Top row, from left: Bacchus close concentric paperweight; Scrambled paperweight; Baccarat close pack paperweight. Middle row, from left: Baccarat close pack paperweight; Baccarat paperweight, 1976; St. Louis white carpet ground paperweight. Bottom row, from left: Saint Louis White Overlay paperweight; Bigaglia scrambled paperweight; Saint Louis four-color crown antique.


text:
Sibling Relationships
Sibling Similarities And Differences, Siblings In Non-western Cultures, Sibling Relationships Across The Life Span
Relationships with extended kin, spouses, parent and child, and siblings are all affected by a changing social world. Family size (one indicator of sibling structure) is shrinking in many societies. The International Database of the U.S. Bureau of the Census (2001) reports an all-time low of 2.76 children per woman, down from 4.17 in 1960. Growing up with fewer siblings (or none, as is mandated in much of China) has profound implications in terms of intrafamily relationships, inheritance possibilities, and obligations and responsibilities for family members.
Dimensions of the sibling relationship. Sibling relationships can be analyzed according to a number of factors, including position within the sibling system, roles assumed by different siblings, family norms for children's expected behavior, the extent of coalition formation within the sibling system, and the functions siblings perform for each other. Expected behavior for siblings may depend on where the child is in the sibling hierarchy (oldest, middle, or youngest child) and whether the child is male or female. At all ages, sisters are reported to be, and report themselves to be, closer to one another than are brothers or cross-sex sibling pairs. Position and sex may dictate role behavior (e.g., who assumes outside versus inside chores or acts as caretaker for younger siblings). Coalitions foster sibling solidarity, counter the power of parents or other sibling subgroups, and develop to strengthen siblings' positions in times of conflict. Siblings serve many functions for one another. Some of these include serving as a "testing ground" for one another when experimenting with new behaviors or ideas before exposing them to parents or peers; serving as teachers; practicing negotiation skills; and learning the consequences of cooperation and conflict and the benefits of commitment and loyalty. Older siblings may serve a protective function, "translate" parental and peer meanings for younger brothers and sisters, and act as pathbreakers when new ideas or behaviors are introduced into the family. For example, parents may object less when a younger son decides to get his ear pierced, or a younger sister decides to have the small of her back tattooed, because an older sibling already weakened parental resistance. Lastly, it is within the sibling group that children first experience feelings of fairness and justice. Siblings compete for resources within the family, and if resources (such as affection, time, attention from parents, space, or material goods) are scarce, children watch closely to ensure that they are getting their fair share (Ihinger 1975). What appears to distinguish middle childhood sibling behavior of children in the United States from its non-Western counterpart is that it reflects a family system based upon independent relationships. It mirrors the prototypical Western family as a culture of individualism (as compared to a culture of collectivism). The consequence of such behavior is intergenerational and interpersonal independence (Kagitcibasi 1996).
Kinship - Descent, Kinship Terminology, Residence Rules
Sibling Relationships - Sibling Similarities And Differences
Sibling Relationships - Siblings In Non-western Cultures
Sibling Relationships - Sibling Relationships Across The Life Span
Sibling Relationships - Stepsiblings And Half-siblings
Sibling Relationships - Conclusion
Other Free Encyclopedias
Marriage and Family EncyclopediaRelatives & Extended Family
Copyright © 2019 Net Industries and its Licensors All Rights Reserved

redpajama_set_name: RedPajamaCommonCrawl


Product prices and availability are accurate as of 2019-04-19 04:47:24 UTC and are subject to change. Any price and availability information displayed on http://www.amazon.com/ at the time of purchase will apply to the purchase of this product.
We are happy to stock the excellent The North Face Mudder Trucker Hat.
With so many available recently, it is great to have a name you can recognise. The The North Face Mudder Trucker Hat is certainly that and will be a superb purchase.
For this price, the The North Face Mudder Trucker Hat comes widely respected and is a popular choice with many people. The North Face Accessories have included some great touches and this equals good value for money.


text:
\section{Implementation Details} \label{sec:impl}

\subsection{HCMoCo{}}

\noindent\textbf{Network Details.} For the proposed instantiation of HCMoCo{}, we implement the sample-level modality-invariant representation learning target by maintaining a memory pool, which is adapted from an open-sourced implementation \footnote{\url{https://github.com/HobbitLong/PyContrast}}. The memory pool is updated in a momentum style with the momentum of $0.5$. For global embeddings, we sample $16384$ negative samples from the memory pool. For other hyper-parameters, we use a batch size of $224$, a learning rate of $0.03$, a temperature of $0.07$ for all three contrastive learning targets. For the pre-train, 4 NVIDIA V100 GPUs are used. The training process is divided in two steps. The first step only pre-train the model using sample-level modality-invariant representation learning target for $100$ epochs. The second stage adds the other two learning targets and trains for another $100$ epochs. The whole training process takes approximately 48 hours.

\noindent\textbf{Mixing Heterogeneous Datasets.} Since we mix several heterogeneous human datasets for pre-train, we need to mask out the missing modalities. For example, when we use NTU RGB+D and MPII for pre-train. The former dataset has all the required modalities, while the latter one misses depth maps. Therefore, for the hierarchical contrastive learning targets, we mask out the missing depth embeddings of MPII for all the positive pairs sampling. By using the masking technique, it is possible to combine multiple heterogeneous datasets into this pre-train paradigm as long as there are at least two common modalities.

\noindent\textbf{Datasets for Pre-train.} For NTU RGB+D, we only use the version with 60 actions~\cite{shahroudy2016ntu}. With the provided RGB-D videos, we uniformly sample one frame from every 30 frames, which makes $143648$ samples. The RGB and depth frames are calibrated by the correspondences provided by the 2D keypoints positions on RGB and depths. For MPII and COCO, we use the full training sets for pre-train.

\subsection{DensePose Estimation}

For the DensePose~\cite{guler2018densepose} estimation, we use the official open-sourced implementation \footnote{\url{https://github.com/facebookresearch/detectron2}}. For the full training set, we train the network for $130000$ iterations with a batch size of $16$, a learning rate of $0.01$ on 4 NVIDIA V100 GPUs, which takes around $80$ hours to train. For the $10\%$ training set, we train the network for $13000$ iterations with a learning rate of $0.005$ and other settings the same, which takes 9 hours to train. The $10\%$ training set is uniformly sampled from the default ordered training set.

\subsection{RGB Human Parsing}

For the RGB human parsing on Human3.6M~\cite{h36m_pami}, we use the official HRNet~\cite{sun2019deep} semantic segmentation implementation \footnote{\url{https://github.com/HRNet/HRNet-Semantic-Segmentation}}. Different ratios of training settings are uniformly sampled from the default ordered full training set. For the full training set, we train the network for $50$ epochs with a learning rate of $0.007$, a batch size of $40$ on 2 NVIDIA V100 GPUs. For other data-efficient settings, we train the network for $150$ epochs with other settings the same. We use the the standard dataset split protocol, where the subjects $1, 5, 6, 7, 8$ are for training and the subjects $9$ and $11$ are for evaluation.

\subsection{Depth Human Parsing}

For the depth human parsing on NTURGBD-Parsing-4K, we use the same implementation as that of RGB human parsing. To use the HRNet to encode depth maps, we repeat the depth dimension for three times to fit the RGB input, which is also how HCMoCo{} deals with depth inputs. For all training settings, we train the network for $150$ epochs with a learning rate of $0.007$, a batch size of $80$ on 2 NVIDIA V100 GPUs. Even though the encoder is used to deal with depth inputs, we still initialize it using ImageNet pre-train for that it might help with the performance proved by some previous works~\cite{xiong2019a2j}.

\subsection{3D Pose Estimation on Depth}

For the 3D pose estimation from depth maps on ITOP~\cite{haque2016towards}, we choose to adapt the official implementation \footnote{\url{https://github.com/zhangboshen/A2J}} of A2J~\cite{xiong2019a2j}. The original implementation uses ResNet as the backbone. And we switch to HRNet. Since the original implementation only provides validation scripts, we re-implement the whole training pipeline. We change the original normalization method where a global mean and variance is counted for a global normalization. Instead, we perform an online instance normalization where we only centralize each depth pixel to zero mean but do not normalize its variance, since its a better way to prevent the over-fitting to the relatively small dataset. We train the network for $50$ epochs with a learning rate of $0.00035$ and a batch size of $12$ on one NVIDIA V100 GPU. As for the dataset, we use the side-view of ITOP since the depth maps in pre-train are side views. Following the official dataset split, there are $17991$ samples for training and $4863$ for testing. Following the practice of A2J~\cite{xiong2019a2j}, we initialize the encoders using ImageNet pre-train.

\subsection{Cross-Modality Supervision}

To experiment with the cross-modality supervision, we choose the downstream task of human parsing on NTURGBD-Parsing-4K. The modalities to experiment with are RGB and depth. To make the experiment fair and the networks to converge faster, the backbones are initialized by CMC~\cite{tian2020contrastive} pre-train. The following descriptions are for the setting of `RGB$\rightarrow$Depth', where the source modality is RGB and the target modality is depth. To implement `Depth$\rightarrow$RGB', one can simply switch the source and target modalities. At training time, a randomly initialized segmentation header, which is the same one used for human parsing experiments, is attached to the dense mapper network of RGB. Then the network is trained with both the hierarchical contrastive learning targets $\mathcal{L}$ and a cross-entropy loss $\mathcal{L}'$ for the supervision of the segmentation. For the `No Contrastive' baseline, we only train with $\mathcal{L}'$. As for the `CMC' baseline, the network is supervised by both the learning target proposed by CMC~\cite{tian2020contrastive} $\mathcal{L}$ and the segmentation loss $\mathcal{L}'$. Note that, during the whole training time, including the CMC pre-train, the target modality of NTURGBD-Parsing-4K is not exposed to better simulate the application scenario. In order to build the connection between RGB and depth during training time, we mix the NTURGBD-Parsing-4K with NTU RGB+D which is the same one used for our pre-train. At inference time, we attach the trained segmentation head to the mapper network of depth. Since the dense embeddings of RGB and depth are aligned thanks to our hierarchical contrastive learning targets, it is reasonable for the segmentation head to be able to handle the dense embeddings of depth.

\subsection{Missing-Modality Inference}

We also use human parsing on NTURGBD-Parsing-4K to experiment with our extension of missing-modality inference. The basic setup is the same as that of the cross-modality supervision experiments. At training time, we take the dense embeddings of both RGB and depth together for a max pooling operation for a simple feature-level fusion. Then the fused dense embedding is passed to a segmentation header, which is the same one used by the human parsing experiment, to produce the segmentation prediction. The network is supervised with both the hierarchical contrastive learning targets $\mathcal{L}$ and a cross-entropy loss $\mathcal{L}'$ for segmentation supervision. Similarly, the `No contrastive' baseline does not use any contrastive learning targets. The `CMC' baseline uses the contrastive learning target proposed in CMC~\cite{tian2020contrastive} as $\mathcal{L}$. At inference time, if RGB is missing, then the dense embedding of depth is passed to the trained segmentation header for prediction. Since the dense embeddings of RGB and depth are aligned and the segmentation header is trained with the fusion of both embeddings, missing one of them will still produce reasonable predictions.

\section{More Quantitative Results} \label{sec:moreres}

\noindent\textbf{DensePose Estimation.} Due to the page limitation, we could not report all metrics for the DensePose~\cite{guler2018densepose} estimation. Therefore, we report them in this supplementary material. As shown in Tab. \ref{tab:denseposedetails}, detailed results of all settings mentioned in the main paper are listed. Specifically, for the initialization of the network, we test with the network randomly initialized (`From Scratch') and the network initialized by ImageNet pre-train (`IN Pre-train'). As for the ratio of training data, we test with the full training set and $10\%$ of the training set. As for the pre-train datasets, we test with two combinations: NTU RGB+D $+$ MPII and NTU RGB+D $+$ COCO. As for the backbone, we test with HRNet-W18 and HRNet-W32. Compared with the baseline and two other state-of-the-art pre-train counterparts, our method outperforms them in most of the metrics. Especially, our method has advantages in GPS and GPSM, which are two critical metrics for DensePose quality. Additionally, we also report full results of the ablation study. The detailed results further validates the analysis in the main paper.

\noindent\textbf{RGB Human Parsing.} We further report detailed RGB human parsing results on Human3.6M~\cite{h36m_pami} that could not fit into the main paper. As shown in Tab. \ref{tab:human36mdetails}, we report the per-class IoU for all the settings reported in the main paper. Similarly, for the initialization of the network, we test with the network randomly initialized (`From Scratch') and the network initialized by ImageNet pre-train (`IN Pre-train'). As for the ratio of training data, we test with the full training set, $20\%$, $10\%$ and $1\%$ of the training set. The pre-train datasets are NTU RGB+D $+$ MPII. In most classes, our method outperforms comparison methods. Moreover, we also report per-class IoU for the four settings in ablation study, which are in line with our analysis in the main paper.

\noindent\textbf{Depth Human Parsing.} We report detailed depth human parsing results on NTURGBD-Parsing-4K. As shown in Tab. \ref{tab:nturgbddetails}, we report the per-class IoU for all the settings reported in the main paper. We initialize the networks using ImageNet pre-train. Two ratios of the training set, \ie full and $20\%$, are tested. We also change the backbone to PointNet++~\cite{qi2017pointnet++} (`PN++'). Since it is a point-based backbone, the `background' class is ignored and not included in the calculation of mIoU. The per-class IoU results also agree with the conclusion in the main paper that our method is superior than other comparison methods.

\noindent\textbf{Cross-Modality Supervision.} As shown in Tab. \ref{tab:appdetails}, we report detailed per-class IoU for the experiments of cross-modality supervision. In both `RGB$\rightarrow$Depth' and `Depth$\rightarrow$RGB' settings, our method outperforms other baseline methods in all classes. Especially, other baseline methods barely make correct predictions while ours makes a huge improvement.

\noindent\textbf{Missing-Modality Inference.} As shown in Tab. \ref{tab:appdetails}, we list detailed per-class IoU for the experiments of missing-modality inference. In both `Only RGB' and `Only Depth' settings, our method outperforms baseline methods in most classes. Therefore, the detailed results further validates the conclusions made in the main paper.

\section{More Qualitative Results} \label{sec:vis}

More qualitative results of RGB human parsing on Human3.6M~\cite{h36m_pami} and depth human parsing on NTURGBD-Parsing-4K are shown in Fig. \ref{fig:10_human36m}, Fig \ref{fig:100_human36m} and Fig. \ref{fig:v2_nturgbd}. We choose to visualize both the full training set and $10\%$ training set for RGB human parsing. The segmentation results produced by our pre-train model are superior than those of other comparison methods, especially in data-efficient settings. For challenging classes like hands and elbows, our method is capable of producing correct predictions constantly while other methods struggle. The depth map is a challenging modality for the dense prediction task like semantic segmentation. Our method manages to produce reasonable predictions that are better than those of other comparison methods.

\clearpage
\input{tables/densepose_details}

\clearpage
\input{tables/human36m_details}

\clearpage
\input{tables/nturgbd_details}
\input{tables/app_details}

\clearpage
\begin{figure*}[t]
    \begin{center}
        \includegraphics[width=0.9\linewidth]{assets/10_human36m.pdf}
    \end{center}
    \caption{Qualitative Results of RGB Human Parsing on Human3.6M with $10\%$ of the Training Set.}
    \label{fig:10_human36m}
\end{figure*}
\clearpage
\begin{figure*}[t]
    \begin{center}
        \includegraphics[width=0.9\linewidth]{assets/100_human36m.pdf}
    \end{center}
    \caption{Qualitative Results of RGB Human Parsing on Human3.6M with the Full Training Set.}
    \label{fig:100_human36m}
\end{figure*}
\clearpage
\begin{figure*}[t]
    \begin{center}
        \includegraphics[width=0.9\linewidth]{assets/v2_nturgbd_parsing_4k.pdf}
    \end{center}
    \caption{Qualitative Results of Depth Human Parsing on NTURGBD-Parsing-4K with the Full Training Set.}
    \label{fig:v2_nturgbd}
\end{figure*}

\end{document}

\section{Experiments}
\label{sec:exp}

\subsection{Experimental Setup}
\noindent \textbf{Implementation Details.}
The default RGB and depth encoders are HRNet-W18~\cite{sun2019deep}.
The default datasets for pre-train are NTU RGB+D~\cite{liu2019ntu} and MPII~\cite{andriluka14cvpr}. The former provides paired indoor human RGB, depth, and 2D keypoints, The latter provides in-the-wild human RGB and 2D keypoints. Mixing human data from different domains helps our pre-train models adapt to a wilder domain.

\noindent \textbf{Downstream Tasks.}
We test our pre-train models on four different human-centric downstream tasks, two on RGB images and two on depth.
\textbf{1)} DensePose estimation on COCO~\cite{guler2018densepose}: DensePose aims at mapping pixels of the observed human body to the surface of a 3D human body, which is a highly challenging task.
\textbf{2)} RGB human parsing on Human3.6M~\cite{h36m_pami}.
Human3.6M provides pure human part segmentation, which aligns with our objectives.
We uniformly sample 2fps of the video for training and evaluation.
\textbf{3)} Depth human parsing on NTURGBD-Parsing-4K.
\textbf{4)} 3D pose estimation from depth maps on ITOP~\cite{haque2016towards} (only side view).
For all the above downstream tasks, we use the pre-train backbones for end-to-end fine-tune.

\noindent \textbf{Comparison Methods.}
Since there are few previous human-centric multi-modal pre-train methods, we propose to use general multi-modal contrastive learning methods CMC~\cite{tian2020contrastive} and MMV~\cite{alayrac2020self} as the baselines. Although there are other multi-modal contrastive learning works, they either require the multi-view calibration~\cite{hou2021pri3d} or focus on multi-modal downstream tasks~\cite{liu2021contrastive, hazarika2020misa} and therefore are not suitable for comparison. In addition, for RGB tasks, we also experiment under two settings, one initializes encoders with supervised ImageNet~\cite{krizhevsky2012imagenet} (IN) pre-train while the other does not.

\input{tables/ablation

\subsection{Performance on Downstream Tasks}
\noindent \textbf{DensePose Estimation.}
As shown in Tab. \ref{tab:densepose}, we test DensePose estimation~\cite{guler2018densepose} under two settings: full and $10\%$ of the training data. The trained models are tested on the full validation set of DensePose. Firstly, if not using IN pre-train, our pre-train model significantly outperforms both `From Scratch' and two baseline methods. Especially under $10\%$ of training data, \textbf{12.7\%} improvement in terms of GPS AP is observed. And our pre-train model even outperforms that using IN pre-train by \textbf{4.13\%} in terms of GPS AP. When we use IN pre-train as initialization, which is a common practice for 2D tasks, our method still outperforms all the baselines. Our method surpasses IN pre-train by \textbf{7.2\%} and \textbf{5.4\%} in terms of GPS/GPSM AP under $10\%$ setting. To further test the performance of in-domain transfer, we also pre-train models using training sets of NTU RGB+D and COCO. The performance gain under $10\%$ setting further improves to \textbf{9.7\%} and \textbf{7.5\%} in terms of GPS/GPSM AP.

\noindent \textbf{RGB Human Parsing.}
As shown in Tab. \ref{tab:human36m}, we test four settings on Human3.6M~\cite{h36m_pami}: full, $20\%$, $10\%$ and $1\%$ training data. In all settings, our method outperforms all baselines in all metrics. On full training data, we outperform IN pre-train by \textbf{5.6\%} in terms of mIoU. The performance gain increases with the amount of training data decreases. It is worth noticing that with only $10\%$ of training data, our method outperforms IN pre-train with full training data.

\input{tables/nturgbd
\noindent \textbf{Depth Human Parsing.}
As shown in Tab. \ref{tab:nturgbd}, we test the pre-train depth backbone on our proposed Dataset NTURGBD-Parsing-4K with all training data and $20\%$ training data. We outperform all baselines on two settings. Especially, only using $20\%$ of training data, we surpass IN pre-train by \textbf{6.4\%} and MMV~\cite{alayrac2020self} by \textbf{4.6\%} in terms of mIoU.

\input{tables/itop
\noindent \textbf{3D Pose Estimation.}
As shown in Tab. \ref{tab:itop}, we test the pre-train depth backbone on ITOP~\cite{haque2016towards} with six different ratios of training data. Our pre-train model outperforms all baselines on most settings. With only $10\%$ training data, the accuracy of our method outperforms that of IN pre-train with all training data. It is also worth noticing that $0.1\%$ of training data are $17$ samples, which makes this a few-shot learning setting. With such limited training data, IN pre-train barely produce meaningful results, while our method improves the accuracy by \textbf{48.2\%}.

\subsection{Ablation Study}
In this subsection, we perform a thorough ablation study on HCMoCo{} to justify the design choices. As shown in Tab. \ref{tab:ablation}, we firstly report the results of only applying sample-level modality-invariant representation learning. Then we add dense intra-sample contrastive learning and sparse structure-aware contrastive learning in order. To further demonstrate the effect of the `soft' design in dense intra-sample contrastive learning, we also report results of the `hard' learning target, which takes the form of a classic InfoNCE~\cite{oord2018representation}. We report the results of the ablation study on all four downstream tasks under data-efficient settings.

For DensePose estimation, it is important to learn feature maps that are continuously and ordinally distributed, which is the expected result of soft dense intra-sample contrastive learning. The performance gain of the soft learning target over the hard counterpart justifies the observation and the learning target design. The dense intra-sample contrastive learning also shows superiority on three other downstream tasks, which shows the importance of fine-grained contrastive learning targets for dense prediction tasks.

Explicitly injecting human prior into the network through sparse structure-aware contrastive learning also proves its effectiveness by further improving the performance on DensePose.
Thanks to the strong hints provided by 2D keypoints, the performance of 3D pose estimation is improved. Moreover, the sparse structure-aware contrastive learning boosts the performance of human parsing both on RGB and depth maps by \textbf{1.9\%} and \textbf{2.8\%} respectively in terms of mIoU. Although 2D keypoints are sparse priors, they still provide the rough location of each part of the human body,
which facilitate the feature alignment of same body parts.
To summarize, the sparse and dense learning targets both contribute to the performance of our methods, which is in line with our analysis.

\subsection{Performance on HCMoCo{} Versatility}
\noindent \textbf{Cross-Modality Supervision.}
We test the cross-modality supervision pipeline on the task of human parsing on NTURGBD-Parsing-4K because it has two modalities and respective dense annotations. Two baseline methods are adopted: 1) using CMC~\cite{tian2020contrastive} contrastive learning target; 2) no contrastive learning target. For a fair comparison, the backbones of all methods are initialized by CMC~\cite{tian2020contrastive} pre-train. At training time, the target modality of training data is not available. We experiment on two settings where we supervise on RGB, test on depth (RGB $\rightarrow$ Depth), and vice versa (Depth $\rightarrow$ RGB). As shown in Tab. \ref{tab:crossmod}, our method outperforms both baselines under two settings. Specifically, our method improves the mIoU of both settings by \textbf{29.2\%} and \textbf{23.0\%}, respectively. Even compared to methods with direct supervision, we can achieve comparable results.

\input{tables/cross_mod

\input{tables/missing_mod
\noindent \textbf{Missing-Modality Inference.}
For missing-modality inference, we report the experiments on the same dataset and same baselines as above. As shown in Tab. \ref{tab:missingmod}, with no pixel-level alignment, the two baseline methods struggle in two missing-modality settings \ie `Only RGB' and `Only Depth'. While our method improves the segmentation mIoU by \textbf{24.3\%} and \textbf{19.6\%} on two settings.

\input{figures/fast_converge

\subsection{Further Analysis}
\noindent \textbf{Faster Convergence.}
One of the advantages of pre-training is the fast convergence speed when transferred to downstream tasks. Our HCMoCo{} also shows superiority in this feature. We log the validation mIoU of Human3.6M human parsing at different training epochs. As shown in Fig. \ref{fig:fast_conv}, compared with IN pre-train and CMC~\cite{tian2020contrastive}, our pre-train model is able to converge within a few training epochs in both the full training data and data-efficient settings.

\input{tables/changebackbone
\noindent \textbf{Changing Backbone.}
So far our experiments are all performed on HRNet-W18. To further demonstrate HCMoCo{}'s performance on other backbones, for the 2D backbone, we also experiment with HRNet-W32~\cite{sun2019deep}. For the depth backbone, we choose to test with PointNet++~\cite{qi2017pointnet++}. For the RGB pre-train model, we experiment on the $10\%$ DensePose estimation. For the depth pre-train model, we experiment on the $20\%$ NTURGBD-Parsing-4K. As shown in Tab. \ref{tab:changebackbone}, our method outperforms its pre-train counterparts by a reasonable margin, which is in line with our previous experimental results.

\section{Our Approach}
\label{sec:method}
In this section, we first introduce the general paradigm of HCMoCo{} (\ref{sec:general}). Following the design principles (\ref{sec:int}), hierarchical contrastive learning targets are formally introduced (\ref{sec:loss}). Next, an instantiation of HCMoCo{} is introduced (\ref{sec:inst}). Finally, we propose two applications of HCMoCo{} to show the versatility (\ref{sec:ext}).

\subsection{HCMoCo{}}
\label{sec:general}
As shown in Fig. \ref{fig:02_general}, HCMoCo{} takes multiple modalities of perceived human body as input. The target is to learn human-centric representations, which can be transferred to downstream tasks. The input modalities can be categorized into dense and sparse representations. Dense representations $I_d^*$ are the direct output of imaging sensors, \eg RGB, depth, infrared. They typically contain rich information but are low-level and noisy. Sparse representations are structured abstractions of the human body, \eg 2D keypoints, 3D pose, which can be formulated as graph $I_s^* = G(V,E)$. Different representations of the same view of a human should be spatially aligned, which means intra-sample correspondences can be obtained for dense contrastive learning.
HCMoCo{} aims to pre-train multiple encoders $E_d^*$ and $E_s^*$ that produce embeddings
of dense representations
and sparse representations for downstream tasks transfer.

To support dense downstream tasks, other than the usual sample-level global embeddings used in \cite{he2020momentum, chen2020simple, chen2020improved, grill2020bootstrap, liu2020self, tian2020contrastive}, we propose to consider different levels of embeddings \ie global embeddings $f^g$, sparse embeddings $f^s$ and dense embeddings  $f^d$
\footnote{For easier understanding of the notations, the superscripts of $f$ and $M$ stand for the kind of embeddings. The subscripts stand for the kind of representations (`g' for `global'; `d' for `dense'; `s' for `sparse').}
, which are defined as follows:
\textbf{1)} For dense representations $I_d$, the global embedding is obtained by applying a mapper network $M^g_d$ to the mean pooling $\mathcal{M}$ of the corresponding feature map, which is formulated as $f_d^g = M^g_d \circ \mathcal{M} \circ E_d(I_d)$. Similarly, for sparse representations $I_s$, the global embedding is defined as $f_s^g = M^g_s \circ \mathcal{M} \circ E_s(I_s)$.
\textbf{2)} Sparse embeddings have the same size as that of sparse representations. Formally, for sparse representations $I_s = G(V, E)$, where $V \in \mathbb{R}^{J\times K}$, the corresponding sparse embedding is defined as $f_s^s = M^s_s \circ E_s(I_s)$, where $f_s^s \in \mathbb{R}^{J\times K'}$, $M^s_s$ is a mapper network. For dense representations, the corresponding sparse features are pooled from the dense feature map using the correspondences $\mathcal{G}$. Then the sparse features are mapped to sparse embeddings as $f_d^s = M^s_d \circ \mathcal{G} \circ E_d(I_d)$.
\textbf{3)} Dense embeddings are only defined on dense representations, which is formulated as $f_d^d = M^d_d \circ E_d(I_d)$.
With three levels of embeddings defined, we formulate the overall learning objective as
\begin{equation} \label{eq:whole}
    \mathcal{L} = \lambda_g\mathcal{L}_g(f^g) + \lambda_d\mathcal{L}_d(f^d) + \lambda_s\mathcal{L}_s(f^s),
\end{equation}
which is analyzed and explained as follows.

\input{figures/framework

\subsection{Principles of Learning Targets Design}
\label{sec:int}
In this subsection, we analyze the intuitions when designing learning targets, which makes the following three principles.
\noindent \textbf{1) Mutual Information Maximization}: Inspired by~\cite{wu2018unsupervised, poole2019variational}, we propose to maximize the lower bound on mutual information, which has been proved by many previous works~\cite{he2020momentum, chen2020simple, chen2020improved, tian2020contrastive} to be able to produce strong pre-train models.
\noindent \textbf{2) Continuous and Ordinal Feature Distribution}: Inspired by the property of human-centric perception, it is desirable for the feature maps of the human body to be continuous and ordinal. The human body is a structural and continuous surface. The dense predictions, \eg human parsing~\cite{gong2017look, li2017multiple, gong2018instance}, DensePose~\cite{guler2018densepose}, are also continuous. Therefore, such property should also be reflected in the learned representations. Besides, for an anchor point on human surfaces, closer points have higher probabilities of sharing similar semantics with the anchor point than that of far away points. Therefore, the learned dense representations should also align with such ordinal relationship.
\noindent \textbf{3) Structure-Aware Semantic Consistency}: Sparse representations are abstractions of the human body, which contains valuable structural semantics about the human body. Instead of identity information, the human pose and structure understanding are the keys to our target downstream tasks. Therefore, it is reasonable to eliminate the identity information and enhance the structure information by enforcing structure-aware semantic consistency where semantically close embeddings (\eg embeddings of left hands from different samples) are pulled close and vice versa.

\subsection{Hierarchical Contrastive Learning Targets}
\label{sec:loss}
Based on the above three principles, we formally define hierarchical contrastive learning targets in this subsection.

\noindent \textbf{Sample-level modality-invariant representation learning} aims at learning a joint latent space at the sample level using global embeddings, which fulfills the first principle. Inspired by
\cite{tian2020contrastive}, the learning target can be formulated as
\begin{equation}
    \mathcal{L}_g = -\underset{\underset{f_1^g \in F_1^g}{F_1^g, F_2^g \in S_g}}{\mathbb{E}} \left[ \text{log}\frac{\text{exp}(f_1^g \cdot \bar{f_2^g} / \tau)}{\sum_{f_2^g \in F_2^g} \text{exp}(f_1^g \cdot f_2^g / \tau)} \right],
\end{equation}
\noindent where $F_*^g$ is a set of global embeddings of one modality, $S_g$ is the set of $F_*^g$ of all modalities, $\bar{f_2^g}$ is the embedding of the paired view of that of $f_1^g$, $\tau$ is the temperature. It should be noticed that $f_1^g$ can be sampled from the global embeddings of either dense or sparse representations.

\noindent \textbf{Dense intra-sample contrastive learning} is operated on the paired dense representations. For any two paired dense embeddings $f_{d1}^d, f_{d2}^d \in \mathbb{R}^{H\times W\times K'}$,
to simultaneously satisfy the first and the second principle, the dense intra-sample contrastive learning target between them is defined in a `soft' way as
\begin{equation}
    \mathcal{L}_d^{12} = -\underset{\underset{m, n}{x, y}}{\mathbb{E}} \left[ \mathcal{W}_{xy}^{mn} \text{log} \frac{\text{exp}(f_{d1}^d(x, y) \cdot f_{d2}^d(m, n)) / \tau)}{\sum\limits_{x', y'} \text{exp}(f_{d1}^d(x,y) \cdot f_{d2}^d(x', y') / \tau)} \right],
\end{equation}
\noindent where $\mathcal{W}_{xy}^{mn}$ is the weight, $\tau$ is the temperature, $(x, y), (m, n), (x', y')$ are coordinates on the dense representation, $1 \le x, x', m \le H$, $1 \le y, y', n \le W$. The above equation is a generalized version of InfoNCE~\cite{oord2018representation}. InfoNCE is a special case when $\mathcal{W}_{xy}^{mn}$ is set to $1$ if $x = m$ and $y = n$ else $0$.
We use the normalized distances as the weights, which is formulated as
\begin{equation}
    \mathcal{W}_{xy}^{mn} = \frac{\text{exp}(\sqrt{(x - m)^2 + (y - n)^2})}{\sum_{x', y'}\text{exp}(\sqrt{(x - x')^2 + (y - y')^2})}.
\end{equation}
For each pair of dense representations, the above learning target is calculated between each pair of dense embeddings. Therefore, the whole learning target is defined as
\begin{equation}
    \mathcal{L}_d = \underset{\underset{f_{d1}^d, f_{d2}^d \in F_1^d, F_2^d}{F_1^d, F_2^d \in S_d}}{\mathbb{E}} \mathcal{L}_d^{12}(f_{d1}^d, f_{d2}^d),
\end{equation}
where $F_*^d$ is a set of dense embeddings of one modality, $S_d$ is the set of all $F_*^d$, $f_{d1}^d$ and $f_{d2}^d$ are two paired embeddings. It should be noticed that the `soft' learning target cannot guarantee an ordinal feature distribution. Instead, it serves as a computationally efficient relaxation of the requirement of ordinal distribution.

\noindent \textbf{Sparse structure-aware contrastive learning} takes two sparse representations $f_1^s$ and $f_2^s$ as inputs. The paired features $f_{1j}^s$ and $f_{2j}^s$ (\ie features of the $j$-th joint) should be pulled close while unpaired features are pushed away. The two sparse representations can be sampled from the same or different modalities, intra- or inter-sample. The intra-sample alignment satisfies the first principle. The inter-sample alignment follows the third principle. The sparse structure-aware contrastive learning target is formulated as
\begin{equation}
    \mathcal{L}_s = -\underset{\underset{j; f_1^s, f_2^s \in \{F_1^s, F_2^s\}}{F_1^s, F_2^s \in S_s}}{\mathbb{E}} \left[\text{log}\frac{\text{exp}(f_{1j}^{s}\cdot f_{2j}^{s} / \tau)}{\sum\limits_{j'; f_i^s \in \{F_1^s, F_2^s\}} \text{exp}(f_{1j}^s \cdot f_{ij'}^s / \tau)} \right],
\end{equation}
where $F_*^s$ is a set of sparse embeddings of one modality, $S_s$ is the set of $F_*^s$, $\tau$ is the temperature, $f_1^s, f_2^s$ are sampled from the union of $F_1^s$ and $F_2^s$.
To conclude, the overall learning target is formulated as Eq. \ref{eq:whole},
where $\lambda_*$ are the weights to balance the targets.

\subsection{Instantiation of HCMoCo{}}
\label{sec:inst}
In this section, we introduce an instantiation of HCMoCo{}. As shown in Fig. \ref{fig:03_framework}, for dense representations, we use RGB and depth. Large-scale paired human RGB and depth data is easy to obtain with affordable sensors \eg Kinect. These two modalities are the most commonly encountered in human-centric tasks~\cite{chen2014detect, h36m_pami, gong2017look, li2017multiple, gong2018instance}. Moreover, a proper pre-train model for depth is highly desired. Therefore, RGB and depth are reasonable choices of human dense representations, both of which are easy to acquire and important to downstream tasks. For sparse representations, 2D keypoints are used, which provide positions of human body joints in the image coordinate. Off-the-shelf tools~\cite{8765346, mmpose2020} are available to quickly and robustly extract human 2D keypoints given RGB images. Using 2D keypoints as the sparse representation is a good balance between the amount of human prior and acquisition difficulty.

For RGB inputs $I_d^1$, an image encoder $E_d^1$~\cite{sun2019deep} is applied to obtain feature maps $E_d^1(I_d^1)$. Similarly, for depth inputs $I_d^2$, an image encoder~\cite{sun2019deep} or 3D encoder~\cite{qi2017pointnet, qi2017pointnet++} $E_d^2$ can be applied to extract feature maps $E_d^2(I_d^2)$. 2D keypoints $I_s$ are encoded by a GCN-based encoder~\cite{zhao2019semantic} $E_s$ to produce sparse features $E_s(I_s)$. Mapper networks comprise a single linear layer and a normalization operation.

As for the implementation of contrastive learning targets, we choose to use a memory pool to store all the global embeddings which are updated in a momentum way. Sparse and dense embeddings cannot all fit in memory. Therefore, for the last two types of contrastive learning targets, the negative samples are sampled within a mini-batch.

\input{figures/otherapp

\subsection{Versatility of HCMoCo{}}
\label{sec:ext}
On top of the pre-train framework HCMoCo{}, we propose to further extend it on two direct applications: cross-modality supervision and missing-modality inference.
The extensions are based on the key design of HCMoCo{}: dense intra-sample contrastive learning target. With the feature maps of different modalities aligned, it is straightforward to implement the two extensions, which are shown in Fig. \ref{fig:04_otherapp}.

\noindent\textbf{Cross-Modality Supervision} is a novel task where we train the network on the source modality, while test on the target modality. This is a practical scenario where people transfer the knowledge of some single modality dataset to other modalities.
At training time, an additional downstream task head (\eg segmentation head) $D$ is attached to the backbone of the source modality. The hierarchical contrastive learning targets $\mathcal{L}$ together with downstream task loss $\mathcal{L}'$ are used for end-to-end training. At inference time, $D$ is attached to the backbone of the target modality. The extracted feature maps of the target modality are passed to $D$ for prediction.

\noindent\textbf{Missing-Modality Inference} is another novel task where we train the network using multi-modal data and inference on single modality. Multi-modal data collection in practice would inevitably result in data with incomplete modalities, which brings the requirement of missing-modality inference.
At training time, the feature maps of multiple modalities are fused using max-pooling and fed to a downstream task head $D$. Similarly, hierarchical contrastive learning targets $\mathcal{L}$ and downstream task loss $\mathcal{L}'$ are used for co-training. At inference time, the feature map of a single modality is passed to $D$ for missing-modality inference.

\input{figures/nturgbd-parsing-4k

\input{tables/densepose

\input{tables/human36m

\vspace{10pt}

\section{NTURGBD-Parsing-4K Dataset}
Although RGB human parsing has been well studied~\cite{chen2014detect, gong2017look, li2017multiple, gong2018instance}, human parsing on depth~\cite{hernandez2012graph, nishi2017generation} or RGB-D data has not been fully addressed due to the lack of labeled data. Therefore, we contribute the first RGB-D human parsing dataset: NTURGBD-Parsing-4K. The RGB and depth are uniformly sampled from NTU RGB+D (60/120)~\cite{shahroudy2016ntu, liu2019ntu}. As shown in Fig. \ref{fig:dataset}, we annotate 24 human parts for paired RGB-D data. The partition protocols follow that of \cite{h36m_pami}. The train and test set both have $1963$ samples. The whole dataset contains $3926$ samples. Hopefully, by contributing this dataset, we could promote the development of both human perception and multi-modality learning.

\section{Introduction}
\label{sec:intro}

As a long-standing problem, human-centric perception has been studied for decades, ranging from sparse prediction tasks, such as human action recognition~\cite{shahroudy2016ntu, liu2019ntu, yan2018spatial, chen2021channel}, 2D keypoints detection~\cite{lin2014microsoft, andriluka14cvpr, sun2019deep, xiao2018simple} and 3D pose estimation~\cite{h36m_pami, reddy2021tessetrack, martinez2017simple},
to dense prediction tasks, such as human parsing \cite{gong2017look, li2017multiple, gong2018instance, chen2014detect} and DensePose prediction~\cite{guler2018densepose}.
Unfortunately, to train a model with reasonable generalizability and robustness, an enormous amount of labeled real data is necessary, which is extremely expensive to collect and annotate.
Therefore, it is desirable to have a versatile pre-train model that can serve as a foundation for all the aforementioned human-centric perception tasks.

With the development of sensors, the human body can be more conveniently perceived and represented in \textbf{multiple modalities}, such as RGB, depth, and infrared.
In this work, we argue that \textit{the multi-modality nature of human-centric data can induce effective representations} that transfer well to various downstream tasks, due to three major \textbf{advantages}:
\textbf{1)} Learning a modal-invariant latent space through pre-training helps efficient task-relevant mutual information extraction.
\textbf{2)} A single versatile pre-train model on multi-modal data facilitates multiple downstream tasks using various modalities.
\textbf{3)} Our multi-modal pre-train setting bridges heterogeneous human-centric datasets through their common modality, which benefits the generalizability of pre-train models.

We mainly explore two groups of modalities as shown in Fig. \ref{fig:teaser} a): dense representations (\eg RGB, depth, infrared) and sparse representations (\eg 2D keypoints, 3D pose).
Dense representations can provide rich texture and/or 3D geometry information. But they are mostly low-level and noisy.
On the contrary, sparse representations obtained by off-the-shelf tools~\cite{8765346, mmpose2020} are semantic and structured. But the sparsity results in insufficient details.
We highlight that it is non-trivial to integrate these heterogeneous modalities into a unified pre-training framework for the following two main \textbf{challenges}:
\textbf{1)} learning representations suitable for dense prediction tasks in the multi-modality setting;
\textbf{2)} using weak priors from sparse representations effectively for pre-training.

\textbf{Challenge 1: Dense Targets.}
Existing methods~\cite{liu2020p4contrast, hou2021pri3d} perform contrastive learning densely on pixel-level features to achieve view-invariance for dense prediction tasks.
However, those methods require multiple views of a static 3D scene~\cite{dai2017scannet}, which is inapplicable for human-centric applications with only single view.
Furthermore, it is preferable to learn representations that are continuously and orderly distributed over the human body.
In light of this,
we generalize the widely used InfoNCE~\cite{oord2018representation} and propose a dense intra-sample contrastive learning objective that applies a soft pixel-level contrastive target, which can facilitate learning ordinal and continuous dense feature distributions.

\textbf{Challenge 2: Sparse Priors.}
To employ priors in contrastive learning, previous works~\cite{khosla2020supervised, wei2020can, assran2020supervision} mainly use the supervision to generate semantically positive pairs. However, these methods only focus on the sample-level contrastive learning, which means each sample is encoded to a global embedding.
It is not optimal for human dense prediction tasks.
To this end, we propose a sparse structure-aware contrastive learning target, which uses semantic correspondences across samples as positive pairs to complement positive intra-sample pairs.
Particularly, leveraging sparse human priors leads to an embedding space where semantically corresponding parts are aligned more closely.

To sum up, we propose \textbf{HCMoCo{}}, a \textbf{H}uman-\textbf{C}entric multi-\textbf{Mo}dal \textbf{Co}ntrastive learning framework for versatile multi-modal pre-training.
To fully leverage multi-modal observations, HCMoCo{} effectively utilizes both dense measurements and sparse priors using the following three-levels hierarchical contrastive learning objectives:
\textbf{1)} sample-level modality-invariant representation learning;
\textbf{2)} dense intra-sample contrastive learning;
\textbf{3)} sparse structure-aware contrastive learning.
As an effort towards establishing a comprehensive multi-modal human parsing benchmark dataset, we label human segments for RGB-D images from NTU RGB+D dataset~\cite{shahroudy2016ntu}, and contribute the \textbf{NTURGBD-Parsing-4K} dataset.
To evaluate HCMoCo{}, we transfer our pre-train model to four human-centric downstream tasks using different modalities, including DensePose estimation (RGB)~\cite{guler2018densepose}, human parsing using RGB~\cite{h36m_pami} or depth frames, and 3D pose estimation (depth)~\cite{haque2016towards}.
Under full set and data-efficient training settings,
HCMoCo{}
constantly achieves better performance than training from scratch or pre-train on ImageNet.
To name a few, as shown in Fig. \ref{fig:teaser} b), we achieve \textbf{7.16\%} improvement in terms of GPS AP on $10\%$ training data of DensePose estimation;
\textbf{12\%} improvement in terms of mIoU on $20\%$ training data of Human3.6M human parsing.
Moreover, we evaluate the modal-invariance of the latent space learned by HCMoCo{} for dense prediction on NTURGBD-Parsing-4K with two settings: cross-modality supervision and missing-modality inference.
Compared against conventional contrastive learning targets, our method improves the segmentation mIoU by \textbf{29\%} and \textbf{24\%} for the two settings, respectively.
To the best of our knowledge, we are the first to study multi-modal pre-training for human-centric perception.

The main \textbf{contributions} are summarized below:
\textbf{1)} As the first endeavor, we provide an in-depth analysis for human-centric pre-training,
which is formulated as
a challenging multi-modal contrastive learning problem.
\textbf{2)} Together with the novel hierarchical contrastive learning objectives, a comprehensive framework HCMoCo{} is proposed for effective pre-training for human-centric tasks.
\textbf{3)} Through extensive experiments, HCMoCo{} achieves superior performance than
existing methods,
and meanwhile shows promising modal-invariance properties.
\textbf{4)} To benefit multi-modal human-centric perception, we contribute an RGB-D human parsing dataset, NTURGBD-Parsing-4K.

\section{Discussion and Conclusion}
In this work, we propose the first versatile multi-modal pre-training framework HCMoCo{} specifically designed for human-centric perception tasks. Hierarchical contrastive learning targets are designed based on the nature of human datasets and the requirements of human-centric downstream tasks. Extensive experiments on four different human downstream tasks of different modalities demonstrated the effectiveness of our pre-training framework. We contribute a new RGB-D human parsing dataset NTURGBD-Parsing-4K to support research of human perception on RGB-D data. Besides downstream task transfer, we also propose two novel applications of HCMoCo{} to show its versatility and ability in cross-modal reasoning.

\noindent \textbf{Potential Negative Impacts \& Limitations.}
Usage of large amounts of data and long training time might negatively impact the environment. Moreover, even though we did not collect any new human data in this work, human data collection could happen if our framework is used in other applications, which potentially raises privacy concerns. As for the limitations, due to limited resources, we could only experiment with one possible instantiation of HCMoCo{}. And for the same reason, even though the theoretical possibility exists, we do not have the chance to further scale up the amount of human dataset and network size.

\section{Related Work}
\label{sec:relatedwork}

\noindent \textbf{Human-Centric Perception.}
Many efforts have been put into human-centric perception in decades. Lots of work in 2D keypoint detection~\cite{lin2014microsoft, andriluka14cvpr, sun2019deep, xiao2018simple} has achieved robust and accurate performance. 3D pose estimation has long been a challenging problem and is approached from two aspects, lifting from 2D keypoints~\cite{h36m_pami, reddy2021tessetrack, martinez2017simple} and predicting from depth map~\cite{haque2016towards, xiong2019a2j}. Human parsing can be defined in two ways. The first one parses garments together with visible body parts~\cite{gong2017look, li2017multiple, gong2018instance}. The second one only focuses on parsing human parts~\cite{chen2014detect, h36m_pami, hong2021garmentd}. In this work, we focus on the second setting because the depth and 2D keypoints do not contain the texture information needed for garment parsing. There are a few works~\cite{hernandez2012graph, nishi2017generation} about human parsing on depth maps. However, the data and annotations are too coarse or unavailable. To further push the accuracy of human-centric perception, DensePose~\cite{guler2018densepose, tan2021humangps} is proposed to densely model each human body surface point. The cost of DensePose annotation is enormous. Therefore, we also explore data-efficient learning of DensePose.

\noindent \textbf{Multi-Modal Contrastive Learning.}
Multi-modality naturally provides different views of the same sample which fits well into the contrastive learning framework. CMC~\cite{tian2020contrastive} proposes the first multi-view contrastive learning paradigm which takes any number of views. CLIP~\cite{radford2021learning} learns a joint latent space from large-scale paired image-language dataset. Extensive studies~\cite{patrick2021compositions, hazarika2020misa, han2020self, rouditchenko2020avlnet, patrick2020multi, alayrac2020self} focus on video-audio contrastive learning. Recently, 2D-3D contrastive learning~\cite{hou2021pri3d, liu2020p4contrast, liu2021contrastive} has also been studied with the development in 3D computer vision. In this work, aside from commonly used modalities, we also explore the potential of 2D keypoints in human-centric contrastive learning.

redpajama_set_name: RedPajamaArXiv


Fair Trade Judaica is a nonprofit organization building a Fair Trade movement in the American Jewish community. They provide educational resources through their website and community presentations; work with fair trade artisans to design new Judaica products; and help bring those products to consumers through fairs and outreach to Judaica/synagogues/fair trade stores.
Their Judaica products include Jewish Blessing Flags, and a variety of other products in collaboration with fair trade businesses.
Fair Trade Judaica is a proud member of the Fair Trade Federation.
Fair Trade Judaica wholesales their products, provides on-line purchasing, and serves as a wholesale representative for five fair trade businesses.
"One Hundred Under $100: Tools for Empowering Global Women"


The Fat Cat 7ft. "Pockey Table" . This 2 in 1 Gametable evolved out the original Pockey Table. We took the best idea in the industry in years and enhanced it with our exclusive GLD latch system, power cord channel, and leg levelers just to mention a few of the features.
Excellent service from Darting.Com. They matched the best price n the internet. Product arrived in perfect condition. Recommend inside delivery since the table is very heavy and it takes two strong folks to move it off the truck. Air hockey is terrific; pool table is difficult to keep level since it sits in a slightly different position each time it is flipped over. Would recommend this product.


The post Which Is Better? Review – Crocs Vs Coleman Snow Boots For Men appeared first on Saif Ahmed Khatri's Blog.
Space on White: Must See Review! Vegetable Peeler, Y Peelers For Poatato, Veggie, Apple, Fruit Skin, Carrot And T..


text:
Song Lists
VOX EAGLE – Single Premier: "Can't Stop"
On December 14, 2019 December 14, 2019 By EclecticMusicLoverIn Reviews
Regular readers of this blog know I write about a lot of indie artists and bands, as one of the things that drives me is wanting to support them in what little way I can. One of my absolute favorites – both from a musical and personal standpoint – is VoxEagle. Essentially the music project of Australian-born and now Colorado-based singer-songwriter, producer and multi-instrumentalist Andy Crosby, VoxEagle first burst onto the music scene in the beginning of 2017 with the release of the electro-pop single "No Sleep". The terrific song has been streamed over 279,000 times on Spotify. He then followed up with a second single "Come Over", both of which were featured on his debut EP Flamingo Paradiso Pt. 1., released in July 2017. I loved both songs so much they ended up on my Top 100 Songs of 2017 list.
Also in 2017, Andy and his wife relocated from bustling Manhattan to the bucolic solitude of Evergreen, Colorado, high in the Colorado Rockies west of Denver. There, he built his own recording studio, which he dubbed "The Eagles Nest", and set to work on his first full album, the brilliant genre-bending TriumAvium, which was released in October 2018. You can read my album review and interview with Andy here. One of the tracks on the album titled "Wander" went all the way to #1 on my Weekly Top 30 this past February.
Now he returns with a wonderful new single "Can't Stop", which I'm thrilled to premier today. The song officially drops on December 16 on all major digital music sites, some of which are included at the end of this post. "Can't Stop" has a soulful dance pop vibe, and captures the infectious energy we first encountered on "No Sleep."
That energy is the essence of VoxEagle's unique, yet eclectic, sound. In our interview, he told me "VoxEagle is a musical energy; I hate to call it a band or whatnot. It's vibe I suppose is with me at the helm, [though] the whole idea of VoxEagle in the beginning was to collaborate with various artists." He added that his main overriding objective in making music is that "It's just gotta have big melodies and be real energetic."
The track opens with spacey, almost seductive synths that quickly expand like a giant flower unfolding its petals into a beautiful explosion of sound and color. Starting with a deep, thumping bassline as a foundation, VoxEagle layers a rich palette of sparkling, dreamy synths, guitar, and percussion, including what sound like bongo drums, to create an exuberant groove that just makes you feel good. He has a fine, casual singing voice, and I like when he freestyles some of the lyrics. It all builds to an exhilarating crescendo before fading out with those spacey synths and pulsating bass. I love it!
Although "Can't Stop" was written prior to the occurrence of a personal tragedy Andy recently experienced with the death of his best friend of 30 years, it really hits home for him, serving as a sort of anthem for his philosophy about life and his music career. He became so despondent over his friend's death that he actually considered throwing in the towel on making music. Thankfully, he came around to realizing it would be impossible for him to stop, as making music is the one thing that keeps him sane at the end of the day – a sentiment I and many others I know can strongly identify with.
Those feelings are perfectly expressed in the lines "Every time I get the hook wrong, I stay back all night just to fix it cuz we can't stop until we catch the feeling. All I know is that we've come too far. Too far to turn back now. Can't stop these feet when they start moving, no." I hope VoxEagle never stops making songs for us to enjoy.
Connect with Vox Eagle: Facebook / Twitter / Instagram
Stream: Spotify / Soundcloud / Apple Music
Purchase: iTunes / Google Play
Can't StopColoradoElectro-psychElectronicIndie Musicindie popPop/RockPsychedelic RockSynthpopVox Eagle
New Song of the Week – NATH JACKSON: "Oncoming Storm"
Top 30 Songs for December 15-21, 2019
One thought on "VOX EAGLE – Single Premier: "Can't Stop""
manrogue
Very infectious beat. Vox is definitely one of your favorites. Good review.
Follow ECLECTIC MUSIC LOVER on WordPress.com
SURRIJA – Single Review: "Nothing Love"
Top 30 Songs for January 12-18, 2020
100 BEST SONGS OF THE 2010s (100-1)
Top 30 Songs for January 5-11, 2020
Top 30 Songs for December 29, 2019-January 4, 2020
Top 100 Songs of 2019
DIARY OF AN OUTLAW – Single Review: "BIGTIME"
MANDALAN ft. CADENCE XYZ – Single Review: "How We Live"
DARKSOFT – Single Review: "Cybersecurity"
REVOLUTION RABBIT DELUXE – Album Review: "Swipe Left"
Top 30 Songs for December 8-14, 2019
SURRIJA - Single Review: "Nothing Love"
100 BEST SONGS OF THE 2000's (100-1)
Song Review: THE OCCASIONAL ANGELS - "Fool For Love"
MISSIO - Album Review: "The Darker the Weather // The Better the Man"
Ten Greatest Rock Songs: The 1960s
HOUSE OF HARM - EP Review: "Coming of Age"
20 Best Disco Songs of All Time
THE CLEAR - Single Review: "Sunlight"
Artist Spotlight: THE MAYAN FACTOR
Music is King – A music website focused on helping independent artists reach a broader audience
World Music Charts Popular International Song Charts


Into an exclusive patented solution for your business.
Fee Technology has an exclusive patent for Profit Solver®, cutting edge technology to diagnose your business.
Our highly qualified consultants and analysts can show you the path to profitability.
Contact us at Fee Technology global headquarters in Austin, Texas.
Fee Technology's Profit Solver® technology can set and adjust fees to achieve your desired profit.


Thank you George! I'm glad you liked it.
Thank you Karen. Yes, about lost love, and perhaps rekindling.
Great use of the opening line, title and closing line… I really liked how you wove them together with the story. Nice vivid imagery, too; I can almost feel the cold of that mountain.
I can picture the frigid mountain cabin and feel the brutal cold. What a great story, and clocking in at a lean mean word count! I'm just dying to know what happened to put them on that mountain.
Ouch. Why does the end of the world always crush your soul so?
Oh I get it. The mountain is a metaphor….
Nice job with this story. I felt cold and warm in equal measure. It had a brevity that was refreshing and left me wanting more.
Yes, exactly right on the metaphor. Thank you for reading.


text:
Publication | Return of the Unchartable Soul film stills and script
The Menteur - Paris School of Art and Culture - Paris, France - June
The literary journal of the Paris School of Art and Culture is publishing stills from the film Return of the Unchartable Soul in its annual publication of The Menteur along with the script which is the poem of the same name.
Screening | Return of the Unchartable Soul film
Paris School of Art and Culture - Paris, France - June
The opening of the University's new Reid Hall will include a screening of the film.
Cascadia Poetry Festival - Anacortes, Washington, USA - May
The Cascadia Poetry Festival returns May 9-12 in Anacortes, Washington.
Passes are $25. In addition to a tribute to Sam Hamill, poet, editor, translator and Co-Founder of Copper Canyon Press, there will be the launch of two anthologies, the Samthology for Sam Hamill and a bilingual poetry anthology in Spanish and English, Make It True meets Medusario.
Among the scheduled poets are José Kozer (winner of the 2013 Neruda Award from the Chilean government), Washington State Poet Laureate Claudia Castro Luna, Copper Canyon Press co-founder William O'Daly, Shin Yu Pai, Stephen Collis, Tim McNulty, Stephen Kuusisto, Rebecca Seiferle, Ian Boyden, Lyn Coffin and Kent Johnson. There will be a shakuhachi flute performance by longtime Hamill collaborator Christopher Yohmei Blasdel.
Official Selection | Lost Sock Collection film
SHORT To The Point Film Festival - Bucharest, Romania - May
SHORT TO THE POINT (STTP) is an international network of distribution, broadcast and promotion of short films. STTP screens short films projected in unconventional cinemas across the world.
Screening | Don't Tell Me I'm Beautiful film
Slippery Elm - Findlay University - Findlay, Ohio, USA - May
Slippery Elm is the literary journal of Findlay University. Its new multimedia wing features art films and experimental films.
Official Selection | Don't Tell Me I'm Beautiful film
Lincoln Short Film Festival - Lincoln, Nebraska, USA - May
The 2019 Lincoln Short Film Festival features short films by producers and directors from the Great Plains US states of Nebraska, Colorado, South Dakota, Kansas, Iowa, and Missouri.
Films will be screened on the Cube, a 750 sq. ft. LED screen in the Railyard district that sits directly across from the Pinnacle Bank Arena. LSFF and the Cube Art Project is an ongoing series of narrative films, animations, and video art sponsored by the City of Lincoln, Nebraska.
Festival screening Friday 3rd May 2019.
FADFest Film-Art-Detroit - Detroit, Michigan, USA - April, May
FADFest celebrates Fine Art and Short Film and is held in Detroit's beautifully repurposed Annex Art Gallery.
FADFest - All Together Now: A Celebration of Art, Film & Music combines multiple types of art all for show at Michigan's own Annex Gallery. The innovative event will highlight local musicians from a variety of genres, a range of fine artists, and short films from local, national, and international filmmakers.
Festival screenings April 26-28 & exhibitions May 3-5.
Semi-Finalist | Return of the Unchartable Soul film
Cadence Poetry Film Festival - Seattle, Washington, USA - April
Verse meets visuals in motion at Northwest Film Forum (NWFF) in April 2019. Cadence: Video Poetry Festival, presented by Northwest Film Forum, programmed in collaboration with Seattle author Chelsea Werner-Jatzke, is a series of screenings, workshops, and discussions on the genre of video poetry, throughout National Poetry Month. Entering its second year, Cadence is growing considerably to fill a gap in the presentation of video poetry in the Pacific Northwest.
Featuring four screenings, one each Thursday of the month, the festival's inaugural Artist-in-Residence, generative workshops for youth and adults, and a juried selection of open submissions, Cadence fosters critical and creative growth around the oft-overlooked medium of video poetry.
Newlyn International Film Festival - Penzance, Cornwall, UK - April
The varied festival programme aims to encourage filmmakers from all backgrounds as well as attracting new audiences to short film. The whole of the imposing Acorn Theatre Penzance is used for screenings, workshops and a festival party.
The selected feature films will be given a priority screening slot. The Newlyn Film Festival is truly a celebration of film, created for filmmakers and those who love film . Our festival is about more than just watching films, and we will create a friendly and welcoming atmosphere for all attendees to enjoy and discuss between screenings in comfortable and relaxed environments.
Festival screenings Friday 5th-Sunday 7th April 2019.
Manchester International Film Festival - Manchester, United Kingdom - March
Over 150 films in a line-up made up of narrative and documentary features, narrative and documentary shorts, experimental films, music videos, animations and VR will screen at the Odeon in Manchester's historic Great Northern Warehouse over the week 2nd-10th March.
For the first time the festival will take place over a week, testament to the events growth over its short history and the strength of films submitted. After five years the festival has developed a reputation amongst the independent filmmaking community as a warm, welcoming festival with a personal touch.
Official Selection | School of Life film
Miniature Film Festival - Vancouver, Canada - November
Since 2015, the Miniature Film Festival has celebrated the best in micro short, one-minute films. The Miniature Film Festival is a small but enthusiastic celebration of tiny films. This unique, one-night event will feature films from around the globe that capture the best storytelling in under a minute.
Featured Artist | Selections from Baptism for the Dead
Ma'am Presents: Womxn - Kansas City, Missouri, USA - November
Ma'am Presents is a space for all womxn-identifying individuals to present poetry, short stories, personal essays, music or any other inspired material. Ma'am is an independent feminist arts zine based in Northwest Missouri.
Juteback Poetry Film Festival - Fort Collins, Colorado, USA - October
Poetry and filmmaking converge for this unique, one-night event. The Juteback Film Festival (JPFF) will feature films from around the globe, highlighting the creative migration of two art forms: video and poetry, sometimes known as Videopoetry or poetry film.
JPFF is Colorado's only poetry film festival and one of only two poetry film festivals screening in the U.S. today.
Reading | Selections from Floodplain
Women's Press Club - Kansas City, Missouri, USA - September
The Women's Press Club has a history of bolstering women in publishing and printing in Northwest Missouri for over 100 years.
Seattle's Arts in Nature festival - Seattle, Washington, USA - August
Seattle's Arts in Nature Festival is held at Camp Long, one of Seattle's green-space gems. The Festival will feature the work of 40+ artists over two days in August. The festival celebrates art and nature with diverse programming.
Performance | Lost Sock
Kuala Lumpur, Malaysia - September
In the interactive performance art, Lost Sock, Sander performs improvisational dance while interviewing audience members. Topics focus around grief and loss and letting go.


text:
Shirley Percival
Wise Owl Learning was founded in 2012 by Shirley Percival. Shirley has been involved in education for over 25 years as a university lecturer and private tutor. She has two grown up boys, two dogs and has lived in the Basingstoke area for 20 years.
Shirley is passionate about education and has a BSc Hons in Science and Technology, a MSc in Science, Technology & DNA Profiling and studied for a D Phil, specialising in Forensic Science and Criminal Technologies.
Winnie Tang
Winnie studied for a BSc and Masters in Chemistry at the University of Surrey where she also obtained a PhD in Electronic Engineering. During her PhD studies she taught, demonstrated and supervised undergraduate students in various modules within Chemistry..
Winnie has worked academically with children and young people of a variety of ages and abilities on a voluntary basis, including those with learning difficulties.
Winnie currently lives with her partner in Church Crookham and their dog Nala, a German Shorthaired Pointer.
Jonny Treston
Jonny is a talented mathematician with a passion for sciences. He has joined us at Wise Owl, in order to expand our mathematics and science capability at GCSE and A level; he has a natural ability to explain higher level maths and sciences in a way that boosts confidence in the most hesitant students! Fluent in German, Jonny is an outdoors enthusiast, expert skier, mountaineer, and enjoys cycling and many other sports. He will be a tremendous member of our team!
Chloe Bourner
Chloe is a fluent French speaker, having spent some of her childhood living in Normandy. Her love of the language led her to study for a BA (Hons) in French at the University of Warwick. During her degree Chloe was lucky enough to spend more time in Normandy, academically teaching English from ages 11-23 and immersing herself in French life and culture. Chloe offers French tuition at all levels, and English support up to GCSE level.
Chloe currently lives in her family home in Romsey with her dog Monty, where they both enjoy all the beautiful walks nearby.
Annie Saba
Annie graduated with a first class honours degree in Psychology from the University of Surrey, and has worked as an Assistant Psychologist within NHS mental health services. A keen swimmer, hiker and pianist, Annie also has a love of reading and enjoyed English literature at A level. Annie also enjoys travelling and has visited the U.S. and Mexico, where she worked as a volunteer helping local families. Annie is fully DBS checked.
Annie will initially be offering GCSE tuition in psychology, English language and literature, and religious studies.
Alex Millard
Alex is a Biological Sciences graduate with a keen interest in Ecology and Science Communication. Completing her Biology degree at the University of East Anglia, Alex has studied the topic in depth; including cancer, cell biology, plant biology, anatomy, immunology, evolutionary biology and more. Alex will be offering tuition in triple sciences up to GCSE and Biology A level.
Alex currently lives in her first home with her partner (hoping to get a furry friend to join them soon !). She is also part of a local dance company, weekly Film and monthly Book Club.


text:
Q: Sending file through ssh2, scp I need to send a copy from server1 to server2 using ssh2. Both are using ubuntu, and I'm trying sending it with the scp command:
scp test.txt userName@server2_direction:/folder_destination

But I'm getting an error:
test.txt: No such file or directory

I tried putting the complete url: /users/asd/my_user/folder/test.txt, but it doesn't work either.
Any hints?

A: You are in the wrong directory on the source computer. Check if there is test.txt in the current directory.
$ ls -l test.txt

redpajama_set_name: RedPajamaStackExchange


The RS4 takes its place among a long line of high performance Audis. Drastic changes in exterior design coupled with a four-wheel drive system and a more powerful engine combine to create one excellent automobile.
On the outside the A4 can be identified by its Xenon headlamps and deep front bumper with air intakes on either side. The rear can be identified by the large bore exhaust pipes and discreet boot spoiler.
The A4's engine has been enhanced mechanically. The 4.2-litre V8 has been improved with the addition of direct injection and with custom-made internal parts to greatly increase the engines output.
The cars hood and side wings are made from lightweight aluminium instead of steel. This helps to improve the car's weight distribution and handling balance. The noted improvements combine making for a better overall driving experience.
Inside the RS4 the stylish bucket seats adjust for maximum comfort and support. Inside, the cabin boasts many high-level standards, such as a Bose stereo system, climate control and sport mode, all activated by controls located on the steering column.
The RS4 is a fairly expensive vehicle. Insurance runs high, and running costs won't be cheap either. Even if you choose the more fuel efficient diesel engine.
The front passengers have adequate head and legroom, while rear passengers also enjoy plenty of space. The boot is fairly large and can accommodate most reasonably packages.
The controls in the cabin are excellent. They are clearly labeled and easy to find. They are all logically placed. The car's Multi Media Interface system controls vehicle navigation and audio settings.
The RS4 is excellent on comfort. The Dynamic Road Control System allows suspension coverage over the roughest terrain. The seats that come standard with the car are adjustable and very comfortable regardless of passenger size.
Accessing the RS4 is not difficult at all. Heavily bolstered front seating compartments require a little more effort. The boot is quite accessible though with its wide aperture.
The RS4 is a super easy car to park thanks to all the Audi Automotive equipment. It provides great front and rear vision, and comes equipped with both front and rear parking sensors. The car's steering is light and blends right into the mix, and the end result is easy parking. The wheels are a little wide however and the driver should use caution when parallel parking.
The RS4 is an expensive car that delivers driving excitement with a powerful engine that delivers low rev torque and high rev punch accompanied by an awesome V8 noise, how could it not be exciting. The braking system on the RS4 is excellent, suspension is good, and the grip is top-notch. The steering occasionally feels a little lacking, but over all the RS4 delivers a splendid ride.
The RS4 would make a good family car too. It provides plenty of room for all of its passengers and could easily accommodate a family with smaller kids or a family with older children. The car offers plenty of boot space, and the RS4 has offers plenty in the way of passenger comfort even on long trips.
The RS4 is not a practical first car. It is expensive to purchase and expensive to run. Even though it is an easy car to drive it offers too much power for a beginning driver, and insurance would be costly.
This is a quality car. The RS4 makes use of quality materials inside and out and that is very obvious from the look and feel of the car. The RS4 sports and delivers a high performance image.
The RS4 is going to attract a lot of attention and could easily catch a thief's eye. That's why it has been fitted with a Thatcham–approved alarm system. There are other optional security add-ons available, such as; double glazed windows, and tracking devices.
The RS4 has been designed with safety in mind. The car comes equipped with the usual array of various airbags, ABS, ESP, Xenon headlights, a strong body shell, and other features that help to enhance the safety of the vehicle.
The RS4 comes equipped with a Bose sound system. The system includes a CD player and tuner with ten speakers. The sound quality is great, and an automatic noise compensation system automatically adjusts the sound level according to the level of exterior noise.
Most of the interior colors are comprised of dark greys and blacks, there are lighter options available. The cabin's chrome and silver trim keeps the cabin from feeling too dark. The instrumentation and displays have red backlighting, and leather trim comes standard with the RS4.
The RS4 comes with a high price tag, but that price is matched by the RS4's amazing ability. It's easy to drive, comfortable, and delivers an excellent performance when driven fast. The engine also performs well and the 4-wheel drive system comes in handy in all types of weather.


text:
Books interview: Elisabeth Schimpfössl
The lecturer in sociology and policy at Aston University discusses what led her to writing about Russia and its elites
Matthew Reisz
Twitter: @MatthewReiszTHE
What sort of books did you read as a child?
Growing up, my father relentlessly fed me difficult and disturbing novels processing the Nazi period. The book I remember most vividly is Youth Without God by the Austrian-Hungarian novelist Ödön von Horváth, dealing with rising fascism in Germany. One of more cheerful ones was Judith Kerr's semi-autobiographical When Hitler Stole Pink Rabbit.
Which books encouraged you to explore Russia and the world of elites?
My interest in the world of elites came long after my interest in Russia. I became fascinated by the French sociologist Pierre Bourdieu and all those (at first glance) not so relevant details he describes that help reproduce France's social structure. I adored the philosopher and sociologist Georg Simmel and his essays on how fashion trickles down the social hierarchy and how the nobility is constrained by the principle of noblesse oblige. My interest in Russia came through music rather than literature. The Viennese part of my family has always been obsessed with music and excellent at playing it. I was utterly useless, but loved listening to it, in particular Russian 20th-century composers.
Which texts gave you guidance in researching and writing about plutocrats?
There are no texts that are massively useful. I was inspired by the oral history projects run by the French sociologist Daniel Bertaux, whose team collected life stories to research social mobility in Soviet and post-Soviet Russia (such as Living Through Soviet Russia). The most fascinating accounts were those that documented the survival strategies of formerly privileged strata after 1917 and the continuity of their cultural and social resources throughout the century. Orlando Figes takes up quite a number of these stories in The Whisperers.
What general accounts of daily life in today's Russia would you recommend to non-specialists?
I would recommend the films of Andrey Zvyagintsev. His Leviathan is merciless in its grimness and bluntness, as is his recent, Oscar-nominated Loveless, but they capture the atmosphere of today's Russia.
What is the last book you gave as a gift, and to whom?
When Nietzsche Wept, by the Stanford psychotherapist Irvin D. Yalom. It is set in Vienna in 1882 and contains fabulous characters such as Lou Andreas-Salomé, with her sharp mind and flamboyant male entourage including Nietzsche, Freud and Rilke. I sent one copy to a dear friend who was leaving London to return to Vienna and another to a friend with a broken heart.
What books do you have on your desk waiting to be read?
Rojava, War and the Future of Syria's Kurds by Thomas Schmidinger, and Sashenka, the first book in Simon Sebag Montefiore's Moscow Trilogy. If someone turns con woman Anna Delvey's story into a book, I will read it right away. There is something deeply confusing yet fascinating about con artists. Delvey managed to convince New York's high society that she was a wealthy heiress with plans for an art foundation it was worth investing in.
Elisabeth Schimpfössl is lecturer in sociology and policy at Aston University and the author of Rich Russians: From Oligarchs to Bourgeoisie (Oxford University Press).
Books interview: Katherine West Scheil
The professor of English at the University of Minnesota talks about her love of mystery and adventure and how this translates to writing about past lives
By Matthew Reisz
Books interview: Nadine Akkerman
The author of Invisible Agents: Women and Espionage in Seventeenth-Century Britain on unreliable narrators and spymistresses
Books interview: Serinity Young
The author of on the figures, real and mythical, who sparked her flights of imagination
EnquireCourses


text:
Books MoMA Publications Rauschenberg: Canyon, One on One Series - Paperback
Rauschenberg: Canyon, One on One Series - Paperback
15 USD https://store.moma.org/books/moma-publications/rauschenberg-canyon-one-on-one-series---paperback/894-894.html
$13.45 Member
Members save up to 20% & free shipping $35+ Details
Become a member at checkout to save 20% on your first purchase and get free shipping.
Members also save 10% every day and 20% during Member Shopping Days.
Members at the Access category and above get free standard shipping in the contiguous 48 states on purchases over $35. Surcharges may apply for oversized or special items. Exclusions apply.
Item# 894-894
Gift Wrap Option
[$0.00] 0 [$5.00] 1
Buy two or more titles from the One on One series and receive 20% off, members receive 30%
This item is final sale and non-returnable.
Item successfully added to cart!
Pickup in-store: Soho / Museum Store /
Available for in-store pickup at these locations:
In New York City? You'll have the option of buying online and picking up at our stores by selecting "In-Store Pickup" during checkout.
MoMA Design Store, Soho
Monday 11:00 a.m.-7:00 p.m Monday 11:00 a.m.-7:00 p.m.
Tuesday 11:00 a.m.-7:00 p.m Tuesday 11:00 a.m.-7:00 p.m.
Wednesday 11:00 a.m.-7:00 p.m Wednesday 11:00 a.m.-7:00 p.m.
Thursday 11:00 a.m.-7:00 p.m Thursday 11:00 a.m.-7:00 p.m.
Friday 11:00 a.m.-7:00 p.m Friday 11:00 a.m.-7:00 p.m.
Saturday 11:00 a.m.-7:00 p.m Saturday 11:00 a.m.-7:00 p.m.
Sunday 11:00 a.m.-7:00 p.m Sunday 11:00 a.m.-7:00 p.m.
Check Holiday Hours
November 25 Thanksgiving Day: Closed
December 24 Christmas Eve: 10:00 a.m. - 5:00 p.m.
December 25 Christmas Day: Closed
December 26: 11:00 a.m. - 7:00 p.m.
December 27 - 30: 10:00 a.m. - 8:00 p.m.
December 31 New Years Eve: 10:00 a.m. - 6:00 p.m.
January 1 New Years Day: 10:00 a.m. - 6:00 p.m.
For everyone's health and safety, COVID-19 vaccination (age 5+) and masks (age 2+) are required for all customers. Learn more.
The Museum Store
894-894 null
Description Details Shipping & Returns
To look inside this book, click here.
By Leah Dickerman
In the mid-1950s Robert Rauschenberg began making what he called "Combines"—radically experimental works that mix paint and other art materials with things found in daily life. These hybrid creations offered a dramatic counterpoint to the gestural abstraction that prevailed in contemporary American painting. Canyon (1959), one of the artist's best-known Combines, is a large canvas bearing paint, a postcard, a man's shirt, photographs, newspaper clippings, wood, a flattened metal can and paint tube, a piece of glass, and, thrusting out from its surface, a stuffed bald eagle. Leah Dickerman's essay examines the genesis of this startling and enigmatic work and positions it within a key period in Rauschenberg's groundbreaking career. 48 pp.; 35 illus.
Each volume in the One on One series is a sustained meditation of a single work from the collection of The Museum of Modern Art. A richly illustrated and lively essay illuminates the subject in detail and situates that work within the artist's life and career as well as within broader historical contexts. This series is an invaluable guide for exploring and interpreting some of the most beloved artworks in the Museum's collection. View the entire series here.
7.25w x 9"h
Year of Design
Leah Dickerman
https://store.moma.org/books/moma-publications/rauschenberg-canyon-one-on-one-series---paperback/894-894.html
Sign up to receive notifications when this item is back in stock.
Sign up for promotions, new releases, early access and more.
We will notify you via email once this item is back in stock.
At MoMA Design Store, all of the designs we sell are curator-approved and authentic. We ensure the integrity of our products through research and by working closely with the designers. Our products embody the spirit of good design objects in MoMA's collection. Some of them are actual designs represented in the Museum's collection.


A little cheat this week as by writing my post on Saturday I get to add Red Nose day news which this week has been building up to. Its been a busy week, ballet viewing, parents evening, a new car, RND (another dress up day at school 🙂 ) and the normal comings and goings of a family week. It was also a week when terrorism struck London again and the sadness and frustration which accompanies such an attack reminds you of the important things in life.
I finished Bee Wilson's This is Not a Diet Book. I have it on Kindle and imagine its quite a short paperback, but so worth the read about our attitudes to food, children's diets etc.. All excellent counsel on a very important subject presented in a common sense approach.
On Monday, I got to see Little Miss dance at her ballet viewing class. It was lovely to watch and a relaxing hour of dance. She has now been dancing for 5 years and her grace and poise is beautiful. I never got to have any dance lessons when I was a child and by encouraging her to ballet I hoped that she would not be as clumsy or awkward as I am. She's not and has discovered a passion and delight in dance which is a pleasure to watch.
Mmm I'm really stuck on this category, the most creative activity at work was when I made some red nose animations to introduce a competition we held for RND. My current work is very office based and formal and I rarely work directly with the children so it was fun to design the competition and then introduce the task to them with my work. My favourite little animation was Disco Dom, he was such a dude to design.
I wore a red nose on Friday but not the foam nose which I had bought as this was taken by Little Man who wanted an extra one to complete the class' collection of the 10 red noses. I had a pin badge and considering I am often plugged into headphones on my runs, it seemed quite apt.
With an 80s 24 hour dance a thon on Radio 2 on Monday, I heard a lot of 80s tunes and loved being taken back in time to my childhood. Whilst loving the music, I was unimpressed with some of the DJs knowledge of the 80s, Jeremy Vine took some convincing that Gorbachev wasn't the USSR president in 1984.
Whilst its Mothers' day tomorrow I don't think we'll be able to do our original plan of going out for a family day. Little Man suffers with chest infections and has been coughing badly today so I think a day resting and being warm indoors is in order. For years I've been trying to visit the ornamental gardens in a neighbouring town and been thwarted each time. However, I am looking forward to opening the envelopes which came home from school on Friday, nothing beats a homemade card and sharing cuddles with my two little ones.


text:
package org.knopflerfish.framework;

import java.io.IOException;
import java.net.ContentHandler;
import java.net.URLConnection;

import org.osgi.framework.Constants;
import org.osgi.framework.ServiceEvent;
import org.osgi.framework.ServiceListener;
import org.osgi.framework.ServiceReference;
import org.osgi.service.url.URLConstants;

/**
 * Wrapper which delegates an MIME ContentHandlers
 * OSGi registered ContentHandlers
 *
 * <p>
 * Each instance of ContentHandlerWrapper  tracks ContentHandlers
 * for a named MIME type and selects the best from all available services.
 * </p>
 */
public class ContentHandlerWrapper
  extends ContentHandler
{

  FrameworkContext       framework;
  String                 mimetype;
  String                 filter;
  ServiceReference<ContentHandler> best;

  ContentHandlerWrapper(FrameworkContext       framework,
			String                 mimetype) {

    this.framework = framework;
    this.mimetype  = mimetype;

    filter =
      "(&" +
      "(" + Constants.OBJECTCLASS + "=" +
      ContentHandler.class.getName() + ")" +
      "(" + URLConstants.URL_CONTENT_MIMETYPE + "=" + mimetype +
      ")" +
      ")";

    final ServiceListener serviceListener =
      new ServiceListener() {
        public void serviceChanged(ServiceEvent evt) {
          @SuppressWarnings("unchecked")
          final
          ServiceReference<ContentHandler> ref =
              (ServiceReference<ContentHandler>) evt.getServiceReference();

          switch (evt.getType()) {
          case ServiceEvent.MODIFIED:
            // fall through
          case ServiceEvent.REGISTERED:
            if (best == null) {
              updateBest();
              return ;
            }

            if (compare(best, ref) > 0) {
              best = ref;
            }
            break;
          case ServiceEvent.MODIFIED_ENDMATCH:
            // fall through
          case ServiceEvent.UNREGISTERING:
            if (best.equals(ref)) {
              best = null;
            }
          }
        }
      };

    try {
      framework.systemBundle.bundleContext.addServiceListener(serviceListener, filter);

    } catch (final Exception e) {
      throw new IllegalArgumentException("Could not register service listener for content handler: " + e);
    }

    if (framework.debug.url) {
      framework.debug.println("created wrapper for " + mimetype + ", filter=" + filter);
    }
  }

  private int compare(ServiceReference<?> ref1, ServiceReference<?> ref2) {
    final Object tmp1 = ref1.getProperty(Constants.SERVICE_RANKING);
    final Object tmp2 = ref2.getProperty(Constants.SERVICE_RANKING);

    final int r1 = (tmp1 instanceof Integer) ? ((Integer)tmp1).intValue() : 0;
    final int r2 = (tmp2 instanceof Integer) ? ((Integer)tmp2).intValue() : 0;

    if (r2 == r1) {
      final Long i1 = (Long)ref1.getProperty(Constants.SERVICE_ID);
      final Long i2 = (Long)ref2.getProperty(Constants.SERVICE_ID);
      return i1.compareTo(i2);

    } else {
      return r2 -r1;
    }
  }

  private void updateBest() {
    try {
      @SuppressWarnings("unchecked")
      final
      ServiceReference<ContentHandler>[] refs
        = (ServiceReference<ContentHandler>[]) framework.systemBundle.bundleContext
          .getServiceReferences(ContentHandler.class.getName(), filter);
      if (refs != null) {
        best = refs[0];
        for (int i = 1; i < refs.length; i++) {
          if (compare(best, refs[i]) > 0) {
            best = refs[i];
          }
        }
      }
    } catch (final Exception e) {
      // TODO, handle differently!? this should not happen.
      throw new IllegalArgumentException("Could not register url handler: " + e);
    }
  }

  private ContentHandler getService() {
    ContentHandler obj;

    try {
      if (best == null) {
        updateBest();
      }

      if (best == null) {
        throw new IllegalStateException("null: Lost service for protocol="+ mimetype);
      }

      obj = framework.systemBundle.bundleContext.getService(best);

      if (obj == null) {
        throw new IllegalStateException("null: Lost service for protocol=" + mimetype);
      }

    } catch (final Exception e) {
      throw new IllegalStateException("null: Lost service for protocol=" + mimetype);
    }

    return obj;
  }

  @Override
  public Object getContent(URLConnection urlc) throws IOException {
    return getService().getContent(urlc);
  }

  @Override
  public Object getContent(URLConnection urlc,
                           @SuppressWarnings("rawtypes") Class[] classes)
      throws IOException
  {
    return getService().getContent(urlc, classes);
  }

  @Override
  public String toString() {
    final StringBuffer sb = new StringBuffer();

    sb.append("ContentHandlerWrapper[");

    final ServiceReference<ContentHandler> ref = best;
    sb.append("mimetype=" + mimetype);
    if(ref != null) {
      sb.append(", id=" + ref.getProperty(Constants.SERVICE_ID));
      sb.append(", rank=" + ref.getProperty(Constants.SERVICE_RANKING));
    } else {
      sb.append(" no service tracked");
    }

    sb.append("]");

    return sb.toString();
  }
}

redpajama_set_name: RedPajamaGithub


The first man-made plastic was invented in Britain in 1854 by Alexander Parkes. He publicly demonstrated it at the 1862 International Exhibition in London, calling the material he produced "Parkesine." Derived from cellulose, Parkesine could be heated, molded, and retain its shape when cooled. It was, however, expensive to produce, prone to cracking, and highly flammable.
But as we all know, the plastic injection molding industry has evolved over the years from producing combs and buttons to producing a vast array of products for many industries including automotive, medical, aerospace, consumer products, toys, plumbing, packaging, and construction.
Nowadays, injection molding is used to create many things such as wire spools, packaging, bottle caps, automotive dashboards, pocket combs, and most other plastic products available today. Injection molding is the most common method of part manufacturing. It is ideal for producing high volumes of the same object. Some advantages of injection molding are high production rates, repeatable high tolerances, the ability to use a wide range of materials, low labor cost, minimal scrap losses, and little need to finish parts after molding.
Portuguese plastics injection industry dates back from 1935, when the first company started producing Backelite (the first real syntetic plastic) utility goods. All this happened inside an industrial context that encompassed the 1929 big Crisis, the Industrial conditioning regimen of 1931 and the Great Exhibition of Portuguese Industry in 1932. And everything evolved from there.
Plastic injection manufacturing from Portugal, nowadays, injects and exports parts for all over Europe, for such diverse markets as packaging, automotive, transport, in fact all available areas you can think of.
Over the last few years, the industry has invested in new equipments all around and, most of all, in automation and quality control.