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Home / Ambrose Akinmusire / Prelude (To Cora)
Prelude (To Cora)
Ambrose Akinmusire (tp), Walter Smith III (ts), Aaron Parks (p), Chris Dingman (vib), Joe Sanders (b), Justin Brown (d). Junko Watanabe (vcl), Logan Richardson (as)
Recently winner of both the 2007 Carmine Caruso International Jazz Trumpet Solo Competition and 2007 Thelonious Monk International Jazz Competition, Ambrose Akinmusire has got plenty of experience performing with such famed musicians as Joe Henderson, Joshua Redman, Steve Coleman, Ron Carter, Jimmy Heath, Herbie Hancock, Wayne Shorter and Jason Moran. Ambroses musical trajectory continues to grow in more than one direction, drawing from the most unconventional sources, unravelling the most comfortable conceptions of limitation.
All the elements that comprise this musical conception are finally featured in CD, his expected recording debut as a leader. A solid set that shows persistent re-evaluations and aspirations to evolution and beauty in front of a solid sextet that includes pianist Aaron Parks, the tenor sax of Walter Smith III and the special collaboration of fellow altoist Logan Richardson.
Dreams of the Manbahsniese 7:39
Vibe Solo / Intro 1:06
Aroca 8:46
HumSong 6:04
M.I.T.A.G. (My inappropriate Soundtrack To a Genocide) 5:23
Turmpet Intro/ Dedication to Ruby 2:22
Ruby 9:27
Trapped In a Dream 5:36
Dingmandingo 9:18
Stablemates 5:17
01. Dreams of the Manbahsniese (Akinmusire) 7:39
02. Vibe Solo / Intro (Akinmusire) 1:06
03. Aroca (Akinmusire) 8:46
04. HumSong (Skidrow Anthem) 6:04
05. M.I.S.T.A.G. (Akinmusire) 5:23
06. Trumpet Intro (Akinmusire) 2:22
07. Ruby (Akinmusire) 9:27
08. Trapped in a Dream (Aaron Parks) 5:36
09. Dingmandingo (Walter Smith III) 9:18
10. Stablemates (Benny Golson) 5:17
All compositions by Ambrose Akinmusire, unless otherwise indicated
Ambrose Akinmusire (trumpet), Aaron Parks (piano), Joe Sanders (bass), Justin Brown (drums), Chris Dingman (vibraphone), Walter Smith III (tenor sax). Special Guests: Junko Watanabe (vocals on #1,5,7), Logan Richardson (alto sax on #5,9).
Recorded in Brooklyn, New York City, on April 21, 2008
Sound engineer: Andy Taub
Mixed & mastered by Dave Darlington
Artwork & design: Alicia Vergel de Dios
Produced by Ambrose Akinmusire
"On Prelude: to Cora, trumpeter Ambrose Akinmusire offers a wide-open musical perspective, inviting a wealth of influences to shape a personalized approach to improvisation and composition. Akinmusire seems content with allowing the music to fall where it may; eschewing trends and any pre-conceived notions about what is expected from a debut recording.
The Oakland, California-native generously shares the spotlight with his ensemble of like-minded collaborators. Pianist Aaron Parks, vibraphonist Chris Dingman, tenor saxophonist Walter Smith III, bassist Joe Sanders and drummer Justin Brown each bring a voice of distinction, leading the way for much of the session. Brown's beat-box-meets-Tony Williams fury on the opening "Dreams of the Manbahsniese" is a fascinating contrast to Akinmusire's flowing theme. Dingman is downright hypnotic on "Vibe Solo Intro" and "Aroca." Parks demonstrates imaginative solos and contributes the pop-influenced tune "Ghost Ship."
Although Akinmusire's trumpet playing is not the primary focus here; there is plenty of space allotted for the leader's acrobatic blowing. The spontaneity and forcefulness on "HumSong (Skidrow Anthem)" balances nicely with the more melancholic "Trumpet Intro/Dedication to Ruby." The young trumpeter's straight-ahead chops are showcased on Benny Golson's "Stablemates," performed as a duet with Parks.
Prelude: to Cora is so full of unbound creativity, it will be interesting to see what this emerging artist has in store for the jazz world."
-John Barron
www.allaboutjazz.com
"New visions and colorful dreams emanate from the horn and pen of trumpeter ambrose akinmusire. You would expect the first place winner of the 2007 thelonious monk international jazz trumpet competition to come out with his horn a-blazing, voraciously showing his chops.
While he does have the honored award and sharpened abilities, his debut recording, Prelude: to Cora, is one that walks quietly yet carries a huge stick of inspiring, focused and enlightened music that is well beyond his youthful expectations.
Like his contemporaries (pianist Robert Glasper, drummer Kendrick Scott and guitarist Mike Moreno), Akinmusire draws inspiration not just within the jazz continuum, but also from his environment: urban and classical persuasions, historical reflection and socially conscious motivation. An example is heard on the audacious opening piece "Dreams of the Manbahsniese"—with its construction and execution of melody, the instrument arrangements, classically trained voice by singer Junko Watanabe and the coloring of electronics. It's a conglomeration of new sounds for a new millennium.
All of the music was composed by Akinmusire, with the exception of three tracks, including the lone standard "Stablemates" by saxophonist Benny Golson. Instead of the obligatory hip-funk-swing session, the compositions are well-thought-out and thought-provoking. There are riveting pieces like Aaron Parks' "Ghost Ship," where Akinmusire's warm tone exudes both melancholy and optimism; or the haunting, cinematic vastness of "M.I.S.T.A.G. (My Inappropriate Soundtrack to a Genocide)," with soul-wrenching voice and wailing horns. Though Akinmusire has burning chops (touches of Clifford Brown, Tomasz Stanko and Terence Blanchard), he altruistically shares his light with the other musicians, purposed towards the larger musical picture and revealing his quiet leadership.
And speaking of the band, Akinmusire is also joined by other young newcomers who donate stimulating performances. Chris Dingman's chilled vibe work highlights the soulful "Aroca," while Walter Smith and Logan Richardson create fiery sax exchanges on "Dingmandingo." Aaron Parks, another bright pianist, joins Akinmusire in a wonderful duo on the swinging "Stablemates," both showing brilliance and a respect to the jazz masters of the past. Last, but surely not forgotten, is the stalwart (and lively) foundation of bassist Joe Sanders and drummer Justin Brown. This is an exceptional debut from an unassuming yet extremely talented new voice."
Marcus Strickland
The Town Musicians
Johannes Wallmann
Pablo Ablanedo
Angel From the South (Digipack)
Magnus Thuelund
Twentytwodollarfishlunch
Mark Zubek
Casually Introducing
Walter Smith III
Cerebral Flow
Logan Richardson
Walter Smith III & Mark Small
The Barcelona Session
Raynald Colom
Escaping the Dark Side (Digifile)
Jupiter Conjunct
Aaron Burnett


When we built our farm buildings—garage, goat barn, and garden shed—we placed them all close (within twenty feet) to the house so that they would be easily accessible in bad weather, especially the deep snowfalls we can get. To build them on the same level, we had to cut into the highest point on our property, leaving a fairly steep bank that is about four feet high. Barb did not like the bare bank, so she has been scattering native wildflower seeds across it and has deliberately planted some phlox, purple coneflowers, costmary, and lantana. Now the bank is bearing a wildflower garden with cosmos, Queen Anne's lace, daisies, chicory, black-eyed susans, and any number of other wildflowers that bloom periodically throughout the season. Our cat, Calli, likes to hunt among the flowers and weeds searching for bunnies, grasshoppers, and crickets that live and feed among the plants. This morning, she was treated to some bigger game.
In my July 27, 2018 post, "Our Deer Hunter", I introduced you to a yearling doe who was abandoned by her mother on our farm this past fall. When her mother disappeared, she was a baby fawn still bearing all her spots. We were surprised that she managed to survive the winter alone in our woods, probably because there was so little snowfall over most of the winter allowing her to feed on all the nuts, grass, and bark that she could find on our 12.5-acre farm. My son, Michael, said we should adopt her, but I don't think we have to. I think she has adopted our farm as her home. She lives in the woods and the ravine that bisects our farm during the day and feeds in our field during the nights. She has become a frequent sunrise visitor to our field and vegetable garden. Peeper Pond Farm has become her home and she is less afraid of us than she appears to be of the deep forest that flanks Cave Mountain. We have ushered her out of our garden a couple of times, but we can't seem to frighten her away.
When I let Calli out this morning, she hadn't touched her breakfast meal. She was quite adamant about going outdoors first, which is very unusual. I thought she might be ill, so I kept an eye on her through the windows. I let her out onto the front porch and watched her determinedly trot down the stairs and towards the driveway on the opposite side of the house. She stopped briefly in the driveway at the corner of the house and then began to walk hesitantly along the back side of the house towards the garage where Barb's wildflower garden is located. I thought she was preparing to chase a rabbit that has been frequenting the wildflowers for a morning meal, so I grabbed the phone thinking it would be a good opportunity for a picture and slowly opened the main entrance door and stepped out onto the porch behind her to watch the scene.
To my surprise, it wasn't a bunny she was after; it was our abandoned doe. She was standing there beside the garage chomping greedily on some succulent plants she had found there. What was even more unusual about it was that it was 7:45 AM and the sun had been up for a full hour. No deer has ever approached so close to our house before during broad daylight! Yet, there she was, standing there casually eating her breakfast, staring unphased at Calli and me as though we were the oddity. I activated the cell phone camera and slowly approached her, as I have done several times before. The doe never flinched, but watched me with interest. When I got within eight feet of her, I raised the camera and took a picture of her—the one that appears at the top of this post. She gave me a distinct look of curiosity, but still didn't flinch. I extended my hand and began to approach her when the doe swung her head slowly to my side. I then realized that Calli was also approaching her and was about two feet to my right.
The doe apparently began to feel uncomfortable with both of us approaching her at the same time, so she backed a few steps away from us, but continued to stand her ground. I then knelt down onto the driveway to present less of a threat. However, Calli continued to approach the doe. Soon, the doe lowered her head and started to slowly approach Calli. Suddenly, they both froze with their eyes fixed on one another, trying to decide which of them should make the first move. Calli was within one foot of the doe's nose. They contemplated one another for a few seconds until the doe twitched her white tail. At that moment, Calli charged at her and the doe jumped back in surprise and darted back about fifteen feet from her, then turned back to see if Calli was pursuing.
Calli was initially surprised by the doe's quick retreat, and had stopped briefly in her tracks until the doe stopped retreating. Then, Calli began to walk towards her again. This time, Calli didn't make a direct approach. She moved slowly to the left and began to walk across the doe's path, as though she was trying to corral her or communicate that she wasn't allowed in the wildflower garden. As usual, the doe just contentedly watched Calli make her statement with a cold indifference. I guess this made Calli somewhat disappointed, so she again charged the doe, eventually chasing her back into the woods along the ravine. Having satisfied herself that the doe finally understood her message, Calli turned around and strolled back to me for some attention with her tail raised high in victory.
I was able to take a number of close-up pictures of the scene. I wish I could have filmed a video clip, but the video function involves a one or two second delay that made it hard to capture what I wanted to film. However, I did take a total of five still photos at various moments, all of which I have included with this post.
Our resident doe is quite curious about us, despite the fact that she isn't tame. I guess that the doe imprinted with our property when her mother left her at the end of our field before disappearing. Since then, the fawn learned how to survive on her own while waiting patiently for her mother to return. We never posed a threat during the many times she saw us in the distance and has, perhaps, accepted us as guardians rather than a threat. Whenever we have approached her, she has looked at us with casual interest rather than fear. If anything has startled her, it has been Calli's efforts to chase her away. Even so, Calli has never growled at or displayed threatening behavior when the doe has approached her. In fact, each time the deer has approached Calli, she has backed away until she senses that doe isn't threatening her. Then, she will charge at the doe to see if she can scare it. When the doe retreats, Calli will chase her for a short distance, then check to see if she has sufficiently established her superiority. It may be a game she plays with the deer, but it always looks as though she is trying to hunt them.
One of our friends told us that deer often fear cats, which might explain her apparent success. Deer can be rather flighty, but it's hard for me to believe that such a relatively large animal would have reason to be frightened by a housecat. Calli is still a very playful kitten and not yet a full year old. We still call her our Deer Hunter, anyway. I'm sure she's quite proud of her success.
As for the doe, I find myself intrigued by her lack of fear of us. Both Barb and I have approached her several times, and she appears to be as curious about us as we are of her. Is it possible that she was abandoned so young that she never learned to fear humans? Has she seen us enough times from our field that we have become familiar to her so she doesn't perceive us as a potential threat? She has never raced away from me even though I have tried to frighten her out of our garden twice. Each time she has left, but only with casual indifference, not with fear. It appears that we may have taught her to stay out of the garden, because she hasn't invaded it since the last time I convinced her to leave. However, she has appeared in other locations and still allows me to get close to her with no fear. I find myself curious to know if she would sniff my extended hand someday or allow me to touch her. I have a friend who operates a deer farm, which might eventually be a better home for her, if she lacks the natural fear she should have of us and other potential threats. It will probably be some time before I know for certain how accepting she truly is of us, but I will continue to test her apparent patience. I will be sure to let you know if and when our resident Peeper Pond Farm doe finally decides what to make of us. Until then, the doe will remain one of the most memorable natural curiosities of our life here at Peeper Pond Farm.


DRAMATIC FOOTAGE!! NJ Police Officer And Gunman Get Into A WILD WEST STYLE Gun Fight . . . Look At How Man BULLETS They Fire!!
Following a New Jersey Supreme Court ruling earlier this month, that dashcam footage of fatal police involved shootings should be released in most cases as a matter of public interest, the Atlantic County Prosecutor's Office is releasing dashboard camera footage of the March 2014 police-involved shooting death of a Williamstown man in Atlantic City.
There has been some criticism that the police fired TOO MANY BULLETS after the suspect was down.


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\section{Introduction}
\label{intro}
Prenatal examinations during different trimesters depend heavily on ultrasound (US) scanning, which is well-recognized as real-time, non-invasive and radiation-free. Biometric measurements interpreted from US images are foundations for the evaluation of fetal and maternal health across different gestational ages \citep{hadlock1985estimation}. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=1.0\linewidth]{Figure_1.jpg}
	\caption{Volumetric ultrasound of the whole fetal head. (a) Illustration of the 3D ultrasound imaging process around the fetal head. Varying fetal poses are allowed during our acquisition. (b) Anatomical definition of the whole fetal head region (area above the blue line). Skull (includes \textit{P}, \textit{F}, \textit{E}, \textit{S}, \textit{T} and \textit{O}) is only a sub-region of the whole head. (c)-(e) Coronal, traverse and sagittal planes from a fetal head ultrasound volume. Arrows denote the various occluded sites (blue arrows), deficient and ambiguous boundaries (green arrows) around the fetal head.}
	\label{fig:challenges}
\end{figure}

Among all the biometrics, measurements focusing on fetal head are major indicators accepted by sonographers, which are explicit in reflecting the growth stage of fetus. By combining the measurements of fetal head with that of other anatomical structures, like fetal abdomen and femur, sonographers can further estimate the fetal weight and hence gain better insights for diagnosis. However, limited by 2D US scanning, current clinical measurement pipeline often exposes the diagnosis of fetal head to potential risks. First, 2D measurements obtained from approximated geometry primitives, like line and ellipse, are obviously rough in describing the complex 3D geometry of fetal head. Sonographers often need multiple 2D measurements, like Head Circumference (HC) and Biparietal Diameter (BPD), to justify their diagnoses \cite{rueda2014evaluation}. Second, selecting standard planes which contain key anatomical substructures is a prerequisite for measurement. Bias of sonographers in this selection step often enlarges the discrepancy in diagnoses \cite{Ni_umb_plane}. Previous automatic solutions \cite{yu2008fetal,wu2017cascaded,li2018automatic} for fetal head measurement partially address the problems, but they are still limited by the 2D US imaging. \par

As shown in Fig. \ref{fig:challenges}, 3D US is emerging as a promising alternative in circumventing the aforementioned problems \cite{Tutschek}. It provides a broad volumetric field of view and therefore enables sonographers to inspect the fetal head anatomy in an ever straightforward way (Fig. \ref{fig:challenges}(a)). Volumetric scanning is less expert-dependent than 2D scanning and thus alleviates the risk of discrepancy in image acquisition. Biometric measurements extracted from 3D US, such as volume, are more representative and comprehensive than 2D ones for diagnosis. Volumetric metrics may also provide earlier indicators than planar ones for prognosis \cite{Tutschek}. \par

Although 3D US is attractive for fetal head imaging, efficient and effective tools for whole fetal head segmentation and quantitative analysis running on the massive volume are still absent. As illustrated in Fig. \ref{fig:challenges}(c)-(e), automated segmentation solutions need to tackle the following challenges possessed by volumetric US of whole fetal head: \textit{i)} the poor image quality resulting from the speckle noise and low resolution, \textit{ii)} inevitable boundary ambiguity caused by low tissue contrast and long-span shadow occlusion caused by the severe acoustic attenuation on skull, particularly in the far field, \textit{iii)} the large varieties of fetal head in appearance (particularly the inner structures), scale, orientation and shape across different fetal poses and gestational ages. \par

Much work have been proposed for prenatal volumetric US segmentation. Semi-automatic solutions, like VOCAL (GE Healthcare), were investigated in clinic to segment fetal anatomical structures \cite{Luewan_vocal_placentaV}. These semi-automatic solutions often simplify the segmentation and discard many important details. In \cite{Dahdouh}, Dahdouh \textit{et al.} explored both intensity distributions and shape priors to segment fetus. Feng \textit{et al.} exploited boundary traces to extract fetal limb volume in \cite{Feng_limb}. Recently, Namburete \textit{et al.} proposed a 3D deformable parametric surface to represent and fit fetal skull for fetal brain maturity evaluation \cite{Namburete_head}. Although shape models provide proper constraints for robust fitting, they are initialization-dependent and rough in capturing the case-specific boundary details. Traditional machine learning methods, like random forests, were leveraged to segment fetal brain structures \cite{Yaqub_brain}. Structured Random Forest was further used to segment fetal skull \cite{cerrolaza2017fetal}. \par

Deep neural networks (CNN) have drastically taken over the traditional methods in US image segmentation \cite{Shen_review,torrents2019segmentation}. Characterized with the end-to-end dense mapping, fully convolutional network (FCN) \cite{Long_fcn} was adopted by \cite{wu2017cascaded} for 2D prenatal US image segmentation with a high performance. A 3D FCN with Recurrent Neural Network was further developed by \cite{yang2017towards} to segment the whole fetus, placenta and gestational sac in early gestational ages. Namburete \textit{et al.} \cite{Namburete2018fully} used the shape model to generate skull masks and trained deep neural networks to segment fetal skull in 3D US volumes. They reported the average segmentation DSC as 83$\%$. Recently, Cerrolaza \textit{et al.} \cite{cerrolaza2018deep} proposed to combine the acoustic shadow casting map with a deep network to segment 3D fetal skull, also achieving an average DSC of 83$\%$. A deep conditional generative network was further developed for the 3D fetal skull reconstruction from few 2D slices by \cite{cerrolaza20183d}, reporting the average DSC of 90$\%$. \par

Although the tasks in \cite{cerrolaza2017fetal,Namburete2018fully,cerrolaza2018deep,cerrolaza20183d} are similar to the work in this paper, their focuses are the fetal skull segmentation, while our task is segmenting the whole fetal head. As shown in Fig. \ref{fig:challenges}(b), whole fetal head is defined as the region of fetus on top of the plane determined by the fetal lower jaw and cervical spine \textit{C2} (denoted as the blue line in Fig. \ref{fig:challenges}(b)). It not only includes the skull (\textit{P, F, E, S, T, O}), but also the maxillo-facial structures of fetus (\textit{M, Z, Y, C}). Segmentation of whole fetal head is more informative than skull segmentation for fetal growth evaluation. However, since the maxillo-facial areas of fetus often present larger non-rigid deformations, occlusions and boundary deficiencies than the skull (Fig. \ref{fig:challenges}(c)-(e)), our task is much more difficult than the skull segmentation. \par

\begin{figure*}[h]
	\centering
	\includegraphics[width=0.9\linewidth]{Figure_2.jpg}
	\caption{Schematic view of our segmentation solution. (a) Architecture of our proposed segmentor. Encoder-decoder design with 3D operators digests the whole US volume. Hybrid attention scheme is combined to enhance the feature maps in key sites. Deep supervision with side branch boosts the network training. (b) Cascaded segmentors in an Auto-Context framework for segmentation refinement. Summation of US volume and probability map of whole fetal head is the input of each context level.}
	\label{fig:framework}
\end{figure*}

In this paper, we propose the first fully-automated solution to segment the whole fetal head in US volumes. In order to fully explore the context in the whole US volume, we firstly formulate the segmentation task as an end-to-end volumetric mapping under an encoder-decoder deep architecture. Making every feature descriptor to be representative is crucial for deep networks, especially for our volumetric network which suffers severe limitations from computation resources. Therefore, we propose a hybrid attention scheme (HAS) and combine it with our segmentor to promote discriminative features and suppress the non-informative volumetric features in a composite and hierarchical way. HAS brings minimal computation overhead and proves to be effective in helping segmentor combating boundary ambiguity and deficiency. To enhance the spatial consistency in the segmentation, we further organize multiple segmentors in a cascaded fashion to refine the results by explicitly revisiting the global shape context encoded in the predictions from predecessors. With experiments on a large dataset, our method presents the ability to tackle a wide range of gestational ages with superior segmentation performance (average DSC as 96$\%$), high agreements with experts and decent reproducibilities (mean standard deviation 11.524 mL). The automated segmentation will not only benefit the extraction of representative biometrics in fetal head, but also have potentials in boosting many advanced prenatal studies, like brain alignment \cite{Namburete2018fully}, volume stitching \cite{Gomez_3Dus_regis} and longitudinal analysis \cite{Namburete_head}. Code is publicly available \textcolor{blue}{\url{https://github.com/wxde/USegNet-DS-HAS}}. \par

\section{Materials and Methods}
\label{MaM}
\subsection{Datasets}
To cover the most important gestational ages where fetal head is intensively examined, we built a dataset consisting of 100 US volumes of fetal head acquired from 100 healthy pregnant women volunteers, with a gestational age (GA) ranging from 20 to 31 weeks. All the data acquisition have been approved by the local Institutional Review Board. All the volunteers that participated in this study have reviewed and signed the consent forms. All the volumes were anonymized and acquired by an experienced sonographer using an US machine (S50, SonoScape Medical Corp., Shenzhen, Guangdong Province, (China)) with an integrated 3D probe. The probe has a $75^{\circ}$ scan angle to ensure a complete scanning of the whole fetal head. Fetus is in static state during scanning and free fetal poses are considered during acquisition. Varying scanning orientation of US probe is accepted to ensure the acquisition quality. The original size of volume is 388$\times$258$\times$440 with a spacing of 0.38$\times$0.38$\times$0.38 $mm^{3}$. An expert with 10-year experience manually delineated all volumes as ground truth. Being skilled in using the annotation software \textit{ITK-SNAP} \cite{itk-snap}, the expert needs about 2 hours to finish the annotation of one volume. All the annotation results are double-checked by a senior expert with 20-year experience. We then randomly split the dataset into 50, 50 volumes for training and testing. Regarding the varying fetal head pose, the training dataset is firstly augmented to 600 with flipping and $180^{\circ}$-step rotation around three axes. We then augment the training data by applying the Random Erasing \cite{zhong2017random} to randomly erase a sub-region with 0 at a random position around the whole fetal head to mimic the ubiquitous acoustic shadow. Finally, the training dataset is augmented to 2112 volumes. \par

\subsection{Methodology}
Our proposed framework is elucidated in Fig. \ref{fig:framework}. The system directly takes the whole US volume as an input. Our deep encoder-decoder architecture then densely labels each voxel in the volume, and generates intermediate probability maps for the foreground and background. Deep supervision is attached to boost training efficiency. Attention modules are injected in both upsample path and skip connections to form the hybrid filtering on volumetric features (Fig. \ref{fig:framework}(a)). Multiple segmentors then follow a cascaded fashion to refine the volume predictions level by level. Final output of the system is the segmentation of whole fetal head (Fig. \ref{fig:framework}(b)). \par

\subsection{3D Encoder-Decoder Architecture for Dense Labeling}
With the interleaved convolution, pooling and non-linearity layers to extract features and organize the semantic hierarchy, convolutional neural networks have become the dominant workhorse for medical image segmentation \cite{Shen_review}. Among all the architectures, FCN \cite{Long_fcn} with encoder-decoder design is a popular choice for dense pixel-wise end-to-end mapping. \par

Because 3D FCN has orders of magnitude of parameters than 2D ones, processing the whole US volume with 3D FCN is challenging under limited GPU memory. Previous researches often resort to slice \cite{Namburete2018fully,cerrolaza2018deep} and tri-plane based 2D FCN. However, they discard the spatial dependency and thus sacrifice accuracy \cite{dou2016automatic}. 3D patch-based FCN with an overlap-tiling stitching strategy is another attempt for volumetric segmentation \cite{yang2017hybrid}. Although it explores spatial cues in 3D patches, it is time-consuming and loses global context in the volume to regularize its segmentation. To directly process the whole US volume with 3D FCN, the architecture of 3D FCN should be carefully tailored. Also, all features should learn to be highly task-relevant. We will elaborate the details of our 3D FCN designs in this section. Improving the discriminative power of features with attention mechanism is introduced in the section follows. \par

As parameterized in Fig. \ref{fig:framework}(a), we customize a 3D U-net \cite{Ronneberger_unet} with long skip connections bridging encoder and decoder paths as our backbone. Concatenation operator is taken to merge feature volumes between encoder and decoder paths. Each convolutional layer (Conv) is followed by a batch normalization layer (BN) and a rectified linear unit (Relu). With the whole US volume as input, we focus on tuning the number of pooling layers, Conv layers and kernel sizes in our architecture to balance the input volume resolution and GPU memory constraints. All these factors affect the receptive field size and feature hierarchy of deep networks in perceiving global and local contexts. We finally opt for 2 successive Conv+BN+Relu layers as a block between each two max-pooling layers. There are totally 3 max-pooling layers in our encoder path. We select 3$\times$3$\times$3 kernel for shallow Conv layers, 5$\times$5$\times$5 kernel for deep Conv layers to further enlarge the receptive field. \par

Suffering from the gradient vanishing problem along the long backpropagation path in deep networks \cite{Glorot_vanish}, deep layers are often over-tuned while the shallow layers are under-tuned during training. To maintain the training efficacy for all layers of our 3D deep network, we adopt the deep supervision mechanism to replenish the gradient flow with auxiliary losses and shorten the backpropagation path for shallow layers \cite{Lee-ds,dou2017_ds}. \par

As shown in Fig. \ref{fig:framework}(a), besides the main loss function at the end of network, deep supervision mechanism exposes shallow layers to the extra supervision of $\mathcal{M}$ auxiliary loss signals via $\mathcal{M}$ side branches. These branches share the same ground truth with the main loss function but have shortened backpropagation path length. These paths build a composite loss signal and therefore enhance the gradient flow in updating the parameters of shallow layers. The basic formulation of deep supervision is as follows:
\begin{equation}
\small
\label{eq:loss_ds_basic}
\begin{split}
\mathcal{L}(\mathcal{X},\mathcal{Y};W,w) = &\mathcal{L}(\mathcal{X},\mathcal{Y};W) + \\
&\sum_{m \in \mathcal{M}}{\mathcal{L}_m(\mathcal{X}, \mathcal{Y};W,w^m)} + \lambda(||W||^2),
\end{split}
\end{equation}
where $\mathcal{X}$, $\mathcal{Y}$ are training pairs, $W$ is the weight of main network, $w = (w^1,...,w^m)$, are the weights of side branches.

Several consecutive deconvolution operations are often used in the side branches to upsample the outputs as the same size as ground truth label \cite{dou2017_ds}. However, the deconvolution is very computation intensive and the number of it in these side branches is often times as that in the main network. Thus, this setting consumes a lot of GPU footprint. Recently, Lin \textit{et al.} \cite{lin2017refinenet} proposed to directly downscale the ground truth label to fit different branches and hence remove the heavy deconvolutions. As shown in Fig. \ref{fig:framework}(a), there is no deconvolution based upsampling in our side branches. We also downscale the ground truth label to 1/2 and 1/4 to fit the size of two branches, respectively. To increase non-linearity while keep computation cost, we use a Conv layer with 1$\times$1$\times$1 kernel to output the probability maps in each branch. Our modification preserves the effectiveness of deep supervision and makes more GPU memory available for the main network to explore. The final composite loss function for our deeply supervised network is accordingly modified as Eq. \ref{eq:loss_ds_resize},
\begin{equation}
\small
\label{eq:loss_ds_resize}
\begin{split}
\mathcal{L}(\mathcal{X},\mathcal{Y};W,w) = &\mathcal{L}(\mathcal{X},\mathcal{Y};W) +\\
&\sum_{m \in \mathcal{M}}{\mathcal{L}_m(\mathcal{X}, S_m(\mathcal{Y});W,w^m)} + \lambda(||W||^2)
\end{split}
\end{equation}
where cross entropy is the metric for main loss function $\mathcal{L}$ and auxiliary $\mathcal{L}_m$. $S_m(\cdot)$ indicates the downscale operation on ground truth label $\mathcal{Y}$. \par

\subsection{Hybrid Attention Scheme to Promote Features}
\label{ssc:attention}
Decoder path and skip connections assign the U-net variants advantages in distilling global context and preserving local details for fetal head segmentation. However, as the deconvolution successively upsamples the feature maps, not only the features representing the fetal head, but also the features of non-head region are learned at different scales. As these feature maps propagate along the decoder path, the final segmentation will be adversely affected. At the same time, feature maps at the shallow layers of encoder path not only contain the detailed boundary cues of fetal head, but also include abundant background noise. Skip connections convey these features to the decoder path to enhance the boundary details at the risks of bringing about false positives. Therefore, filtering the features to be task-relevant and focus on fetal head region becomes very important under the limited feature capacities and computation resource. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=1.0\linewidth]{Figure_3.jpg}
	\caption{Schematic illustration of our proposed attention module.}
	\label{fig:attention_module}
\end{figure}

Attention mechanism becomes an attractive solution for the problem. It borrows the idea from human beings in applying the limited computation resources to the focus of view. Irrelevant features are suppressed under the mechanism to improve the learning efficiency and efficacy. Recently, attention mechanism becomes popular in image analysis \cite{wang2017residual} and natural language processing \cite{bahdanau2014neural}. In \cite{wang2018deep}, Wang \textit{et al.} proposed a framework with attention setting to improve multi-scale features for prostate segmentation in 2D US images. Schlemper \textit{et al.} further investigated the attention mechanism as a general module for both medical image classification and segmentation \cite{schlemper2019attention}. However, they only explored the attention module to filter the feature maps on skip connections and ignored the upsample path, and also the composite filtering effect. In this work, we propose a hybrid attention scheme (HAS) to progressively refine the features maps in both skip connection and upsample path at different scales. \par

As the building block of HAS, we firstly introduce the design of our attention module (AM) (Fig. \ref{fig:attention_module}). Given the feature maps $F(\mathcal{X})$ as input, we apply a Conv layer with 1$\times$1$\times$1 kernel to shrink the feature map channels to 32 as $P(\mathcal{X})$. This operation reduces the computation cost of our attention module and enhances the interactions among channels. We then attach two consecutive Conv layers with 3$\times$3$\times$3 kernel to produce the un-normalized attention weights $W(\mathcal{X})$:
\begin{equation}
\label{eq:attention_weights}
\small
W(\mathcal{X}) = \mathcal{H}(F(\mathcal{X});\theta)
\end{equation}
where $\theta$ represents the parameters learned by $\mathcal{H}$ which contains the mentioned 3 Conv layers. After that, we approach the attention map $A(\mathcal{X})$ by normalizing $W(\mathcal{X})$ across the channel dimension with a \textit{Softmax} function:
\begin{equation}
\label{eq:attention_softmax}
\small
a_{c,r,d}^{k}=\frac{exp(w_{c,r,d}^{k})}{\sum_{k}exp(w_{c,r,d}^{k})}
\end{equation}
where $w_{c,r,d}^{k}$ denotes the value at spatial location $(c,r,d)$ and \textit{k}-th channel on $W(\mathcal{X})$, while $a_{c,r,d}^{k}\sim(0,1)$ denotes the normalized attention weight at spatial location $(c,r,d)$ and \textit{k}-th channel on $A(\mathcal{X})$. After getting the normalized attention map, we then multiply it with the $P(\mathcal{X})$ in an element-by-element manner to generate the refined feature map $T(\mathcal{X})$. With the attention mechanism, $\mathcal{H}(\theta)$ will learn to indicate and enhance the semantic features of fetal head boundary in $P(\mathcal{X})$ with higher $a_{c,r,d}^{k}$, while suppress the non-head regions with lower attention values (see our Results section). To fully make use of the $P(\mathcal{X})$ and $T(\mathcal{X})$, we concatenate them together and output the final 64-channel attention features after a Conv layer. \par

As shown in Fig. \ref{fig:framework}, to thoroughly exploit the feature filtering effect of attention mechanism, different from \cite{wang2018deep,schlemper2019attention}, our HAS implants the AM in both skip connections (denoted as \textit{SAM}) and upsample path (denoted as \textit{UAM}) at different scales in out network. HAS forms a composite and hierarchical feature selection in the segmentor. Specifically, we allocate a SAM in each skip connection and an UAM after each feature concatenation in the upsample path. UAM enforces the feature interaction among the concatenated feature maps and then selects the most discriminative features among them for further decoding. The auxiliary loss branches are attached behind the UAMs to ease the learning with filtered and discriminative features. \par

\subsection{Refinement with Auto-Context Scheme}
\label{ssec:refine}
Neighboring predictions are beneficial to support the decision on current location and address boundary ambiguity. Thanks to the whole US volume input and large receptive field size in our network design, our network can get arbitrary access to the context dependencies in long or short ranges. We hence combine our HAS based network with a classic iterative refinement framework, Auto-Context \cite{Tu_atoctxt}, to explore varying context and better recover the boundary of fetal head in ambiguous sites. \par

Auto-Context is designed to learn the context encoded in images and probability maps. It proves to be an elegant scheme for successive labeling refinement. As shown in Fig. \ref{fig:framework}(b), it stacks a series of models in a way that, the model at level $k$ can simultaneously revisit the appearance context in the intensity image and the shape context in the probability map which is generated by the model at level $k-1$. Eq.~\ref{equation2} illustrates the general iterative process of a typical Auto-Context scheme,
\begin{gather}
\label{equation2}
\mathcal{Y}^{k} = \mathcal{G}^{k}(\mathcal{J}(\mathcal{X}, \mathcal{Y}^{k-1})),
\end{gather}
where $\mathcal{G}^{k}$ is the mapping function of the segmentor at level $k$, $\mathcal{X}$ and $\mathcal{Y}^{k-1}$ are the US volume and the probability map of fetal head from level $k-1$, respectively. $\mathcal{J}$ is a join operator to combine $\mathcal{X}$ and $\mathcal{Y}^{k}$, which is set as an element-wise summation in this work. Summation saves the computation cost and performs better than concatenation \cite{wu2017cascaded}. Limited by GPU memory, we train the network in level $k$ after finishing the training of level $k-1$. The probability map in level 0 is initialized with the constant value 0. By compromising between the time efficiency in training/testing and the performance gain, we use 2 context levels in total ($k=2$). The last context level outputs the final refined segmentation. \par

\section{Experimental Results}
\label{Results}
\subsection{Implementation and Evaluation Criteria}
We implemented our framework in \textit{Tensorflow}. Training and testing were run in a NVIDIA GeForce GTX TITAN X GPU (12GB). All the Conv layers were initialized from truncated normal distributions. As a trade-off between image quality and segmentation performance, we downscale the original US volume with a factor of 0.4 on each dimension for input. The final segmentation result was resampled back to the original full resolution for evaluation. We updated the weights of all layers with an Adam optimizer (batch size=1, initial learning rate=\textit{1e-5}, moment term is 0.5). The training epoch in each Auto-Context level was set to 30. For the testing time, our final method only needs about $2$ \textit{seconds} to segment an US volume. \par

For segmentation evaluation, we target to assess the region, boundary and voxel-wise similarities with 5 criteria. They include the Dice similarity coefficient (DSC, $\%$), Conformity (Conf, $\%$), Jaccard (Jacc, $\%$), average distance of boundaries (Adb [mm]),  Hausdorff distance of boundaries (Hdb [mm]). DSC indicates the mutual overlap between segmentation and ground truth. Conformity ($Conf=(3DSC-2)/DSC$) provides wider range and can be more sensitive and rigorous than DSC, as suggested by \cite{Chang_Conform}. Adb is used to describe the average distance from segmentation surface to ground truth. Hdb is sensitive to boundary outliers and emphasizes the worst labeling cases \cite{Rueda_Challenge}. DSC, Jaccard, Adb and Hdb are defined as following,
\begin{equation}
DSC(E,G) = \frac{2|E\cap G|}{|E|+|G|}, Jacc(E,G) = \frac{|E \cap G|}{|E \cup G|},
\label{eq:dice, jaccard}
\end{equation}
\begin{equation}
\begin{split}
Adb(E,G) = &\frac{1}{2}(\frac{\sum_{v_{i}\in e_{G}}min_{v_{j}\in e_{E}}dist(v_{i},v_{j})}{|G|} +\\
&\frac{\sum_{v_{j}\in e_{E}}min_{v_{i}\in e_{G}}dist(v_{j},v_{i})}{|E|}),
\label{eq:adb}
\end{split}
\end{equation}

\begin{equation}
\begin{split}
Hdb(E,G) &= max(H(e_{G}, e_{E}), H(e_{E}, e_{G})) \\
H(e_{G}, e_{E}) &= max_{v_{i}\in e_{G}}\left\{min_{v_{j}\in e_{E}}dist(v_{i},v_{j})\right\} \\
H(e_{E}, e_{G}) &= max_{v_{j}\in e_{E}}\left\{min_{v_{i}\in e_{G}}dist(v_{j},v_{i})\right\}.
\end{split}
\label{eq:hdb}
\end{equation}
where $E$ and $G$ are segmentation and ground truth. $|\cdot|$ calculates the volume of segmented object. $e_{E}$ and $e_{G}$ are the surfaces of segmentation and ground truth. $v_{i}$ is the vertex on the surface, $dist(v_{i},v_{j})$ is the Euclidean distance between vertex $v_{i}$ and $v_{j}$. \par

\subsection{Quantitative and Qualitative Analysis}
\label{quanti_quali}
Hence forth, we will denote our basic 3D segmentor network without deep supervision and HAS as \textit{USegNet}. As shown in Table \ref{table:quanti_diff_method}, we firstly compare the USegNet with several competitors, like the 3D deconvolution network (\textit{3D-DeconvNet}) \cite{noh2015learning}, 2D USegNet (\textit{2D-USegNet}) and 3D patch-based USegNet (\textit{p-USegNet}) to prove the effectiveness of our backbone architecture. The USegNet shares the same encoder-decoder layout with 3D-DeconvNet, 2D-USegNet and p-USegNet. Whereas, 3D-DeconvNet lacks the skip connections between encoder and decoder, 2D-USegNet takes slices with original resolution as input and outputs slices with the same sizes. p-USegNet digests 64$\times$64$\times$64 3D patches and generates the prediction of whole US volume with an overlap-tiling stitching strategy \cite{yang2017hybrid}. We keep proper settings for all the compared methods for fair comparisons. \par

\begin{table*} \caption{Quantitative comparison of different segmentation methods}
	\label{table:quanti_diff_method}
	\centering
	\tiny
	\resizebox{0.9\textwidth}{!}
	{
		\begin{tabular}{c|c|c|c|c|c}
			\toprule[2pt]
			\multirow{2}{*}{\bf{Method}} &\multicolumn{5}{c}{\bf{Metrics}} \\
			\cline{2-6}
			& DSC [\%] 		& Conf [\%]		& Jacc [\%]		& Adb [mm]		& Hdb [mm]		 	\\
			\hline
			p-USegNet 		&93.31		&85.53		&87.53		&0.8908		&7.839 	 		\\
			2D-USegNet 		&94.31		&87.86		&89.27		&0.8023		&6.861		 	\\
			3D-DeconvNet 	&94.51		&88.31		&89.62		&0.6804		&6.334		 	\\
			USegNet			&94.83		&89.06		&90.20		&0.6186		&5.400		 	\\
			USegNet-DS		&94.95		&89.33		&90.42		&0.6225		&5.409		 	\\
			\hline
			USegNet-DS-UAM		&95.57		&90.70		&91.54		&0.5242		&4.785		 	\\
			USegNet-DS-SAM		&95.63		&90.83		&91.64		&0.5362		&4.702		 	\\
			USegNet-DS-HAS		&95.85		&91.30		&92.05		&0.5070		&4.876		 	\\
			USegNet-DS-HAS-Ctx &\textbf{96.05}	&\textbf{91.74}		&\textbf{92.42}		&\textbf{0.4793}		&\textbf{4.609} 		\\
			\hline
			\toprule[2pt]
		\end{tabular}
	}
\end{table*}

Lacking of global contextual information for guidance, p-USegNet presents the worst results in terms of both shape and boundary similarities among the compared methods. With original whole slice as high resolution input and slice based large training dataset, 2D-USegNet gets better results than p-USegNet but still has high boundary distance errors suffering from the lack of spatial regularization. 3D-DeconvNet achieves 1.2 percent DSC improvements over p-USegNet and reduces the Adb error for about 15$\%$ over 2D-USegNet. This proves the importance of adopting 3D operators and taking whole volume as input to exploit global context to benefit whole fetal head segmentation. By establishing skip connections to revisit detailed boundary cues in multi-scale feature maps, our USegNet further refines the results over 3D-DeconvNet with 0.3 percent improvement in DSC. Finally, by adding the deep supervision (DS) to boost the training process, USegNet-DS gets another 0.1 percent improvement in DSC. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=1.0\linewidth]{Figure_4.jpg}
	\caption{Two cases (first and second row) to show the comparison of Hausdorff distance [\textit{mm}] among different methods. From left to right: 2D-USegNet, p-USegNet, USegNet-DS-UAM and USegNet-DS-HAS-Ctx. The color bar is annotated with \textit{mean} in the center, \textit{min} and \textit{max} on the ends.}
	\label{fig:surf_dist}
\end{figure}

Based on USegNet-DS, we then move to conduct ablation study on our introduced modules, including the SAM, UAM, HAS and Auto-Context (Table \ref{table:quanti_diff_method}). Locating on the feature flow of main network, UAM (USegNet-DS-UAM) presents significant improvement over USegNet-DS, about 0.6 percent in DSC. Selectively enhancing the detailed boundary features of fetal head and discarding the noise in background as the upsampling progresses, UAM proves its importance in our architecture. Benefiting from the feature filtering effect on skip connections, SAM (USegNet-DS-SAM) brings  0.7 percent improvement in DSC and reduces the Adb error about 12$\%$ over USegNet-DS. This result reflects that, the feature maps from shallow layers of encoder path indeed contain redundant and irrelevant features. SAM suppresses these kind of features and hence improves the segmentation. Also, because UAM charges the main stream of feature flow in the network, UAM can bring higher improvements than SAM. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=1.0\linewidth]{Figure_5.jpg}
	\caption{Segmentation result of six cases in the testing set. These cases have different shapes, sizes and gestational ages. Hausdorff distances [\textit{mm}] from the segmentation surface to ground truth are illustrated to provided a more detailed illustration. The color bar is annotated with \textit{mean} in the center, \textit{min} and \textit{max} on the ends.}
	\label{fig:attention_seg_comp}
\end{figure}

Combining the SAM and UAM to form the composite and hierarchical feature filtering, HAS (USegNet-DS-HAS) only adds little computation overhead but contributes more segmentation improvements than the SAM-only and UAM-only based models. It brings the highest refinement over USegNet-DS (about 0.9 percent in DSC, 19$\%$ in Adb). At this point, the whole fetal head segmentation performance of USegNet-DS-HAS is already very promising. The averaged absolute relative error in voxel number between our segmentation and ground truth gets as low as $3.40\%$. Considering the computation cost in training/testing and performance gain, our Auto-Context scheme only stacks two USegNet-DS-HAS with same configurations. We denote the stacked models as USegNet-DS-HAS-Ctx. As Table \ref{table:quanti_diff_method} shows, USegNet-DS-HAS-Ctx presents betterment on all metrics compared to USegNet-DS-HAS (about 0.2 percent in DSC, 0.4 percent in Jacc). Compared to the results reported by \cite{Namburete2018fully,cerrolaza2018deep,cerrolaza20183d} for fetal skull segmentation (as highest as 90$\%$ in DSC), our task is more challenging and our method  achieves better results. \par

With two cases as shown in Fig. \ref{fig:surf_dist}, we visualize the Hausdorff distance from different whole fetal head segmentation surfaces to the ground truth. Lacking of proper spatial context to guide and regularize the segmentation, the results of 2D-USegNet and p-USegNet are rough and visually implausible. The use of UAM (USegNet-DS-UAM) obviously reduces the surface distances of most boundary points. Benefiting from the hybrid feature filtering effect of HAS and Auto-Context, USegNet-DS-HAS-Ctx further narrows the surface distances. More visualization results of USegNet-DS-HAS-Ctx are shown in Fig. \ref{fig:attention_seg_comp}. It can be observed that, our proposed method conquers the poor image quality, scale and shape variations, occlusion and boundary ambiguities of whole fetal head in US volumes, and finally presents promising segmentation results. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=0.95\linewidth]{Figure_6.jpg}
	\caption{Two cases (first and second row) to explicitly compare the segmentation details of different methods. From left to right, sagittal slice of a whole fetal head in right, middle and left region. Red, green and yellow curves denote the contour from ground truth, USegNet-DS and our USegNet-DS-HAS.}
	\label{fig:seg_curve_comp}
\end{figure}

In Fig. \ref{fig:seg_curve_comp}, we illustrate an explicit comparison between USegNet-DS and our USegNet-DS-HAS to show the effectiveness of hybrid attention. As we can observe, both USegNet-DS and USegNet-DS-HAS can properly fit the ground truth around fetal skull regions. However, suffering from the irrelevant features in the background, USegNet-DS tends to under-segment the whole fetal head around the fetal facial and neck areas (blue arrows). The boundaries in these areas are hard to recognize due to the lack of hard bone structures. With Fig. \ref{fig:attention_map_comp}, we further show a case (USegNet-DS vs. USegNet-DS-HAS) to reveal the impact of our proposed hybrid attention. Through the point-to-point comparisons, we can see that, the probability maps produced by USegNet-DS are still fuzzy and low around fetal head boundaries. Whereas, the maps produced by USegNet-DS-HAS are compact and high around the whole fetal head, even in the severely occluded spots. This phenomenon demonstrates that our hybrid attention scheme not only suppresses the false positive prediction in non-head regions but also enlarges the gap between foreground object and background noise. \par

\begin{figure}[h]
	\centering
	\includegraphics[width=0.9\linewidth]{Figure_7.jpg}
	\caption{Advantages of HAS in enhancing the prediction maps. From top to bottom: coronal, traverse and sagittal slice of a whole fetal head; segmentation ground truth (green) overlaid on slices; probability map of whole fetal for three slices produced by USegNet-DS; probability map of whole fetal for three slices produced by USegNet-DS-HAS. Color map denotes the probability range. White crosshair is used to facilitate the point-to-point comparisons.}
	\label{fig:attention_map_comp}
\end{figure}

In Table \ref{table:SAM_UAM_Number}, we conduct the ablation study on the number of SAM and UAM. The base model for the experiments is USegNet-DS. Its results are listed for reference. As shown in Fig. \ref{fig:framework}, we define our experimental setting as follows. SAM-1 denotes the USegNet-DS only with the module SAM 1. SAM-12 denotes the USegNet-DS with both SAM 1 and SAM 2 modules in the skip connections. USegNet-DS-SAM is our model with all the 3 SAMs. UAM-1 denotes the USegNet-DS with only the UAM 1 module before the second deconvolution layer. UAM-12 denotes the USegNet-DS with both UAM 1 and UAM 2 modules. USegNet-DS-UAM is our model with all the 3 UAMs. For the experiments on UAM, if it is removed, then the auxiliary loss branch is attached to the concatenation layer before it to keep the training process to be fair. As we can see, using only one SAM and UAM, SAM-1 and UAM-1 can already bring obvious refinement on the segmentation (about 0.5 percent in DSC). Increasing the number of SAM and UAM also increases the performance. However, the increment is decreasing and the performance comes to a saturation in USegNet-DS-SAM and USegNet-DS-UAM. When compared with the computation cost of USegNet-DS, the SAM and UAM modules only add slight computation overhead. SAM-12 and UAM-12 consistently improve all the segmentation metrics over SAM-1 and UAM-1, except the Hdb. We interpret this phenomenon as that, SAM-2 and UAM-2 locate at the middle semantic levels and may miss some very detailed features that are only conveyed by SAM-3. They therefore present slight degradation in the strict metric Hdb which emphasizes worst boundary outliers. \par

\begin{table*} \caption{Comparison about different numbers of SAM and UAM}
	\label{table:SAM_UAM_Number}
	\centering
	\scriptsize
	\resizebox{0.9\textwidth}{!}
	{
		\begin{tabular}{c|c|c|c|c|c|c}
			\toprule[2pt]
			\multirow{2}{*}{\bf{Network Layout}} &\multicolumn{6}{c}{\bf{Metrics}} \\
			\cline{2-7}
			& DSC [\%] 		& Conf [\%]		& Jacc [\%]		& Adb [mm]		& Hdb [mm]	&Time(s)		 \\
			\hline
			USegNet-DS		&94.95		&89.33		&90.42		&0.6225		&5.409		&0.78 	\\
			\hline
			SAM-1	&95.44		&90.42		&91.30		&0.5556		&4.939		&0.76		  \\
			SAM-12	&95.60		&90.75		&91.59		&0.5380		&5.149		&0.78  \\
			USegNet-DS-SAM		&95.63		&90.83		&91.64		&0.5362		&4.702		&1.11  \\
			\hline
			UAM-1	&95.49		&90.51		&91.39		&0.5374		&4.832		&0.77  \\
			UAM-12	&95.54		&90.62		&91.48		&0.5350		&5.024		&0.81  \\
			USegNet-DS-UAM		&95.57		&90.70		&91.54		&0.5242		&4.785		&0.88  \\
			\hline
			\toprule[2pt]
		\end{tabular}
	}
\end{table*}

\begin{figure}[h]
	\centering
	\includegraphics[width=1.0\linewidth]{Figure_8.jpg}
	\caption{Correlation and Bland-Altman agreement on measuring the fetal head volume.}
	\label{fig:bland_altman}
\end{figure}

After getting the fetal head segmentation, we can then obtain some useful biometrics, like the volume. We adopt the correlation coefficient and Bland-Altman agreement \cite{Rueda_Challenge} to comprehensively evaluate the discrepancy among the volume size derived from expert annotations and our USegNet-DS-HAS-Ctx segmentations. As shown in Fig. \ref{fig:bland_altman}, tested on the 50 varying volumes, our solution achieves high correlation (0.990) and agreement (-1.6$\pm$19.5 mL with 95\% of the measurements locate in the $\pm$1.96 standard deviation in Bland-Altman plot) in measuring the fetal head volume when compared to the expert. This high correlation and agreement indicate that our solution may serve as a promising alternative in assisting experts to analyze whole fetal head volumes. \par

\begin{figure*}[h]
	\centering
	\includegraphics[width=0.92\linewidth]{Figure_9.jpg}
	\caption{Fetal head volume (mL) measurement reproducibility (right) against three pre-defined scanning directions (left). Blue mesh represents the whole fetal head. Three volumes of a fetus in the same group.}
	\label{fig:vol_repro}
\end{figure*}

As shown in Fig. \ref{fig:challenges}, subject to the strong acoustic reflection on fetal skull, different fetal head orientations or scanning directions can arouse various shadows and occlusions. The appearance of whole fetal head in US volumes can hence drastically change. In this regard, keeping high reproducibility and being robust against scanning direction variation become crucial requirements before our methods can be applied in real clinical scenarios. Accordingly, we newly collected 156 volumes from 52 volunteers to validate the reproducibility of our solution (3 volumes per volunteer. Free fetal pose and varying GA from 21 to 31 weeks). The same US machine, S50 from SonoScape Medical Corp., Shenzhen, Guangdong Province, (China) was used for image acquisition. Each volunteer was scanned along three predefined directions, as shown in Fig. 9, where the Anterior(A), Posterior (P), Left (L), Right (R), Superior (S), Inferior (I) axes are sketched to denote fetal head orientation. For each volunteer, a volume is acquired along each direction. All the 3 volumes from the same volunteer are collected as a group. All the data are anonymized and the acquisition is approved by the local Institutional Review Board. All the volunteers have reviewed and signed the consent forms. Fig. \ref{fig:vol_repro} shows the box-plot of volume measurements generated by our USegNet-DS-HAS-Ctx for each group. We can observe that, our method suffers little from the pose or scanning variations, and attains remarkable reproducibilities (the mean of standard deviation of all groups is 11.524 mL, minimum is 1.960 mL, maximum is 49.869 mL) over all groups in measuring the whole fetal head volumes. \par

\section{Discussion}
\label{Discuss}
Automated analyses of US volume have appealing potentials in promoting the prenatal examinations and bringing about changes to the traditional clinical work-flow. Accurately segmenting the whole fetal head in the volume may provide ever precise biometrics in describing the fetal growth. However, automated segmentation of whole fetal head in US volume is non-trivial due to the poor image quality, varying fetal poses and massive volume data. In this paper, we approach the task by proposing a fully-automated solution with high performances and good reproducibilities. \par

Whereas, there still exist several key points for future study. \textit{First}, since our method enables the automated extraction of fetal head volume, conducting the population study about the precise fetal head volume against GA becomes more tractable than ever. Previously, due to the lack of efficient tools in analyzing US volumes, there is no widely accepted reference chart of fetal head volume. This then limits the use of 3D US in supporting prenatal diagnoses. Only with the population study and the associated reference charts, volumetric measurement of fetal head can really benefit the fetal health monitoring. To achieve this goal, we need to collect more volume data and enhance our solution to cover a broad GA range. \textit{Second}, in the population study, ultrasound images will be acquired across different subjects, sites, devices, sonographers, GAs and etc. Unpredictable appearance shift in US images often happens during the acquisition due to different imaging conditions. Deep neural networks tend to suffer from this kind of appearance shift and be severely degraded \cite{yang2018generalizing}. Improving the generalization ability and robustness of deep neural networks to handle varying imaging conditions is critical for automated ultrasound image analysis, especially for the population study. Leveraging the shared shape prior \cite{yang2018generalizing} or fine-tuning the deep model for each acquisition site with few samples \cite{gibson2018inter} will be considered for our task. \textit{Third}, to accelerate the collection and annotation of large dataset for population study, we should greatly reduce the time and cost in manually annotating the volumes. Currently, the volume annotation is very expensive and time-consuming (more than 2 hours for one volume). Assisting the experts during annotation with machine learning powered algorithms, like the interactive segmentation \cite{wang2018interactive}, is highly demanded in our scenario. \textit{Finally}, based on our proposed automated segmentation, we should try to conduct longitudinal study to analyze the development pattern of fetal head volume. This kind of study may provide earlier and better indicators than 2D measurements for the prognosis of rare diseases, like the intrauterine growth restriction (IUGR) syndrome. \par

Considering the computation burden of the volumetric segmentation, we need to further reduce the computation cost of 3D deep networks to enable larger volume input with higher resolution for better segmentation results. We can consider the checkpointed backpropagation techniques \cite{chen2016training} to save more GPU memory for training with high resolution input. In \cite{yang2019fetusmap}, we explored the checkpointed backpropagation and provided clear evidences that higher resolution volumetric input can promote the localization of multiple fetal landmarks than low-resolution ones in 3D ultrasound. Real-time feature is not strongly required in current 3D ultrasound applications, however, network architectures like the pseudo-3D networks \cite{Qiu_pseudo3d} and Mobilenets \cite{howard2017mobilenets} to reduce the computation cost of convolution kernel should be seriously investigated in the near future. Also, as the fetal pose and scale vary greatly across subjects and timepoints, efficient detection strategies, like Faster R-CNN \cite{ren2015faster} in 2D or 3D form \cite{xu2019efficient}, to locate the fetal head in volume can greatly narrow the search space and hence reduces the computation burden in segmentation. Finally, the segmentation result should be fully explored to facilitate more advanced applications or be complementary to each other, like the landmark detection, standard plane localization and longitudinal analysis of fetal brain. \par

\section{Conclusions}
\label{Conclusions}
In this work, we propose the first fully-automated solution for the precise segmentation of whole fetal head in US volumes. The task is pending and lacking satisfying solution before this work. We highlight our work with a hybrid attention scheme. It imposes a composite and hierarchical feature filtering effect on our 3D encoder-decoder backbone for better feature learning under the limited GPU resources and deep network layers. With experiments and demonstrations, our proposed modules are proved to be effective. Promising segmentation accuracy, remarkable correlations and agreements with experts, and high reproducibilities against scanning variations indicate that, our work may have potentials to assist sonographers in reviewing fetal growth from a new perspective. \par

\section{Acknowledgements}
This work was supported in part by the National Key R$\&$D Program of China (No. 2019YFC0118300), Shenzhen Peacock Plan (No. KQTD2016053112051497, KQJSCX20180328095606003), Medical Scientific Research Foundation of Guangdong Province, China (No. B2018031) and National Natural Science Foundation of China with Project No. U1813204. \par

\bibliographystyle{elsarticle-num}

redpajama_set_name: RedPajamaArXiv


Madrid System is exercised under Madrid Agreement (1981) and the Madrid Protocol (1989). It is supervised by the International Bureau of the World Intellectual Property Organization (WIPO) which is located in Geneva, Switzerland. The framework was fundamentally acquainted to provide comfort in the arrangement of registration of marks. Each region has an alternate framework for registration of trademarks. The framework empowers the registration of trademarks in numerous jurisdictions worldwide that are a part of Madrid Union's 85 member nations by recording a single application in the Applicant's national trademark office, or regional trademark office.
A man to secure his trademark needed to get his stamp enrolled in the nation where he needs to ensure it. Be that as it may, a trademark conceded by a specific nation served to secure the enthusiasm of the holder just in that nation and any individual who needed to ensure his trademark internationally was hence required to file for trademark registration in every individual nation where such assurance was looked for. This made a ton of disarray like the multiple gathering of applications and bothers to the potential candidate and furthermore expanded the expenses for him. In order to put an end to this chaos, India became the 90th member of the Madrid protocol on July 8, 2013 in order to provide the local businesses to protect their trademarks in various jurisdictions by just filing one application with one set of essential fee that is to be paid. This further allows the owner of the brand to organize registration of international trademark which in turn brings confidence to do business in India.
As soon as India became the member of the Madrid Protocol there were few amendments that were made to the Indian trademark law in order to satisfy its norms with the Madrid system. The Trademarks (Amendment) Act, 2010 was passed on 21st September 2010 which led to the formation and incorporation of Chapter IVA (containing sections 36A to 36G) in the Trademarks Act 1999 that focuses on the "Special Provisions relating to Protection of Trademarks through international registration under the Madrid Protocol." This amendment was done to bring the Madrid protocol in force in India.
Formerly, Indian brand owners that needed to ensure their trademarks in different purviews needed to enlist neighborhood to advice in every jurisdiction and comprehend the significant laws, which could be unreasonably time-consuming and exhausting. Likewise, trademark owners outside of India needed to draw in nearby guidance keeping in mind the end goal to enlist their Trademark Registrations in India. The registration procedure was frequently long. A smooth registration process (through household recording) could take up to a few years. The Madrid framework has not just facilitated the method of applying for the trademark yet, in addition, simplified its consequent administration. Presently candidates can assign India and get trademark security or get "international registration" in India in view of an 'essential application' or 'fundamental registration' in a Protocol member nation.
The regulatory body for Madrid Protocol is International Bureau of World Intellectual Property Organization and the appropriate office for Indian Applicants to file an application under Madrid Protocol / International Application in the Indian IP office.
The term of registration under Madrid Protocol is a time period of 10 years which can be further renewed.
A legal entity or natural person who has a commercial establishment in, or is a domicile or national of, a country which is member to the Madrid protocol can file an application through this protocol.
It has a unified exchange trademark application process by which a man can get security worldwide through a single application and a single arrangement of expenses.
It is a more helpful and knowing technique.
It prevents the unreasonable gathering of applications.
The Protocol Applications might be in French, English or Spanish (which is clearly leeway in non-French talking purviews like India.
The procedure is less tedious.
Any adjustment in the subtle elements of the correct holder (Eg. name, address and so forth) can be changed by sending one single report to the International Bureau and no endeavors must be made at each national office.
Under the Madrid Protocol, it is truly simple to include and along these lines assign the part nation at a later date under a similar International registration. The recording and upkeep expenses related to the international registration are bring down after some time than keeping up a few separate national registrations.
The international registration can stretch out just to Protocol domains.
The Indian Trademarks registry has not possessed the capacity to engage the national filings on account of the absence of labor. Since India has embraced the Madrid framework, it would be significantly more hard to investigate both National and International filings in the meantime and with same labor.
The Indian Trademarks registry has not possessed the capacity to engage the national filings as a result of the absence of labor. Since India has embraced the Madrid framework, it would be much more hard to investigate both National and International filings in the meantime and with same labor.
Under the convention, the international applications must be prepared inside year and a half. Along these lines, the national filings may get postponed.
The international application is reliant on the essential application/registration, thus, if there is any adjustment in the "fundamental" application or registration (like cancelation), these progressions should consequently apply to the international applications also.
Assigning the responsibility for International registration to elements dwelling or having an association with Non-Madrid Protocol state is denied under the guidelines of the Madrid Protocol. This is bad for India since it has business interests with a few non-part nations and now it should document national applications to manage these nations.
If an Indian fundamental stamp is crossed out or restricted in initial five years then the International check will correspondingly be scratched off or constrained. After the expiry of this five-year term, be that as it may, the international registration winds up noticeably free.
Madrid Protocol overall offers a kind advantage to all local organizations and business people to ensure their trademark portfolios over the world along with a convenient and cost-effective way of obtaining trademark protection. In like manner, the choice of picking India as an assigned nation has facilitated the financial aspects of documenting trademark applications for some international gatherings.
For any International or National Trademark Registrations in India, you can consult with Company Vakil and their Specialized Trademark Attorney's would be happy to help you.


okay, after you get a windows xp bootable cd, place it in your cd rom or cd rewriter, now boot your computer through cd. in windows xp, during the splash screen, press f4 and then choose boot from cd. now, you will see a message on the screen saying that �press any key to boot from cd� once you get into the setup, you will observe that setup is inspecting your system and loading setup files. once you get the welcome to setup screen, press enter to setup windows. the licensing agreement will be displayed next press f8 to agree it. the next screen will give you an option to do a repair. you will see that �if one of the following windows xp installations is damaged, setup can try to repair it� use up and down arrow keys to select windows xp installation and then press r to begin the repair process. you must let the repair run. setup will now check your disks and start copying installation files, this may take few minutes. after the copying file process, you will be required to reboot. note: reboot will happen automatically, you will see a red progress bar.
�, press shift+f10, this is the security hole in this operating system, you will now see a command console which will enable you to access your system. now, you have to type nusrmgr.cpl and then press enter, to execute your command. believe it or not, you will now gain a graphical access to your user account in the control panel. now, you can do anything, you can change the passwords of the accounts or you can even remove the passwords. after you have completed the manipulations, go to command prompt and type exit. wait for the repair process to get over, then reboot your system and find the change. you can also stop the password prompt, for this you must follow the same procedure given above. but, only one thing you have to change, in the command prompt type in control userpassword2, a screen will popup, choose log on without being asked for the password. note: after making changes in the command prompt or through the command prompt, always exit using the exit command, this will save the changes.
password reset disk have you ever thought of a password reset disk?
�. this will initiate a wizard; click next until the wizard asks for your login passwords. enter your login password and then click next. the creation of your password disk will begin, once it is finished, give a label and save it in a safe and secure place. you now know, how to create a password reset disk, but you must be wondering how you would use this disk. it is very easy to use; you must restart your system for this, when you get the login screen, leave the password box blank. this will bring in the login failure box; you will see the option of password reset disk. don�t panic, insert your password reset disk and follow the procedure. through this procedure you will be able to put a new password.
enter the administrator account, go to control panel and then to user accounts. click on the particular user account and then click on remove password. by this way you will be able to remove the password of the particular user account. reboot your system in the normal boot up mode and find the change. note: if your windows xp box is not on a network, then there will be no administrator password. actually, the non-network systems have a hidden administrator with no password.
Forgot Your Windows XP Administrator Password? Here's How To Recover It!
Getting the Administrator password can be done with a free program that can be found here: http://www.loginrecovery.com/Use the program to create a floppy boot disk (or CD), and restart your computer with the disk in the drive, so your computer boots the software on the disk instead of booting Windows. It will then display an encryption key, which you should write down on a peice of paper if you're using a CD boot disk. If you're using a floppy disk, then you don't need to write it down. Just wait a minute for it to record the encryption key to the disk. Remove the disk, turn off your computer by pressing and holding the power button, and then turn it back on. Windows will boot up. Insert the disk back into the computer (or any computer with internet access. It doesn't have to be the one you're recovering the administrator password for) and go to http://www.loginrecovery.com/instructions.php and follow the instructions on that webpage. The administrator password will be emailed to you.If this doesn't work, then you can always completely re-install Windows by purchasing a Windows CD-ROM.
Just make sure you backup any important files beforehand.
boxes. But in reality, you'll find the option to create a PDF file in the Print dialog box.
This is where most, if not all, PDF creation tools allow you to create a PDF.
Provides forums and several tutorials. Topics include spyware prevention, spyware removal, Hijackthis logs, spyware news and warnings, anti-spyware software, spam, viruses, security and others.
Describes anti-spyware web sites and products that are either suspicious in their nature or are deceptive. It also lists legitimate products. This is a very helpful article and you should check here before using the anti-spyware product that you are considering.
Provides descriptions, pricing information, and ratings for spyware removal, adware removal, virus removal and prevention tools along with snoop prevention tools, firewalls, file deletion tools, cleanup tools, encryption tools, and cookie managers.
List of known Browser Helper Objects (BHOs).
Recommended link to an article entitled Rogue/Suspect Anti-Spyware Products & Web Sites which can help you determine which products are safe.
Twitter is a social networking and micro-blogging service that enables its users to send and read other users' updates known as tweets. Tweets are text-based posts of up to 140 bytes in length. Updates are displayed on the user's profile page and delivered to other users who have signed up to receive them. Senders can restrict delivery to those in their circle of friends (delivery to everyone being the default). Users can send and receive updates via the Twitter website, SMS, RSS (receive only), or through applications such as Tweetie, Twitterrific, Twitterfon and Feedalizr. The service is free to use over the web, but using SMS may incur phone services provider fees.
You can now sneak away with every single trick, tactic, formula, shortcut, strategy and Jealously-Guarded secrets that these two underground marketers use to generate huge piles of cash at will using Twitter!
We have spent over 3 months researching & putting together this step-by-step blueprint which will show you every little trick & technique to exploding your online business & making crazy money from Twitter.
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l Secret Applications and Tools To Automate Your Twitter Marketing.
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Yahoo Messenger Services recently change some firewall settings that's why most of users have sign in problem.
All the Older Version of Yahoo Messenger will no longer be able to connect through firewall.
So You Need to UPGRADE the Yahoo Messenger with 9.0 Version then you can signin Under Firewall.
What is a firewall?A firewall is security software that prevents unauthorized access to your computer. Many company networks and some home networks have firewalls to keep their computers more secure. Firewalls protect against viruses and other types of malicious software.
the video in your required format.
As every person related to IT field know on Conficker worm effect more than 9 million machines (as per external report) it is pretty impressive for such a classic infection vector.
Conficker is a simple worm; it exploits a Microsoft Windows vulnerability, that can only be utilized over a local network as it uses the SMB protocol, ad uses an initial infection vector of running an "autorun" on removable media.
In my previous blog I write you and show you in practical that how you can protect your computer from virus. When you insert removable media USB in computer it start automatically and when you click on that USB "Autorun" file run automatically and virus comes in your computer.
There are two classifications of individuals who try to break into computer systems -- hackers and crackers. Hackers are individuals with proficient computer skills who exploit weaknesses in system programming in order control a computer system. Crackers break into computer accounts and damage the system using the privileges that are available to the stolen account or use the stolen account to mask their identity when attempting to break into other computer systems on the local network or the Internet. How can these renegades be foiled? The hacker is stopped by closing down the "holes" the s/he finds.
Computer & Telecommunication Services regularly installs software patches to close down security holes and continuously monitors possible break-in points. The cracker is stopped by good password security and good password security is in your hands.
Never tell someone else your password. Richard Stockton College feels so strongly about this aspect of password protection that it is specifically stated in the acceptable use policy in Standard 2. "Users may not disclose the password of an account or otherwise make the account available to others who have not been authorized to use the account. Users are responsible for all usage of their accounts and are expected to take appropriate safeguards to assure that their passwords are not known to others." Never write down your password. Anyone observing your login will see where it is located and can retrieve it for their use -- or misuse -- when you aren't around. Even if they don't observe your login, they will look for anything written down and posted in the vicinity of your workstation (e.g., the side of the monitor, bottom of the keyboard, on the keyboard tray.).
Never send your password to someone else in an electronic mail message. You cannot be sure of the security measures taken on the systems that your message passes through or the security of the system where your message is delivered.
configuration file or programmable keyboard key. Anyone sitting at your computer has immediate access to your password. Use a password that you can type easily and quickly. This will thwart anyone attempting to get your password by watching over your shoulder as you type.
All of our central systems and file servers require you to change your password every ninety days. If you suspect someone might know your password, don't wait for it to expire. Change it immediately!
You should change your password more frequently if you TELNET to Stockton's computer systems from another site. The Internet is a complex web of computer networks and security levels vary. At less secure sites it is possible for a really determined cracker to monitor communications packets and capture your username and password.


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Luxury wine
Wine museum
Serving Those Who Served: Six brands of wine owned by veterans in the United States
Jean H. Vannatta
"I will never leave a fallen comrade", "I will never leave an aviator behind me" and "Semper Fidelis" (Always Loyal) are at the heart of the beliefs and values of the US military. Memorized in initial formation, their meanings are experienced throughout the service. But even after hanging up the uniform, veterans still feel the inexorable pull of standing up for those beliefs.
These veteran winemakers, vineyard owners and brand owners use their civic occupations to support their veteran siblings.
1. Dauntless Wine Company, Willamette Valley, OR
Proceeds from these Dauntless Wine Company packs go to Folds of Honor / Photo by Ben Martin
After serving in Iraq, Marine Corps veterans Ben Martin and Paul Warmbier and Navy Corpsman veteran Ryan Mills founded the Dauntless Wine Company. Understanding that good wine comes from good grapes, their majority team of veterans craft wines from grapes grown in the Willamette Valley.
Location and quality are important, but Dauntless believes that the people who take care of the vines are also paramount. The team created the Dauntless Veteran Foundation (DVF) to help veterans translate skills learned in the military into agriculture.
2. Tackitt Family Vineyards, Paso Robles, California
A Warriors Help Warriors event at Tackitt Family Vineyards / Photo by Robert Binder
While serving in the US Navy Explosive Ordnance Disposal (EOD), Leon Tackitt was responsible for securing explosive materials. Upon retirement, he and his wife took over the family business, Tackitt Family Vineyards, in Paso Robles.
Photo by Robert Binder
Some of Leon's EOD siblings had more difficult transitions to civilian life, so the Tackitts established EOD cellars to provide donations to organizations that support the EOD community. These include the EOD Warrior Foundation, which provides support to injured service members and their families, as well as the families of deceased service members.
3. Purple Heart Wines, Sonoma County, California
Peter Mondavi Sr. served in the United States Army in World War II
Winemaker John Moynier served as a squadron training non-commissioned officer in the US Air Force from 1968 to 1972. A graduate of the University of California, Davis, he was a winemaker with C. Mondavi & Family from 1985 until upon his recent retirement in 2021.
Marc Mondavi with the Purple Heart bottle
In 2015, Moynier developed the blend for Purple Heart Wines Sonoma-called Vin Rouge, which is part of the C. Mondavi & Family portfolio. Napa Valley winemaker and pioneer Peter Mondavi Sr. was also a veteran, having served in the United States Army during World War II.
Each year Purple Heart Wines donates to the Purple Heart Foundation. Named after the medal awarded to service members injured or killed in war, the Purple Heart Foundation supports veterans and their families.
4. Bella Luna Estate Wines, Paso Robles, CA
Sherman Smoot (L) flew F-4Js for the Navy and Kevin Healey (R) was in the military in Vietnam / Photo by Amy Hinrichs
Kevin Healey and Sherman Smoot grew up as best friends, although they served their country in different ways: Kevin was in the military in Vietnam, and Sherman flew F-4Js for the navy. The two came together at their home in Paso Robles to found Bella Luna Estate Winery.
Sporting an image of the Distinguished Flying Cross, Bella Luna produces Fighter Pilot Red, a single-vineyard Zinfandel sourced from the Adelaida District AVA. A portion of the wine sales support the children of injured and deceased veterans.
5. Singing Water Vineyard, Texas Hill Country, TX
Andy and Missy Ivankovich worked in different career fields before turning to wine production, and Andy's time as a team leader in the US Air Force had a lasting impact on the couple. As co-owners of Singing Water Vineyards, the Ivankoviche continue the winery's tradition of championing Texas Hill Country wines and supporting veterans.
Singing Water's Freedom Red Blend supports the U.S. military and veterans through organizations such as United Service Organizations (USO). The USO aims to keep "military personnel connected to their families, homes and countries" during their tenure and transition to civilian life.
6. Nine Iron Wine, California
Every bottle of Nine Iron's red, white and rosé sold provides donations to Camp Hope / Photo bu Renee Morone
After several deployments over 14 years, Brandon Casella left the military to work as director of wine and spirits sales. Casella met Blake Barnes on the golf course, and although Barnes had a civilian background, he shared Casella's passion for golf, wine, and service to those who served.
The duo founded Nine Iron Wine. Every bottle of red, white and rosé Nine Iron's sold provides donations to Camp Hope. Located in Houston, Texas, Camp Hope helps veterans and families of veterans with post-traumatic stress disorder (PTSD) on their journey to recovery.
JUSTIN Vineyards & Winery Adds Lewis Cellars To Its Luxury Wine Portfolio | New
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napa valleyunited states
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Chilean luxury wine brand, Lapostolle, extends its heritage with the launch of Two Piscos
JULIET, NEW LUXURY WINE BRAND, LAUNCHES WITH SUSTAINABILITY AT THE FOREFRONT
Bronco Partners Examines Debt Consolidation vs. Debt Settlement

redpajama_set_name: RedPajamaCommonCrawl


Should I cancel my trip to New York this weekend?
The gruesome point everyone is overlooking is that in a week or so the stench from THOUSANDS of decomposing bodies will fill this city with a smell that will not only deter tourists, but drive locals away as well.
There is no way even the TINIEST portion of the debris will be removed by the time you get here.
Do not come to New York unless you would enjoy something like that.
Wake up, folks. The horrors are not over yet.
Heard that the air is not that healthy either with all that stuff floating in the air. Our trip to New York was to arrive tomorrow. We canceled our trip and will plan in again next year.
If it were me, I would not come. I personally feel that the US is bracing for major retaliation. When we strike back, it will not be pretty, and there is no way of knowing when or where it will be. I would not take the chance right now by flying abroad. When we do make a strike, some sort of counter retaliation may follow.....I just wouldn't chance it.
I can't say whether you should go to New York or not, but I can relate a similar experience I had in 1999.
I had tickets to go to Istanbul. A week before my departure, the huge earthquake leveled a portion of the city. I agonized for 2 days over whether or not to go. Ultimately, I decided that the last thing that the city needed was the economic disaster of the loss of tourist revenue right on the heels of the enormous loss of life and property. My instinct turn out to be correct, as the people of Istanbul were incredibly thankful to us and the other travelers who came inspite of the tragedy.
Thanks for all the messages and advice.
I cancelled my flight this morning. Some of you were asking where I am coming from. I live in London.
I think the devastation in NY is too great to even contemplate normality. The relative with whom I was supposed to stay in New Jersey told me not to go and my friend in Boston basically said the same.
I will visit though, next year. I think the whole world shares the American people's sorrow. Certainly, in London, the mood is quite grim and Heathrow has still suspended all US flights.
Can you change your trip around so that you go to DC first? Maybe sightsee in DC and then Boston and save NY for last. It won't be the same right now but at least some time may elapse and things in NY could be at least a bit more "normal." Another suggestion - maybe go somewhere else besides NY as a third choice. There are many historic sites within hours of DC such as Williamsburg, VA, and Gettysburg, PA that are chock full of American history and are very nice and interesting places to visit. Amerian hearts are broken but not our spirits!
I've though about your question since yesterday (I have to use a computer in a cafe). Come another time--come in the spring--when we will be agagin available we hope to see to your enjoyment of (still) a glorious city.
I totally agree with Elizabeth. Chris, I know you have already canceled, so this is for anyone else considering coming in the next 2 weeks or so. The tension here is almost thicker than the smoke. Not a smile anywhere. Bomb threats almost every hour. A lot of people are still in shock. It was absolutely gut wrenching to see people jumping out of windows to their deaths. Watching it on TV is really very different than being there, feeling it, smelling it. I am about out of my skin at this point. Little to no sleep, sirens 24 hrs a day, migranes from whatever is floating around in the air, and jumping at any loud noise or shadow overhead. I haven't even gotten started on the grief as we discover yet another customer, friend, associate, etc. who is missing. Not only were there hundreds of office workers there, but there were also hundreds of people who have appointments or visit there regularly (we had no way of knowing that they had gone there that day). We haven't begun to start on doldrums, we are still on shock, denial, and numbing pain. And, communication is nil. In my home and office we have no long distance telephone, no fax, spotty cell phone connection, & practically no internet access. Strange though, I got on Fodors but could not get CNN, CNBC, Yahoo, Google, or anything else. The water supply will be the next to go.
Ellen, thank you for posting. You've given us a real understanding of what life is like for those of you in New York. I do hope that somehow you, and all New Yorkers, in fact all of us, can find a way to back to some semblance of life before 9-11-01.
Please keep us updated on how you're doing -- the day-to-day trials of your life help clarify the reality of these incomprehensible events.


text:
import datetime

def _timestamp():
    now = datetime.datetime.now()
    return now.strftime("%Y-%m-%d-%H-%M")

redpajama_set_name: RedPajamaGithub


text:
1982 Renault 5 Turbo 1
5 Turbo 1
Blue Olimpic
Red - Blue
Manual 5 Speed
ASI Gold Plate
RDS00147
Contact us to request the assistance of our experts on "1982 Renault 5 Turbo 1".
Pellegrino Spallanzani
Petru Moldovanu (Russian Language)
*Chassis: VF1822000B0001360
*Engine: 840B7 No. 1261
*Power 158 HP- kW 118,00
*Current State: Restored
*Certificate: ASI Gold Plate No. 29850
The History of the Renault 5 Turbo 1
"The little French Pest"
The Renault 5 Turbo has just turned 40, the sports version of the well-known French utility car produced since 1972 which, in this version, gains muscle as a bodybuilder. Produced by the transalpine brand between 1980 and 1985, the "supercharged beast" has managed to earn a poster in the room of many fans.
Born as a basis for the rally variant, it also had some success in this dimension, winning some races of the world championship.
The project took off in the late seventies and gave a new genetic identity to the Renault 5, distorted in style and mechanical architecture, thanks also to the central rear arrangement of the powerful engine, combined with rear-wheel drive.
The stylistic impact was really strong, very striking. We certainly cannot speak of a car full of grace, but its look does not leave you indifferent and, if you take it, it does so incisively, fueling the fever of passion. In the road version, the Renault 5 Turbo delivers 160 horsepower, developed by a 1.4-liter in-line 4-cylinder engine, which pushes vigorously, especially at high revs, thanks to the turbocharging, with a pressure of 0.86 bar.
Remarkable performances, which intimidated many supercars of the time, especially in the passage from 0 to 100 kph, covered in just 6″5. The maximum speed reached was of 200 kph.
The Renault 5 Turbo marked the introduction into the range of the technical expertise of the turbocharger acquired in the races and prefigured the customs clearance of this technology on an industrial scale. Its appeal still today makes it a flattered, sought-after, desired car.
Our Renault 5 Turbo 1
The car is in excellent condition of bodywork and mechanics, and has been the subject of a recent restoration, completely repainted in the current Blue Olympic color.
The engine has also been completely restored, with the replacement of many vital mechanical parts and today it is able to deliver all its original power.
The internal instrumentation is the original one, fully functional.
In the passenger compartment, the newly restored seats in blue leather and blue/red fabric stand out. The carpet has also been completely renovated in the same orange shade.
The toolbox located on the rear window is really characteristic.
The Momo Corse steering wheel, with three blue spokes, is covered in the same leather as the seats, creating a pleasant color combination.
The alloy wheels are perfect and without scratches, with blue inlays.
It is not easy to find models in these excellent conditions on the market, which represent highly sought after objects by fans of the French company.
Today it is possible to view the car online, by booking a call with one of our commercial operators, via Skype, Zoom, Whatsapp and Google Meet platforms.
Click and drag to navigate