1 Tuner + 1 or more non tuner LIGHT monsters. Once per turn you can change the battle position of 1 face up monster your opponent controls. During battle between this attacking card and a defense posi..
Change all face-up monsters on the field to face-down Defense Position. During the End Phase, flip all face-down Defense Position monsters your opponent controls face-up, and your opponent draws 1 car..
Activate only when a Defense Position monster you control, whose DEF is higher than its ATK, is destroyed by battle. Special Summon up to 2 monsters whith the same name as that monster from your deck ..
This cards effect changes depending on the number of DARK monsters in your Graveyard. 1 or fewer: This card loses 400 ATK. 2 to 4: This card gains 400 ATK. 5 or more: You can send this card to the..
Select 1 face-up Synchro Monster. Return it to its owner's Extra Deck. Then, if all of the monsters that were used for the Synchro Summon of that monster are in your Graveyard, you can Special Summon ..
During your End Phase, Special Summon 1 "Engine Token" (Machine-Type/ EARTH/ Level 1/ ATK 200/ Def 200) in Attack Position. Once per turn, you can have this card gain 1000 ATK. If you do, it is destro..
Both players can Tribute Summon "Ancient Gear" monsters with 1 less Tribute than required. When this card is destroyed and sent to the Graveyard, you can Special Summon 1 "Ancient Gear" monster from y..
When this card is Special Summoned by the effect of a "Gladiator Beast" monster, add 1 "Gladiator Beast" card from your Graveyard to your hand. At the end of the Battle Phase, if this card attacked or..


Thank you for visiting Scripture Prayer for Marriages. To fully understand the principles of praying scripturally, please read the teaching "Why Pray Scripture?" .
Hearing God: As it is rare to hear an audible voice from God, one of the ways God will speak to us is through His written Word and a small still voice inside us. Before reviewing the scripture references below that address what the Bible says about marriage ask the Holy Spirit of revelation to join you. When certain verses capture your attention more than others, meditate and incorporate those into your personal daily prayer life.
Love bears up under anything and everything that comes, is ever ready to believe the best of every person, its hopes are fadeless under all circumstances, and it endures.
Finally, both of you, live in harmony with one another; be sympathetic, love as brothers, be compassionate and humble. Do not repay evil with evil or insult with insult, but with blessing, because to this you were called so that you may inherit a blessing.
For, whoever would love life and see good days must keep his tongue from evil and his lips from deceitful speech. He must turn from evil and do good; he must seek peace and pursue it. For the eyes of the Lord are on the righteous and his ears are attentive to their prayer, but the face of the Lord is against those who do evil.
We hold up our marriage to you. We understand that our union together is a picture of our relationship with you and it is Holy. We commit our marriage to You, Lord, that You would cause our thoughts to become agreeable with Your will and we would have success in our marriage relationship.
We agree to be kind and tenderhearted toward each other according to Your word, forgiving one another and blessing one another. We choose to speak the truth in love and listen well to each other. Make us strong where we are weak, Lord.
Help us to pray together, to trust in You and walk in obedience to Your word so that we may have peace and joy in our marriage. Give us revelation of Your great love and an understanding of how to love each other. Teach (husband) how to love (wife) like Christ loves the church and teach (wife) to honor and respect (husband).
Forgive us for our self-centeredness, anger, lust, and foolishness. Give us your patience and help us not to blame and accuse one another. Help us to see You as our source of acceptance, healing, justification, and provision.
Bring healing to our lives, Lord. Comfort and mend those areas where our hearts are broken. and deliver us from rejection and sorrow. Have mercy on us, Lord, and put Your mercy in our hearts for one another. Today we choose openly to receive your healing, love, forgiveness, acceptance, favor, harmony, unity, truth, patience, kindness, joy, and faithfulness and self-control into our lives.
Help us to remember that our bodies are not our own—that they belong to each other and to You. We choose not to deprive each other of sexual love and to remain faithful to our mates alone.
We ask that you remove the blinders from our eyes so we can see the truth and walk courageously in it. We resist the enemy that would try to destroy our marriage. We bind the powers of divorce, division, seduction, rejection, judgment, criticism, addiction, control, self-pity, harshness, unkindness, lying and deception. We put to death the words we have spoken against one another. We forgive each other for past offenses. We agree that the enemy is bound away from this marriage and invite the Holy Spirit into our lives.
Lord, surround us with Godly counsel. Separate us from those who would seduce us into ungodliness. Give us Godly, loving, Christian friends and a church family where we can worship You and grow in the knowledge of Your will. Thank you for teachers, preachers, apostles, evangelists, prophets, and other gifted people who minister to us and help us to mature in You. Draw us into Your word so that we can be cleansed and receive wisdom for our lives.
Lord, thank You for your faithfulness. Thank you that you watch over Your Word to perform it. Thank you that You are at work within us causing us to desire and to do your will. You are the Lord, Almighty, and if You are for us, who can be against us? We love you, Lord, and we entrust our marriage to you.
I hold this couple, (husband) and (wife) before You and ask for Your presence and mercy in their lives. If they do not know You, Lord, reveal Your unfailing love to them. Teach them to pray together, to trust in You and walk in obedience to Your Word so that they may have Your peace and joy in their marriage.
We pray that they will be kind and tenderhearted toward each other, forgiving one another and blessing one another. We pray that they would speak the truth in love and listen well to each other.
Draw them into Your Word that they would be cleansed and receive wisdom for their lives. Give them revelation of Your great love towards them and an understanding of how to love each other. Teach him to love his wife like Christ loves the church and teach her to honor and respect him.
Surround them with Godly counsel. Separate them from those who would seduce them into ungodliness. Give them Godly, loving, Christian friends and a church family where they can worship You and grow in Your love for one another. Bring forth laborers to minister to them.
Forgive them for their selfishness, anger, lust, and foolishness. Put a stop to any blaming and accusing. Help them to look to You as their source of acceptance, healing, justification and provision.
Bring healing to their lives. Comfort them and mend their broken hearts and deliver them from rejection and sorrow. Have mercy on them, Lord.
We stand in the gap for them. We resist the enemy on their behalf. We bind the powers of division, divorce, seduction, rejection, judgment, criticism, addiction, control, self-pity, harshness, unkindness, lying and deception. We thank you, Lord, that the blinders are removed from their eyes and they can see the truth and walk in it. Give them courage and show them how to resist evil.
We loose Your healing, love, forgiveness, acceptance, favor, harmony, unity, truth, patience, kindness, joy, and faithfulness and self-control into their lives.
Lord, thank you that all things are possible with You. Thank you that You watch over Your Word to perform it. Thank you for Your great and precious promises and Your mighty love that covers all. We look forward to seeing Your hand at work in their marriage relationship.
Scripture Prayer for Marriages was written to lift up and encourage the body of believers; sharing your testimony is one of the ways that we continue to overcome the enemy.


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text:
\section{Introduction}

We propose a new method for domain adaptation based on a single target image.
As shown in Fig.~\ref{fig:teaser}, given a trained GAN for domain $A$, and a single image \ensuremath{I_B}{} from domain $B$,
our approach learns to find a corresponding image in domain $B$ for any image in domain $A$. We can achieve this by fine-tuning the GAN for domain $A$ to obtain a second GAN that generates images in domain $B$. The two GANs share a latent space so that a single latent code will generate two corresponding images, one in domain $A$ and one in domain $B$.
The main selling point of our method is that it achieves superior quality than the state of the art in single shot domain adaption.
Our method is computationally lightweight and only takes a few minutes on a single GPU, so that it can be widely applied.

In order to do this, we leverage multiple existing components, including two excellent pre-trained networks: First, we use StyleGAN2~\citep{Karras2019stylegan2} as a pre-trained GAN. A follow-up version has been published on arXiv~\citep{Karras2021}, but the code only became available after we finished all experiments. Second, we use a pre-trained network for image embedding, CLIP~\citep{radford2021learning}, to encode images as vectors.
Third, we use the pioneering idea of StyleGAN-NADA~\citep{gal2021stylegannada}, which builds upon StyleCLIP~\citep{patashnik2021styleclip}, to encode a domain gap (or domain shift) as vector in CLIP embedding space. Fourth, we leverage II2S~\citep{zhu2020improved} as GAN embedding method to transfer image $I_{B}$ into domain $A$ to obtain a better estimation of the domain gap.

Even though the visual quality of StyleGAN-NADA is already impressive when used as a single image domain adaption method, we identified multiple technical issues that can be improved to achieve another large jump in visual quality. First, and most importantly, StyleGAN-NADA was designed for zero-shot domain adaptation, and does not have a good solution to model the domain gap based on a single example image. Their reference implementation models the domain gap as a vector from the average image in domain $A$ to the given image \ensuremath{I_B}{} in CLIP embedding space. However, this leads to overfitting in practice and the transfer results lose  attributes of the input images, so that input images from domain $A$ get mapped to images that are all too similar to \ensuremath{I_B}{} in domain $B$. We identify a better solution to this problem. In fact, the domain gap should be modeled as a vector from the image \ensuremath{I_B}{} to its analog in domain $A$, so that the image in domain $A$ shares salient within-domain attributes with the reference image. We therefore need to solve an inverse $B$-to-$A$ domain-transfer problem, which we propose to tackle using the state-of-the-art GAN embedding method II2S~\citep{zhu2020improved}. A key insight is that we can use a heavily regularized version of the II2S GAN inversion method to do the reverse problem of transferring any related image (from a domain $B$) into the domain $A$, helping to characterize the semantic domain gap better than previous work.
Further extensions enable us to fine tune the modeling of the domain gap to explicitly model which attributes of the input image should be kept. Second, we propose multiple new regularizers to improve the quality. Third, we propose a technical improvement to the heuristic layer selection proposed in StyleGAN-NADA that is more straightforward and robust.

In summary, we make the following contributions:
\begin{enumerate}
  \item We reduce the mode collapse/overfitting problem which often occurs in one-shot and few-shot domain adaptation. Our results look similar to the target domain images with fewer artifacts. These results are also faithful to the identities of the source domain images and able to capture fine details.
  \item Our domain adaptation provides more freedom to control the "similarity" between images across domains that share a common latent-code, which makes a large number of downstream applications possible, e.g., pose adaptation, lighting adaptation, expression adaptation, texture adaptation, interpolation, and layer mixing, using state-of-the-art image editing frameworks.

\end{enumerate}

\section{Related Work}

\paragraph{Domain adaptation.}

Domain adaptation is the task of adapting a model to different domains. Different works in this area~\citep{bousmalis2016domain,Bousmalis_2017, na2020fixbi,Wang_2020,Kang_2019} try to learn diverse domain independent representations using the source domain to make predictions, such as image classification, in the target domains. More importantly, generating diverse representations of images by combining natural language supervision has been of interest to the computer vision and NLP research communities~\citep{frome2013devise}. Recently, OpenAI's Contrastive Language-Image Pretraining (CLIP)~\citep{radford2021learning} work established that transformer, and large datasets, could generate transferable visual models. In CLIP, both images and text are represented by high dimensional semantic-embedding vectors, which can then be used for zero-shot learning.

\paragraph{GAN-based domain adaptation.}
In the GAN domain, various models and training strategies have been proposed for few-shot domain adaptation tasks~\citep{Bousmalis_2017,NEURIPS2018_4e4e53aa,Li_2020,Liu_2019}. Most relevant to our work, the domain adaptation methods~\citep{patashnik2021styleclip,gal2021stylegannada,Jang_2021,Song:2021:AgileGAN} that build upon StyleGAN~\citep{karras2019style,Karras2019stylegan2,Karras2020ada} demonstrate impressive visual quality and semantic interpretability in the target domain. These methods can be broadly classified into few-shot and single-shot domain adaptation methods.

A notable few-shot method, StyleGAN-ADA~\citep{Karras2020ada} proposes an adaptive discriminator augmentation method to train StyleGAN on limited data. Another work, DiffAug~\citep{zhao2020differentiable}, applies differentiable transformations to the real and generated images for robust training. A discriminator related approach, FreezeD~\citep{mo2020freeze}, freezes lower layers of the discriminator to achieve domain adaptation. Toonify~\citep{gittoonify} interpolates between the model-weights of different generators to generate samples from a novel domain. A more recent work~\citep{ojha2021fewshot}, reduces overfitting on limited data by preserving the relative similarities and differences in the instances of samples in the source domain using cross domain correspondence.

\paragraph{Latent space interpretation and semantic editing.}

GAN interpretation and understanding of the latent space has been a topic of interest since the advent of GANs. Some notable works in this domain~\citep{bau2018gan,bau2019seeing,harkonen2020ganspace,shen2020interfacegan,tewari2020stylerig}  have led to many GAN-based image editing applications. More recent studies into the activation space of StyleGAN have demonstrated that the GAN can be exploited for downstream tasks like unsupervised and few-shot part segmentation~\citep{zhang2021datasetgan,tritrong2021repurposing,abdal2021labels4free, collins2020editing, bielski2019emergence}, extracting 3D models of the objects~\citep{pan2021do,chan2020pigan} and other semantic image editing applications~\citep{zhu2021barbershop, tan2020michigan, wu2020stylespace, patashnik2021styleclip}.

Image embedding is one of the approaches used to study the interpretability of the GANs. To enable the semantic editing of a given image using GANs, one needs to embed/project the image into its latent space. Image2StyleGAN~\citep{abdal2019image2stylegan} embeds images into the extended StyleGAN space called $W+$ space. Some followup works~\citep{zhu2020domain,richardson2020encoding,tewari2020pie} introduce regularizers and encoders to keep the latent code faithful to the original space of the StyleGAN. Improved-Image2StyleGAN (II2S)~\citep{zhu2020improved} uses $P_N$ space to regularize the embeddings for high-quality image reconstruction and image editing. We use this method to embed real images into the StyleGAN and show that our domain adaptation preserves the properties of the original StyleGAN in Sec~\ref{sec:results}.

Image editing is another tool to identify the concepts learned by a GAN.
In the StyleGAN domain, recent works~\citep{harkonen2020ganspace,shen2020interfacegan,tewari2020stylerig,10.1145/3447648}  extract meaningful linear and non-linear paths in the latent space. InterfaceGAN~\citep{shen2020interfacegan}  finds linear directions to edit latent-codes in a supervised manner. On the other hand, GANSpace~\citep{harkonen2020ganspace} extracts unsupervised linear directions for editing using PCA in the $W$ space. Another framework, StyleRig~\citep{tewari2020stylerig}, maps the latent space of the GAN to a 3D model. StyleFlow~\citep{10.1145/3447648} extracts non-linear paths in the latent space to enable sequential image editing. In this work, we will use StyleFlow to test the semantic editing of our domain adapted images.

In the area of text-based image editing, StyleCLIP~\citep{patashnik2021styleclip} extends CLIP to perform GAN-based image editing. StyleCLIP uses the CLIP embedding vector to traverse the StyleGAN manifold, by adjusting the latent-codes of a GAN, in order to make a generated image's CLIP embedding similar to the target vector, while remaining close to the input in latent space. A downside to this approach is that these edits are unable to shift the domain of a GAN outside its original manifold. However, their use of CLIP embeddings inspired StyleGAN-NADA~\citep{gal2021stylegannada}, which creates a new GAN using refinement learning to do zero-shot domain adaptation. Although unpublished, they also demonstrate one-shot domain adaptation in their accompanying code. The original and target domain are represented by CLIP text embeddings. The difference of the embeddings represents a direction used to shift the domains. Although in the accompanying source-code~\citep{gitnada}, they use a bootstrap-estimate of the mean CLIP image embedding of the original domain, and use a reference image or its CLIP image embedding to represent the new domain.

\section{Method}

Our approach involves fine-tuning a GAN trained for some original domain $A$, e.g. FFHQ faces, to adapt it to a new related domain $B$. In our approach, the images in $A$ and the images in $B$ are related to each-other by a common latent code. Any image which can be generated or embedded in domain $A$ can be transferred to a corresponding and similar image in $B$. We use the CLIP embeddings as a semantic-space in order to model the difference between domains $A$ and $B$, and we use StyleGAN~\citep{STYLEGAN2018, Karras2019stylegan2} as the image generator.
A key to our approach is to preserve directions within  and across domains as illustrated in Fig.~\ref{fig:domain-xfer-clip-vectors}.
Before fine-tuning the GAN for domain $A$ (to obtain the GAN for domain $B$), we determine a domain-gap direction. This direction, called \ensuremath{v^\text{ref}}{}, is a vector in CLIP embedding space which points towards a reference image \ensuremath{I_B}{} which is in domain $B$ from its corresponding image \ensuremath{I_A}{} in which is in domain $A$.
We use the CLIP image-embedding model \ensuremath{E_I}{} to find
\begin{align}
    \ensuremath{v^\text{ref}}{} &= \ensuremath{E_I}{}(\ensuremath{I_B}{}) - \ensuremath{E_I}{}(\ensuremath{I_A}{}).
\end{align}

Finding \ensuremath{I_A}{} in domain A for a given image in domain B is a significant limitation in the current state of the art, StyleGAN-NADA~\citep{gal2021stylegannada}, as they use the mean of domain $A$. The mean of domain A is a very crude approximation for \ensuremath{I_A}{}. Instead, we propose an inverse domain adaption step, by projecting the image \ensuremath{I_B}{} into the domain $A$ to find a sample that is more similar and specific to the reference image than the mean of domain $A$. In principle, this problem is also a domain adaption problem similar to the problem we are trying to solve, just in the inverse direction. The major difference is that we have a pre-trained GAN available in domain A.

We use the II2S GAN-inversion method~\citep{zhu2020improved} in order to find a latent code for an image similar to \ensuremath{I_B}{} that is plausibly within domain $A$. The I2S and II2S methods use  an extended version of $W$ space from StyleGAN2. The $W$ code is used 18 times, once for each style block in StyleGAN2. When allowing each element to vary independently, the resulting latent space is called $W+$ space~\cite{abdal2019image2stylegan, abdal2020image2stylegan++, zhu2020improved}. I2S showed that the additional degrees of freedom allow GAN inversion for a wider set of images with very detailed reconstruction capabilities, and II2S showed that an additional regularization term to keep the latent codes close to their original distribution made latent-code manipulation more robust.
II2S uses a hyperparameter, $\lambda$, which can be increased in order to generate latent codes using more regularization, and therefore in higher density regions of the $W+$ latent space. The effect of this parameter is shown in Fig.~\ref{fig:ii2s-lambda}. The value suggested in the original work was $\lambda=0.001$, however, low values of lambda allow II2S to find latent codes that are too far away from the latent-codes produced by the mapping network of the original GAN and thus produce images that are less plausible to have come from domain $A$, underestimating the gap between domains. In the context of domain shift we find it is useful to use $\lambda=0.01$ as illustrated in Fig.~\ref{fig:ii2s-lambda}.
The result is a latent code \ensuremath{w^{\text{ref}}}{} in $W+$ space which is shifted towards a high-density portion of the domain $A$.
Then the image generated from that code, \ensuremath{I_A}{}, is an image in domain $A$ that corresponds to \ensuremath{I_B}{}.

\begin{figure}[htbp]
    \centering
    \includegraphics[width=0.85\textwidth]{Figures/ii2s-lambdas.png}
    \caption{An illustration showing how II2S embeds \ensuremath{I_B}{} in the original StyleGAN domain $A$, shown for two different values of $\lambda$. Reference images from other domains are shown in the top row. The value recommended by~\citet{zhu2020improved} is shown in the second row, and the value used in this work is shown in the third row. Although there is some subjectivity involved, we believe that the large value $\lambda=1\mathrm{e}{-2}$ is needed for II2S to find images that plausibly could belong to the domain $A$, which in this case is FFHQ faces. }
    \label{fig:ii2s-lambda}
\end{figure}

\paragraph{Training}
As illustrated in Fig.~\ref{fig:ii2s-lambda}, we use II2S to find an image \ensuremath{I_A}{} which we consider to be similar to \ensuremath{I_B}{} but still plausibly within a domain $A$. In principle, it is possible that II2S finds \ensuremath{I_A}{} so that \ensuremath{I_B}{} is similar enough to be considered the same, in which case the two domains overlap. However, we are concerned with the cases where the domains are different, and the vector \ensuremath{v^\text{ref}}{} indicates the direction of a gap, or shift, between domain $A$ and domain $B$. We use refinement learning to train a new generator, $G_B$, so that images generated from $G_B$ are shifted parallel to \ensuremath{v^\text{ref}}{} in CLIP space, relative to images from $G_A$. The desired shift is indicated by the red arrows in Fig.~\ref{fig:domain-xfer-clip-vectors}.
During training, latent codes $w$ are generated using the mapping network of StyleGAN2.
Both $G_A$ and $G_B$  are used to generate images from the same latent code, but the weights of $G_A$ are frozen and only $G_B$ is updated during training. The goal of refinement learning is that $G_B$ will preserve semantic information that is \textit{within} domain $A$ but also that it will generate image shifted \textit{across} a gap between domains.
When refining the generator for domain $B$, we freeze the weights of the StyleGAN2 `ToRGB' layers, and the mapping network is also frozen. The overall process of training is illustrated in Fig.~\ref{fig:training-process}.

\begin{figure}[htbp]
    \centering
    \includegraphics[width=0.85\columnwidth]{Figures/Domain-XFer-Clip.png}
    \caption{The vectors in the CLIP image embedding space, $E_I$, which control domain adaptation. Each domain is depicted here as a dashed outline; the vectors $v^\text{ref}$ and $v^\text{samp}$ cross between the two domains and are used to refine a generator for domain $B$. Corresponding images should be shifted in the same direction. The vectors $v_A$ and $v_B$ model important semantic differences within each domain that should also be preserved by domain transfer. $G_A(w)$ and $G_B(w)$ are corresponding images for an arbitrary latent-code $w$ encountered during training. Style mixing (shown on the right) shifts a part of the latent code towards the reference image effecting the result in both domains.}
    \label{fig:domain-xfer-clip-vectors}
\end{figure}

\begin{figure}[htbp]
    \centering
    \includegraphics[width=0.95\columnwidth]{Figures/Process-Diagram.pdf}
    \caption{A process diagram for domain transfer. White rectangles indicate calculations, computed values are shown on the connecting lines. The four loss-calculations are indicated by blue rectangles, and the learnable weights of StyleGAN2 (all weights except the mapping network and the ToRGB layers) are indicated in green.  }
    \label{fig:training-process}
\end{figure}

The goal of training is to shift CLIP embeddings from domain $A$ in a direction parallel to \ensuremath{v^\text{ref}}{}. We use the vector \ensuremath{v^\text{samp}}{} to represent the current domain shift of the network $G_B$ during training, on a single sample. We have
\begin{align}
    \ensuremath{v^\text{samp}}{}&=\ensuremath{E_I}{}(G_B(w))-\ensuremath{E_I}{}(G_A(w))
\end{align}
as a cross-domain vector for corresponding images generated from the same $w$ latent code using the two generators.
We use the loss
\begin{align}
    L_\text{clip\_across} &= 1-\text{sim}(\ensuremath{v^\text{ref}}{}, \ensuremath{v^\text{samp}}{}),
\end{align}
where  $\text{sim}({\bm{b}}{a}, {\bm{b}}{b}) = \frac{{\bm{b}}{a}^T{\bm{b}}{b}}{\|{\bm{b}}{a}\|\|{\bm{b}}{b}\|}$
is the cosine similarity score.  This loss term is minimized when the domain shift vectors are parallel.

It is important that the reference image \ensuremath{I_B}{} matches the generated image, $G_B(\ensuremath{w^{\text{ref}}}{})$, both in a semantic sense, as measured by the similarity of the CLIP embeddings, and also in a visual sense. We accomplish this using two losses:  $L_\text{ref\_clip}$ and $L_\text{ref\_rec}$. The first loss measures the change in the CLIP-embeddings of the original and reconstructed reference image,
\begin{align}
    L_\text{ref\_clip} &= 1-\text{sim}\left(
            \ensuremath{E_I}{}\left(\ensuremath{I_B}{}\right), \ensuremath{E_I}{}\left(G_B(\ensuremath{w^{\text{ref}}}{})\right)
        \right),
\end{align}
ensuring that the $G_B$ can reconstruct the embedding. Unlike $L_\text{clip\_accross}$, this loss term is not based on a \textit{change} in embeddings between the two domains, instead it guides $G_B$ by aligning it to a global embedding in CLIP space, ensuring that $I_B$ remains fixed in the domain of $G_B$.

The second loss term is a reconstruction loss based on perceptual and pixel-level accuracy,
\begin{align}
    L_\text{ref\_rec} &=
        L_\text{PIPS}\left(\ensuremath{I_B}{}, G_B(\ensuremath{w^{\text{ref}}}{})\right)
        + L_2\left(\ensuremath{I_B}{}, G_B(\ensuremath{w^{\text{ref}}}{})\right)
\end{align}
where
 $L_\text{PIPS}$ is the perceptual loss from~\citet{zhang2018unreasonable}, and $L_2$ is the squared euclidean difference between pixels.
 The purpose of this loss is to ensure that the visual, and not just the
 semantic, qualities of the image are preserved. This is necessary in addition
 to $ L_\text{ref\_clip}$ because, while the CLIP embeddings do capture many
 semantic and visual qualities of the image, there are still many perceptually distinct
 images that could produce the same CLIP embedding. This is visible in
 Fig.~\ref{fig:Ablation_study}, without the reconstruction loss $G_B$ fails to
 preserve some important visual qualities (such as symmetry) of the input.

There is a tendency for GANs to reduce the variation during training, especially in few-shot fine-tuning.  We combat this by preserving the semantic information that is \textit{not} related to the domain gap.  A semantic change that is not related to the change in domains should not be affected by $G_B$. Therefore, the vector connecting the reference and sample images within the domain $A$ should be parallel to the corresponding vector in domain $B$.  Let
$ v_A = \ensuremath{E_I}{}(G_A(w)) - \ensuremath{E_I}{}(\ensuremath{I_A}{}) $
be a vector connecting a sample image with latent-code $w$ to the reference image in the CLIP space.  This vector represents semantic changes that are \textit{within} domain $A$, and we want the matching semantic changes to occur within the domain $B$. Let
$v_B = \ensuremath{E_I}{}(G_B(w)) - \ensuremath{E_I}{}(\ensuremath{I_B}{})$
denote the corresponding vector in domain $B$.  We introduce the loss
\begin{align}
    L_\text{clip\_within}& = 1-\text{sim}(v_A, v_B),
\end{align}
which is minimized when the two within-domain changes are parallel.

The final loss is then a weighted sum of losses
\begin{align}
L &= L_\text{clip\_across} + \lambda_\text{clip\_within}L_\text{clip\_within} + \lambda_\text{ref\_clip}L_\text{ref\_clip} + \lambda_\text{ref\_rec}L_\text{ref\_rec},
\end{align}
with empirically determined weights of $\lambda_\text{clip\_within}=0.5$, $\lambda_\text{ref\_clip}=30$, and $\lambda_\text{ref\_rec}=10$.
Together, these four loss terms guide the refinement process for $G_B$.  Among these losses, $L_\text{clip\_across}$ was proposed by StyleGAN-NADA~\citep{gal2021stylegannada}. The other losses are novel contributions of this work.

\paragraph{Style Mixing}
After the training step, the generator $G_B$ generates images that are semantically similar to the reference image \ensuremath{I_B}{}. However, we have observed that the visual style may not be sufficiently similar. We attribute this to the idea that the target domain may be a \textit{subset} of the images produced by the new generator $G_B$.  This issue was addressed in StyleGAN-NADA~\citep{gal2021stylegannada} using a second latent-mining network in order to identify a distribution of latent codes within the domain of $G_B$ that better match the reference image. Our approach exploits the structure of latent codes in $W+$ space. Latent vectors in $W+$ space can be divided into 18 blocks of 512 elements, each impacting a different layer of StyleGAN2. Empirically, the latter blocks of the $W+$ code have been shown to have more effect on the style (e.g. texture and color) of the image whereas the earlier layers impact the coarse-structure or content~\citep{zhu2021barbershop} of the image.   We partition the latent code in the image into $w= (w_C,w_S)$ where $w_C$ consists of the first $m$ blocks of the $W+$ latent code that capture the content of the image, and $w_S$ consists of the remaining blocks and captures the style. In this work, we will use $m=7$ unless otherwise specified.  Then we transfer the style from a reference image using linear interpolation, to form $\hat{w} = (w_C, \hat{w}_S)$ where
\begin{align}
 \hat{w}_S &= (1-\alpha) w_S + \alpha(w_S^\text{ref}),
 \label{eg:eight}
\end{align}
and $w_S^\text{ref}$ is last $(18-m)$ blocks of $w^\text{ref}$.
Consider the distribution of images generated from random $w$ drawn according to the distribution of latent codes from the mapping network of StyleGAN2.
If $\alpha=0$, then the distribution of images $G_B(\hat{w})$ includes the reference image, but encompasses a wide variety of other fine visual styles. If $\alpha=1$, then the images $G_B(\hat{w})$ will still have a diverse content, but they will all very closely follow the visual style of \ensuremath{I_B}{}. An important application of this method is in conditional editing of real photographs. To achieve that, first we take a real input image $I_\text{real}$ and invert  it in domain $A$ using II2S on the generator $G_A$ in order to find a $W+$ latent code $w_\text{real}$. Then $G_B(w_\text{real})$ generates a corresponding image in domain $B$. We can then compute $\hat{w}_\text{real}$ by interpolating the style codes (\ref{eg:eight}) so that the final image $G_B(\hat{w}_\text{real})$ is similar to $I_\text{real}$ but has both content and the visual style shifted towards domain $B$.

\section{Results}
\label{sec:results}

In this section, we will show qualitative and quantitative results of our work. The only other published method that accomplishes similar one-shot GAN domain adaptation which we are aware of is~\citet{ojha2021fewshot}. They focus on few-shot domain adaptation, but they also demonstrate a capability to solve the one-shot problem. The most closely related work to our approach is StyleGAN-NADA~\citep{gal2021stylegannada}, which is unpublished at the time of submission, however we compare to it as the main competitor.
The paper mainly discusses zero-shot domain adaptation, but the approach can also accomplish one-shot domain adaptation, as demonstrated in their accompanying source-code. Moreover, it demonstrates impressive improvements over the state of the art and even beats many SOTA few-shot methods considering the visual quality.
As our method can still significantly improve upon the results shown in StyleGAN-NADA, this underlines the importance of our idea in reducing overfitting.
We compare against additional approaches in the appendix.

\paragraph{Training and Inference Time.}
Given a reference image, the training time for our method is about 15 minutes for 600 iterations on a single Titan~XP GPU using ADAM as an optimizer with the same settings as~\citet{gal2021stylegannada}. We use a batch size of 4. At inference time, there are different applications. In a basic operation, GAN generated images can be transferred with a single forward pass through a GAN generator network, which works in 0.34 seconds. Considering a more advanced operation, where existing photographs are embedded into a GAN latent space, the additional embedding time has to be considered. This embedding time is only 0.22 seconds using e4e~\citep{tov2021designing} and about two minutes using II2S~\citep{zhu2020improved}.

\paragraph{Visual Evaluation.}
In Fig.~\ref{fig:Comparison_with_sota}, we show a comparison of our results on faces against the two most relevant competing methods -- StyleGAN-NADA~\citep{gal2021stylegannada} and few-shot-domain-adaptation~\citep{ojha2021fewshot}. The results show that our method remains faithful to the original identity of the embedded images in domain $A$, while the other two methods suffer from overfitting, i.e., collapsing to narrow distributions which do not preserve salient features (for example the identity of a person).
We show additional visual results in the supplemental materials, including results on cars and dogs and results for fine-tuning the domain adaptation.

\begin{figure*}[th]
    \centering
    \includegraphics[width=\linewidth]{Figures/Exp2_Comparison_with_SOTA.png}
    \caption{Comparison of our framework with state-of-the-art frameworks for StyleGAN domain adaptation. We compare with StyleGAN-NADA~\citep{gal2021stylegannada} and the few-shot method of~\citet{ojha2021fewshot}. Each row corresponds to a different reference image \ensuremath{I_B}{}, and each column is a different real image $I_\text{real}$ from domain $A$. Notice that our method is able to match the styles of the reference images, while StyleGAN-NADA fails to maintain the content of the images in domain $A$ (for example the identity of a person is lost). On the other hand, the few-shot method suffers from severe mode collapse.}
    \label{fig:Comparison_with_sota}
\end{figure*}

\paragraph{User Study.}
We also perform a user study by collecting 187 responses from Amazon Mechanical Turk in order to compare the visual quality and the domain transfer capabilities of our framework compared to the competing methods.
When asked which method generates higher quality images from domain $B$, 73\% of users preferred our approach to StyleGAN-NADA, and 77\% selected ours over Few-shot~\citep{ojha2021fewshot}.
When asked which method is better at maintaining the similarity to a corresponding source image in domain $A$, we found that 80\% of the responses chose our approach over StyleGAN-NADA, and 91\% preferred our approach to Few-shot.
Our method outperforms the competing works in terms of the quality of the generated image, and the similarity of the generated image to the source image from domain $A$.  According to the user study, the other methods produced images that are more similar to \ensuremath{I_B}{}, but that is also an indication of overfitting and mode collapse.

\paragraph{Ablation study.}
We perform an ablation study to evaluate each component of our framework. In Fig.~\ref{fig:Ablation_study}, we show the effect of II2S embedding, different losses and style mixing/interpolation on the output.

\begin{figure*}[htb]
    \centering
    \includegraphics[width=\linewidth]{Figures/Ablation_study.png}
    \caption{Ablation study of the losses and style mixing used in our framework. From left to right: the reference image \ensuremath{I_A}{} and several images from domain $A$, the baseline approach (StyleGAN-NADA), adding II2S instead of using the mean of domain $A$, adding $L_\text{ref\_clip}$, $L_\text{clip\_within}$, and then using style mixing. The top row shows reconstructions of the image \ensuremath{I_A}{} using $G_B$. }
    \label{fig:Ablation_study}
\end{figure*}

\paragraph{Image editing capabilities.}
Another important aspect of our method is that we are able to preserve the semantic properties of the original StyleGAN (domain $A$) in domain $B$. We can make edits to the images in domain $B$ via the learned generator $G_B$ without retraining the image editing frameworks on the new domain. Fig.~\ref{fig:Attributes_control} shows image editing capabilities in the new domain $B$.
We use StyleFlow edits such as lighting, pose, gender etc. to show the fine-grained edits possible in the new domain.

\begin{figure*}[th]
    \centering
    \includegraphics[width=\linewidth]{Figures/Exp5_attributes_control.png}
    \caption{Image editing capabilities of the new domain $B$ using StyleFlow~\citep{10.1145/3447648}.

    This figure shows the editing results of the embedded real image $I_{real}$ transferred to domain $B$. Notice that our method preserves the semantic properties of the original StyleGAN.
    }

    \label{fig:Attributes_control}
\end{figure*}

\paragraph{Limitations}
Our method has several limitations (See Fig.~\ref{fig:Failded_case}). Some of these limitations are inherent due to the challenging nature of the problem of single-shot domain adaptation. Other limitations can be addressed in future work.
First, when we find the initial image in domain $A$ that corresponds to the input in domain $B$, we do not attempt to control for the semantic similarity. Future work should encourage the images to have similar semantics.
Second, we can only transfer between related domains. For example transferring FFHQ faces into the domain of cars is not explored in this paper.
Third, also relevant to the original distribution of the StyleGAN, embeddings into the StyleGAN work best when the objects are transformed to the canonical positions (for example face poses that are the same as FFHQ). Extreme poses of the objects in the reference images sometimes fail.

\begin{figure*}[th]
    \centering
    \includegraphics[width=\linewidth]{Figures/Failed_case.png}
    \caption{Some failure cases of our method. In these examples, we observe that the identity of the face is compromised a bit more than in typical examples of our method.}
    \label{fig:Failded_case}
\end{figure*}

\section{Conclusions}
We propose a novel method for single shot domain adaption. The main achievement of this work is to obtain results of unprecedented quality while reducing overfitting observed in previous work.
The technical key components of our work are a method to model the domain gap as vector in CLIP embedding space, a way to preserve within-domain variation, and  several extensions for fine-grained attribute-based control. We also introduce several new regularizers and a style mixing approach.

\newpage

redpajama_set_name: RedPajamaArXiv


text:
// Copyright 2020 The Chromium Authors
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

/**
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import {assertNotReached} from 'chrome://resources/js/assert.js';

import {ButtonBarState} from './cellular_types.js';

/** @polymerBehavior */
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    /**
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   * Initialize the subflow.
   */
  initSubflow() {
    assertNotReached();
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  /**
   * Handles forward navigation within subpage.
   */
  navigateForward() {
    assertNotReached();
  },

  /**
   * Handles backward navigation within subpage.
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  navigateBackward() {
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redpajama_set_name: RedPajamaGithub


Driving lessons in an automatic car, why make driving harder than it needs to be, learn in an auto.
Work related road safety, from DVLA licence checks to online knowledge testing and on road assessment and re-training I can help local companies fulfil their legal obligation under Health & Safety legislation.
Driving refresher courses available for those who lack confidence in their driving. Training also available for those drivers wishing to improve their standards and take an Advanced Driving Test.


text:
Home WORLD NEWS Beijing 2022 Winter Olympics: From summer to winter Games hosts
Beijing 2022 Winter Olympics: From summer to winter Games hosts
The logo may be new but some of the venues will be familiar as Beijing transforms from summer to winter Olympic host city
What has swimming great Michael Phelps got to do with curling?
On the surface not much, but turn water into ice at an iconic Olympic venue, and it is a different story.
The 10-lane pool where the American won eight gold medals in 2008 has been transformed into a four-track curling rink for the 2022 Games as Beijing prepares to become the first city to host both summer and winter Olympics.
It is not just the Chinese capital that is swapping sporting seasons; there are athletes trading trainers for skates as they pursue a dual Olympic dream.
With 100 days to go before the action gets under way on 4 February, we look at how some familiar places and faces are getting ready for the biggest winter sports event.
From Water Cube to Ice Cube
Beijing will be reusing five of its venues from the summer Games as part of its sustainability aims.
One of the most memorable from 2008 was the Water Cube – a box-shaped building, clad in a honeycomb of transparent cushions and lit up in blue that hosted the swimming, diving and artistic swimming.
The big bubble-wrapped box had its facilities, lighting and ventilation renovated to become the curling venue, and once the Games are over it will switch seasonally between an aquatics and ice venue.
From swimming…
The other key venue from 13 years ago, the Bird's Nest stadium, will also get another Olympic appearance when it hosts the opening and closing ceremonies.
At the summer Games, this was where the athletics took place as well as the ceremonies that included the handover to London 2012 featuring David Beckham and a double-decker bus.
The Birds Nest hosted extravagant opening and closing ceremonies in 2008
Ice hockey will be taking place at two of the venues used in 2008 – the National Indoor Stadium that hosted rhythmic gymnastics, trampoline and handball and the Wukesong Sports Centre that was home to the basketball.
Short-track and figure skating is being held at the Capital Indoor Stadium, where volleyball was played in the summer Games.
The United States won men's Olympic gold in basketball at the Wukesong Indoor Stadium in 2008…
There is one new venue in the existing Olympic Park, where the National Speed Skating Oval – nicknamed 'The Ice Ribbon' – now stands on an area that was formerly used for hockey and archery.
The only other site being used in Beijing is the new Big Air Shougang, which is on a former steel mill site away from the Olympic Park, while the sliding, skiing and snowboard events take place at two other hubs linked within an hour by rail from Beijing.
'I had a week off' – athletes switching from summer to winter Games
While Greg Rutherford (left) is seeking a winter Games appearance six years after his most recent summer one, Vincent de Haitre (right) is aiming to do it six months after his appearance in Tokyo
The Beijing 2008 venues are very familiar to British long jumper Greg Rutherford, who reached the final in the Bird's Nest on his Olympic debut four years before winning gold in London.
If things go to plan, he could be in a bobsleigh this time round having been named in one of the Great Britain bobsleigh squads that will attempt to qualify for the Games.
While still relatively uncommon, there are enough precedents of athletes switching between summer and winter Games for this to be a realistic prospect – although no Briton has won medals at both.
Another member of Britain's bobsleigh squad, Montell Douglas, is also hoping to compete in Beijing again, having raced in the women's 100m in 2008.
Canadian Vincent de Haitre is taking the switch to another level, though, as he aims to compete in Beijing just 180 days after appearing at the Tokyo Games.
Already a dual Olympian, having competed in speed skating at Pyeongchang 2018 before track cycling in Tokyo, the 27-year-old has a year less than normal to make the transition from summer to winter after the coronavirus pandemic delayed the summer Games by a year.
He is the first athlete to attempt to do this in such a short time, although up until 1992 winter and summer Games were held in the same year so there are athletes who have competed at both in the same year.
"I had a week off when I got back, which is not a lot," he told BBC Sport. "When the cyclists had their off-season I was training with the skaters and when the skaters had their off-season, I went to the Olympics. It's been a real struggle to stay healthy and also stay motivated.
"Doing sport is inherently a painful activity – your heart-rate goes so high you taste blood, your legs burn, your back is sore all the time, your knees hurt, you have to see the physios almost twice a week.
"It's choosing effort and hard effort on a day-to-day basis and you've not had a chance to just not do anything.
"I have earned the respect of my team-mates [in both sports] by challenging myself with something very few have ever done and no-one has done it in this time frame.
"This has definitely been the most challenging thing I've had to do in my athletic career. At the moment getting to the Olympics is the goal, but once there just being there is not the goal any more, it's to be competitive."
From stopping rain to creating snow at venues
The snow machines will be busy for Beijing 2022
It is not only the indoor venues and some athletes who have been transformed from summer to winter.
With Beijing's average temperature in February above freezing and winters generally dry, the obvious challenge for a Winter Games is a lack of snow.
Zhangjiakou – which is hosting most of the ski and snowboard events – averages eight inches of the white stuff per year, while Yanqing – home to the sliding and alpine skiing – gets just two inches.
The answer, obviously, is fake it.
It will not be the first time Beijing has used manmade weather to combat the natural conditions at a Games, with the Chinese government having seeded the clouds before the 2008 opening ceremony to create a downpour elsewhere and keep the stadium dry.
That involved firing rockets packed with silver iodide crystals into rain clouds over the suburbs of Beijing.
This time it will be snow cannons firing super-cool water from reservoirs and lakes to eject ice crystals and create pistes on the slopes.
Beijing is not the first winter Games to lack snow – Pyeongchang 2018 and Sochi 2014 both used artificial snow and even Vancouver in 2010 had trucks of it being brought in to the melting Cypress Mountain venue.
Remember the words of the International Olympic Committee at the time of the Chinese bid for the Games: "Due to the lack of natural snow the look of the venue may not be aesthetically pleasing."
There is, perhaps, a silver lining in that (snowless) cloud – it could be a quick switch back to summer sports again afterwards.
See also T20 World Cup: Ireland's Curtis Campher take four wickets in four balls against Netherlands
Previous article'Touch Of France' awarded TV Show Of The Year
Next articleSarkodie's disrespect has cost me a lot – Patapaa
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WORLD NEWS October 22, 2021
Tottenham: Nuno Espirito Santo appointment 'turning into a nightmare'
WORLD NEWS September 27, 2021
What is climate change? A really simple guide

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Discussion in 'Star Trek: Voyager' started by Farscape One, Jul 27, 2018.
The Data thread in the TNG forum inspired this.
I understand there are only so many positions on a starship the size of Voyager, but this one bit is puzzling.
If we go by the actual insignia on Chakotay, then he was a Lt. Commander, which makes sense since the original XO, Cavit, was also a Lt. Commander.
So why does Tuvok get to be one, also, 3 years into the journey home? In fact, aside from the start when the other positions were getting filled... Chief Engineer, Conn... and Paris' reinstatement at the end of season 6, no one else got promoted. I understand that the rest likely wouldn't due to the aforementioned available positions, but why did Tuvok get to be promoted?
And if Tuvok, as a department head, gets to move up a rank, why not Harry? At least maybe a few years in, or in the final season. I always found it funny how there were a bunch of Lts. all around the ship but he was senior over them due to being a bridge officer. And I understand that actually does happen. But Janeway couldn't throw him a bone?
I mean, his contributions were good.
He was on the design team when the Warp 10 barrier was broken. He upgraded Astrometrics with Seven, which Astrometrics was a massive contribution throughout the rest of the series. He was key in saving the ship from the Hirogen. He was on the design team for the Delta Flyer. He helped the ship get a lot closer to home in "TIMELESS". He commands the ship during some watches, including many night shifts. He's even been killed more than once. Not to mention his regular duties, he seemed quite capable and proficient.
So why no Lt., junior grade?
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I always assumed Chakotay was only a Lt. Commander before he resigned his commission to join the Maquis, which was why he was reinstated as one onboard Voyager. Tuvok, given his wealth of experience, really should've made Lt. Commander far earlier. Kim, yeah he probably should've gotten promoted at least once, but with the very episodic format of Voyager maybe the PTB though the casual viewer might get confused, lol.
why did Tuvok get to be promoted?
Because he's the captain's friend.
But yeah, Harry should have been promoted at some point in the show. Though there were issues between Garrett Wang and Berman and Braga, so maybe they intentionally kept Harry the Eternal Ensign because of that?
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Kim, yeah he probably should've gotten promoted at least once, but with the very episodic format of Voyager maybe the PTB though the casual viewer might get confused, lol.
Why would the PTB think it would be confusing? That he was an ensign in one episode and a Lt. the next. I find it more confusing that he was the head of his department with only the rank of Ensign. Was he the head of Ops right from the start? Was it the particular position he was originally assigned before the ship was abducted by the Caretaker? Or did he replace someone that was killed? Sorry if I sound snarky.
He was Ops Officer from the start.
Regarding Chakotay, I figured he was a Lt. Commander before he resigned to join the Maquis.
No need to apologise, I was making more of a comment on the Voyager producers who were more focused on syndication and trying to lure in more casual viewers than aiming to tell a cohesive over-arching story (something that Voyager would've benefited from, given the premise), as such the status quo is always reset for the next episode--hence the crew complement is always given as 150-ish, there are an abundance of shuttles, torpedoes and other essential resources (except when suddenly there isn't).
Kim was fresh from the Academy and made the Chief Ops Officer, which I can only assume he got for being top of his class--seeing as how the ship would carry more experienced (though maybe not as qualified) officers and non-coms that could fill the role. It might've been better had he come aboard as a junior officer then promoted up (after being rescued from Ocampa) when the original Ops Manager was killed in transit.
Kim probably should have been promoted, but Tuvok can be a commander, while the XO is a Lt. Commander. Janeway was just being considerate of Tuvok's upcoming retirement, if they ever get home.
Was Chakotay ever addressed as "lieutenant commander"?
While "commander" is appropriate for both Commanders and Lieutenant Commanders, all the Lieutenant Commanders on TNG, DS9, and Voyager were at least sometimes addressed by their full rank, so if my memory is correct and Chakotay was always called "commander" then the most likely explanation, IMO, is his insigia is wrong (or we are reading it wrong).
The network wanted a show basically like Gilligan's Island: nobody ever develops or changes so you can miss a few episodes (or seasons) and pick it up again with no problem. I agree that's stupid, but that's what the network wanted. So Harry couldn't get promoted.
Tuvok could because he wore the wrong rank insignia for all of the first season. The network could be convinced that no harm would result since his new rank matched his previous appearance.
IMO. Ranks and promotions on Voyager were severely messed up by the writers. Making it hard to understand the continuity within regular Trek. Much of the background Starfleet things were just thrown out the window in order to make some plot writing work.
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'Military' rank and job assignation can sometimes seem at odds within the chain of command. It's worth noting that not every crewman or officer is going to automatically move upwards in rank otherwise everyone would make Admiral. Normally specific exams have to taken. Regards service, that can also be taken into account (usually a number of years or outstanding leadership, which would apply to Tuvok), but recognition of service doesn't always translates as movement within the ranks either. Field commissions where rank is assigned is of the authority of the Captain, hence the Marquis on Voyager - complete with a slightly different insignia. (Tom's insignia was regular and of course Tuvok's).
I don't think Harry actually needed to move upward in rank to do his job. To be fair he did night shift command but it just seemed more appropriate to give First and Second officer rank for leadership recognition and authority.
No, he never was referred to as "Lieutenant Commander" in dialogue. But in addition to his rank insignia, Cavit was a Lt. Commander (addressed as such in dialogue) so it makes sense Chakotay would hold the same rank he did.
Doesn't the fact that Tom Paris got demoted for a year and a half before being re-promoted shoot that theory down?
Additionally, wasn't there a theory that Tuvok was demoted as a result of his actions in Prime Factors from LCDR to LT?
The one interesting aspect of this theory is that if it holds, Janeway never promoted anybody at all.
That is, she had clearance from Starfleet to make Paris a full Lieutenant as reward for his betrayal of the Maquis, and she used that. But all the other people she first had to demote, and then later restored their rank after they had "done the time". With all other characters, this is pretty much explicitly true (Paris' demotion to junior Lieutenant is not explicated as a punitive measure, but this being Paris, you don't need to assume much!). Tuvok is so messed up that there is no "explicit" there, which is the reason why this theory can be put forth in the first place.
Janeway might well have felt she did not have the authority to promote anybody if she couldn't contact Starfleet Accounting. She had already done the Maquis ranks thing, and this was trouble enough for her: if she declared "no can do" for any further juggling, she would be on firmer ground when it came to managing her forcibly drafted pirates and all.
Apart from this, promotions in Starfleet could be just like promotions in the real world: you have to collect all the right tick marks in all the right boxes before getting one. It's not just time served, or heroic deeds performed, or courses completed, but the sum total of all those. And the audience cannot tell whether Tuvok or Kim has ticked all the boxes or not.
Yes, that theory exists, but I'd disregard it. When Tuvok's insignia was corrected, Tom and B'Elanna also had their insignia changed from being full Lieutenants to being Lieutenant JG, as the producers still wanted Tuvok to have clear seniority over the two of them. Tom at least wasn't involved in Tuvok and B'Elanna's thing in Prime Factors and thus there would be no reason to punish him.
Incidentally, I'm reminded of an amusing nitpick website in regards to Fury from season 6 which includes flashback scenes set during the first season. In these scenes, Tuvok has Lt. Commander's pips on, and as the website put it "I'm not sure how to flag this one. It is wrong, but it's also consistent."
Tom at least wasn't involved in Tuvok and B'Elanna's thing in Prime Factors and thus there would be no reason to punish him.
"No reason to punish Tom Paris" doesn't convince me at all, is what I'm saying.
In any case, none of the ranks change at a specific moment directly tying to "Prime Factors". Tuvok's pips fluctuate back and forth across the length and breadth of "Cathexis" before settling, while Paris and Torres get their adjustments when convenient. Some of this we may treat as in-universe, some we may decide to turn a blind eye on.
Yup, good old Phil Farrand. I've got all the books.
Then again, a reward/punishment mechanism would seem like a priority for people stuck in a desperate situation... They did set up the "replicator rations" sanctions system that persists for years after the replicators get repaired.
And Janeway is using rank as a stick readily enough; why not use it as a carrot? But if you indeed are stranded tens of thousands of lightyears from your garden, you might use the carrots sparingly, and perhaps indeed only for soothing the people who first got the stick and have now abandoned their naughty ways. In which case promoting Tuvok just for extra carrot cake would destabilize rather than stabilize the community - but interpreting this as re-promoting after Tuvok got the stick for "Prime Factors" (but only after Janeway prevaricated a bit) would make it a stabilizing move on Janeway's part.
I view Chakotay as always being a (provisional) full Commander - he is never called "Lieutenant" in any context. His faulty neck badge should just be ignored.
Voyager was isolated from the rest of Starfleet - a closed system. They assimilated the odd stray - Neelix, Kes, the Borg, the Equinox crew - and a few unlucky extras got killed but otherwise there wasn't much movement for the crew. Handing out promotions wouldn't really do anything for their relative status unless the higher-ups also got demoted.
I remember someone on these boards basically claimed that any Starfleet officer who didn't rise up to the command ranks was an abject failure, and that there was no point to them ever having joined Starfleet in the first place. But that's not realistic. After a certain point, promotions no longer become automatic; selection becomes more rigorous. The command grades also carry an extra level of responsibility that not everybody is suited for. And that's just fine. In the real-life navy, many officers are quite content to spend their careers in the Lieutenant grades, and then retire.
It never ceases to amaze me that the number of pips on a character could be wrong. Like, the way the pips work to us is so clearcut and obvious, you would think that actors and crews would notice things like this. Even if actors don't care and its just a costume/uniform to them, wouldn't you think someone would notice the number or style of pips changing randomly throughout the season? I wouldn't expect everyone on production to be a Star Trek nerd like us, I'm sure for 99% of the people there it's just a job, but come on!
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text:
LoveSick Radio New Single "Bloodshot Eyes" Hit's Classic Rock Magazine Weekly Track List!
LoveSick Radio Release New Blues Tinged Single, "Bloodshot Eyes"
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Download / Stream "Bloodshot Eyes" Online:
LoveSick Radio (LSR) play unapologetic blue-collar rock n' roll on steroids! Big guitars, big vocals with punk and blues-infused swagger creating a fresh sound all their own.
LSR has performed at some of the most buzzed about festivals and events such as NHL All-Star Game with Fall Out Boy and O.A.R.; the Changjiang International Music Festival in China, the Vans Warped Tour, and has shared the stage with the likes of the Bon Jovi, Scott Weiland of Stone Temple Pilots, Dorothy, Josh Todd & The Conflict, Wayland, Bobaflex, Blue October, Kid Rock, Steel Panther, Three Days Grace, Buckcherry, Hinder, Twenty One Pilots, Paramore, Dead Sara, to name a few. Additionally, between live shows and writing/recording, the band found time to bring their live energy stage show overseas for tours in Japan, South Korea & Singapore.
LoveSick Radio has received great airplay, landing in the top 50 Billboard pop chart with their 1st single. LSR received multiple placements on several MTV shows (including an appearance on "My Super Sweet Sixteen") and MTV2featured the band as one of its "Bands on the Rise."
Thanks to LoveSick Radio's road work and live shows, the band boasts a strong, loyal fan base. The band recently began production on their new record with guitarist David Harris co-producing with Joe Viers ( Twenty One Pilots, Bobaflex ) & producer Warren Huart (The Fray, Aerosmith) on new tracks
Bloodshot Eyes Blues Rock Classic Rock Magazine Lovesick Radio


Seventh phase of UP election was conducted on 8 March, 2017. This was the last phase, among total number of 7 phases. The election results were declared on 11 March. BJP energed out as a clear winner. They are brought into power with massive majority, winning 312 seats out of 403. SP and Congress alliance couldn't do any wonder and they could only win 54 seats. BSP, despite being a former winner in UP, was able to secure a mere tally of 19 seats.
UP Elections 2017 was a major political event. The elections are declared for the complete state, and political scenario of the state is very clear. Elections in uttar Pradesh were held in February and March , 2017. There were total seven phases, in which elections were held. Voting percentage has been above 60%, in starting two phases it was close to 65% even. Considering this, we can say that people participaed in good numbers, however a better voter turnout was expected. Uttar Pradesh election results were also awaited for a long time. The UP election results were declared on 11 March, a very important day for every political party of the state. All the participating parties were focusing on winning the elections of Uttar Pradesh legislative assembly.
After these Uttar Pradesh assembly elections 2017, we have seen the new government coming in power. Current term of Uttar Pradesh legislative assembly is going to be over on 27-05-2017. We were covering UP elections results and live updates along with latest happening and trends of the election.
There are 403 assembly seats in this state, more than any other state of India. Winning in this region proves that the party is politically strong. Around 14 crore voters are eligible to cast their votes, largest tally of voters in a single state. Such large tally of voters belongs to different communities. Every party tries to turn the voters of every community to in their favor; however it is a tough task to do. In 2012 elections, SP won the elections with clear majority. They managed to bag 224 seats clearly turning the elections in their favor. Result of Uttar Pradesh elections was declared in favour of SP, allowing Akhilesh Yadav to take oath as CM for first time.
The Uttar Pradesh Elections 2017 were carried out in 7 different phases. Voting on first phase took place on 11, Feb 2017 and the last phase have seen voting on 8, March 2017. Counting of votes took place on 11 March, 2017 and UP election results was declared on same day.
The 2012 assembly elections saw a majority of win by Samajwadi Party. Akhilesh Yadav was appointed as CM of the state by party president Mulayam Singh Yadav. It was a test of Akhilesh to prove his leadership. In the upcoming elections, Akhilesh was the Chief Minister candidate of SP. Akhilesh's party has seen few downs and controversies in the recent past. They have to win trust of voters despite all the negative publicity party is gaining, due to clash of thoughts between Akhilesh and Mulayam. In 2017 elections, Akhilesh couldn't win even respectable number of seats and only 47 seats were won by SP.
BSP is another main party which was eyeing to form their government for coming term. Mayawati led BSP promoted them as best choice for the elections, but voters didnt agree with her. Party didn't perform well in the 2017 elections, gaining only 19 seats. BSP won 80 seats in 2012 elections,a d this time the number got worse, reducing their chance of comeback in next term too. The party will try to regain its strength to come in power again.
Another main national parties, BJP was also testing their fate in UP elections. It was a litmus test for BJP after their brave move of demonetizations. Post such a big decision, this was the first time when common man spoke with their votes. The party is led by PM Narendra Modi, and they scored very well by winning 312 seats. The party attained a magical number, surpassing majority by a margin of 110 seats.
Among these contenders, Congress was also trying to reclaim its lost esteem in these elections. Last few years have not been good for the party. UP have 403 assembly seats, winning more constituencies here meant to increase there presence in Vidhan Sabha. Congress was likely to target most of the constituencies in order to win with majority, however their alliance with SP left them with very less number of seats. The party won only 7 seats, which is not a good number, party wll be very disappointed with their performance. This is not what they would have expected.
The state has severe issues including poverty, high crime rate, minimum development and basic education etc. However politicians take spotlight away from these important issues. UP politics and election agenda is generally kept limited to communities and casts. A large population of UP is below poverty line. Many other issues are quality of education, basic infrastructure, power supply, development of rural areas, and crime rate in the state, etc. These issues should be raised by parties to improve condition of Uttar Pradesh. Corruption has made the state's condition even worse, taking away most of the money allotted for development and constructions.
You would like to know: Assembly Constituencies of Uttar Pradesh and Candidates List.
Uttar Pradesh legislative assembly type is Bicameral, it has one lower house and one upper house. Lower house has 403 assembly constituencies and upper house (Vidhan Parishad) has 100 Members. 1 Member is nominated from Anglo-Indian community. It has 80 parlimentary constituencies.Such large number of seats in assembly elections contains voters of various communities. It is a diverse state where people have various issues and expectations from the government. Deep political knowledge of the state is necessary to turn the voters in your side. Every parties' political experts looks closely to every move made by politicians. This state has not seen a party scoring consecutive wins in recent years.
Numerous Opinion polls are conducted by some news and survey agencies. Opinion survey predicts people's view by taking opinion of small sample of people. These surveys are conducted in various regions of the state to get a better prediction. In most of the surveys, BJP have an edge over other parties. BSP and SP are experiencing a close fight in terms of votes. Congress doesn't look good and is out of competition. None of the survey suggested that they will win by majority in 2017 elections.
Uttar Pradesh is one of the biggest State of India and it has largest state in term of Population. As per elections held in 2012, 127492836 electors were in UP and 59.40% voters used their votes. 75725793 voters participated in the last election.
Since independence, congress has ruled Uttar Pradesh till 1974. In 1974 assembly elections, people of Uttar Pradesh chose some other party over congress for the first time in the history of Republic of India. After this election, congress hardly ruled the state till now. Janta Party was the first party to defeat congress in the country. After which various other parties got introduced in Uttar Pradesh elections. In recent years, SP and BSP were considered as main contenders of the elections. In 2017 elections, they faced tough competitions from Bhartiya Janta Party, which won 312 seats.
Uttar Pradesh has 403 Vidhan Sabha seats and 80 Lok Sabha seats, which are highest in India. 202 seats are required to form the government. In last assembly elections (2017) Bharatiya Janata party won 312 seats out of 403 and formed the Government.
Among the 403 Vidhan Sabha seats, 85 seats are reserved for scheduled caste candidate. Out of 80 parliamentary constituencies, 17 seats are reserved for scheduled caste. Due to such large number of assembly seats in the state, elections are carried out in numerous phases. In this region of the country, people from different caste and religion are inhabited. Muslim voters plays an important role in forming the government. Various caste are also focussed by politicians to ensure a marginal win.


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However, not all supplements around are safe to use. We are sad to hear the foods were not to your liking and that the deliveries would be automatic. Hello Lori, Thank you so much for your review. In the future, I will make a point to select food items that I now know to be more to my liking than others. Tomorrow is my last day on turbo take off.
We read customer reviews and expert ratings for Nutrisystem, to learn what's good and bad in this diet. We also review the "Turbo13" program, provide a sample menu, and review the food taste. Here are the top complaints and the worst-rated food: (Updated). NUTRISYSTEM REVIEWS AND RATINGS. See what real customers who lost weight and changed their lives have to say. You can lose weight, improve your health and have more energy, too.. lose weight fast shop plans. Researching weight loss programs? Read reviews about Nutrisystem, topics include the Counseling Team, the meals, personal results and more.


Nutrition is the foundation of health. It determines our mental capacity for learning, regulates our mood, and provides us with sufficient energy and rest. Although it is the foundation of health for most, it can be the last stop for intervention.
Many of the clients seen at Nutrition Advancement may have tried everything for their health concerns with no luck, not really addressing the root problem which is inflammation. Science now points to the root causes of every health condition we fear as inflammation. The food we eat is the only thing that changes for most of us as our environment typically stays the same with where we live and work.
The process of digestion is inflammatory. The immune system is involved in our digestion and can easily over-produce inflammation in response to poor health, chronic physical stress or emotional stress, environmental toxin exposure or heredity. The digestive tract we have was replicated from our mother and that is how we start life. Should she be in poor health we will be also. Add to this all the things we do to either help or hurt our health through food, exercise, stress and sleep over a lifetime.
Addressing inflammation through nutrition should be the first approach to actually heal. Many of the clients address most —if not all— the health complaints they come in with by the 5th visit. Healing is possible when we properly address inflammation through highly specific nutrition therapy so you can get back to life!


text:
New Music Seance:
2007: Concert No. 1, Nature Vivante
Event Type: Music Program Origin: Other Minds
Summoning the specters of musical forbears, and channeling the spirits of their successors, the 2007 New Music Seance Concerts featured five hours of hypnotic solo piano music performed by Sarah Cahill, or on the eerily disembodied Yamaha Disklavier, with additional spirit raising interludes by the Kate Stenberg and Eva-Maria Zimmerman violin-piano duo. The concerts were held in the intimate, candle lit setting of the historic Arts & Crafts styled Swedenborgian Church in San Francisco. The first concert entitled "Nature Vivante", includes the title piece by Wladimir Vogel, and works by Dane Rudhyar, Phil Collins, Alan Hovhaness, Josef Hauer, George Antheil, Dan Becker, Peter Garland, Hans Otte, as well as the world premiere of "Rippling the Lamp", a stunning piece for violin and tape by Charles Amirkhanian. For a complete description of all the pieces heard please download the Program Guide which can be found at http://www.otherminds.org/pdf/Program_web.pdf
Part 1 of 19:
Musical Selections: Prayer, Lament, and Death March, Op. 1 (1914) (8:22) / Dane Rudhyar [U.S. Premiere]
Performers: Sarah Cahill, piano
Genres: 20th Century Classical; New Music
Subject: Piano music
People: Rudhyar, Dane, 1895-1985; Cahill, Sarah
Recording Date: 2/24/2007
Musical Selections: Pleasant Dreaming (2006) (8:06) / Phil Collins
Genres: 21st Century Classical; New Music
People: Collins, Phil, 1951-; Cahill, Sarah
Musical Selections: Kirgiz Suite, Op. 73: Variations [1st movement] (1951) (3:09) / Alan Hovhaness
Performers: Kate Stenberg, violin
Eva-Maria Zimmermann, piano
Subject: Suites (Violin and piano)
People: Hovhaness, Alan, 1911-2000; Stenberg, Kate (Violinist) ; Zimmermann, Eva-Maria (Pianist)
Musical Selections: Kirgiz Suite, Op. 73: A Kirgiz Tala [2nd movement] (1951) (1:18) / Alan Hovhaness
Musical Selections: Kirgiz Suite, Op. 73: Dance [3rd movement] (1951) (0:53) / Alan Hovhaness
Musical Selections: Zwölftonspiel (2. September 1956) (3:12) / Josef Matthias Hauer [U.S. premiere]
Subject: Violin and piano music
People: Hauer, Josef Matthias, 1883-1959; Stenberg, Kate (Violinist) ; Zimmermann, Eva-Maria (Pianist)
Musical Selections: Fünf Stücke, Op. 41, No. 5 "Jazz" (1925) (1:43) / Josef Matthias Hauer [U.S. premiere]
Musical Selections: Rippling the Lamp, for violin and tape (2006-07) (7:58) / Charles Amirkhanian [World premiere]
Performers: Kate Stenberg, violin with tape
Genres: 21st Century Classical; Electro-Acoustic / Electronic
Subject: Electro-acoustic; Violin music; Electronic and violin music
People: Amirkhanian, Charles; Stenberg, Kate (Violinist)
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Strophe d'album [1st movement] (1917-21) (0:32) / Wladimir Vogel [U.S. premiere]
Performers: Eva-Maria Zimmermann, piano
People: Vogel, Wladimir; Zimmermann, Eva-Maria (Pianist)
Part 10 of 19:
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Prélude Gris [2nd movement] (1917-21) (1:21) / Wladimir Vogel [U.S. premiere]
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Nettement Désagréable [3rd movement] (1917-21) (1:01) / Wladimir Vogel [U.S. premiere]
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Morceau Poétique [4th movement] (1917-21) (1:28) / Wladimir Vogel [U.S. premiere]
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Lasse et Plaintif [5th movement] (1917-21) (1:17) / Wladimir Vogel [U.S. premiere]
Musical Selections: Nature Vivante-Six Pieces Expressionnistes: Joyeux [6th movement] (1917-21) (1:03) / Wladimir Vogel [U.S. premiere]
Musical Selections: Woman Sonata (1923) (6:34) / George Antheil
Subject: Sonatas (Piano)
People: Antheil, George, 1900-1959; Zimmermann, Eva-Maria (Pianist)
Musical Selections: XTC Blues (2005) (2:43) / Dan Becker [World premiere]
Genres: New Music; Mechanical Music
Subject: 21st century classical; Mechanical musical instrument music; Player piano music; Piano music; Disklavier
People: Becker, Dan, 1960-
Musical Selections: Don't Make Me Go Back to LA (2003) (6:51) / Dan Becker [World premiere]
Musical Selections: Hermetic Bird, to Kuniharu Akiyama (1996) (5:12) / Peter Garland
People: Garland, Peter, 1952-; Cahill, Sarah
Musical Selections: Das Buch der Klänge, No. 10 ("The Book of Sounds") (1979-82) (10:49) / Hans Otte
People: Otte, Hans, 1926-; Cahill, Sarah


I came across Crabble from a post on Macworld and was actually taken back by its sheer simplicity. Ok a piece of plastic is going to do magic! whatever…Ofcourse I did order and have been using it for almost a month now.
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Dori is intelligent, affectionate, brilliant red and black with a fantastic championship pedigree. Dori is the daughter of VA1 world champion Chakira vom Pendler. She is also a world famous World Vize Sieger VA2 Quenn vom Löher Weg SchH3, IPO3 (paternal) granddaughter and a VA1 Pakros d'Ulmental SchH3, IPO3 (maternal) granddaughter.
Georgia is our newest "Mom in Training" who will begin her Schutzhund training this year and compete in 2017 and have litters later in 2017. She is a loving, affectionate brilliant red and jet black puppy with a spectacular pedigree. She is a world famous VA1 Remo von Fichtenschlag granddaughter and a World Sieger VA1 Larus von Batu and World Sieger VA1 Yasko vom Farbenspiel granddaughter.
Kada is a beautiful, spirited, perennial puppy with a heart full of love. She is a V titled show dog, a Vize World Sieger VA2 Quenn von Lower Weg IPO1 granddaughter with the great Ursus von Batu, Netun von Bad Boll, and Ulk von Arlett in her pedigree.
Anna brings excellent German Shepherd conformation, exceptionally loving disposition, and rich red with jet black to her litters. She is spectacularly beautiful and literally stops passers-by on the street with her beauty. Anna is the first generation daughter of VA1 NASS Sieger Champion Tuareg von Bad-Boll and VA3 Xyra vom Drei Birkenzwinger and has 2X VA1 Larus von Batu, 2X VA1 Yasko von Farbenspiel, VA1 Yello von St.-Michaels-Berg and many other world champions in her pedigree. She competed for SG and earned her Schutzhund training in 2017.
Nelly is a beautiful dog just beginning her show career. She has World Champion Siegers (2006) Zamp von Thermados and (2000) Ursus von Batu and top producers VA Dux Della Valcuvia and Esko in her pedigree.
Vee is especially beautiful with her brilliant red and special tan and silver lines in her jet black top coat. She is a World Sieger VA2 Quenn vom Loher Weg and VA Negus granddaughter and brings the lines of famous VA1 Ursus von Batu and famous VA1 Zamp von Thermodos in her pedigree.
Viktoria is a beautiful long coat female with many world champions in her pedigree including the famous 2X VA1 World Champion Vegas du Haut Mansard and World Champion VA1 Pakros d' Ulmental. She brings beautiful deep black and red long coat features and an exceptionally loving disposition to our kennel.
Xena is a beautiful black and red champion female with one the finest pedigrees in the world. She is a V1 show champion. She is the granddaughter of VA Furbo Degli Achei son of the great VA Quenn vom Loher Weg. She has the great Zamp von Thermados, Ursus von Batu, and Hill von Farbenspiel in her pedigree, a who's who of the greatest producing champions in recent history.
Zoe's pedigree is a "who's who" of many famous German top producers including 2x World Sieger VA1 Yasko vom Farbenspiel SchH3, Kkl 1a, Nicos, World Vize Sieger VA2 Quantum von Arminius SchH3, Kkl 1a, VA1 Zamp vom Thermodos SchH3, Kkl 1a, V Dux de Cuatro Flores SchH3, Kkl 1a, World Sieger VA1 Pakros vom d' Ulmental, Vegas; Plus many other VA producers including VA Esko von Dänishchen Hof, VA Nero vom Nobachtal, VA Enzo vom Buckhorn , VA Yello v. St.-Michaels-Berg, VA Nando vom Gollerweiher, and VA Mentos vom Osterberger-Land.


The private hire driver was not the only one left shaken when a stone smashed the window of his taxi - he had passengers, including a one-year-old child, in the back of the car.
The attack happened at around 6.30pm on Saturday night on York Road, opposite Killingbeck Asda.
The large stone hurled at the taxi can be seen in the video - as well as the shattered glass that covered the inside of the car and the road.
Leeds Private Hire Drivers Organisation (LPHDO), shared the video with Leeds Live to highlight the dangers faced by private hire drivers in certain parts of Leeds.
The organisation led more than 100 private hire drivers in a go-slow protest through Leeds city centre this morning hoping to bring attention to attacks like these, which the LPHDO says are on the up.
Member Zahid Mahmood said: "This is exactly what the protest was about - public safety.
"That passenger or driver could have been any single one of us.
"It's not just drivers, it's passengers at risk too. It's every single night."
West Yorkshire Police confirmed officers received a report of the front passenger window of the taxi being smashed by youths throwing stones outside the Asda store on York Road.
A spokesman said: "The taxi was carrying passengers at the time but no-one was injured.
"The suspects were described as six white youths, aged 10 to 12, wearing dark clothing.
"Officers searched the area but were unable to trace those involved."
The criminal damage has been recorded and officers from the local neighbourhood policing team have been made aware.
Anyone with any information that could assist the investigation is asked to contact officers via 101 quoting crime number 13170575498.
West Yorkshire Police addressed drivers' concerns about stone throwing and said 'hotspot' areas were being targeted.
A spokesman said: "One of the issues that drivers are highlighting has been around stone throwing and other similar crime and anti-social behaviour affecting them and their business.
"Incidents like this are completely unacceptable and we are working with the drivers and our partner agencies to put long-term problem-solving measures in place to tackle the issue.
"A number of key hotspot areas have been identified, primarily in east Leeds, and we are working with our neighbourhood teams to target those areas to deter any further offences. We will be working to identify those involved and will take appropriate action, including exploring the full range of control measures available through Leeds Anti-Social Behaviour Team.


Mesut Ozil was among the goals as Arsenal left it late to see-off Watford 2-0 at the Emirates this afternoon and extend our winning run to five games in the Premier League.
Alexandre Lacazette wasted the first big chance of the game as he chipped wide of the post when clean through on goal while Watford were also causing some problems of their own at the other end but the game remained goalless at half-time.
The visitors continued to create after the break as Arsenal struggled to get going but the introductions of Alex Iwobi and Danny Welbeck midway through the second half changed the game in our favour.
Iwobi created the opening goal as his cross was put through his own net by Watford defender Craig Cathcart while under pressure from Lacazette in the 80th minute.
Arsenal doubled their lead just a few minutes later as Welbeck robbed possession in midfield and the move led to Lacazette crossing for Ozil to sweep home from 8 yards to make it 2-0 and secure another vital three points.
The win means Arsenal have now won our last five league games and seven in all competitions while he now sit level on points with fourth-placed Tottenham – who have a superior goal difference.