home that wasn't just right.
Even so, I might get back in the game. Brien still sends me listings. They appear like little bursts of hope in my inbox.
Perhaps I'll rethink my parameters and start looking at condos and townhouses, even though sharing a wall with my neighbor is the very thing from which I'd like to extricate myself.
Who knows. If adulthood has taught me anything, it's to continually downgrade my expectations.
Contact staff writer Leigh Tauss at ltauss@indyweek.com. Support independent local journalism. Join the INDY Press Club to help us keep fearless watchdog reporting and essential arts and culture coverage viable in the Triangle.
Editor's note: The INDY would like to thank realtor Shannon Brien and mortgage broker James Hedges, who donated their time for this story.
house buying Affordable Housing News The Color of Law gentrification Raleigh Fuquay-Varina Wake County housing cost housing price housing trends
Win at Housing
How to WIN at Buying a Home
As Triangle home prices continue to rise, millennials will need to make compromises when shopping for homes. While you may not get a home within the beltline, there are still good values to be found in surrounding communities like Johnston county. It may not be centrally located, but the entire Triangle is rapidly changing. Downtown Clayton used to be a ghost town in prior years, but now has many wonderful bars, breweries, and restaurants, as well as farmers markets and events. Also, millennials might find that as they age, living downtown, with growing crowds and traffic jams, starts to lose its appeal.
DWK 27 days ago


And what a ride it's been – thanks for joining me! My name is Lori and I was diagnosed with breast cancer in 2002. After chemo, surgery and five years of endocrine therapy, I thought I was home free. But that's not how cancer works. Cancer hangs on, psychologically and physically, until it returns or we die of something else. In my case, it came back in 2011, showing up in my bones. Since then I've learned to live with the ups and downs of metastatic breast cancer, the scans that can show progression, the changes of treatments and their side effects. Along the way I've built a community of friends and colleagues that journey with me.
My blog is my chance to share, question, speculate, yell, wonder and discuss life – life with cancer, life as a wife, mother, daughter, sister and friend, life with peaks and valleys, in all its glory and in the ditches. In the years since it began, it's changed a lot – and so have I. I'm humbled to share it with you, and honored when you chime in. So grab a glass of wine or a cup of tea, and let's get to know each other!
I'm finally here. Great photo!
TY…John wants the photo credit but I don't know how to put that under the pic!
I'm going to share this blog with my mom. Thanks so much for creating this, Lori.
You have some wonderful posts on your blog. We would love to share them through our site that shares the patient experience. Would you be willing to contact me for a larger conversation?
Thank you for sharing what you have.
when i read your tag line the words 'of chemo,cancer and red,red wine' made me flash back to those huge syringes of that red chemo drug that turns your urine red in less than an hour.
Thanks for taking asterisk into etcetera—they are kissing cousins in some way. Do you know the poetry of Alicia Ostriker? She has a set of poems on her mastectomy that run with blood and red, red wine. She is a very dear friend.
I agree they are certainly related! I do not know Alicia's work. Thanks for sending me in her direction!
Is there a way I could contact you by e-mail?
I just read I want you to know on Reader's Choice. What a beautiful story about courage and love. Thanks for shaing it!
Peace and love and joy to you.
My name is Michael Foti and I am the Director of Business Development and Syndication for Empowered Doctor. Our company, Empowered Doctor, focuses on the latest healthcare news. Currently, we are news affiliates of The Chicago Tribune, Arizona Central, and CBSNewYork, supplying their website's health sections with the latest advances against cancer. Over the course of our extensive research on the topic of breast cancer, we came across your blog and I found it to have a plethora of great information and I believe it would be very appealing to our readers. We've recently launched a program to identify and publcizie someof the top breast cancer-related blogs on the web. As such, I'd like to congratulate you as your blog has been selected as one of Empowered Doctor's Top Breast Cancer Blogs. I've included the code below which will allow you to copy and paste it into your site in order to display your award badge. Again, I'd like to congratulate you on your selection, and if you have any questions, please feel free to contact me. Additionally, be sure to visit both CBSNewYork.com/health (on the right hand side under advances against cancer) and ChicagoTribune.com/health (and then click the cancer central tab) to see the continuous work we do for the fight againstcancer.
I'm unable to include the code for the award badge within this post, but please email me at michaeljfoti@gmail.com and I'd be glad to provide it to you.
How do I subscribe to your posts?
Hi Sharon! So sorry I've neglected this! There should be a box near the top where you can enter your email address. I HOPE I will have time to clean up the page a bit in the coming weeks.
Hi Lori! I just got reading through a few of your posts and I had a quick question. I am involved in the cancer community and was hoping you could email me back when you get the chance. Thanks! – emilywalsh688@gmail(dot)com.
Forgive me but I am trying to figure out your story. Are you metastatic now? I hope not and your about doesn't say that but your recent post did but four years on chemo and my brain doesn't always track as well as I might like.
Anyway, delighted to find you site! There is much more for me to explore here. You and I both chose the same, exuberant theme!
Hi Marcy, great to "meet" you. I suppose this could use an update! I am an MBC patient – diagnosed Sept 2011, after 2 years of knowing it was there but unable to find it.
Glad you found me and I love forward to checking out your blog as well. Love the theme!
Sorry to hear that. Dang. But it's a good decade to be metastatic in or that is what I am hoping! I was diagnosed stage IV ovarian only after my lung collapsed. The down side of being fit. I, too, am trying to bring a political frame into cancerland.
Hi Lori! My name is Cameron Von St. James and I had a quick question for you! I was wondering if you could email me at your earliest convenience at cvonstjames AT gmail DOT com 🙂 I greatly appreciate your time!!
Your blog is an amazing place! Thank you for sharing your thoughts on breast cancer. I am very interested to know more!
I am a graduate student working on a project funded by Stanford to study how women with breast cancer use social media for informational support. We are planning to send out a brief survey via Twitter. Would you mind if we tag you on this Twitter mention?
Thank you so much! I am glad you found my site. By all means tag me and I will try to get the word out.
Thank you Lori for your support! We really appreciate it and I am very excited that I found this blog!
Hi Lori..I was/am looking for info and came across your blog. I myself was diagnosed in 2005 then again in 2010. I was recently told I have mets. to bones. I am just starting this next phase. I really hope we can touch base at some point. Thanks for sharing this journey..
I'm so sorry, Cathy. But I'm here. Please let me know how I can help.
Wishing you the very, very best of outcomes tomorrow. I'm here for support, whatever your future holds!
Hi Lori thank you for checking in on me. My journey physically has been rough but mentally I'm holding on mainly because of the out pour of strength and support and love from my friend. They have this thing where they each send me links on days that I have chemo to music that they have downloaded and want me to listen to. It makes them feel like they're sharing the journey with me. I lost my father a year ago to cancer so it is very personal to me. He fought with dignity and strength and I plan on tackling this battle the same way! How are you feeling?
I'm glad to hear you're getting such loving support! At this point, my treatments are quite tolerable and I'm doing well. Hang in there!
Hi Lori, I found you on Twitter and then was reading the content on your blog! Your message, story, and location (SoCal) inspired me to connect. We are a clinical genetics laboratory in Irvine CA and are preparing to start testing for Hereditary Cancer Risk this month. I personally have a family history of brain (husband and mother in law), prostate (uncle), and ovarian cancers. Other executive leaders have personal and family (breast, lymphoma, TP53 gene mutation) ties to cancer as well. I wasn't sure if you have heard about genetic testing but I wanted to connect after reading your story. We are always looking to grow our network of people like you who – whether patient, caregiver, or provider – probably see life through a different set of eyes due to these experiences. I would welcome you to come visit the lab and meet you in person. Please take a look at our website and I hope you have the opportunity to chat with you in the near future.
Hi Amy – just getting back here after too long a hiatus. Thanks for reaching out. I certainly am familiar with genetic testing and always appreciate a new resource! My schedule is busy through mid November but I'd love to come down and learn more!
Hello Lori, I just found your blog and am pleased to meet you. I look forward to reading more. Although the topic is difficult, your writing is very light and lively.
Hi Rita – thanks so much for stopping by. I hope you'll join the conversation now that I'm back to posting more frequently!!
Lori – this is Shawna from Rethink Breast Cancer. Would love to get in touch but don't have your new email. Please message me when you get a chance. Will I see you in Lisbon?


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For forty years, the 18-hole Magnolia layout at the Walt Disney World Resort was a highly regarded end of season venue on the PGA Tour but, sadly, that arrangement ended in 2012.
I think a visit to Disney to play should be on most visiting golfer's Orlando things to do … first and foremost the courses Magnolia, Palm and Lake Buena Vista are in the good category rather than anything beyond that, let's be clear these will not blow you away. Some may say even a little predictable – but even if this is true, the courses here are enjoyable and add in a little Disney goodwill and the child in all of us will come out. Since Osprey Ridge left the Disney fold, Magnolia is probably the best of the three 18 hole layouts here and has been a mainstay on the PGA Tour for 40+ years with a couple of wins for Jack Nicklaus and Tiger Woods over the years. The course is in a tranquil setting, with more than 1500 of the fragrant Magnolias dotted around the 18 holes, 11 holes have water on them. A favourite hole on the front nine is the par-3 6th with the trademark Mickey Mouse bunker at the front and this part of the course is beautiful with a great looking lake between the 6th and the 7th holes. The obvious choice on the back nine for pick of the holes is the 17th; driving across a lake to a dog-legging right to left fairway and then avoiding more water short and right of the green just stands out – having my one and only birdie here helps too! If golf is meant to be fun, then it certainly happens here at Disney.
Good fun, yes, but 'twould be even better if they kept the tees and greens in better shape.