text:
"""py.test configuration"""
import os
from sys import version_info
from pathlib import Path
import numpy as np
import nibabel as nb
import pandas as pd
import pytest
import tempfile
import pkg_resources

from .utils.bids import collect_data

test_data_env = os.getenv(
    "TEST_DATA_HOME", str(Path.home() / ".cache" / "stanford-crn")
)
test_output_dir = os.getenv("TEST_OUTPUT_DIR")
test_workdir = os.getenv("TEST_WORK_DIR")

data_dir = Path(test_data_env) / "BIDS-examples-1-enh-ds054"

@pytest.fixture(autouse=True)
def add_np(doctest_namespace):
    doctest_namespace["PY_VERSION"] = version_info
    doctest_namespace["np"] = np
    doctest_namespace["nb"] = nb
    doctest_namespace["pd"] = pd
    doctest_namespace["os"] = os
    doctest_namespace["pytest"] = pytest
    doctest_namespace["Path"] = Path
    doctest_namespace["datadir"] = data_dir
    doctest_namespace["bids_collect_data"] = collect_data
    doctest_namespace["test_data"] = pkg_resources.resource_filename(
        "niworkflows", "tests/data"
    )

    tmpdir = tempfile.TemporaryDirectory()

    doctest_namespace["tmpdir"] = tmpdir.name

    nifti_fname = str(Path(tmpdir.name) / "test.nii.gz")
    nii = nb.Nifti1Image(np.random.random((5, 5)).astype("f4"), np.eye(4))
    nii.header.set_qform(np.diag([1, 1, 1, 1]), code=1)
    nii.header.set_sform(np.diag([-1, 1, 1, 1]), code=1)
    nii.to_filename(nifti_fname)
    doctest_namespace["nifti_fname"] = nifti_fname

    cwd = os.getcwd()
    os.chdir(tmpdir.name)
    yield
    os.chdir(cwd)
    tmpdir.cleanup()

@pytest.fixture
def testdata_dir():
    return data_dir

@pytest.fixture
def ds000030_dir():
    return Path(test_data_env) / "ds000030"

@pytest.fixture
def workdir():
    return None if test_workdir is None else Path(test_workdir)

@pytest.fixture
def outdir():
    return None if test_output_dir is None else Path(test_output_dir)

redpajama_set_name: RedPajamaGithub


Inspired by the beauty of nature this Colmslie Beach Reserve wedding shoot fused a bohemian mix of botanical greenery, recycled wood and intricate lace with a love filled vibe. Set amongst two magnificent rows of Jacaranda trees in Brisbane as the sun set.
The ceremony set up featured our stunning ghost chairs, being clear they enhanced the natural greenery of the surrounding reserve.
The bride made her way up the aisle to her eagerly waiting groom to the boho chic styled ceremony set up with recycled timber doors which created a simple, natural and chic backdrop, framed perfectly by botanical greenery and star jasmine plants.
For more infomation and inclusions on our boho chic ceremony set up package, view Brisbane Wedding Decorators packages here.
Once they sealed the deal and enjoyed a glass of bubbles at their relaxed cocktail hour setting, showcasing our elegant wooden top bar tables and white bar stools, styled with our cute 'hubby' and 'wifey' chair signs.
Then bride and groom then enjoyed a little time alone together to kick back, relax and take in all the magic together at a stunning styled picnic setting with champagne and nibbles.
Precious time that we recommend every couple includes in their wedding day schedule as the day really will fly by so quickly so a little time out to relax, reflect and just be will be in the moment.
Our gorgeous hair pin style wooden serving table was used to present canapes, drinks and refreshments for the guests.
Our sweet pink lemonade dispenser looked so refreshing!
Brisbane Wedding Decorators loved creating this botanical styled wedding shoot at Colmslie Beach Reserve in Brisbane. If you enjoyed this shoot and are looking for ceremony and cocktail hour decor hire for your special day, we would love to hear from you. Contact us now for more information and to confirm availability for your special day.
If you enjoyed the style of this Colmslie Beach Reserve wedding photo shoot, Brisbane Wedding Decorators would love to hear from you to discuss the styling of your special day.
Contact us now for more information and to confirm availability for your special day.
Florals New England Flower Co.


text:
layout: bod
categories:
  - board
name_first: "Michael"
name_last: "Chladek"
seat: "Elected"
position: "Treasurer"
term_start: "2020"
term_expires: "2024"
committees:
  - "Membership & Volunteers"
date: 2021-01-01
image: "/images/people/board/michael-chladek.jpg"
permalink: /board-of-directors/michael-chladek.html
published: true
active: true
---

Michael is a health outcomes researcher with a background in the social sciences who also dabbles in web development. Throughout these endeavors, Michael is particularly interested in exploring how data, research, and the stories they tell can make people's lives, communities, and society better. He has a PhD in human development from the University of Chicago and a BA in psychology from the University of Missouri–Columbia. He first started attending Chi Hack Night in 2017, and since then he's been working with the Chicago Justice Project breakout group to explore patterns in how Chicago media outlets may over- or under-represent crime across the city. In his free time, you'll likely find him either reading a book or playing a Zelda game.


For practically any significant enthusiast, watch fanatic or premium of hand enjoys the principle of determining the attributes of outstanding hand watches is necessary to developing a superb option. While purchasing on the web for watch the web characteristics fairly a great deal of details when a possible point of view consumer can examination a wristwatch company or edition, however it is really crucial comprehend what top qualities stand for an outstanding watch. In this post I explore the 4 P's to discover to identify the absolute best Smart Watch in the industry.
Watches generated by the finest Smart sight firms have documents that go as much back greater than a century from supply time. Their devotion to fantastic Smart motions causes no question concerning the beauty of skillfulness and condition like a name brand name. Thoroughly made watch was created with goal to meet the specific desires and also requires of your distinctive industry and lifestyle. Some Smart perspective famous brand names are much more aviation/aerospace dependent, some are very h2o-styled, some will be more vehicle-encouraged, among others are totally layout-influenced. If your life-style is far more o2, sea, or terrain-structured that will certainly determine if all the brand name of watch goes properly with your own wants and needs.
Being a serious watch collector, a principal issue needs to be the great top quality and also difficulty from the on the inside solutions that produce the watch action. Outstanding tact watch test could have numerous countless moving inner aspects that create specific activities for specific timekeeping. For Smart motions vehicle motions view, referred to as mechanical or personal-winding, these brand names with fingertips-finished at home activities typically have a very better good quality than those that might be volume-made or outsourced employees. Smart quartz and very-high quality quartz arm watches which function from the power supply source could have added-exact timekeeping characteristics which can be normally just measured up to by aerospace hand watches.
Elegant watches are not simply extremely little timekeeping systems, yet additionally very easily transportable features of painstaking craft. The status along with the business by yourself might perhaps order exceptional prices in the initial buy; nonetheless experienced workmanship and exclusivity of little versions usually can accumulation to keep or improve rate of an arm watch with time. Smart see specialists recognize that any enduring collector requirement to search in to these functions that may provide a vital upbeat impact on the a lot more expression resale well worth. Maybe summarized by showing essentially when looking for an extraordinary arm enjoy the manufacturer, Workout, Difficulties and Design are normal items to consider. These items are examine if a watch will have the performance history in order to fulfill your needs, constantly maintain dependability and also security and slide within the appropriate cost range to your individual paying out investing budget plan. Strategy buying Smart checks out as cost precisely the same way you would certainly with excellent pictures. Eventually you will certainly have a fantastic option of incredible watches for a long time to experience.


text:
Democratic candidates line up to challenge Yoho
Andrew Caplan @AACaplan
Yvonne Hayes-Hinson, Dushyant Gosai and Tom Wells each hope to be his opponent in the race for Florida Congressional District 3.
In just five months, registered Democrats will pin their favorite candidate against incumbent Ted Yoho in a congressional race to represent North Central Florida.
Yoho, R-Gainesville, has represented District 3 in Congress since 2012 and looks toward what he has previously said will be his fourth and final term in office.
The district includes Alachua, Clay, Putnam, Bradford and Union counties, as well as parts of Marion County.
In 2012, Yoho edged incumbent Cliff Stearns by less than 1,000 votes to take the seat. He is a large-animal veterinarian and Gainesville resident.
Yoho has been a strong proponent for the Second Amendment, earning himself an "A" grade from the National Rifle Association. He has pushed for better medical coverage for veterans and has attempted to get a VA clinic built in Clay County.
Yoho was unavailable for comment.
Last year, he voted for a bill that passed and allowed the federal government to withhold funds from states and local governments if they didn't enforce immigration laws. He also voted against a disaster relief appropriations bill that amended and suspended the debt ceiling for a short time frame to help provide more funding for Hurricane Harvey and Irma relief efforts. The bill ultimately passed.
Three candidates have filed to run against him, all Democrats, who will appear on the August primary ballot. The winner will likely face Yoho in November.
Yvonne Hayes-Hinson, Dushyant Gosai and Tom Wells each hope to be that opponent.
Hayes-Hinson, 70, is a former Gainesville City Commissioner. She's a University of Florida graduate and lifelong educator.
Her time on the commission was her only time holding a political office. She said she's running for District 3 to fix issues within the area that have gone unresolved.
"Frankly, I felt a void in my community," she said. "I've been feeling it for some time."
Hayes-Hinson said her focus will be on education, universal health care, justice reform and protecting the Earth from gas emissions and fracking.
"These conservatives have forgotten where we live," she said.
Gosai, 58, is a resident of Hernando in Citrus County. He said he is a former hotel owner and educator at City College, an online college with locations around Florida. He said he would like to solve issues in education, first and foremost.
"I'm a firm supporter that we should have a free undergraduate education for everybody in this country," he said.
Gosai said he believes free education could be accomplished as early as 2025, but said he didn't want to disclose how to accomplish it.
He holds a bachelor's degree in accounting and is a licensed lawyer in India. He said he holds a master's degree in fraud management and a doctorate in accounting.
"I don't have any political background," he said. "I have a different way of thinking compared to politicians and that's how I would stand out. I'm not there to do the politics, I'm there to work for the people."
Wells, 67, is a Gainesville resident who ran as an independent against Yoho in 2016. Wells received less than 4 percent of the vote.
He said the first run was a "practice lap" and that he intends to hit the campaign trail more seriously this time around. He said he's tired of seeing corrupt politicians make decisions that have negative effects on constituents.
"I've been banging my head against the wall since 1981," Wells said.
Wells can often be spotted at rallies and protests. On Tuesday he plans to join a group in Palatka after school officials expressed interest in arming teachers as a way to prevent school shootings.
His campaign has several pillars, including addressing poverty, tougher gun laws, destroying the fossil fuel industry and health care.
"Money isn't going to health care, it's going to corporate profits," Wells said.

redpajama_set_name: RedPajamaCommonCrawl


TEGUCIGALPA, Honduras –Deposed President Manuel Zelaya made a dramatic return to Honduras' capital yesterday, taking shelter from arrest at Brazil's embassy and calling for negotiations with the leaders who forced him from the country at gunpoint.
The interim government initially ordered a 15-hour curfew, and then extended it to a 26-hour shutdown of the capital, but thousands of Zelaya supporters ignored the decree and remained outside the embassy, dancing and cheering.
Others in the capital rushed home, lining up at bus stands and frantically looking for taxis.
Electricity was cut off for hours at a time on the block housing the embassy and in areas of Tegucigalpa where news media offices are located.
Later, Defense Minister Lionel Sevilla said all flights to Tegucigalpa had been suspended indefinitely.
Chants of, "Yes we could! Yes we could!" bellowed from the crowd outside the Brazilian Embassy.
Zelaya said he was trying to establish contact with the interim government to negotiate a solution to the standoff that started when soldiers flew him out of the country June 28.
"As of now, we are beginning to seek dialogue," he said by telephone, giving few details.
He also summoned his countrymen to come to the capital for peaceful protests, and urged the army to avoid attacking his supporters.
"It is the moment of reconciliation," he said.
President Roberto Micheletti, who took power after Zelaya's ouster, has promised to step aside following an election scheduled for November.
The interim government was clearly caught off guard by the turn of events.
Only minutes before he appeared publicly at the embassy, officials said reports of his return were a lie.
US Secretary of State Hillary Rodham Clinton urged both sides to look for a peaceful solution to the crisis.


text:
\section{Introduction}\vspace{-2mm}
Generative Adversarial Networks (GANs)~\cite{NIPS2014_5ca3e9b1,karras2019style,karras2019analyzing} has demonstrated versatile image editing capability, especially by discovering the spontaneously learned interpretable directions that can manipulate corresponding image attributes~\cite{radford2015unsupervised,jahanian2019steerability,shen2019interpreting,goetschalckx2019ganalyze,voynov2020unsupervised}. Concretely, given a random latent code $w$, image editing can be achieved by pushing the latent vector along a specific semantic direction (\textit{e.g.}, age, gender):
\begin{equation}
I{'}=G(w+\alpha \times \mathop{\bold{n}}^{\rightarrow}),
\label{eq1}
\end{equation}
where $I{'}$ is the edited image, $G$ is the generator, $\alpha$ is a scaling factor, and $\bold{\mathop{n}\limits ^{\rightarrow}}$ is the interpretable direction.

As a consequence, recent attempts~\cite{abdal2019image2stylegan,abdal2019image2stylegan2,richardson2020encoding,zhu2020domain} aim to migrate this power to real image editing by inverting an image to the latent code $w$. There are two {keys} for this task, \romannumeral1) whether the inverted code can faithfully reconstruct the original input, and, \romannumeral2) whether the pre-acquired semantic directions can be successfully applied. However, existing methods seem to stuck in a paradox, as achieving one end will inevitably sacrifice the other. As shown in Fig.~\ref{fig:teaser}, I2S, I2S++, and pSp~\cite{abdal2019image2stylegan,abdal2019image2stylegan2,richardson2020encoding} concentrate only on obtaining faithful reconstruction, but the inverted codes show limited editability. In contrast, latent codes obtained from In-domain inversion~\cite{zhu2020domain} (Fig.~\ref{fig:teaserd}) are regularized in the semantically meaningful domain at the expense of fidelity. We argue that balancing these two factors solely based on a single image is extremely challenging, as there is no indicator to shed light on the editable domain in the latent space, preventing the optimization from obtaining a perfect balance between two factors.

In this paper, we resolve the above problem by introducing consecutive images, which can be either a video segment or the same person with different poses, to form a joint optimization process. The rational behind our alternative solution is that the \emph{continuity} brought by consecutive images can be used as an indicator to constrain the \emph{editability}. In particular, to ensure each of the inverted latent codes is editable, we jointly optimize multiple latent codes by enforcing each of them is semantically accessible from one of the other code with a simple linear combination. In addition, we further explore {the sequential continuity} for \emph{fidelity}, by injecting multi-source supervision that common regions of the reconstructed images should be consistent in all the consecutive images. We establish dense correspondences between input images, and then apply the obtained correspondences to warp each of the reconstructions to the neighbors for a consistent and coherence measurement.

To evaluate the proposed method, we construct a real video-based dataset RAVDESS-12, and another consecutive images dataset synthesized by manipulating attributes from the generated images of StyleGAN~\cite{karras2019analyzing}. Extensive experiments demonstrate the superior performance of our solution over existing methods in terms of editability and reconstruction fidelity. Furthermore, our method is capable to perform various applications, \textit{e.g.}, video-based GAN inversion, unsupervised semantic transfer, and image morphing.

In summary, our contributions are three-fold:
\begin{itemize}
    \item We propose an alternative GAN inversion method for consecutive images, and delve deep into the continuity of consecutive images for GAN inversion.
    \item We tailor two novel constraints, one is the mutually accessible constraint that formulates consecutive images inversion as a linear combination process in the latent space to ensure editability, and the inversion consistency constraint that works in the RGB space to guarantee reconstruction fidelity, by measuring reconstruction consistency across {images}.
    \item We demonstrate optimal performances in terms of editability and reconstruction fidelity, and we support various new applications like video-based GAN inversion and unsupervised semantic transfer.

\end{itemize}

\section{{Related Work}}\vspace{-2mm}

\textbf{Image Editing via Latent Space Exploration.}
Generative models show great potential in synthesizing versatile images by taking random latent codes as inputs. Recent works show that the latent space of a pre-trained GAN encodes rich semantic directions. Varying the latent codes with a specific direction can edit the image with the target attribute. In particular, Radford~\textit{et al}.~\cite{radford2015unsupervised} observe that there are directions in the latent space corresponding to adding smiles or glasses on the faces. Ganalyze~\textit{et al}.~\cite{goetschalckx2019ganalyze} explore the memorability direction in the latent space by a fixed assessor. Jahanian~\textit{et al}.~\cite{jahanian2019steerability} study the steerability of GANs to fit some image transformations. Shen~\textit{et al}.~\cite{shen2019interpreting} explore the semantic boundary in the latent space of the binary attributes. Voynov~\textit{et al}.~\cite{voynov2020unsupervised} discover the semantic directions hidden in the latent space by an unsupervised model-agnostic procedure. Varying the latent codes with such directions can manipulate the corresponding attributes of the output images. It is natural to transferring these techniques on real image editing. Before that, it is required to invert a real image back to the latent code.

\textbf{GAN Inversion.}
To realize real image editing, GAN inversion methods are proposed to inference a latent code of an input image based on the pre-trained GAN~\cite{perarnau2016invertible,zhu2016generative,abdal2019image2stylegan,creswell2018inverting,pan2020exploiting}. These methods can be categorized into two classes, optimization-based and encoder-based. The former individually optimizes the latent code for a specific image, with a concentration on pixel-wise reconstruction~\cite{abdal2019image2stylegan,abdal2019image2stylegan2,creswell2018inverting,NEURIPS2018_e0ae4561}. However, ensuring reconstruction fidelity cannot guarantee the output latent code is editable by the learned directions. On the other hand, encoder-based methods train a general encoder that maps real images to latent codes~\cite{zhu2020domain,richardson2020encoding}. Especially, In-Domain GAN inversion~\cite{zhu2020domain} combines the learned encoder with an optimization process to align the encoder with the semantic knowledge of the generator. However, existing methods do not resolve the problem of editable domain in the latent space, thus they cannot achieve a perfect balance between editability and fidelity. We aim to solve this problem from a novel view of considering multiple consecutive images.

\section{Method}\vspace{-2mm}

\begin{figure*}[t]
\centering
\includegraphics[width=0.99\linewidth]{figure/overview/final.pdf}
\vspace{-3mm}
\caption{The pipeline of the proposed consecutive images based GAN inversion. $G$ is a pretrained generator of StyleGAN and $F$ is a pretrained FlowNet for calculating the bidirectional optical flow. The upper part shows the Mutually Accessible Constraint. Given consecutive images as input, we enforce each of them is semantically accessible from one of the other codes with a simple linear combination. Both~$w_{1}$ and~$\bold{\mathop{n}\limits ^{\rightarrow}}_{k}$ are the optimization targets. The bottom shows the Inversion Consistency Constraint in the RGB space. Note here we only show the forward flow among $I_b$ and $I_{b+1}$ when calculating the $\mathcal{L}_{ICC}$ for simplicity, meanwhile, $\mathcal{L}_{C}$ and $\mathcal{L}_{P}$ are also omitted.}
\label{fig:overview}\vspace{-5mm}
\end{figure*}

\subsection{Overview}\vspace{-2mm}
\label{sec:igi}
The editability of the latent code and the fidelity of the reconstruction are the two vital factors that affect the performance of GAN inversion. To satisfy both sides, we exploit the continuity brought by consecutive images which depict a same subject with different variations. The pipeline of our approach is illustrated in Fig.~\ref{fig:overview}. Given a sequence of consecutive images as input, the proposed method aims to seek their optimal latent codes in the latent space, and then feed them into a pretrained and fixed generator for reconstruction. In particular, \romannumeral1) we define a linear combination mechanism among consecutive images, which would facilitate the editability of the latent codes via a joint optimization with semantic directions, and \romannumeral2) we establish a consistency constraint in the RGB space between the warped results of the reconstructed images and their corresponding originals, promoting the fidelity of the reconstruction. Note that we choose the generator of StyleGAN~\cite{karras2019style} as the pretrained one in our model.

\subsection{Consecutive Images Based GAN Inversion}\label{sec.method}\vspace{-2mm}
\textbf{Mutually Accessible Constraint.}
For each image in an input set, it may gradually change into the others, just as shown in Fig.~\ref{fig:overview}, the mouth opens progressively. Or it can vary to the others in a drastic way, such as the same person at very different poses. In either case, given the first image $I_1$, it can be intuitively assumed that the latent codes of the other images are linear combinations of that of the starting image along with a specific semantic direction $\bold{\mathop{n}\limits^{\rightarrow}}$ (\textit{e.g.}, expression, pose). Then, the latent code $w_{1+k}$ of the image $I_{1+k}$ can be formulated as follows:
\begin{equation}
w_{1+k} = w_{1}+\alpha_k \times \mathop{\bold{n}}^{\rightarrow}, \quad k=1,2,\cdots,T-1,
\label{eq4}
\end{equation}
where $T$ denotes the total number of images. However, the assumption is too strong since consecutive images are likely to vary from one to the others in different attributes. On the other hand, the specific semantic direction $\bold{\mathop{n}\limits^{\rightarrow}}$ can be predefined~\cite{shen2019interpreting,voynov2020unsupervised,shen2021closedform} , but the semantically equivalent input images are required. To cope with images with arbitrary semantic variations, we regard the direction as one of our optimization targets. Note that the scaling factors $\alpha_k$-s are also learnable, and therefore, we absorb them into the direction and reformulate Eq.(\ref{eq4}) as the proposed mutually accessible constraint, which is as follows:
\begin{equation}
w_{1+K} = w_{1}+ \sum_{k=1}^K \mathop{\bold{n}_k}^{\rightarrow}.
\label{eqxxx}
\end{equation}

In this way, we can figure out the latent codes of all the images via a joint optimization of $w_1$ and $\bold{\mathop{n}\limits^{\rightarrow}}_k$-s. Such a simple linear combination mechanism can promote the editability of the latent codes. The main reasons are that, \romannumeral1) each of the inverted latent codes can be regarded as an edited code with respect to one of the others, and \romannumeral2) if the images vary in a specific semantic direction, the scales of variations could be learned adaptively, and more importantly, \romannumeral3) it is able to deal with the variations of different attributes among consecutive images. Moreover, the learned $\bold{\mathop{n}\limits^{\rightarrow}}_k$-s have the potential to be transferred to other latent codes as predefined semantic directions.

\textbf{Inversion Consistency Constraint.}
Once we have the latent codes, we feed them into the generator $G(\cdot)$ to reconstruct consecutive images. For a certain base image $I_b$, its reconstruction $O_b$ with regard to the latent code $w_b$ is calculated by
\begin{equation}
O_b = G(w_b), \quad b=1,2,\cdots,T.
\label{eqxxxx}
\end{equation}

In order to ensure a fidelity of the reconstructions, we particularly consider an inversion consistency between common regions of the reconstructed images and the input consecutive images. Specifically, we tailor an inversion consistency constraint loss based on bidirectional flows in the RGB space. As shown in the bottom of Fig.~\ref{fig:overview}, first, the forward flows $f_{b \Rightarrow b+k}$ between base image $I_{b}$ and the other images $I_{b+k}$ can be calculated by a pretrained FlowNet2~\cite{ilg2017flownet} $F(\cdot)$, which is formulated as follows:
\begin{equation}
f_{b \Rightarrow b+k} = F(I_b,I_{b+k}),
\label{eq6}
\end{equation}
where $k\in\mathbb{Z}\cap[1-b,T-b]-\{0\}$. Then we warp $I_{b}$ with this flow to form the warped images $\hat{I}_{b+k}$, which can be presented as follows:
\begin{equation}
\hat{I}_{b+k} = warp(I_b,f_{b \Rightarrow b+k}).
\label{eq7}
\end{equation}
Also, we have the warped results $\hat{O}_{b+k}$ of the recovered images $O_b$, which is presented as follows:
\begin{equation}
\hat{O}_{b+k} = warp(O_b,f_{b \Rightarrow b+k}).
\label{eq8}
\end{equation}
Since consecutive images describe a same subject, an inherent relationship should be existed among the generated warpings $\{\hat{I}_{b+k}\}$ for each base image $I_b$. And this relationship should be transferred to the other warpings $\{\hat{O}_{b+k}\}$. In the same way, we can calculate the backward flow $f_{b+k \Rightarrow b}$ between $I_{b+k}$ and $I_{b}$, and the corresponding warpings $\{\hat{I}_b^{b+k}\}$, $\{\hat{O}_b^{b+k}\}$. By iterating over all the values of $b$ and $k$, we inject multi-source supervision from the warpings of input images to confine the reconstructions, and the proposed inversion consistency constraint loss ${\cal L}_{ICC}$ is given by
\begin{equation}
\mathcal{L}_{ICC} = \sum_{b}\sum_{k}(\|\hat{I}_{b}^{b+k} - \hat{O}_{b}^{b+k} \|_{2}+\|\hat{I}_{b+k}-\hat{O}_{b+k}\|_{2}),
\label{eq9}
\end{equation}
where $\|\cdot\|_{2}$ denotes a pixel-wise $L_2$ distance.

Moreover, we consider to maintain a pixel-wise consistency between the input images and its corresponding recoveries. A pixel-wise consistency loss ${\cal L}_C$ is therefore introduced in our objective, that is
\begin{equation}
\mathcal{L}_{C} = \sum_{m\in q\cup\{0\}} \|O_{1+m}-I_{1+m}\|_{2},
\label{eq10}
\end{equation}
To guarantee a fine visual perception of the recontructions, we also utilize a perceptual loss ${\cal L}_P$ which is formulated as follows:
\begin{equation}
\begin{aligned}
\mathcal{L}_{P}  = \frac{1}{4} \sum_{j=1}^4 \sum_{m\in q\cup\{0\}} \|\Phi_{V}^j(I_{1+m})-\Phi_{V}^j(O_{1+m}) \|_{2},
\label{eq11}
\end{aligned}
\end{equation}
where $\Phi_{V}^j(\cdot)$ denotes the $j$th layer of a pretrained VGG-16 network, and we follow Abdal \textit{et al}.~\cite{abdal2019image2stylegan} to select the features produced by the \texttt{conv1\_1}, \texttt{conv1\_2}, \texttt{conv3\_2} and \texttt{conv4\_2} layers of VGG-16 for modeling the loss.

Finally, the whole objective function $\cal L$ is defined as follows:
\begin{equation}
{\cal L}=\lambda_1{\cal L}_{ICC}+\lambda_2{\cal L}_{C}+\lambda_3{\cal L}_{P},
\end{equation}
where $\lambda$s denote the balance factors. Then the latent code $w_{1}$ and the directions $\bold{\mathop{n}\limits ^{\rightarrow}}_k$ can be optimized by
\begin{equation}
\label{eq12}
\{w^*_{1}, \bold{\mathop{n}\limits ^{\rightarrow}}_k^*\}= \mathop{\arg\min}_{\{w_{1},\bold{\mathop{n}\limits ^{\rightarrow}}_k\}} \lambda_{1} \mathcal{L}_{ICC}+ \lambda_{2} \mathcal{L}_{C}+ \lambda_{3} \mathcal{L}_{P}
\end{equation}

Note that we follow \cite{abdal2019image2stylegan} that initialize $w_{1}$ with the mean latent vector of $\mathcal{W+}$ space. And the direction $\bold{\mathop{n}\limits^{\rightarrow}}_k$-s are initially set to zero and updated during optimization.

\section{Experiments}\vspace{-2mm}

\begin{table*}[t]
   \caption{Comparisons with existing GAN inversion methods on image reconstruction with four metrics on two datasets. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.2cm}{
       \begin{tabular}{c|c|c|c|c|c|c|c|c}
           \hline
           \multirow{1}{*}{\diagbox{Methods}{Metrics}}       & \multicolumn{4}{c|}{RAVDESS-12 Dataset} & \multicolumn{4}{c}{Synthesized Dataset} \\
           \cline{2-9}
          &{NIQE$\downarrow$}   &{FID$\downarrow$}  &{LPIPS$\downarrow$}   &{MSE$\downarrow$($\times$e-3)}  &{NIQE$\downarrow$}  &{FID$\downarrow$}  &{LPIPS$\downarrow$}  &{MSE$\downarrow$($\times$e-3)}   \\
           \hline
           I2S~\cite{abdal2019image2stylegan}   &3.770    &16.284  &0.162   &8.791                      &3.374&48.909&0.271&35.011         \\
           pSp~\cite{richardson2020encoding}    &3.668    &29.701  &0.202   &22.337                     &3.910&84.355&0.391&46.244                \\
           InD~\cite{zhu2020domain}             &3.765    &18.135  &0.193   &9.963                      &3.152&42.773&0.352&44.645                  \\
           Ours    &\textbf{3.596}     &\textbf{13.136}   &\textbf{0.148}  &\textbf{5.972}             &\textbf{2.807}&\textbf{37.225}&\textbf{0.250}&\textbf{24.395}    \\
           \hline
           \hline

           I2S++~\cite{abdal2019image2stylegan2} &3.358    &0.320   &\textbf{0.003}   &0.174     &2.644         &2.967         &\textbf{0.014} &1.458             \\

           Ours++    &\textbf{3.352}     &\textbf{0.311}    &\textbf{0.003}  &\textbf{0.165}     &\textbf{2.597}&\textbf{2.897}&\textbf{0.014} &\textbf{1.432}    \\

           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:reconstruction}
      \vspace{-8mm}
\end{table*}

\subsection{Implementation Details}\vspace{-2mm}

We implement the proposed method in Pytorch on a PC with an Nvidia GeForce RTX 3090. We utilize the generator of StyleGAN~\cite{karras2019style} pre-trained on the FFHQ dataset~\cite{karras2019style} with the resolution of $1024\times 1024$. The latent codes and semantic directions are optimized using Adam optimizer~\cite{kingma2014adam}. We follow~\cite{abdal2019image2stylegan} that use 5000 gradient descent steps with a learning rate of 0.01, ${\beta}_1 = 0.9$, ${\beta}_2 = 0.999$, and ${\epsilon} = 1e^{-8}$. We empirically set the balancing weights in Eq.~\eqref{eq12} as $\lambda_{1}=1$, $\lambda_{2}=1$ and $\lambda_{3}=1$. And we set $T=5$ in Eq.~\eqref{eqxxxx}, which indicates 5 consecutive images are contained in each input sequence.

\subsection{Experimental Settings}\vspace{-2mm}

\textbf{Datasets. }
We first conduct our experiments on the RAVDESS dataset~\cite{livingstone2018ryerson} with real videos. The original RAVDESS dataset contains 2,452 videos with 24 subjects speaking and singing with various semantic expressions. We select 12 videos of them for evaluation, resulting in 1,454 frames, we name this dataset as RAVDESS-12 Dataset.
Since there are no ground truth latent codes for real images, we cannot subjectively evaluate the inverted code and its editability in the latent space. On the other hand, it is demonstrated that the learned semantic directions work very well in generated images. As a result, we construct a synthesized dataset, containing 1000 samples that were randomly generated by StyleGAN. For each sample, we vary its latent code with 5 random combinations of the $\alpha$ value (ranging from -3 to 3) and semantic direction (acquired from InterfaceGAN~\cite{shen2019interpreting}), producing 5000 images. We record the latent codes of the original samples, the corresponding editing specifications, and the edited latent codes for evaluating the editability. GAN inversion methods will invert the original samples and edit them to target attributes for comparisons.

\textbf{Competitors.}
We mainly compare with four GAN inversion methods: Image2StyleGAN (I2S)~\cite{abdal2019image2stylegan}, Image2StyleGAN++ (I2S++)~\cite{abdal2019image2stylegan2}, In-domain Inversion (InD)~\cite{zhu2020domain}, and pSp network~\cite{richardson2020encoding}. All the methods are inverted to the same $\mathcal{W+}$ latent space of StyleGAN, applying the same directions for editing. It is worth noting that I2S++ introduces the additional noise space $\mathcal{N}$ for small details recovery. A main problem is that, the inverted two latent codes in the $\mathcal{W+}$ and $\mathcal{N}$ spaces are highly coupled, but the learned semantic directions are optimized in $\mathcal{W+}$ only. Applying them changes the $\mathcal{W+}$ space latent code but leaves the noise vector unchanged, these unpaired vectors yield ``ghosting'' artifacts after editing (see Fig.~\ref{fig:semantic_exp}). As a result, we mainly use it for reconstruction comparisons, and we also extend our method to include the noise space, named as Ours++.

\input{inversion_quality}

\textbf{Evaluation Metrics. }
For the quantitative comparisons, we use four metrics, Naturalness Image Quality Evaluator (NIQE)~\cite{mittal2012making}, Fréchet Inception Distance (FID)~\cite{heusel2017gans}, Learned Perceptual Image Patch Similarity (LPIPS)~\cite{zhang2018unreasonable}, and pixel-wise Mean Square Error (MSE), for evaluating the reconstruction fidelity. Especially, FID computes the Wasserstein-2 distance between the distribution of input and output images. NIQE evaluates the quality perceived by a human, which is a completely blind assessment with no request for the GT image. Since there is no GT for the semantic editing task on the real RAVDESS-12 Dataset, we use FID and NIQE to evaluate real image editing results.

\subsection{Evaluation on Image Reconstruction}\vspace{-2mm}
\textbf{Quantitative Evaluation. }We first evaluate the reconstruction fidelity of the inverted codes. Quantitative comparisons are shown in Tab.~\ref{table:reconstruction}. We can see that our method significantly outperforms three editable GAN inversion methods (upper part) on both the real dataset and the synthesized one. Especially for the pixel-wise difference metric MSE, we largely improve the state-of-the-art by 31\%. This indicates that the proposed joint optimization successfully incorporates complementary information from neighboring images. Besides, by involving the noise space $\mathcal{N}$, I2S++ and Ours++ achieve the most faithful reconstruction compared with other methods. Thanks to the inter-image coherence, we further push the reconstruction record a bit.

\textbf{Qualitative Evaluation.} Qualitative comparisons are shown in Fig.~\ref{fig:reconstruction_exp}. We can see that the Image2StyleGAN cannot recover the image color correctly. Meanwhile, the pSp and InD cannot recover the finest facial details of the original images (see teeth in the first row). Compared with the above three methods, our method can reconstruct faithful appearance details. Unsurprisingly, I2S++ recovers the finest details among all competitors. That mainly because of their optimization performed in the $\mathcal{N}$ space encodes high-frequency information. We also depict our results optimized in $~\mathcal{N}$ space, and we preserve the original color better than I2S++~(see the second row).

\input{semantic_editing1}

\begin{table}[t]
   \caption{Quantitative evaluation on real image manipulation with two blind metrics on the RAVDESS-12 Dataset. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.1cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Metrics}        &{I2S~\cite{abdal2019image2stylegan}}      &{pSp~\cite{richardson2020encoding}}   &{InD~\cite{zhu2020domain}}   & Ours\\
           \hline
           NIQE  $\downarrow$         &   3.776         &5.242          &3.693                &\textbf{3.254}          \\
           FID  $\downarrow$          &   21.609        &30.128         &19.271               &\textbf{15.482}         \\
           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:semantic}
      \vspace{-8mm}
\end{table}

\input{semantic_editing2}

\subsection{Evaluation on Image Editing}\vspace{-2mm}

In this section, we evaluate our GAN inversion method on real image editing as well as synthesized images. We conduct two editing tasks based on the inverted latent codes, the first one is semantic manipulation and the second is image morphing.

\subsubsection{Semantic Manipulation}\vspace{-2mm}

Semantic manipulation aims to edit a real image by varying its inverted codes along with a specific semantic direction. We use five semantic directions (\textit{i.e.}, gender, pose, smile, eyeglasses, and age) acquired by~\cite{shen2019interpreting} in the experiment

\begin{table}[t]
   \caption{Quantitative evaluation on image manipulation with four metrics on the Synthesized Dataset. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.1cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Metrics}        &{I2S~\cite{abdal2019image2stylegan}}      &{pSp~\cite{richardson2020encoding}}   &{InD~\cite{zhu2020domain}}   & Ours\\
           \hline
           NIQE  $\downarrow$                      &   3.390         &3.917          &3.193      &\textbf{3.163}          \\
           FID  $\downarrow$                       &   35.894        &58.342         &48.867     &\textbf{33.872}         \\
           LPIPS  $\downarrow$                     &   0.399         &0.452          &0.424      &\textbf{0.347}         \\
           MSE$\downarrow$($\times$e-3)          &   89.671        &126.642           &101.563      &\textbf{70.224}         \\
           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:semantic_g}
      \vspace{-8mm}
\end{table}

\textbf{Qualitative Evaluation.} The qualitative comparisons on real data are shown in Fig.~\ref{fig:semantic_exp}. We can see that our manipulated faces have visually more plausible results than those of the competitors. In particular, the manipulated results gained by Image2StyleGAN~\cite{abdal2019image2stylegan} present noisy artifacts with pose changes and glasses are entangled with the age attributes, revealing that the edited latent codes are escaped from the editable domain. Similar situations can be found in I2S++~\cite{abdal2019image2stylegan2}. The manipulated faces based on pSp~\cite{richardson2020encoding} are almost unchanged. This is because pSp focuses on learning a direct mapping from the input to latent code, ignoring the editability. This problem is addressed by In-domain inversion~\cite{zhu2020domain}, but it also sacrifices the reconstruction quality. In contrast, thanks to the jointly considered inherent editability constraint between consecutive images, our inverted latent codes are more semantically editable, leading to more disentangled manipulations. On the other hand, the noise space optimization methods (right part of Fig.~\ref{fig:semantic_exp}) show obvious noise artifacts than the others with pose changes, this is because the pre-optimized noise vector is not suitable for the edited latent vector $w$. However, Ours++ can disentangle gender from glasses better than the inverted vector from I2S++.

To evaluate whether the obtained inversion can be edited by arbitrary directions, we force the editing direction different from the semantic changes contained the input sequence on the synthesized dataset. The qualitative comparisons on the synthesized data are shown in Fig.~\ref{fig:semantic_exp_g}. Similar to the evaluation on real data, I2S produces obvious artifacts, pSp fails to edit the results, and InD cannot preserve the original identity. Our manipulated results are more similar with the GTs, which indicates that our inverted codes are much closer with the GT latent codes and also inherit their editability.

\textbf{Quantitative Evaluation.} We present the quantitative comparisons in Tab.~\ref{table:semantic} and Tab.~\ref{table:semantic_g}. Our method achieves the best results on both the RAVDESS-12 Dataset and the Synthesized Dataset. In particular, for the blind metric NIQE, our edited results achieve 13.8\% improvement over the state-of-the-art method, which indicates that our editing is more visually plausible. Quantitative results on the Synthesized Dataset can evaluate whether the inverted codes are close enough with the GT code such that we can reuse their semantic information. From two non-blind metrics LPIPS and MSE, we can see that our edited results are very similar to the GTs. Thanks to our semantically accessible regularization in the latent space, our inverted latent codes show a strong editability compared with the competitors.

\input{image_morphing}

\subsubsection{Image Morphing}\vspace{-2mm}

Image morphing aims to fuse two images semantically by interpolating their latent codes. It is another way to evaluate whether the inverted codes indeed lie in the latent space and reuse the semantic knowledge. For the high-quality inverted codes, their interpolated results should also stay in the editable domain and the semantic varies continuously. Qualitative comparisons are shown in Fig.~\ref{fig:image_morphing}. We can see that the morphing results produced by Image2StyleGAN~\cite{abdal2019image2stylegan} have noticeable artifacts. Meanwhile, the results produced by pSp~\cite{richardson2020encoding} are unrealistic with the unnatural hairs. In contrast, our method presents high-quality results with a continuous morphing process. We also present the quantitative evaluation on the morphing task in Tab.~\ref{table:morphing} and Tab.~\ref{table:morphing_g}, we can see that our inversion results outperform the other inversion methods on both the real dataset and the synthesized one.

\begin{table}[t]
   \caption{Quantitative evaluation on image morphing with two blind metrics on the RAVDESS-12 Dataset. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.1cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Metrics}        &{I2S~\cite{abdal2019image2stylegan}}      &{pSp~\cite{richardson2020encoding}}   &{InD~\cite{zhu2020domain}}   & Ours\\

           \hline
           NIQE  $\downarrow$   &4.255        &5.350       &4.051    &\textbf{3.688}                    \\
           FID  $\downarrow$    &40.627        &38.474     &38.925           &\textbf{37.695}         \\

           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:morphing}
      \vspace{-7mm}
\end{table}

\begin{table}[t]
   \caption{{Quantitative evaluation on image morphing with four metrics on the Synthesized Dataset. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.1cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Metrics}        &{I2S~\cite{abdal2019image2stylegan}}      &{pSp~\cite{richardson2020encoding}}   &{InD~\cite{zhu2020domain}}   & Ours\\

           \hline
           NIQE  $\downarrow$   &3.389            &3.800            &3.212                 &\textbf{3.115}                    \\
           FID  $\downarrow$    &31.776        &30.192                          &{21.901}     &\textbf{18.621}    \\
           LPIPS  $\downarrow$  &0.472         &0.467            &{0.469}          &\textbf{0.402}         \\
           MSE$\downarrow$($\times$e-3)   &141.432         &121.834            &{125.674}          &\textbf{98.354}         \\

           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:morphing_g}
      \vspace{-8mm}
\end{table}

\input{semantic_transfer}

\subsection{Semantic Transfer}\vspace{-2mm}

As discussed in Sec.~\ref{sec.method}, both the latent code~$w$~and the semantic direction~$\bold{\mathop{n}\limits ^{\rightarrow}}$ can be unsupervisedly obtained after inversion. Besides the latent code, our acquired direction~$\bold{\mathop{n}\limits ^{\rightarrow}}$~represents the semantic changes of the input images. Given the input images as reference, we can transfer its semantic changes to the target faces.

The transfer results are shown in Fig.~\ref{fig:semantic_trans}. We can see that the semantic attributes of target faces are modified following the reference image set. Note that there are more than one attribute has been changed in the reference. For example, in the right example, the mouth and pose varies simultaneously but we can still capture those changes. This shows that our acquired direction is disentangled with the referenced and can be applied on other faces. Other than existing supervised~\cite{shen2019interpreting,shen2020interfacegan} or unsupervised~\cite{shen2021closedform,voynov2020unsupervised} learning of interpretable directions, this sheds light on a new exemplar-based learning of semantic direction.

\subsection{Ablation Study}\vspace{-2mm}

In this section, we analyze the efficacy of our two components: mutually accessible constraint (MAC) and inversion consistency constraint (ICC). Note that without these two components, our method equals to the Image2StyleGAN inversion and we set it as our baseline. By unplugging one of these two constraints, we yield two variants of ``$w/o$ MAC'' and ``$w/o$ ICC''. In this case, $\mathcal{L}_{ICC}$ is removed and all the latent codes are optimized simultaneously.

\begin{table}[t]
   \caption{Ablation study on image reconstruction with four metrics. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.05cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Variants}    &{NIQE$\downarrow$}   &{FID$\downarrow$} &{LPIPS$\downarrow$}   &MSE$\downarrow$($\times$e-3) \\

           \hline
           Baseline                         &3.770              &16.284            &0.162                     &8.791                                \\
           $w/o$ MAC     &3.685               &13.375            &0.151                       &8.065                             \\
           $w/o$ ICC      &3.765                 &14.791            &0.160                       &8.508                               \\
           Ours            &\textbf{3.596}         &\textbf{13.136}   &\textbf{0.148}            &\textbf{5.972}       \\
           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:ablation_inversion}
      \vspace{-7mm}
\end{table}

\begin{table}[t]
   \caption{Ablation study on semantic manipulation with two blind metrics. $\downarrow$ denotes the lower the better and the best results are marked in \textbf{bold}.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.1cm}{
       \begin{tabular}{c|c|c|c|c}
           \hline
           {Metrics}    &Baseline   &$w/o$ MAC    &$w/o$ ICC   &Ours     \\
           \hline
           {NIQE$\downarrow$}               &3.776    &3.659     &3.398    &\textbf{3.254}\\
           {FID$\downarrow$}                &21.609   &16.121.   &17.274   &\textbf{15.482}\\
           \hline
       \end{tabular}
       }
      \end{center}
      \label{table:ablation_semantic}
      \vspace{-9mm}
\end{table}

We perform ablation study experiment on image reconstruction and semantic manipulation tasks on the RAVDESS-12 Dataset. Quantitative comparisons of GAN inversion are shown in Tab.~\ref{table:ablation_inversion}. We can see that every variant outperforms the baseline on all metrics. This indicates both two components contribute to the GAN inversion performance. Meanwhile, variant~($w/o$ MAC) performs better than the variant~($w/o$ ICC), this indicates that the inversion consistency brought by consecutive images contributes more for the GAN inversion task. In Tab.~\ref{table:ablation_semantic} of semantic editing, we observe a different situation. We can see the variant~($w/o$ ICC) performs better than the variant~($w/o$ MAC), this reveals that mutually accessible constraint confines the inverted latent codes to stay in the editable domain. The above two evaluations show that our two constraints work very well following our design principles.

\input{ablation_sem}

We show the results of different variants in Fig.~\ref{fig:abl_semantic} by changing the ``age'' attribute. We can see that the baseline and variant~($w/o$ MAC) entangles with glasses, showing that concentrating only on reconstruction fidelity lacks editability of the inverted codes. In contrast, variant~($w/o$ ICC) and our final result can successfully modify the ``age'' attribute, revealing the strong regularization power of our designed mutually accessible constraint.

\section{Conclusion}\vspace{-2mm}
In this paper, we propose an alternative GAN inversion method for consecutive images, we formulate consecutive images inversion as a linear combination process in the latent space that ensures editability, and transfer the reconstruction consistency across inputs in the RGB space to guarantee reconstruction fidelity. The experiment results demonstrate the effectiveness in terms of editability and reconstruction fidelity. Besides, we also support various new applications like video-based GAN inversion and unsupervised semantic transfer.

\section{Supplemental Results}

\begin{table*}[t]
   \caption{Quantitative evaluations on image reconstruction and semantic editing tasks on the nonlinear dataset.}
       \vspace{-0.6cm}
       \begin{center}
       \setlength{\tabcolsep}{0.2cm}{

       \begin{tabular}{c|c|c|c|c|c|c|c|c}
           \hline
           \multirow{1}{*}{\diagbox{Metrics}{Methods}}        & \multicolumn{4}{c|}{Image Reconstruction} & \multicolumn{4}{c}{Nonlinear Semantic Edit} \\
           \cline{2-9}
                             &I2S   &pSp   &InD  &Ours  &I2S  &pSp  &InD  &Ours\\
           \hline
           NIQE  $\downarrow$                      &   3.632         &3.439         &3.254      &\textbf{2.997}                                        &   3.940         &3.974          &3.703      &\textbf{3.476}          \\

           FID  $\downarrow$                       &   40.098        &62.932        &77.692      &\textbf{33.692}
                                                   &   48.032        &64.224         &51.607     &\textbf{37.039}         \\

           LPIPS  $\downarrow$                     &   0.252         &0.323          &0.414      &\textbf{0.238}
                                                   &   0.489         &0.522          &0.471      &\textbf{0.403}         \\
           MSE$\downarrow$($\times$e-3)            &   30.767        &79.878        &83.294     &\textbf{25.192}
                                                   &   87.361        &118.285        &99.623     &\textbf{69.449}         \\
           \hline
       \end{tabular}
       }

      \end{center}
      \label{table}
\end{table*}

To further demonstrate our method is not restricted with the linear-based editing, we synthesize a new dataset under the nonlinear constraints by StyleFlow~\cite{abdal2021styleflow}. It consists of 1,000 sequences resulting in 5,000 images, with different semantic changes, such as pose, illumination, expression, eyeglasses, gender, and age. We conduct an inversion experiment on it and the results are shown in the Tab.~\ref{table}, Fig.~\ref{fig_rec}. Our method can obtain an accurate reconstruction on this nonlinear dataset. Besides, we also conduct nonlinear semantic editing task using the nonlinear StyleFlow~\cite{abdal2021styleflow}, as shown in the right parts of Tab.~\ref{table} and Fig.~\ref{fig_edit}. Our results are more similar with the GT (see the hair color of ``\emph{age+}'' in the right-bottom corner of Fig.~\ref{fig_edit}). These results prove that our method is not constrained by linear-editing assumption. That because our method is an \emph{optimization-based} GAN inversion method that does not rely on any attribute constraint for the input images, and the optimization is image-specific \emph{without training} a general network.

\begin{figure*}[t]
    \centering
    \subfloat[Original]{
    \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/original/11_1623415862}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/original/4_1623413696}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/original/7_1623414199}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/original/13_1623415718}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[I2S]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/i2s/11_1623415862}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/i2s/4_1623413696}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/i2s/7_1623414199}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/i2s/13_1623415718}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[pSp]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/psp/11_1623415862}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/psp/4_1623413696}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/psp/7_1623414199}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/psp/13_1623415718}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[InD]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ind_final/11_1623415862}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ind_final/4_1623413696}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ind_final/7_1623414199}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ind_final/13_1623415718}
     \end{minipage}
     }
   \hspace{-2.8mm}
    \subfloat[Ours]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ours/11_1623415862}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ours/4_1623413696}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ours/7_1623414199}
     \includegraphics[width=\linewidth]{rebuttal/latex/inversion/ours/13_1623415718}
     \end{minipage}
     }

\vspace{-3mm}\caption{Qualitative comparison on image reconstruction on the nonlinear dataset.}
\label{fig_rec}\vspace{-6mm}
\end{figure*}

\begin{figure*}[t]
    \centering
    \subfloat[GT]{
    \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/original/18_1623415623}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/original/18_1623415606}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/original/17_1623415282}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/original/17_1623415274}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[I2S]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/i2s/18_1623415623_gender_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/i2s/18_1623415623_gender_-1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/i2s/17_1623415279_age_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/i2s/17_1623415279_age_-1}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[pSp]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/psp/18_1623415623_gender_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/psp/18_1623415623_gender_-1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/psp/17_1623415279_age_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/psp/17_1623415279_age_-1}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[InD]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ind/18_1623415623_gender_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ind/18_1623415623_gender_-1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ind/17_1623415279_age_+1}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ind/17_1623415279_age_-1}
     \end{minipage}
     }
   \hspace{-2.8mm}
    \subfloat[Ours]{
     \begin{minipage}{0.18\linewidth}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ours/18_1623415623}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ours/18_1623415606}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ours/17_1623415282}
     \includegraphics[width=\linewidth]{rebuttal/latex/edit/ours/17_1623415274}
     \end{minipage}
     }
    \rotatebox[origin=c]{90}{  --  \hspace{10mm} Age \hspace{10mm} +  \hspace{10mm} + \hspace{10mm} Gender \hspace{10mm} --}
\vspace{-3mm}\caption{Qualitative comparison on semantic edit on the nonlinear dataset.}
\label{fig_edit}\vspace{-6mm}
\end{figure*}

We also give more qualitative comparison on image reconstitution on RAVDESS-12 Dataset and the linear-based Synthesized Dataset in Fig.~\ref{fig:reconstruction_exp}. We can see that our method can reconstruct the most faithful appearances by optimization latent code in the~$\mathcal{W+}$ space. Involving the~$\mathcal{N}$ space largely improves reconstruction quality and Ours++ can reconstruct the correct colors.

The qualitative comparison on image editing task on Synthesized dataset can be seen in Fig.~\ref{fig:semantic_exp_g}. Our edited results are more similar with the ground truths and show the pleasure appearances, which indicates that our inverted latent codes are close enough with the GT codes.
\begin{figure*}[t]
   \centering
    \subfloat[Original]{
    \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000067_2}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[I2S]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/i2s/000067_2}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[pSp]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/psp/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/psp/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/psp/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/psp/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/psp/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/psp/000067_2}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[InD]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/indomain/000067_2}
     \end{minipage}
     }
   \hspace{-2.8mm}
    \subfloat[Ours]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/ours/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/ours/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/ours/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/ours/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/ours/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/ours/000067_2}
     \end{minipage}
     }

   \hspace{-1.5mm}
   \rotatebox[origin=c]{90}{\rev{- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -}}
   \hspace{-3mm}
   \subfloat[I2S++]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/i2s++/000067_2}
     \end{minipage}
    }
   \hspace{-2.8mm}
   \subfloat[Ours++]{
     \begin{minipage}{0.14\linewidth}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-04-01-01-01-08_000124}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-06-01-01-02-19_000115}
     \includegraphics[width=\linewidth]{figure/inversion/truth/02-02-06-02-01-01-24_000066}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000013_2}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000010_2}
     \includegraphics[width=\linewidth]{figure/inversion/truth/000067_2}
     \end{minipage}
     }

\caption{Qualitative comparison on image reconstruction.}
\label{fig:reconstruction_exp}\vspace{-4mm}
\end{figure*}

 \begin{figure*}[t]
   \centering
    \hspace{-5mm}

    \rotatebox[origin=c]{90}{  \textbf{+}  \hspace{15mm} \textbf{Age}   \hspace{15mm} \textbf{--} \hspace{15mm}  \textbf{+}  \hspace{20mm} \textbf{Gender} \hspace{10mm}  \textbf{--}}
    \subfloat[GT]{
    \begin{minipage}{0.195\linewidth}
     \includegraphics[width=\linewidth]{figure/semantic_edit/truth/000083_2_gender_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/truth/000083_2_gender_+2}

     \includegraphics[width=\linewidth]{figure/semantic_edit/truth/000062_2_age_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/truth/000062_2_age_+2}
     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[I2S]{
     \begin{minipage}{0.195\linewidth}
     \includegraphics[width=\linewidth]{figure/semantic_edit/i2s/000083_2_gender_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/i2s/000083_2_gender_+2}

     \includegraphics[width=\linewidth]{figure/semantic_edit/i2s/000062_2_age_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/i2s/000062_2_age_+2}

     \end{minipage}
     }
   \hspace{-2.8mm}
    \subfloat[Ind]{
     \begin{minipage}{0.195\linewidth}
     \includegraphics[width=\linewidth]{figure/semantic_edit/indomain/000083_2_gender_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/indomain/000083_2_gender_+2}

     \includegraphics[width=\linewidth]{figure/semantic_edit/indomain/000062_2_age_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/indomain/000062_2_age_+2}

     \end{minipage}
     }
   \hspace{-2.8mm}
    \subfloat[pSp]{
     \begin{minipage}{0.195\linewidth}
     \includegraphics[width=\linewidth]{figure/semantic_edit/psp/000083_2_gender_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/psp/000083_2_gender_+2}

     \includegraphics[width=\linewidth]{figure/semantic_edit/psp/000062_2_age_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/psp/000062_2_age_+2}

     \end{minipage}
     }
   \hspace{-2.8mm}
   \subfloat[Ours]{
     \begin{minipage}{0.195\linewidth}
     \includegraphics[width=\linewidth]{figure/semantic_edit/ours/000083_2_gender_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/ours/000083_2_gender_+2}

     \includegraphics[width=\linewidth]{figure/semantic_edit/ours/000062_2_age_-2}
     \includegraphics[width=\linewidth]{figure/semantic_edit/ours/000062_2_age_+2}

     \end{minipage}
     }
   \hspace{-2.8mm}
\vspace{-3mm}\caption{Qualitative comparison on semantic editing.}
\label{fig:semantic_exp_g}\vspace{-3mm}
\end{figure*}

redpajama_set_name: RedPajamaArXiv


Our understanding of our craft has come a long way since 2015. I believe that it is now a good time to evolve product management into product thinking. A philosophy, mindset, a common knowledge. But primarily, something that can be acquired by anyone in the organisation.
The people in charge and at the forefront of this change will be product coaches. They will be the custodians of product thinking within an organisation, and tasked with getting people, teams, and organisations to become more product led through product thinking.
In summary; product management will become product thinking, product managers will become product coaches, and this will lead to organisations being more product led.


text:
Q: Getting data from the body in the authorization middleware I have a similar question as enter link description here: if I want to update an object (sent in the body of a PUT) which contains an id, how can I obtain this id in the middleware without sending this Id in the route data?
Example: Given the object:
myObject= new MyObject
{
   id = 1,
   string "blah blah blah"
}

a user who has update rights on MyObject with id = 3 and who has NO UPDATE RIGHTS on MyObject with id = 1 uses Postman to send a PUT with the route /api/values/3 and the body myObject. The authorization middleware will be fooled with the id = 3 and will let the user modify the wrong object.
Of course I could add in the updateMethod a check (if (myObjectId != myObject.id) ...) or I could remove the id from the MyObject, but both solutions seem too much effort for such an edge case. The most straightforward way would be to be able to validate the actual data in the middleware.
Any way to do it? Is there a better approach I have not considered? thanks!

A:
I want to update an object (sent in the body of a PUT) which contains an id, how can I obtain this id in the middleware without sending this Id in the route data?

Achieve the above requirement in middleware/authorization filter, reading the posting data MyObject from request body is easy, but it does not support us write the modified MyObject data back to request body.
If possible, you can try to achieve it in action filter, like below.
public void OnActionExecuting(ActionExecutingContext context)
{
    if (context.HttpContext.Request.Path.StartsWithSegments(new Microsoft.AspNetCore.Http.PathString("/api/values")))
    {
        var user = context.HttpContext.User;

        //code logic here

        //check if request from client contains id in route data
        var routeData = context.HttpContext.Request.RouteValues;

        //update value of MyObject Id property based on your requirement
        object myObject;

        if (context.ActionArguments.TryGetValue("myObject", out myObject))
        {
            MyObject _myobject = myObject as MyObject;

            _myobject.Id = 3;
        }
    }
}

redpajama_set_name: RedPajamaStackExchange


text:
using System;
using System.IO;
using System.Reflection;
using System.Collections.Generic;

namespace HandlebarsDotNet
{
	internal static class BuiltinHelpers
    {
        [Description("with")]
        public static void With(TextWriter output, HelperOptions options, dynamic context, params object[] arguments)
        {
            if (arguments.Length != 1)
            {
                throw new HandlebarsException("{{with}} helper must have exactly one argument");
            }
            options.Template(output, arguments[0]);
        }

        public static IEnumerable<KeyValuePair<string, HandlebarsHelper>> Helpers
        {
            get
            {
                return GetHelpers<HandlebarsHelper>();
            }
        }

        public static IEnumerable<KeyValuePair<string, HandlebarsBlockHelper>> BlockHelpers
        {
            get
            {
                return GetHelpers<HandlebarsBlockHelper>();
            }
        }

        private static IEnumerable<KeyValuePair<string, T>> GetHelpers<T>()
        {
            var builtInHelpersType = typeof(BuiltinHelpers);

			foreach (var method in builtInHelpersType.GetMethods(BindingFlags.DeclaredOnly | BindingFlags.Static | BindingFlags.Public))
            {
                Delegate possibleDelegate;
	            try
	            {
					possibleDelegate = Delegate.CreateDelegate(typeof(T), method);
	            }
	            catch
	            {
		            possibleDelegate = null;
	            }
	            if (possibleDelegate != null)
                {
                    yield return new KeyValuePair<string, T>(
                        ((DescriptionAttribute)Attribute.GetCustomAttribute(method, typeof(DescriptionAttribute))).Description,
                        (T)(object)possibleDelegate);
                }
            }
        }
    }
}


text:
Biografia
Ha ottenuto il suo primo podio continentale agli europei di Dortmund 2011, dove ha ottenuto il secondo posto nei 55 chilogrammi.

Grazie al secondo posto nel torneo europeo di qualificazione olimpica, ha guadagnato la possibilità di rappresentare la  ai Giochi olimpici estivi di , dove ha vinto la medaglia d'argento, dopo essere rimasto sconfitto contro il russo Džamal Otarsultanov nella finale del torneo dei 55 chilogrammi. Nei turni precedenti aveva battuto nell'ordine l'egiziano Ibrahim Farag nel turno di qualificazione, il bulgaro Radoslav Velikov agli ottavi, l'indiano Amit Kumar Dahiya ai quarti e il giapponese Shinichi Yumoto in semifinale.

All'europeo di casa di Tbilisi 2013 è stato estromesso dal tabellone principale della categoria 60 chilogrammi dal russo Opan Sat ed ha avuto la meglio nella finale per il terzo posto, disputata contro il turco Munir Recep Aktas.

L'anno successivo è divenuto campione continentale a Vantaa 2014 e vicecampione mondiale a Tashkent 2014, nella categoria nei 57 chilogrammi.

Ai mondiali di Las Vegas 2015 ha vinto l'oro nei 57 chilogrammi.

Ai Giochi olimpici di  ha trionfato nel torneo dei 57 chilogrammi, battendo in finale il giapponese Rei Higuchi.

Nonostante fosse iscritto, non ha potuto partecipare al torneo mondiale di qualificazione olimpico di Sofia, poiché risultato positivo al COVID-19: ha preso il suo posto il connazionale Beka Lomtadze, campione mondiale in carica nei 61 chilogrammi.

Palmarès
Giochi olimpici
: argento nella lotta libera 55 kg.
: oro nella lotta libera 57 kg.

Mondiali
Tashkent 2014: argento nei 57 kg
Las Vegas 2015: oro nei 57 kg
Parigi 2017: bronzo nei 61 kg.

Europei
Dortmund 2011: argento nei 55 kg
Tbilisi 2013: bronzo nei 60 kg
Vantaa 2014: oro nei 57 kg
Riga 2016: oro nei 61 kg
Novi Sad 2017: oro nei 61 kg
Kaspijsk 2018: bronzo nei 65 kg

Giochi europei
Minsk 2019: argento nei 65 kg

Altri progetti

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


Forest Pulse first used the Services of Barrie Wyatt from nVision Consulting to advise and support us in relation to the charity applying for PQASSO (Practical Quality Assurance System for Small Organisations). The charity commissioned Barrie following very positive feedback from a closely linked organisation with a very similar remit to ourselves who also used the services of nVision Consulting for the same purpose. I found Barrie to be very knowledgeable, and lead us through the process in a very positive way, enabling us recognise our own strengths and weaknesses rather than 'directing us'. He was empathetic, and although the world of disability was not an area he was familiar with he had the skills to give us the direction we needed.
Following our very positive relationship with Barrie when working on PQASSO, the trustees unanimously agreed to commission him on a second occasion, this time to produce an Organisational Strengths Review, funding by the Building Capabilities element of a Big Lottery Grant. Again Barrie was insightful and had a way of drawing our knowledge from us. We were very pleased with the finished report, and I feel it was a very true reflection of the operations of the charity which has proved to be a very successful tool.
I would certainly be happy to recommend Barrie to other organisations, and particularly to small charities such as ourselves as Barrie clearly understood the peculiarities of our position, and the different challenges we face both as a small organisation and as a charity with limited funding and resources.


In Elaine Aron's research on sensitivity, she found that highly sensitive people (HSP's) who had a more difficult childhood were more likely to be shy, anxious or depressed than a matched group of non HSP's.
Those HSP's who had a good childhood experience, were not more likely to be shy, anxious or depressed than a non HSP.
So you see, HSP's are not innately more likely to be shy, anxious or depressed!
An HSP with a difficult childhood just needs to take the time (and get support) to sort out and work through these issues arising from difficult early relationships. HSP's take in so much information that if the experience is difficult to digest, then they have a lot of unsorted material that weighs them down.
We HSP's really benefit from the clarity that results from taking the time to sort things out.
Want help to sort out your early experiences? Call me or email me.


musicians of their time, playing with artists such as Frank Sinatra, Sonny and Cher, the Beach Boys, the Monkees and even the Byrds. In addition to Campbell, other members included keyboardist Leon Russell, guitarist Tommy Tedesco and saxophonist Steve Douglas. And, not to be forgotten, the crew also included a talented young bass guitarist named Carol Kaye, who at 84 now lives in Rosamond.
Kaye was the bassist on Phil Spector and Brian Wilson productions and recorded guitar on Ritchie Valens' huge hit "La Bamba." The story of the Wrecking Crew was made into a documentary now available on Netflix.
* ... HAPPY JACK'S: My friend Gene Bonas, a proud Navy veteran, reminded me of an important event this weekend that is worthy of note. Said Bonas: "Stopped in at my favorite diner to have a delicious hamburger and spoke with the owners of Happy Jack's, Frances and Ruben Rosales. They said they're sponsoring another fundraiser for the Ronald McDonald House this coming Sunday, August 13, from 6 a.m. to 2 p.m. All money collected will go to Ronald McDonald House, and workers are donating their time, wages and tips. Last year Happy Jack's presented Ronald McDonald House $8,000. The goal this year is $10,000. So, for a delicious breakfast or lunch please support this worthwhile fundraiser."
* ... RIP HARRY: Taft College lost a friend and community role model last year with the death of Harry Wilson, who graduated from the school in 1942. Wilson, who died last September at the age of 96, served in both World War II and the Korean war and later worked at ARCO for 30 years before retiring. The college has now announced that Wilson left the college $1.6 million, which will help fund student support services along with starting the Opal Smith and Harry Wilson Endowment for Taft College.
* ... SPOTTED ON TWITTER: "Sometimes I'm good at eating healthy, but other times I have to change into bigger clothes in the middle of a meal."
* ... SPOTTED ON FACEBOOK: "Unfortunately, I don't have the type of rage that makes you want to clean house."
* ... NUESTRO MEXICO: Congratulations to the folks who own Nuestro Mexico, the intimate eatery on 21st Street that consistently serves up quality food at affordable prices. (They also happen to offer an awesome margarita). According to the Californian's Dorothy Mills-Gregg, the restaurant is looking to open a lounge at the corner of Calloway Drive and Hageman Road to serve the fast growing Northwest area.
* ... MEMORIES: Did you know that the Grapevine is actually a community located at the foot of what we now know as the 'Grapevine grade" headed to Los Angeles? According to the Facebook page Kern County of old, "The village and grade are named, not for the once-winding road known as the Grapevine that used to climb the steep mountain canyon, but for the canyon it passed through with its wild grapes that still grow along the original road. Its Spanish name was La Cañada de las Uvas, i.e. Grapevine ravine." Wikipedia added: "The top of the Grapevine is registered as California Historical Landmark #283, where Don Pedro Fages passed through in 1772 during his explorations through California."
years living on the street? Those are long odds for sure, but 35-year-old Tara Hunt is an example of why we should never give up. Hunt has lived a life not for the faint of heart: her mother was murdered, her father died of AIDS and she fell into a life of crime, drugs, deceit and denial. But she refused to quit and today, drug free, sober and determined, is working for county Supervisor Leticia Perez as she climbs the ladder of redemption. I will be chatting with Hunt today (Wednesday) on KERN NewsTalk 96.1 FM as she shares her story of failure, despair and later - success.
* ... KERN RIVER: Hats off to Clinica Sierra Vista which has stepped forward to take an aggressive approach to warning people about the dangers of swimming in the Kern River. Clinica, a non-profit that serves 150,000 people in Kern, Fresno and Inyo counties, has launched a multimedia campaign to keep people out of the river, whose raging waters have claimed at least a dozen lives this summer alone. The health-care organization has embarked on a blitz of radio PSAs, print ads, social media posts and perhaps the most high-profile outreach of all: five billboards – three in English, two in Spanish – that remind valley residents that there's only one sane way to approach the Kern River: by appreciating its beauty from the riverbank. The campaign isn't cheap; Clinica is spending $23,000 to get the word out. But the only cost Clinica CEO Steve Schilling is worried about is the human one. "Keeping our patients and our fellow neighbors healthy and safe is a public health obligation we take very seriously. This is just another way we can help make that happen."
* ... SPOTTED ON TWITTER: "Sorry I used a 3D printer to make an exact replica of your mom and we're dating now."
* ... THE MARK: The popular downtown restaurant The Mark, known for its live music and Tuesday night jazz sessions, has parted ways with manager Jeramy Brown. Meanwhile Meir Brown, owner of Cafe Med, has been brought in as interim general manager to build a new management team.
* ... OVERHEARD: A local hair stylist is telling a customer about why she left her old salon: "I found out the owner put up cameras inside the building, with audio, so she could listen in on all of our private conversations."
* ... TRASH: A big "thank you" to the woman who was spotted picking up trash on the Panorama Bluffs at 6:30 a.m. this week.
* ... GREEN ENERGY: Chris Padham contributed this to our morning: "An interesting read yesterday about energy costs during the summer months. Just paid my PGE bill. I used .06 kilo watt more than last years period. It cost an additional $35 for this year. I now understand why it's called green energy. The whole point is to take as much green from your wallet as they can."
* ... MEMORIES: The old American Jewelry Co. used to be located at the corner of 20th and Eye streets downtown, and outside was an elegant street clock. According to the Facebook page Kern County of Old (an endless source of fascinating historical footnotes) that clock is now in Illinois. One reader noted: "The American Jewelry Co., Bakersfield purchased a Joseph Mayer street clock in 1922. It remained in Bakersfield until Jasper San Filippo purchased it. It is now restored as part of the San Filippo collection in East Barrington, IL at 'Place de la Musique.'"


text:
Home › Post and Broadcast
International Resolution Benefits Wireless Mic Users
Joe Ciaudelli, Sennheiser's director of spectrum affairs, explains the process and significance of Resolution ITU-R 59—international legislation that could prove beneficial to wireless mic operations
Joe Ciaudelli ⋅ Nov 27, 2019
Joe Ciaudelli, Sennheiser's director of spectrum affairs, was recently a member of the U.S. State Department delegation to the international Radiocommunication Assembly. The delegation led an effort to enact Resolution ITU-R 59—international legislation that could prove beneficial to wireless mic operations. Here, Ciaudelli explains the process and significance of the resolution.
Every three to five years, the International Telecommunications Union (ITU)—the United Nations' specialized agency for information and communication technologies—brings together government delegations from around the globe for the World Radiocommunication Conference (WRC) and Radiocommunication Assembly (RA). The events are used to review emerging technologies, negotiate use of radio frequencies that enable wireless devices, harmonize frequency bands to specific applications, and codify binding international treaties. This facilitates cross-border, interference-free wireless communications among different services such as broadcasting, live events, mobile broadband, satellite, aviation, ship-to-shore, public safety, and new applications such as smart cars.
RA-19 and WRC-19 took place Oct. 21 through Nov. 22 in Sharm El-Sheikh, Egypt. A key issue at RA-19 was Resolution ITU-R 59. Calling for harmonization of radio frequency bands used for electronic newsgathering (ENG) and related content, Resolution ITU-R 59 is the fruit of a multiyear, collective effort by audio industry manufacturers, associations and thought leaders. With their support, Sennheiser has been active in the relevant ITU working parties during the past four years leading up to RA-19/WRC-19, contributing technical reports and recommendations that document the exponential growth of news and entertainment programming during this era when radio frequency spectrum available for wireless microphones has been constrained.
Within the U.S. ITU delegation, many practical solutions that were developed through industry collaboration with the Federal Communications Commission (FCC) were integrated into ITU reports and recommendations. These include allowing wireless microphones to operate in suitable alternate frequency bands and refining regulations that factor in real-world conditions, such as permitting wireless mic transmission on channels used for TV broadcast if the local terrain or building shielding makes such operation feasible. These efforts were reinforced in Europe through the ITU delegation from Germany and the Association of Professional Wireless Production Technology (APWPT).
Related: ITU Report Explores the Future of AI in Broadcast, May 31, 2019
Radiocommunication Assembly 2019 (RA-19), Sharm el-Sheikh, Egypt, 21-25 October 2019 Photo by ITU/ D. Woldu
ITU reports, recommendations, and resolutions become global standards. Their ratification is very difficult and time-consuming because they require full consensus at every stage. They must be reviewed and approved without objections, first domestically by the U.S. delegation comprising the FCC, the National Telecommunications and Information Administration (NTIA), the U.S. State Department, and private sector delegates from industries that often have conflicting interests other than those of the content creation industries.
A convincing point is the preeminent role that content creation plays in the economic vibrancy of the nation. The United States is the undisputed leader in TV/radio broadcasting, motion pictures, professional sports, performing arts, and other live cultural events. Copyrighted programming contributes well over $1 trillion to the U.S. economy, supports more than five million high-paying jobs and has the highest export-to-import ratio of any American-made product or service. Wireless microphones are indispensable tools that fuel this ecosystem.
In promoting Resolution ITU-R 59 in particular, the fact that electronic newsgathering is an essential service during emergency situations such as natural disasters was emphasized. At such times, a wireless mic is the front end of a broadcast chain that delivers life-critical information to the public. Regional and global harmonization is essential for cross-border, spontaneous newsworthy events and will benefit any production team that operates internationally.
If consensus for a proposed ITU contribution is gained within the U.S. delegation, it is submitted to the ITU for debate, possible refinement and (hopefully) adoption by all 200-plus members of this U.N. agency. International approval of a contribution is an extremely high hurdle because it is scrutinized through the ears and eyes of the gamut of global viewpoints.
Related: Can RF Mics Be Harmonized Globally?, by Clive Young, March 20, 2019
The U.S. proposal for Resolution ITU-R 59 was vigorously debated during RA-19, with points raised by many administrations, including the Russian Federation, Japan, Germany, United Kingdom and Australia. In the end, the proposal from the United States was ratified with only minor edits.
Resolution ITU 59-2 now instructs the ITU to post a publicly accessible database of links to the relevant regulatory information regarding wireless mic use from the U.N. member nations. It also calls for charts and other documentation that will aid in harmonization of permitted frequency bands among countries.
The next step is to encourage other countries to contribute their information for inclusion in the ITU database. The ITU only accepts information about a country if it comes directly from that country's administration. Wide international participation throughout the four-year study cycle leading up to the next RA and WRC in 2023 will be a key element to harmonization.
Toward that goal, an informational technical paper was presented through the U.S. State Department delegation to the conference of the Inter-American Telecommunications Commission (CITEL) that took place in August. CITEL is a regional coalition for North, Central and South America that forms ITU proposals with consensus from the Americas. It's noteworthy that Canada recently finalized a proceeding that largely harmonizes wireless microphone operation with the United States, including portions of the 941.5–960 MHz band. Also, an invitation to provide the information for the ITU database was distributed to the member nations attending WRC-19.
This is all part of a grassroots effort by a wide variety of stakeholders within the content creation industries. Milestones such as this resolution would not have been achieved without the backing of individuals and associations who included their names in industry petitions or, better yet, wrote to the FCC, the State Department or their elected officials directly.
Tags ⋅ electronic newsgathering ⋅ ENG ⋅ FCC ⋅ Federal Communications Commission ⋅ International Telecommunications Union ⋅ ITU-R 59 ⋅ Joe Ciaudelli ⋅ radio frequency bands ⋅ Radiocommunication Assembly ⋅ RF Spectrum ⋅ Sennheiser ⋅ United Nations ⋅ World Radiocommunication Conference
RF Venue Releases OS X Spectrum Analysis Software for RF Explorer®
RF Venue Releases Vantage OS X Spectrum – Scanning and Monitoring Software for RF Explorer®
RF Venue Releases RF Explorer® RackPRO™
RF Venue Introduces RF Optix™
RF Venue Bows RF Optix
MXL DRUM PA-5K Mic Kit, MM-131 Mic Mate Mobile
New Subjective Tests Challenge Claimed Benefits of High-Resolution Audio
Jands Introduces RF Venue Wireless Products to Australia


text:
John Boehner, Cigarette Aficionado, Goes Green
The former House Speaker and marijuana opponent has now become the weed industry's highest-profile political ally.
By David Paul Morris/Bloomberg/Getty Images.
Growing acceptance of marijuana on the right—in 2017, for the first time, a majority of Republicans supported legalization—has precipitated some elaborate ideological contortions as the party of Nancy Reagan and "just say no" evolves to stay in constituents' good graces. "This is a freedom issue," Rep. Dana Rohrabacher of California explained earlier this year, seizing on decriminalization as a libertarian cause and accusing Attorney General Jeff Sessions, who had rescinded Obama-era guidance to stop pursuing marijuana convictions, of having "forgotten the Constitution and the 10th Amendment." Senator Cory Gardner, whose home state of Colorado has made a killing taxing weed, denounced the effort as "trampling" on states' rights.
Even John Boehner, the famously orange former Speaker of the House and notorious chainsmoker, has decided to go green, announcing on Wednesday that he is joining the board of directors of Acreage Holdings, one of America's largest marijuana companies, to help them navigate state and federal policy in their quest for legalization. In an interview with Bloomberg, which called his career move "a watershed event," Boehner said that his position on weed—to which he was once "unalterably opposed"—had evolved over the years thanks to a "close friend" of his who used marijuana to ease his back pain, as well as his longstanding focus on federal-prison reform. "When you look at the number of people in our state and federal penitentiaries, who are there for possession of small amounts of cannabis, you begin to really scratch your head," Boehner, who says he has never smoked pot, told Bloomberg. "We have literally filled up our jails with people who are nonviolent and frankly do not belong there."
https://twitter.com/ComfortablySmug/status/984102765122252801
A second prominent Republican, former Massachusetts governor Bill Weld, announced that he is also joining Acreage. "Cannabis could be perceived as an exit drug, not a gateway drug" for opioids, he explained, noting that his libertarian view on the matter had led him to support marijuana legalization in Massachusetts as early as 1992. "I was a little bit ahead of the field there," he said.
It is unlikely that Boehner will be getting rich off of his new position, and he remains on the board of tobacco company Reynolds American Inc. (Neither Boehner nor Weld invested in Acreage, though Weld said he might.) The domestic marijuana industry was worth just $10 billion in 2017; a drop in the bucket compared to the $77.6 billion in revenue alone that the tobacco industry pulled in in 2016. Nevertheless, Boehner's announcement and full-throated endorsement of legalization caused significant buzz in the industry. "It is difficult to overstate the impact of this monumental event for the U.S. cannabis sector," Vahan Ajamian, an analyst at Beacon Securities Ltd., told Bloomberg.
Pot Products That We Don't Have Yet but Probably Need
Groovy Feast Cat Food
Genuine free-range mouse tenderloin, with marijuana added. Now you'll actually know why your cat is purring.
2018 Midterms
Paul Ryan Joins G.O.P. Retreat, Abandons Run in 2018
"No regrets," Ryan insists, as Democrats prepare to retake the House.
Last Night on Late Night
Why Trevor Noah Wants California to License Nonviolent Weed Offenders
"Think about it: they set up the weed business, they created the market—and right now, they're watching all these white guys get rich while they're doing time for being ahead of their time."
Exit Interview
"Idiots," "Anarchists," and "Assholes": Boehner Unloads on Republicans
The former House Speaker spends most of his time on the golf course, but he's still stewing over the state of the Republican party.


text:
Pazifischer Krieg bezeichnet mehrere Konflikte:

 Spanisch-Südamerikanischer Krieg (1864–1866)
 Salpeterkrieg (1879–1884)
 Pazifikkrieg (1937–1945) als Teil des Zweiten Weltkriegs


Holtzman, G. (2008) "In Township Tonight: South Africa's Black City Music and Theatre, David B. Coplan, University Of Chicago Press; 2nd edition. 2008, 455 pages, Paperback: ISBN-10: 0226115674, ISBN-13: 978-0226115672.", African Music: Journal of the International Library of African Music, 8(2), pp. 118-119. doi: https://doi.org/10.21504/amj.v8i2.1786.


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"convert decimal to a fraction"


Last month we told you about the Immersion MOTIV platform, which would allow developers to have much more control over the way phones use haptic feedback (the way your phone vibrates). Up to this point, Android haptic feedback has been a very cut and dry affair: it turns on, it turns off, sometimes it's on a little longer, and sometimes it isn't. Immersion, the SDK of which is now available, gives devs the tools to make this a much more varied experience.
Say you are playing a console video game like Uncharted 2 on the PS3. Your controller will vibrate in response to what is going on around you. However, it isn't a standard on/off vibration. There are subtle and profound differences between the feedback that it gives you. It will let you know the difference between major and minor impacts. If a train whizzes by you, the controller's motor may shake very differently than if a massive explosion goes off a few feet away from you. It is another tool to draw you into the game's world and enhance your overall experience.
Up to this point, Android games and apps couldn't utilize anything remotely close to that kind of experience. Immersion SDK will give devs the tools so that they soon will. From soft, feather-like vibrations to a gargantuan tremor that will emit a motorized buzz that can be heard in the next room, Immersion can make it possible.
Two versions of the platform exist: today's new SDK for developers and the MOTIV Integrator, aimed at OEM manufacturers, which has been available since last month.
While these kinds of announcements don't often mean a lot to us non-developer types, when we actually see the results in action, we are astounded at what the tools allowed the developers to create. I have a feeling this will be one of those cases and look forward to seeing the new Android experiences that this door will open.
Devs can check out the Immersion SDK here.
Could Sprint And T-Mobile Be Merging?


David (Karen's brother) has a YouTube Channel and has recently posted a fun spot promoting his new online radio station.
Karen's brother has just launched his web-based online streaming radio station, Exposure with David Sadof. It is a hand-crafted collection of rock, indie and alternative music. The station is curated and hosted by Karen's brother, a Houston DJ whose past shows on terrestrial radio include Explosions In The Glass Palace (KSHU-FM), Exposure (101 KLOL-FM) and Lunar Rotation (KTBZ-FM The Buzz).
It's my brother Richard's birthday today, born on the 25th anniversary of the premier performance of Ravel's Boléro and, like me, once had a full head of hair!
Just a quick post to say Happy Birthday to my niece Jeannot and her dad, my younger brother JP.
The first picture is from the first time I met Jeannot a number of years ago. It was her first birthday.
And here's a picture of my youngest brother, Jeannot's dad, at about the same age. He's posing with our father in the front yard of our house in Louisville KY.


Retro looking TYR athletic swimsuit, navy blue with neon orange flame-like pattern. Tagged size 34, according to TYR size chart that is a US 8. I would say that is accurate, shown here on a size 4 model. Good pre-owned condition. I wish this fit me because I love it, I hope you love it too.