Standish Wine Company is really just one man: Dan Standish, amongst the company of fine old-vine Barossa Shiraz grapes. The core of the Standish Wine Company resides in the fruit from his 1912-planted family owned vineyard in Vine Vale. The fruit goes into two cuvees: The Standish and The Relic. These wines are also joined by other outstanding vineyard fruit sources from throughout the North-West Barossa and Eden Valley, including fruit from the Fechner (Eden Valley), Laycock(Greenock), Schubert (Marananga), Nietschke, Helbig and Lehmann families.
2009 sees the release of all five cuvees for the first time, and they are definitive interpretations of old-vine Barossa Shiraz. Dan's hands-off approach is all about letting the fruit speak: hand-picked, destemmed only, open ferments with wild yeasts, some in concrete eggs, gently basket pressed to French oak (15% new) The wines spend a minimum of two years on elevage with no racking, and are unfined and unfiltered into bottle.


text:
Texans star J.J. Watt says he probably won't play this season if NFL requires face shields
The NFL is considering different types of protective masks but Watt said he's been there, done that and won't do it again.
Author: KHOU 11 Staff, John Birch / CBS Sports
Published: 2:21 PM EDT July 9, 2020
HOUSTON — As the NFL and players union work together on safety protocols to protect players this season, one option could keep Houston Texan J.J. Watt off the field.
Watt told Pro Football Talk that he might skip the season altogether if the NFL requires face shields.
The three-time Defensive Player of the Year wore one early in his career and says he won't ever do it again.
"My second year in the league I thought it'd be cool, I put a visor on my helmet," Watt said Wednesday. "I was like, 'It looks so cool, I wanna put a visor on.' I had it on for about three periods of practice and I said, 'Take this sucker off I'm gonna die out here.' … So now you're gonna put something around my mouth? You can keep that. If that comes into play, I don't think you're gonna see me on the field."
RELATED: Report: NFL, union working on opt-out plan for players
RELATED: NFL's stay-at-home order means no travel for training camp
The NFL has been testing different types of protective masks but hasn't said yet if players would be required to wear them.
The league is also thinking about scrapping the preseason, a move some players support, according to CBS Sports.
Watt hopes it will be safe to play at least some preseason games for the sake of players trying to earn a spot on the roster.
RELATED: Houston Texans announce 2019 preseason opponents
RELATED: Got Texans season tickets? Here are the options you have for going to games
"I think obviously as a vet, personally do I think that preseason games for somebody in my situation is absolutely necessary? I would say, I don't think so," Watt said. "But I think there are a whole bunch of people who need those opportunities and who live for those opportunities."
Either way, Watt said it's too early for final decisions, including whether to play this season.
The NFL is expected to give players the option of sitting out the season if they don't feel safe.
Read more on CBSSports.com.
RELATED: Texans' Bill O'Brien will kneel with players during national anthem
RELATED: Reports: NFL to play Black national anthem in Week 1
RELATED: 'There's a chance now' | Momentum builds to change Washington Redskins' name

redpajama_set_name: RedPajamaCommonCrawl


There are just 7 days remaining for people to pay remaining monies for the magical weekend event we have planned for Yeovil on Saturday 28th April.
We are running a 64 seater executive coach to the Stags last away game of the season, and have booked out a full hotel for our activities.
The hotel will be open pre-game for our exclusive use, and the coach is available for everyone to use to and from Huish Park, where we will then return to our hotel after the match, before a 20 minute walk into the bustling town centre.
The trip was sold just a couple of weeks after going on sale in 2017, which is testament to the great Chris Taylor, whose honour we will be toasting on the trip.
Anyone who paid deposits and is still to pay the £40 remaining balance, are urged to do so within the next 7 days, so we can send all monies to the bus company and the hotel to which we will be sleeping in.
You can contact us via the Facebook pages, and those that know the editor can PM him via facebook.


SAFE's shelter, The Starting Place, provides temporary refuge for adults and their children seeking safety from domestic violence, sexual assault and/or stalking. In 2015 SAFE's shelter provided safe refuge for 70 people. The Starting Place provides dormitory style hosing in a confidential location. The Starting Place is a first-come, first-served shelter and a screening must be completed before intake. Interested clients can complete a screening on the phone by calling our 24-hour help line at 503-397-6161 or 866-397-6161 and speaking with an advocate.
Gift Certificates for Haircuts, Clothing, etc.


This area is designed to provide students with theoretical, conceptual and practical skills that they will need as a consumer and practitioner of journalism, and to enable students to fulfill their responsibilities as citizens in a self-governing democracy. The area will prepare students with essential skills for today's digital media environment. Students will learn how to research, write, interview and produce news content, including written, audio and video components, for newspapers, magazines, television, radio and the Internet, including Web sites and social media.
Suggested minors: Biology, business studies, criminal justice, economics, labor studies, political science, psychology, public affairs and sociology.
COM 35200 - Mass Communication Law Cr. 3.
COM 41401 - Multimedia Design & Production Cr. 3.


I, <bride/groom>, take you, <bride/groom>, to be my <husband/wife>.
and honor you all the days of my life.
in sickness and in health, until death do us part.
and pledge thee my troth, so long as ye both shall live.
through the best and the worst of what is to come as long as we live.
<bride/groom>, Our miracle lies in the path we have chosen together.
is not to avoid changes, but to navigate them successfully.
Let us commit until death parts us.
a faithful and true <husband/wife>.
I, <bride/groom>, take thee, <bride/groom>, to be my wedded <husband/wife>.
your loving and loyal husband from now on.
pleasure and pain, until death parts us.
your loving and loyal wife from now on.
I pledge thee my faith.
to be my <husband/wife>, my friend, and my love.
<man/woman> to be your <husband/wife>, to live together in a holy marriage?
our constant faith and abiding love.
thereto I pledge thee my troth.
the procreation of children and their physical and spiritual nurture.
with my sacred vow before God.
Our miracle lies in the path we have chosen together.
the miracle of making each day work together.
I offer you my love and my support throughout all of our lives.
come illness, trouble, or poverty, I will stand with you.
Take this ring, as a sign of my love and commitment.
on which you rest, the companion of your life.
With you I will walk my path from this day forward.
I came here today to join my life to yours before this company.
I vow here that this love will be my only love.
I will make my home in your heart from this day forward.
according to the law of God, and the faith of Israel."
I <bride/groom>, take thee <bride/groom>, to be my <husband/wife>.
I <bride/groom>, take you <bride/groom>, to be my <husband/wife>.
to celebrate life with you forevermore.
to the future. I promise to honor your traditions as I honor you.
In the future, happy occasions will come as surely as the morning.
Difficult times will come as surely as the night.
When things go joyously, meditate according to the Buddhist tradition.
When things go badly, meditate.
Meditation in the manner of the Compassionate Buddha will guide your life.
To say the words 'love and compassion' is easy.
upon patience and perseverance is not easy.


text:
Q: Delete rows from pd.DataFrame based on a defenition given in a second DF I have two dataframes x and y. DF x contains two grouping variables S and A, and a value variable V. From this DF I want to delete rows for group pairs (S,A) defined in y. DF y contains the variable S and a variable  A_D which together define which (S,A) pair needs to be deleted from x.
However, each element in y['A_D'], (could) contains a string of elements from A, separated by commas. Each of these separate elements should get deleted form x (for the particular element in S).  Moreover, if y['A_D'] contains the element all for a particular element in S it should delete the entire S-group in x.
I found a solution which does what I want but my question is, is there a simpler or more Pythonic way to solve this problem?
import pandas as pd
import numpy as np

# Define x
x = pd.DataFrame({'S': np.repeat(['s1','s2','s3'], 5),
                  'A': [j for i in range(3) for j in ['a','b','c','d','e']],
                  'V': np.random.uniform(size=15) })

# Define y. Which (S,A) pairs should be deleted from x. For 's1' all rows should be deleted.
# For 's2' 'a' and 'd' rows should be deleted and for 's3' the 'c' row should be deleted.
y = pd.DataFrame({'S':['s1','s2','s3'],
                  'A_D':['all','a, d', 'c']})

# My solution:
# expand y to a new DF z. Comma separated elements in 'A_D' become separate elements. Also strip whitespace.
z = []
for i, r in y.iterrows():
    z.append(pd.DataFrame({'S'  : r[0],
                           'A_D': [u.strip() for u in str(r[1]).split(',')]}))
z = pd.concat(z)

# first delete S-groups defined by `all`
x_d = x.merge(z[z['A_D']=='all'],how='left')
x_d = x_d[x_d['A_D']!='all'].drop(columns= 'A_D')

# then drop (S,A) pairs.
x_d = x_d.merge(z[z['A_D']!='all'],how='left', left_on = ['S','A'], right_on = ['S', 'A_D'])
x_d = x_d[pd.isna(x_d['A_D'])].drop(columns= 'A_D').reset_index(drop=True)

# The required result:
print(x_d)

For clarity the objects look like this:
x
Out[1]:
     S  A         V
0   s1  a  0.758516
1   s1  b  0.522200
2   s1  c  0.190511
3   s1  d  0.544617
4   s1  e  0.480378
5   s2  a  0.191016
6   s2  b  0.714625
7   s2  c  0.852788
8   s2  d  0.142410
9   s2  e  0.909382
10  s3  a  0.895031
11  s3  b  0.153444
12  s3  c  0.751675
13  s3  d  0.227501
14  s3  e  0.586467

y
Out[2]:
    S   A_D
0  s1   all
1  s2  a, d
2  s3     c

z
Out[3]:
    S  A_D
0  s1  all
0  s2    a
1  s2    d
0  s3    c

x_d
Out[4]:
    S  A         V
0  s2  b  0.714625
1  s2  c  0.852788
2  s2  e  0.909382
3  s3  a  0.895031
4  s3  b  0.153444
5  s3  d  0.227501
6  s3  e  0.586467

A: Here's my solution, it's at least shorter :)
def filter_group(group, filter_rule):
    return (None if filter_rule == 'all'
            else group[~group["A"].isin(filter_rule.replace(' ', '').split(','))])

x_d = pd.concat(filter_group(x.groupby('S').get_group(grp), filter_rule)
                for grp, filter_rule in dict(zip(y["S"], y["A_D"])).items())

A: x
###
     S  A         V
0   s1  a  0.490194
1   s1  b  0.875381
2   s1  c  0.384808
3   s1  d  0.063960
4   s1  e  0.003159
5   s2  a  0.188624
6   s2  b  0.400527
7   s2  c  0.137458
8   s2  d  0.162291
9   s2  e  0.337899
10  s3  a  0.101296
11  s3  b  0.464031
12  s3  c  0.407629
13  s3  d  0.222498
14  s3  e  0.802472

whatever separated with ', ', ' ,', ','
y
###
    S      A_D
0  s1      all
1  s2  a, d ,c
2  s3      c,d

y['A_D'] = y['A_D'].replace('all', ', '.join(x['A'].unique()))
y = y.assign(A_D=y['A_D'].str.split(',')).explode('A_D')
y['A_D'] = y['A_D'].str.strip()
output = x[~x.set_index(['S','A']).index.isin(y.set_index(['S','A_D']).index)].reset_index(drop=True)
output
###
    S  A         V
0  s2  b  0.400527
1  s2  e  0.337899
2  s3  a  0.101296
3  s3  b  0.464031
4  s3  e  0.802472

redpajama_set_name: RedPajamaStackExchange


Welcome to the Magno-Humphries Inc Conference Room!
This space is available to rent 9 AM to 5 PM Monday through Friday. The room can hold up to 12 people in the room. It can comfortably sit 8 to 10 people at the table.
Tigard Chamber members have 2 to 8 hours of free use depending on their level of membership.
No refunds will be given for less than 48 hours notice of cancellation from reservation time.
Use the scheduler below to reserve the conference room.
Click on the amount of time you would like to use the space.
Click on the date you would like to reserve.
Click on the time you would like your reservation to start and hit confirm.
If you need more time, go through steps 1 through 4 again.


He's not a rough diamond—a pearl-containing oyster of a rustic: he's a fierce, pitiless, wolfish man.
Skype has established its website-centered consumer beta towards the world, right after launching it extensively within the United states and You.K.
Wuthering Quotes is a new digital retelling of Wuthering Heights. This website presents the popular classic in a variety of short, visual quotes.
This project has been created by Rosie Maynard, a third year Publishing and English Literature Student at Bath Spa University.


I've mentioned before that, before we moved to Missouri, my dad came to Missouri with my husband for a month. They were here to put up our equipment building. The equipment building is steel, and I think I've mentioned before that steel buildings are very popular out here.
As you drive around and look at the numerous farms, you will also see numerous of these buildings, including PreFab Buildings. This is because there are many advantages to metal buildings, such as durability.
Wanna come build one for me. Johnnys shed is so fulled I can't store any of my stuff in it.


"While the Kingdom of Saudi Arabia (KSA) takes seriously the threat of terrorism within Saudi Arabia, it has been an ongoing challenge to persuade Saudi officials to treat terrorist financing emanating from Saudi Arabia as a strategic priority," the memo states.
Progress was cited by the Saudi government (due in part to "intense focus" by the United States) to proactively investigate and detain financiers of concern. "Still, donors in Saudi Arabia constitute the most significant source of funding to Sunni terrorist groups worldwide."
"In contrast to its increasingly aggressive efforts to disrupt al Qaeda's access to funding from Saudi sources, Riyadh has taken only limited action to disrupt fundraising for the UN 1267-listed Taliban and LeT-groups that are also aligned with al Qaeda and focused on undermining stability in Afghanistan and Pakistan."
"More needs to be done since Saudi Arabia remains a critical financial support base for al Qaeda, the Taliban, LeT and other terrorist groups," according to the memo signed by Clinton.
The memo states al Qaeda and other militants groups "continue to exploit Kuwait both as a source of funds and as a key transit point."
The UAE's growing global financial power, coupled with weak regulatory oversight, "makes it vulnerable to abuse by terrorist financiers and facilitation networks."
"Appropriate legal measures, including effective prosecution" to hold terrorist financiers publicly accountable, and sending a strong message of deterrence to others.


At the Berkshire Business Growth Hub, we offer impartial business advice, specialist support programmes and a range of events for businesses in Berkshire—all free of charge. Whether you are an established business with ambitious growth plans, a new start-up, a scale-up company or an entrepreneur looking to turn your ideas into a business, we can help.
Check out our search tool for some initial business advice. The easy to use tool will help you instantly access information on a range of topics, including starting a business, finance and funding, business planning, marketing and exporting.


This paper assesses the impact of the severe acute respiratory syndrome (SARS) on the stock market of China. Our results indicate that the Chinese stock market reacts rapidly to the SARS epidemic. We provide strong empirical evidence that the epidemic has an immediate impact on the pharmaceutical and tourism industries. In particular, pharmaceutical companies are benefited from the outbreak of SARS, while the tourism sector is adversely affected. Our results imply the existence of a profitable trading rule during an epidemic.
This paper empirically models China's stock prices using conventional fundamentals: corporate earnings, risk-free interest rate, and a proxy for equity risk premium. It uses the estimated long-run stock price misalignments to date booms and busts, and analyses equity market reforms and excess liquidity as potential drivers of these stock price misalignments. Our results show that China's equity prices can be reasonable well modelled using fundamentals, but that various booms and busts can be identified. Policy actions, either taking the form of deposit rate changes, equity market reforms or excess liquidity, seem to have significantly contributed to these misalignments.
This study examines the degree of price-integration between aggregate equity market indices of Hong Kong, the Chinese Shanghai and Shenzhen A and B share markets, and the international Brent crude oil price. The application of Vector Autoregressive methods reveals that the regions markets are generally price-segmented with the prominent exception of Shanghai B market which is price-integrated with the domestic A share markets in both Shanghai and Shenzhen. The evidence would suggest that Chinese markets are more heavily influenced by domestic events in the long term than external influences.
This paper examines the impact of renminbi revaluation on foreign firm valuation and, by implication, firm prospects. To deal with the potential endogeneity of exchange rate movements, we consider not just official announcements of exchange rate policy but also 27 instances of market-perceived changes in China's currency policy driven by domestic or foreign political pressure. Using information on 12,300 firms in 44 countries, we find that stock returns increased with renminbi revaluation expectations. This reaction was related as much to improved market sentiment as to specific trade channels, however. In terms of trade channels, we find that expectations of renminbi appreciation reduce the relative stock returns of firms providing components and raw materials to China as inputs for the country's exports. There is also some evidence that expectations of renminbi appreciation reduce the stock prices of financiallyconstrained firms.


"Thank you for choosing Weebly to make your on-line purchases and payments for Walnut Hills Athletics. Weebly is being retired this month, so Walnut Hills Athletics is going to Local Events as the new on-line vendor. Please use the links below."


text:
\section{Introduction}
Object detection with transformers (DETR)~\cite{carion2020end} is a recent framework that views object detection as a direct prediction problem via a transformer encoder-decoder~\cite{vaswani2017attention}. Without hand-designed sample selection~\cite{zhang2020bridging} and non-maximum suppression, DETR reaches a competitive performance with Faster R-CNN~\cite{ren2015faster}. However, DETR comes with training and optimization challenges, which needs large-scale training data and an extreme long training schedule. As shown in \figref{mAP} and \secref{voc}, we find that DETR performs poorly in PASCAL VOC ~\cite{everingham2010pascal}, which has insufficient training data and fewer instances than COCO~\cite{lin2014microsoft}.
\begin{figure}[t]
\centering
  \includegraphics[width=1.0\linewidth]{figures/vocAP50.pdf}
  \caption{The VOC learning curves ($\textrm{AP}_{50}$) of DETR and UP-DETR with ResNet-50 backbone. Here, they are trained on \texttt{trainval07+12} and evaluated on \texttt{test2007}. We plot the short and long training schedules, and the learning rate is reduced at 100 and 200 epochs, respectively.}
  \label{mAP}
\end{figure}

With well-designed pretext tasks, unsupervised pre-training models achieve remarkable progress in both natural language processing (\eg GPT~\cite{radford2018improving,radford2019language} and BERT~\cite{devlin2018bert}) and computer vision (\eg MoCo~\cite{he2020momentum,chen2020improved} and SwAV~\cite{caron2020unsupervised}). In DETR, the CNN backbone (ResNet-50~\cite{he2016deep} with $\sim$23.2M parameters) has been pre-trained to extract a good visual representation, but the transformer module with $\sim$18.0M parameters has not been pre-trained. More importantly, although unsupervised visual representation learning (\eg contrastive learning) attracts much attention in recent studies~\cite{he2020momentum,chen2020simple,grill2020bootstrap,cao2020parametric,caron2018deep,asano2019self}, existing pretext tasks can not directly apply to pre-train the transformers of DETR. The main reason is that DETR mainly focuses on spatial localization learning instead of image instance-based~\cite{he2020momentum,chen2020simple,grill2020bootstrap} or cluster-based~\cite{cao2020parametric,caron2018deep,asano2019self} contrastive learning.

Inspired by the great success of unsupervised pre-training in natural language processing~\cite{devlin2018bert}, we aim to unsupervisedly pre-train the transformers of DETR on a large-scale dataset (\eg ImageNet), and treat object detection as the downstream task.
The motivation is intuitive, but existing pretext tasks seem to be impractical to pre-train the transformers of DETR.
To overcome this problem, we propose \textbf{Unsupervised Pre-training DETR (UP-DETR)} with a novel unsupervised pretext task named \textbf{random query patch detection} to pre-train the detector without any human annotations --- we \textit{randomly} crop multiple \textit{query patches} from the given image, and pre-train the transformers for \textit{detection} to predict bounding boxes of these query patches in the given image. During the pre-training procedure, we address two critical issues as follows:
\begin{itemize}

\item[(1)] Multi-task learning: Object detection is the coupling of object classification and localization. To avoid query patch detection destroying the classification features, we introduce \textbf{frozen pre-training backbone} and \textbf{patch feature reconstruction} to preserve the feature discrimination of transformers.
\item[(2)] Multi-query localization: Different object queries focus on different position areas and box sizes. To illustrate this property, we propose a simple single-query pre-training and extend it to a multi-query version. For multi-query patches, we design \textbf{object query shuffle} and \textbf{attention mask} to solve the assignment problems between query patches and object queries.
\end{itemize}

In our experiments, UP-DETR performs better than DETR on PASCAL VOC~\cite{everingham2010pascal} and COCO~\cite{lin2014microsoft} object detection with faster convergence and better average precision. Besides, UP-DETR also transfers well with state-of-the-art performance on one-shot detection and panoptic segmentation. In ablations, we find that \textbf{freezing the pre-training CNN backbone} is the most important procedure to preserve the feature discrimination during the pre-training.

\section{Related Work}
\subsection{Object Detection}
Most object detection methods mainly differ in positive and negative sample assignment. Two-stage detectors~\cite{ren2015faster,cai2018cascade} and a part of one-stage detectors~\cite{lin2017focal,liu2016ssd} construct positive and negative samples by hand-crafted multi-scale anchors with the IoU threshold and model confidence. Anchor-free one-stage detectors~\cite{tian2019fcos,zhou2019objects,law2018cornernet} assign positive and negative samples to feature maps by a grid of object centers. Zhang \etal~\cite{zhang2020bridging} demonstrate that the performance gap between them is due to the selection of positive and negative training samples. DETR~\cite{carion2020end} is a recent object detection framework that is conceptually simpler without hand-crafted process by direct set prediction~\cite{stewart2016end}, which assigns the positive and negative samples automatically.

Apart from the positive and negative sample selection problem, the trade-off between classification and localization is also intractable for object detection. Zhang \etal~\cite{zhang2019towards} demonstrate that there is a domain misalignment between classification and localization. Wu \etal~\cite{wu2020rethinking} and Song \etal~\cite{song2020revisiting} design two head structures for classification and localization. They point out that these two tasks may have opposite preferences. For our pre-training model, it maintains shared feature for classification and localization. Therefore, it is essential to take a well trade-off between these two tasks.

\subsection{Unsupervised Pre-training}
Unsupervised pre-training models always follow two steps: pre-training on a large-scale dataset with the pretext task and fine-tuning the parameters on downstream tasks. For unsupervised pre-training, the pretext task is always invented, and we are interested in the learned intermediate representation rather than the final performance of the pretext task.

To perform unsupervised pre-training, there are various of well-designed pretext tasks. For natural language processing, utilizing time sequence relationship between discrete tokens, masked language model~\cite{devlin2018bert}, permutation language model~\cite{yang2019xlnet} and auto regressive model~\cite{radford2018improving,radford2019language} are proposed to pre-train transformers~\cite{vaswani2017attention} for language representation.
For computer vision, unsupervised pre-training models also achieve remarkable progress recently for visual representation learning, which outperform the supervised learning counterpart in downstream tasks. Instance-based discrimination tasks~\cite{ye2019unsupervised,wu2018unsupervised} and clustering-based tasks~\cite{caron2018deep} are two typical pretext tasks in recent studies. Instance-based discrimination tasks vary mainly on maintaining different sizes of negative samples~\cite{he2020momentum,chen2020simple,grill2020bootstrap} with non-parametric contrastive learning~\cite{hadsell2006dimensionality}. Moreover, instance discrimination can also be performed as parametric instance classification~\cite{cao2020parametric}. Clustering-based tasks vary on offline~\cite{caron2018deep,asano2019self} or online clustering procedures~\cite{caron2020unsupervised}. UP-DETR is a novel pretext task, which aims to pre-train transformers based on the DETR architecture for object detection.

\section{UP-DETR}

\begin{figure*}[ht]
\centering
\begin{subfigure}{0.43\linewidth}
\includegraphics[width=1.0\linewidth]{figures/single.pdf}
\caption{single-query patch (N=3, M=1)}
\label{fig:single}
\end{subfigure}
\begin{subfigure}{0.56\linewidth}
\includegraphics[width=1\linewidth]{figures/multi.pdf}
\caption{multi-query patch (N=6, M=2)}
\label{fig:multi}
\end{subfigure}
  \caption{The pre-training procedure of UP-DETR by random query patch detection. (a) There is only a single-query patch which we add to all object queries. (b) For multi-query patches, we add each query patch to $N/M$ object queries with object query shuffle and attention mask. CNN is not drawn in the decoder of (b) for neatness.}
  \label{main}
\end{figure*}

The proposed UP-DETR contains pre-training and fine-tuning procedures: (a) the transformers are unsupervisedly \textit{pre-trained} on a large-scale dataset without any human annotations; (b) the entire model is \textit{fine-tuned} with labeled data which is same as the original DETR~\cite{carion2020end} on the downstream tasks. In this section, we mainly describe how to pre-train the transformer encoder and decoder with random query patch detection.

As shown in \figref{main}, the main idea of random query patch detection is simple but effective. Firstly, a frozen CNN backbone is used to extract a visual representation with the feature map $f \in \mathbb{R}^{C \times H \times W}$ of an input image, where $C$ is the channel dimension and $H \times W$ is the feature map size. Then, the feature map is added with positional encodings and passed to the multi-layer transformer encoder in DETR. For the random cropped query patch, the CNN backbone with global average pooling (GAP) extracts the patch feature $p \in \mathbb{R}^{C}$, which is flatten and supplemented with object queries $q \in \mathbb{R}^{C}$ before passing it into a transformer decoder.
Noting that the \textit{query patch} refers to the cropped patch from the original image but \textit{object query} refers to position embeddings, which are fed to the decoder.
The CNN parameters are shared in the whole model.

During the pre-training procedure, the decoder predicts the bounding boxes corresponding to the position of random query patches in the input image. Assuming that there are $M$ query patches by random cropping, the model infers a prediction fixed-set $\hat{y} = \{\hat{y_i}\}^N_{i=1} $ corresponding to $N$ object queries ($N>M$). For better understanding, we will describe the training details of single-query patch ($M=1$) in \secref{onepatch}, and extend it to multi-query patches ($M>1$) with object query shuffle and attention mask in \secref{multi-query}.

\subsection{Single-Query Patch}
\label{onepatch}
DETR learns different spatial specialization for each object query~\cite{carion2020end}, which indicates that different object queries focus on different position areas and box sizes. As we randomly crop the patch from the image, there is no any priors about the position areas and box sizes of the query patch. To preserve the different spatial specialization, we explicitly specify single-query patch ($M=1$) to all object queries ($N=3$) as shown in \figref{fig:single}.

During the pre-training procedure, the patch feature $p$ is added to each different object query $q$, and the decoder generates $N$ pairs of predictions $\hat{y} = \{\hat{y_i}\}^N_{i=1} $ to detect the bounding box of query patch in the input image. Following DETR~\cite{carion2020end}, we compute the same match cost between the prediction $\hat{y}_{\hat{\sigma}(i)}$ and the ground-truth $y_i$ using \textit{Hungarian} algorithm~\cite{stewart2016end}, where $\hat{\sigma}(i)$ is the index of $y_i$ computed by the optimal bipartite matching.

For the loss calculation, the predicted result $\hat{y}_i = (\hat{c}_i \in \mathbb{R}^2 ,\hat{b}_i \in \mathbb{R}^4 ,\hat{p}_i \in \mathbb{R}^{C} )$ consists of three elements: $\hat{c}_i$ is the binary classification of matching the query patch ($c_i=1$) or not ($c_i=0$) for each object query; $\hat{b}_i$ is the vector that defines the box center coordinates, its width and height $\{x,y,w,h\}$. They are re-scaled relative to the image size;
$\hat{p}_i$ is the reconstructed feature with $C=2048$ for the ResNet-50 backbone typically. With the above definitions, the \textit{Hungarian} loss for all matched pairs is defined as:
\begin{align}
\mathcal{L}(y, \hat{y}) =& \sum_{i=1}^{N}[\lambda_{\{c_i\}}\mathcal{L}_{cls}(c_i,\hat{c}_{\hat{\sigma}(i)}) + \mathds{1}_{\{c_{i} =1\}} \mathcal{L}_{box}(b_i,\hat{b}_{\hat{\sigma}(i)}) \nonumber\\
 & +\mathds{1}_{\{c_{i} =1\}} \mathcal{L}_{rec}(p_i,\hat{p}_{\hat{\sigma}(i)})].
\end{align}
Here, $\mathcal{L}_{cls}$ is the cross entropy loss over two classes (match the query patch $vs.$ not match), and the class balance weight $\lambda_{\{c_i=1\}}=1$ and $\lambda_{\{c_i=0\}}=M/N$. $\mathcal{L}_{box}$ is a linear combination of $\ell_{1}$ loss and the generalized IoU loss with the same weight hyper-parameters as DETR~\cite{carion2020end}. $\mathcal{L}_{rec}$ is the reconstruction loss proposed in this paper to
balance classification and localization during the unsupervised pre-training, which will be discussed in detail below.

\subsubsection{Patch Feature Reconstruction}
Object detection is the coupling of object classification and localization, where these two tasks always have different feature preferences~\cite{zhang2019towards,wu2020rethinking,song2020revisiting}. Different from DETR, we propose a feature reconstruction term $\mathcal{L}_{rec}$ to preserve classification feature during localization pre-training. The motivation of this term is to preserve the feature discrimination extract by CNN after passing feature to transformers. $\mathcal{L}_{rec}$ is the mean squared error between the $\ell_{2}$-normalized patch feature extracted by the CNN backbone, which is defined as follows:
\begin{align}
\mathcal{L}_{rec}(p_i,\hat{p}_{\hat{\sigma}(i)}) &= \left\| \frac{p_i} {\left\| p_i \right\|_{2}}- \frac{\hat{p}_{\hat{\sigma}(i)}}{\left\| \hat{p}_{\hat{\sigma}(i)} \right\|_{2}} \right\|_{2}^{2}.
\end{align}

\subsubsection{Frozen Pre-training Backbone}
With the patch feature reconstruction, the CNN backbone parameters seriously affect the model training. Our motivation is that the feature after transformer should have similar discrimination as the feature after the CNN backbone. Therefore, we freeze the pre-training backbone and reconstruct the patch feature after the transformers by $\mathcal{L}_{rec}$. Stable backbone parameters are beneficial to transformer pre-training, and accelerate the feature reconstruction.

As described above, we propose and apply feature reconstruction and frozen backbone to preserve feature discrimination for classification. In \secref{freeze_CNN}, we will analyze and verify the necessity of them with experiments.

\subsection{Multi-Query Patches}
\label{multi-query}
For general object detection, there are multiple object instances in each image (\eg average 7.7 object instances per image in the COCO dataset). Moreover, single-query patch may result in the convergence difficulty when the number of object queries $N$ is large. Therefore, single-query patch pre-training is inconsistent with multi-object detection task, and is unreasonable for the typical object query setting $N=100$. However, extending a single-query patch to multi-query patches is not straightforward, because the assignment between $M$ query patches and $N$ object queries is a specific negative sampling problem for multi-query patches.

To solve this problem, we divide $N$ object queries into $M$ groups, where each query patch is assigned to $N/M$ object queries.
The query patches are assigned to the object queries in order. For example, the first query patch is assigned to the first $N/M$ object queries, the second query patch to the second $N/M$ object queries, and so on. Here, we hypothesize that it needs to satisfy two requirements during the pre-training: \textbf{(1) Independence of query patches}. All the query patches are randomly cropped from the image. Therefore, they are independent without any relations. For example, the bounding box regression of the first cropping is not concerned with the second cropping. \textbf{(2) Diversity of object queries}. There is no explicit group assignment between object queries for the downstream tasks. In other words, the query patch can be added to arbitrary $N/M$ object queries ideally.

\subsubsection{Attention Mask}

To satisfy the independence of query patches, we utilize an attention mask matrix to control the interactions between different object queries. The mask matrix $\mathbf{X} \in \mathbb{R}^{N \times N}$ is added to the softmax layer of self-attention in the decoder $softmax\left(QK^{\top}/{\sqrt{d_{k}}}+\mathbf{X}\right) \mathbf{V}$. Similar to the token mask in UniLM \cite{dong2019unified}, the attention mask is defined as:
\begin{equation}\label{eq:mask}
\mathbf{X}_{i, j}=\left\{\begin{array}{ll}
0, & \text { i, j in the same group } \\
-\infty, & \text {otherwise}
\end{array}\right.,
\end{equation}
where $\mathbf{X}_{i, j}$ determines whether the object query $q_i$ attends to the interaction with the object query $q_j$. For intuitive understanding, the attention mask in \figref{fig:multi} displays $1$ and $0$ corresponding to $0$ and $-\infty$ in \eqref{eq:mask}, respectively.

\subsubsection{Object Query Shuffle}
Groups of object queries are assigned artificially. However, during the downstream object detection tasks, there are no explicit group assignment between object queries. Therefore, To simulate implicit group assignment between object queries, we randomly shuffle the permutation of all the object query embeddings during pre-training~\footnote{In our further study, we find that the object query shuffle is not helpful. More details are included in the appendix.}.

\figref{fig:multi} illustrates the pre-training of multi-query patches with attention mask and object query shuffle. To improve the generalization, we randomly mask 10\% query patches to zero during pre-training similarly to dropout~\cite{srivastava2014dropout}. In our experiments, two typical values are set to $N=100$ and $M=10$. Apart from such modifications, other training settings are the same as those described in \secref{onepatch}.

\section{Experiments}
We pre-train the UP-DETR using ImageNet~\cite{deng2009imagenet} and fine-tune the parameters on VOC~\cite{everingham2010pascal} and COCO~\cite{lin2014microsoft} for object detection, one-shot detection and panoptic segmentation. In all experiments, we adopt the UP-DETR model (41.3M parameters) with ResNet-50 backbone, 6 transformer encoder, 6 decoder layers of width 256 with 8 attention heads. Referring to the open source of DETR\footnote{https://github.com/facebookresearch/detr}, we use \textit{the same hyper-parameters} in the proposed UP-DETR and our DETR re-implementation. We annotate R50 and R101 short for ResNet-50 and ResNet-101.

\noindent\textbf{Pre-training setup.} UP-DETR is pre-trained on the ImageNet training set without any labels. The CNN backbone (ResNet-50) is pre-trained with SwAV~\cite{caron2020unsupervised}. As the input image from ImageNet is relatively small, we resize it such that the shortest side is within $[320, 480]$ pixels while the longest side is at most 600 pixels. Given the image, we crop the query patches with random coordinate, height and width, which are resized to $128\times128$ pixels and transformed with the SimCLR-style~\cite{chen2020simple} without horizontal flipping. AdamW~\cite{loshchilov2017decoupled} is used to optimize the UP-DETR, with the initial learning rate of \num{1e-4} and the weight decay of \num{1e-4}. We use a mini-batch size of 256 on 8 V100 GPUs for 60 epochs with the learning rate multiplied by 0.1 at 40 epochs.

\noindent\textbf{Fine-tuning setup.} The model is initialized with pre-training UP-DETR parameters and fine-tuned for all the parameters (including CNN) on VOC and COCO. We fine-tune the model with the initial learning rate \num{1e-4} for transformers and \num{5e-5} for CNN backbone, and the other settings are same as DETR~\cite{carion2020end} on 8 V100 GPUs. The model is fine-tuned with short/long schedule for 150/300 epochs and the learning rate is multiplied by 0.1 at 100/200 epochs, respectively.

\subsection{PASCAL VOC Object Detection}
\label{voc}
\noindent\textbf{Setup.} The model is fine-tuned on VOC \texttt{trainval07+12} ($\sim$16.5k images) and evaluated on \texttt{test2007}. We report COCO-style metrics: AP, $\textrm{AP}_{50}$ (default VOC metric) and $\textrm{AP}_{75}$. For a full comparison, we report the result of Faster R-CNN with the R50-C4 backbone~\cite{caron2020unsupervised}, which performs much better than R50~\cite{li2017fully}. DETR with R50-C4 significantly increases the computational cost than R50, so we fine-tune UP-DETR with R50 backbone.
\begin{table}[!h]
\begin{tabular}{l|lll}
Model/Epoch & AP & $\textrm{AP}_{50}$ & $\textrm{AP}_{75}$ \\\thickhline
Faster R-CNN & 56.1 & \textbf{82.6} & \textbf{62.7} \\\hline
DETR/150      & 49.9 & 74.5 & 53.1 \\
UP-DETR/150     & \textbf{56.1} {\small (+6.2)}& 79.7 {\small(+5.2)}& 60.6 {\small (+7.5)}\\\hline
DETR/300    & 54.1 & 78.0 & 58.3 \\
UP-DETR/300     & \textbf{57.2} {\small (+3.1)}& 80.1 {\small(+2.1)}& 62.0 {\small(+3.7)}\\
\end{tabular}
\caption{Object detection results trained on PASCAL VOC \texttt{trainval07+12} and evaluated on \texttt{test2007}. DETR and UP-DETR use R50 backbone and Faster R-CNN uses R50-C4 backbone. The values in the brackets are the gaps compared to DETR with the same training schedule.}\label{voc_exp}
\end{table}

\noindent\textbf{Results.} \tabref{voc_exp} shows the compared results of PASCAL VOC.
We find that the DETR performs poorly in PASCAL VOC, which is much worse than Faster R-CNN by a large gap in all metrics. UP-DETR significantly boosts the performance of DETR for both short and long schedules: up to \textbf{+6.2 (+3.1)} AP, \textbf{+5.2 (+2.1)} $\textrm{AP}_{50}$ and \textbf{+7.5 (+3.7)} $\textrm{AP}_{75}$ for 150 (300) epochs, respectively. Moreover, UP-DETR (R50) achieves a comparable result to Faster R-CNN (R50-C4) with better AP. We find that both UP-DETR and DETR perform a little worse than Faster R-CNN in $\textrm{AP}_{50}$ and $\textrm{AP}_{75}$. It may come from different ratios of feature maps (C4 for Faster R-CNN) and no NMS post-processing (NMS lowers AP but slightly improves $\textrm{AP}_{50}$).

\figref{fig:vocAP} shows the AP (COCO style) learning curves on VOC. UP-DETR significantly speeds up the model convergence. After the learning rate reduced, UP-DETR significantly boosts the performance of DETR with a large AP improvement. Noting that UP-DETR obtains 56.1 AP after 150 epochs, however, its counterpart DETR (scratch transformers) only obtains 54.1 AP even after 300 epochs and does not catch up even training longer. It suggests that pre-training transformers is indispensable on insufficient training data (\ie $\sim 16.5$K images on VOC).

\subsection{COCO Object Detection}
\noindent\textbf{Setup.} The model is fine-tuned on COCO \texttt{train2017} ($\sim$118k images) and evaluated on \texttt{val2017}. There are lots of small objects in COCO dataset, where DETR performs poorly~\cite{carion2020end}. Therefore, we report AP, $\textrm{AP}_{50}$, $\textrm{AP}_{75}$, $\textrm{AP}_{S}$, $\textrm{AP}_{M}$ and $\textrm{AP}_{L}$ for a comprehensive comparison. Moreover, we also report the results of highly optimized Faster R-CNN-FPN with short (3$\times$) and long (9$\times$) training schedules, which are known to improve the performance results~\cite{he2019rethinking}.

\begin{table*}[ht]
\centering
\begin{tabular}{lll|llllll}
Model & Backbone & Epochs & AP   & $\textrm{AP}_{50}$ & $\textrm{AP}_{75}$ & $\textrm{AP}_{S}$& $\textrm{AP}_{M}$& $\textrm{AP}_{L}$ \\\thickhline
Faster R-CNN $\dagger$~\cite{lin2017feature} & R101-FPN & -& 36.2 & 59.1 & 39.0 & 18.2 & 39.0 & 48.2 \\
Mask R-CNN $\dagger$~\cite{he2017mask} & R101-FPN & -& 38.2 & 60.3 & 41.7 & 20.1 & 41.1 & 50.2 \\
Grid R-CNN $\dagger$~\cite{lu2019grid} & R101-FPN & -& 41.5 & 60.9 & 44.5 & 23.3 & 44.9 & 53.1 \\
Double-head R-CNN~\cite{wu2020rethinking} & R101-FPN & -& 41.9 & 62.4 & 45.9 & 23.9 & 45.2 & 55.8 \\
RetinaNet $\dagger$~\cite{lin2017focal} & R101-FPN & -& 39.1 & 59.1 & 42.3 & 21.8 & 42.7 & 50.2 \\
FCOS $\dagger$~\cite{tian2019fcos} &  R101-FPN & -& 41.5 & 60.7 & 45.0 & 24.4 & 44.8 & 51.6 \\
DETR~\cite{carion2020end} &  R50 & 500 & 42.0 & 62.4 & 44.2 & 20.5 & 45.8 & 61.1 \\
\hline
\hline
Faster R-CNN & R50-FPN & $3\times$& 40.2 & \textbf{61.0} & \textbf{43.8} & \textbf{24.2} & 43.5 &  52.0 \\
DETR (Supervised CNN) & R50 & 150 & 39.5 & 60.3 & 41.4 & 17.5 & 43.0 & 59.1 \\
DETR (SwAV CNN)~\cite{caron2020unsupervised} & R50 & 150 & 39.7 & 60.3 & 41.7 & 18.5 & 43.8 & 57.5 \\
\textbf{UP-DETR} & R50 & 150 & \textbf{40.5} \small (+0.8) & 60.8 & 42.6  & 19.0 & \textbf{44.4} & \textbf{60.0} \\\hline
\hline
Faster R-CNN & R50-FPN & $9\times$ & 42.0 & 62.1 & \textbf{45.5} & \textbf{26.6} & 45.4 & 53.4 \\
DETR (Supervised CNN) & R50 & 300 & 40.8 & 61.2 & 42.9 & 20.1 & 44.5 & 60.3 \\
DETR (SwAV CNN)~\cite{caron2020unsupervised} & R50 & 300 & 42.1 & \textbf{63.1} & 44.5 & 19.7 & 46.3 & 60.9 \\
\textbf{UP-DETR} & R50 & 300 & \textbf{42.8} \small (+0.7) & 63.0 & 45.3& 20.8 & \textbf{47.1} & \textbf{61.7} \\
\end{tabular}
\caption{Object detection results trained on COCO \texttt{train2017} and evaluated on \texttt{val2017}. Faster R-CNN, DETR and UP-DETR are performed under comparable settings. $\dagger$ for values evaluated on COCO \texttt{test-dev}, which are always slightly higher than \texttt{val2017}. The values in the brackets are the gaps compared to DETR.}
\label{coco}
\end{table*}

\begin{figure*}[ht]
\centering
\begin{subfigure}{0.48\linewidth}
\includegraphics[width=0.96\linewidth]{figures/vocAP.pdf}
\caption{AP learning curves of VOC}
\label{fig:vocAP}
\end{subfigure}
\begin{subfigure}{0.48\linewidth}
\includegraphics[width=0.96\linewidth]{figures/cocoAP.pdf}
\caption{AP learning curves of COCO}
\label{fig:cocoAP}
\end{subfigure}
\caption{AP (COCO style) learning curves with DETR and UP-DETR on VOC and COCO. Models are trained with the SwAV pre-training ResNet-50 for 150 and 300 epochs, and the learning rate is reduced at 100 and 200 epochs, respectively.}
\label{fig:APcurves}
\end{figure*}

\noindent\textbf{Results.}
\tabref{coco} shows the results on COCO with other methods.
With 150 epoch schedule, UP-DETR outperforms DETR by 0.8 AP and achieves a comparable performance as compared with Faster R-CNN-FPN (3 $\times$ schedule). With 300 epoch schedule, UP-DETR obtains \textbf{42.8} AP on COCO, which is 0.7 AP better than DETR (SwAV CNN) and 0.8 AP better than Faster R-CNN-FPN (9 $\times$ schedule). Overall, UP-DETR comprehensively outperforms DETR in detection of small, medium and large objects with both short and long training schedules. Regrettably, UP-DETR is still slightly lagging behind Faster R-CNN in $\textrm{AP}_{S}$, because of the lacking of FPN-like architecture~\cite{lin2017feature} and the high-cost attention operation.

\figref{fig:cocoAP} shows the AP learning curves on COCO. UP-DETR outperforms DETR for both 150 and 300 epoch schedules with faster convergence. The performance improvement is more noticeable before reducing the learning rate. After reducing the learning rate, UP-DETR still holds the lead of DETR by $\sim$ 0.7 AP improvement. It suggests that pre-training transformers is still indispensable even on sufficient training data (\ie $\sim$ 118K images on COCO).

\begin{table*}[]
\begin{center}
\begin{adjustbox}{max width=1\linewidth}
\begin{tabular}{c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c|c}
\hline
\multirow{2}{*}{Model}  & \multicolumn{17}{c}{seen class} & \multicolumn{5}{|c}{unseen class} \\
&plant& sofa& tv& car& bottle& boat& chair& person& bus& train& horse& bike& dog& bird& mbike& table& \textbf{AP$^{50}$}& cow& sheep& cat& aero& \textbf{AP$^{50}$} \\
\hline
SiamFC~\cite{bertinetto2016fully} & 3.2 & 22.8 & 5.0 & 16.7 & 0.5 & 8.1 & 1.2 & 4.2 & 22.2 & 22.6 & 35.4 & 14.2 & 25.8 & 11.7 & 19.7 & 27.8 & 15.1 & 6.8 & 2.28 & 31.6 & 12.4 & 13.3  \\
SiamRPN~\cite{li2018high} & 1.9 & 15.7 & 4.5 & 12.8 & 1.0 & 1.1 & 6.1 & 8.7 & 7.9 & 6.9 & 17.4 & 17.8 & 20.5 & 7.2 & 18.5 & 5.1 & 9.6 & 15.9 & 15.7 & 21.7 & 3.5 & 14.2  \\
CompNet~\cite{zhang2019comparison} & 28.4 & 41.5 & 65.0 & 66.4 & 37.1 & 49.8 & 16.2 & 31.7 & 69.7 & 73.1 & 75.6 & 71.6 & 61.4 & 52.3 & 63.4 & 39.8 & 52.7 & 75.3 & 60.0 & 47.9 & 25.3 & 52.1 \\
CoAE~\cite{hsieh2019one} & 30.0& 54.9& 64.1& 66.7& 40.1& 54.1& 14.7& 60.9& 77.5& 78.3& 77.9 & 73.2& 80.5& 70.8& \textbf{72.4}& 46.2& 60.1& \textbf{83.9}& 67.1& 75.6& 46.2& 68.2 \\
Li~\etal~\cite{li2020one}  &33.7&58.2&67.5&72.7&40.8&48.2&20.1&55.4&\textbf{78.2}&79.0&76.2&74.6&\textbf{81.3}&71.6 &72.0&48.8& 61.1 &74.3& 68.5 & \textbf{81.0} & 52.4 & 69.1 \\\hline
DETR  &11.4&42.2&44.1&63.4&14.9&40.6&20.6&63.7&62.7&71.5&59.6&52.7&60.6&53.6 &54.9&22.1& 46.2 &62.7& 55.2 & 65.4 & 45.9 & 57.3 \\
UP-DETR  &\textbf{46.7}&\textbf{61.2}&\textbf{75.7}&\textbf{81.5}&\textbf{54.8}&\textbf{57.0}&\textbf{44.5}&\textbf{80.7}&74.5&\textbf{86.8}&\textbf{79.1}&\textbf{80.3}&80.6&\textbf{72.0} &70.9&\textbf{57.8}& \textbf{69.0} &80.9& \textbf{71.0} & 80.4 & \textbf{59.9} & \textbf{73.1}
\end{tabular}
\end{adjustbox}
\end{center}
\caption{One-shot detection results on VOC \texttt{2007test} set.}
\label{OSD}
\end{table*}

\begin{table*}[]
\centering
\begin{tabular}{l|lll|lll|lll|ll}
Model & PQ & SQ & RQ & PQ$^{th}$ & SQ$^{th}$ & RQ$^{th}$ & PQ$^{st}$ & SQ$^{st}$ & RQ$^{st}$ & AP$^{seg}$ \\\thickhline
PanopticFPN++~\cite{kirillov2019panoptic} &42.4 & 79.3 &  51.6&49.2 & 82.4 &58.8 &32.3 & 74.8 & 40.6 & \textbf{37.7} \\
UPSNet~\cite{xiong2019upsnet} & 42.5 & 78.0 & 52.5 & 48.6 & 79.4 &59.6 &33.4 &  75.9 &41.7& 34.3 \\
UPSNet-M~\cite{xiong2019upsnet} & 43.0 & 79.1 &  52.8 & 48.9 & 79.7 & 59.7 & 34.1 & 78.2    &42.3 & 34.3 \\\hline
DETR~\cite{carion2020end} & 44.3 & 80.0 & 54.5 & 49.2 & 80.6 &60.3 & \textbf{37.0} & \textbf{79.1} & \textbf{45.9} & 32.9\\
UP-DETR & \textbf{44.5} & \textbf{80.3} & \textbf{54.7} & \textbf{49.6} & \textbf{80.7} & \textbf{60.7} & 36.9 & 78.9 & 45.8 & 34.0
\end{tabular}
\caption{Panoptic segmentation results on the COCO \texttt{val} dataset with the same ResNet-50 backbone. The PanopticFPN++, UPSNet and DETR results are re-implemented by Carion~\etal~\cite{carion2020end}.}
\label{panoptic}
\end{table*}

\subsection{One-Shot Detection}
Given a query image patch whose class label is not included in the training data, one-shot detection aims to detect all instances with the same class in a target image. One-shot detection is a promising research direction that can detect unseen instances. With feeding query patches to the decoder, UP-DETR is naturally compatible to one-shot detection task. Therefore, one-shot detection can also be treated as a downstream fine-tuning task of UP-DETR.

Following the same one-shot detection setting as~\cite{hsieh2019one}, we crop the query image patch as the query patch to the DETR decoder. we train DETR and UP-DETR on VOC \texttt{2007train val} and \texttt{2012train val} sets with 300 epochs then evaluate on VOC \texttt{2007test} set.~\tabref{OSD} shows the comparison to the state-of-the-art one-shot detection methods. Compared with DETR, UP-DETR significantly boosts the performance of DETR on both seen (\textbf{+22.8} AP$^{50}$ gain) and unseen (\textbf{+15.8} AP$^{50}$ gain) classes. Moreover, we show that UP-DETR outperforms all methods in both seen (\textbf{+7.9} AP$^{50}$ gain) and unseen (\textbf{+4.0} AP$^{50}$ gain) classes of one-hot detection. It further verifies the effectiveness of our pre-training pretext task.

\subsection{Panoptic Segmentation}
Panoptic segmentation~\cite{kirillov2019panoptic} is a natural extension to DETR by adding a mask head on the top of the decoder outputs. Following the same panoptic segmentation training schema as DETR~\cite{carion2020end}, we fine-tune UP-DETR for box only annotations with 300 epochs. Then, we freeze all the weights of DETR and train the mask head for 25 epochs.

\tabref{panoptic} shows the comparison to state-of-the-art methods on panoptic segmentation with the ResNet-50 backbone. As seen, UP-DETR outperforms DETR\footnote{With a bug fixed in \url{github.com/facebookresearch/detr/issues/247}, the DETR baseline is better than paper report.} with \textbf{+0.2} PQ, \textbf{+0.4} PQ$^{th}$ and \textbf{+1.1} AP$^{seg}$.

\begin{table}[t]
\centering
\begin{tabular}{c|cc|c}
Case & Frozen CNN & Feature Reconstruction &$\textrm{AP}_{50}$ \\\thickhline
DETR & \multicolumn{2}{c|}{scratch transformers} & 74.5 \\\hline
(a) & &  & 74.0 \\
(b) &$\checkmark$ & &  \textbf{78.7} \\
(c)& & $\checkmark$ &  62.0 \\
(d)&$\checkmark$ & $\checkmark$ & \textbf{78.7}\\\hline
\end{tabular}
\caption{Ablation study on frozen CNN and feature reconstruction for pre-training models with $\textrm{AP}_{50}$. The experiments are fine-tuned on PASCAL VOC with 150 epochs.}
\label{Ablation}
\end{table}

\begin{figure}[t]
\centering
  \includegraphics[width=0.49\textwidth]{figures/5curves.pdf}
  \caption{Learning curves of VOC ($\textrm{AP}_{50}$) with four different pre-training UP-DETR models and DETR. The models trained with 150 epochs corresponds to the models in \tabref{Ablation} one-to-one.}
  \label{5curves}
\end{figure}

\begin{figure*}[t]
\centering
  \includegraphics[width=0.8\linewidth]{figures/vis.pdf}
  \caption{The unsupervised localization of patch queries with UP-DETR. The first line is the original image with predicted bounding boxes. The second line is query patches cropped from the original image with data augmentation. The value in the upper left corner of the bounding box is the model confidence.}
  \label{vis}
\end{figure*}

\subsection{Ablations}
For ablation experiments, we pre-train UP-DETR for 15 epochs with the learning rate multiplied by 0.1 at the 10-th epoch on ImageNet. We fine-tune models on VOC object detection following the setup in ~\secref{voc} with 150 epochs\footnote{More ablations are included in the appendix.}.

\subsubsection{Frozen CNN and Feature Reconstruction}\label{freeze_CNN}
To illustrate the importance of patch feature reconstruction and frozen CNN backbone of UP-DETR, we pre-train four different UP-DETR models with different combinations of whether freezing CNN and whether adding feature reconstruction.

\tabref{Ablation} shows $\textrm{AP}$ and $\textrm{AP}_{50}$ of four different pre-training models and DETR on VOC with 150 epochs. As shown in \tabref{Ablation}, not all pre-trained models are better than DETR, and pre-training models (b) and (d) perform better than the others. More importantly, without frozen CNN, pre-training models (a) and (c) even perform worse than DETR. It confirms that freezing pre-trained CNN is essential to pre-train transformers. In addition, it further confirms the pretext (random query patch detection) may weaken the feature discrimination without the freezing pre-training CNN weights.

\figref{5curves} plots the $\textrm{AP}_{50}$ learning curves of four different pre-training models and DETR, where the models in \figref{5curves} correspond to the models in \tabref{Ablation} one-to-one. As shown in \figref{5curves}, model (d) UP-DETR achieves faster convergence at the early training stage with feature reconstruction. The experiments suggest that random query patch detection is complementary to the contrastive learning for a better visual representation. The former is designed for the spatial localization with position embeddings, and the latter is designed for instance or cluster classification.

It is worth noting that UP-DETR with frozen CNN and feature reconstruction heavily relies on a pre-trained CNN model,~\eg SwAV. Therefore, we believe that it is a promising direction for further investigating UP-DETR with random query patch detection and contrastive learning together to pre-train the whole DETR model from scratch.

\subsection{Visualization}

To further illustrate the ability of the pre-training model, we visualize the unsupervised localization results of given patch queries. Specifically, for the given image, we manually crop several object patches and apply the data augmentation to them. Then, we feed these patches as queries to the model. Finally, we visualize the model output with bounding boxes, whose classification confidence is greater than $0.9$. This procedure can be treated as \textit{unsupervised one-shot detection} or deep learning based \textit{template matching}.

As shown in \figref{vis}, pre-trained with random query patch detection, UP-DETR successfully learns to locate the bounding box of given query patches and suppress the duplicated bounding boxes~\footnote{Base picture credit: https://www.piqsels.com/en/public-domain-photo-jrkkq, https://www.piqsels.com/en/public-domain-photo-smdfn. }.
It suggests that UP-DETR with random query patch detection is effective to learns the ability of object localization.

\section{Conclusion}
We present a novel pretext task called random query patch detection to pre-train the transformers in DETR. With unsupervised pre-training, UP-DETR significantly outperforms DETR on object detection, one-shot detection and panoptic segmentation. We find that, even on the COCO with sufficient training data, UP-DETR still performs better than DETR.

From the perspective of unsupervised pre-training models, pre-training CNN backbone and pre-training transformers are separated now. Recent studies of unsupervised pre-training mainly focus on feature discrimination with contrastive learning instead of specialized modules for spatial localization. However, for UP-DETR pre-training, the pretext task is mainly designed for patch localization by positional encodings and learn-able object queries. We hope that an advanced method can integrate CNN and transformers pre-training into a unified end-to-end framework and apply our pre-training tasks to more detection related frameworks.

\section*{Acknowledgement}
This work was supported by the Guangdong Natural Science Foundation under Grant 2019A1515012152.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


We took serious care of this aspect facing important financial investments for assuring the total quality that lets our clients work totally safe with a product guaranteed by external most important international bodies.
In this case the EC logo must be followed by four numbers identifying the certification body. All the Telecrane Silver Line products are certified by external bodies.
Certificate of conformity, application of Council Directive 2006/42/EC on Machinery. Indicates the total conformity of the industrial radio remote controllers for crane use. All the Telecrane Silver Line gamma is certified by EURO Cert.
for the Russian market. All the Telecrane Silver Line gamma is certified for the Russian market.


Meet Jonathan Long AKA JayLong. Jonathan is a songwriter, vocalist, violinist, guitarist, and the frontman for the Los Angles based band Vulpes. Vulpes enjoys playing all sorts of music ranging from indie rock to R&B and hip-hop to electronic. Long has been playing violin since the age of five and does not plan on stopping any time soon. He has been playing for almost twenty years. Long also has a degree in Music Education and Music Performance from Xavier University and tends to focus on the "purpose" within music. According to Long his life is centered around that purpose.
Vulpes is actively looking to play shows so make sure to reach out to JayLong on Gigmor. They have a ton of work on the page including really great covers of massive pop hits like Drake's "Hotline Bling" and Sam Smith & Disclosure's "Latch".
JayLong is a self taught producer. He taught himself how to use ProTools and is classically trained in violin as well as the art of acting– talk about multi-talented. With so many different influences, there is no wonder Long aims to create a fusion of as many genres as possible. For example, Vulpes would play live jazz while using 90's hip-hop samples and a heavily syncopated dubstep bass in the background. Jonathan isn't looking to find a specific sound, but instead a language that he can communicate through. He really seems like he's going places between his smooth voice, talent on multiple instruments, and all of his production experience. Make sure to check out his Gigmor page and give him a follow!
There are new bands and artists joining our network of nearly 50K musicians and 1,000 industry pros on Gigmor every single day. When we find a band we really like, especially one that is established and has a lot of material, we at Gigmor like to shine the spotlight on their hard work.
Today's post is on the band Chisler out of Crystal Lake, Illinois. They have been together for two years and specialize in electronic rock and blues. Lead by guitarist and vocalist Mo Chisler, the band is currently touring around Southern Illinois and Northern Wisconsin. Chisler also consists of drummer Hagop Mikaelin and bassist Zach England. Check out their cover of Citizen Cope's "Bullet and a Target" below.
Mo and the band love playing covers but their originals have generated quite the buzz. Their single "I'm Gone" is well known throughout the communities they play in and is a true blues jam.
Chisler is openly looking to gig and jam with new people so if you like their sound make sure to give them a follow on Gigmor as well as check out their band's website.
Despite Chisler being new to Gigmor they have done a lot to promote their band online. If you're looking for some exposure uploading cover and original live videos onto the web is a good place to start; all emerging bands that have the opportunity to perform live should be utilizing Youtube and Facebook to upload their performances! It's the digital age, and the more online exposure artists have will only increase the chances of someone discovering your music. (We get it you have an emotional attachment to your staple gun and flyer, but it's time to for the stragglers to adapt, even if you do it begrudgingly.) Check out a couple more stellar covers from Chisler below.


text:
Duchenne Muscular Dystrophy (DMD)
A significant unmet need remains in the DMD patient population due to inadequate dystrophin production in critical organs and tissues. Our DMD program is designed to address the genetic basis of DMD by promoting the skipping of specific DMD exons in the nucleus, allowing muscle cells to create a functional dystrophin protein.
Duchenne muscular dystrophy (DMD) is a monogenic X-linked disease caused by mutations in the DMD gene, which encodes for the dystrophin protein. Dystrophin is essential to maintaining the structural integrity and normal function of muscle cells for walking, breathing and cardiac function. In patients with DMD, mutations in the DMD gene can lead to certain exons being misread, resulting in a failure to produce sufficient functional dystrophin. The reduction or absence of functional dystrophin leads to damage to muscle cell membranes, resulting in muscle cell death and progressive loss of muscle function throughout the body.
The Unmet Need in Duchenne Muscular Dystrophy
In patients with DMD, progressive muscle weakness occurs throughout the body.
Early in childhood the large, proximal (close to body core) skeletal muscles are affected and later the distal muscles. The lower body muscles are affected earlier than the upper body muscles. Patients may have difficulty standing, climbing stairs and maintaining balance. Patients may ultimately become dependent upon a wheelchair.
In the later stages of the disease, the heart and diaphragm are affected, and this ultimately contributes to morbidity and mortality.
An ideal treatment would not only be effective in restoring high amounts of dystrophin in the skeletal muscles, but also in the heart and diaphragm given they are key organs responsible for disease morbidity and mortality.
Entrada EEV-Oligonucleotides for Duchenne Muscular Dystrophy
Entrada is developing oligonucleotides conjugated to our Endosomal Escape Vehicle (EEV™) platform for the treatment of patients with DMD.
Our EEV™ Platform is designed to enable high cellular uptake and robust cytosolic delivery of EEV therapeutic candidates, resulting in a greater amount of the oligonucleotide being able to reach its intended target in the nucleus. In preclinical studies, our proprietary oligonucleotide has been shown to promote enhanced exon skipping and dystrophin production. In certain tissues, there is a near complete correction of dystrophin.
Differentiation of EEV-Oligonucleotides
Limited exposure in target tissues and poor endosomal escape have been significant barriers to achieving greater dystrophin levels with oligonucleotide therapies. Because the unique mechanism of action of the EEV™ platform is designed to enable high cellular uptake and robust cytosolic delivery of EEV therapeutic candidates, in preclinical studies we have seen not only significant dystrophin production in the muscles like the heart but also uniform dystrophin production beyond the vasculature within tissues. Given cardiomyopathy is a leading cause of death for patients with DMD, we believe this increased level and higher myocyte distribution of dystrophin observed preclinically may potentially translate to improved cardiac function in patients with DMD.


We can customize the products according to your requirements and we can pack it for Natural Erythritol Powder , Natural Inositol Powder , Natural Protein Powders , we are going to be your most effective company partner. We welcome new and aged clients from all walks of daily life to call us for long term small business interactions and acquiring mutual achievements!
We insist on the principle of development of 'High quality, Efficiency, Sincerity and Down-to-earth working approach' to provide you with excellent service of processing for Natural Erythritol Powder , Natural Inositol Powder , Natural Protein Powders , Our company will continue to serve customers with best quality, competitive price, timely delivery, the best payment term! We sincerely welcome friends from all over the world to visit & cooperate with us and enlarge our business. If you are interested in our goods please do not hesitate to contact us we will be happy to provide you with further information!


text:
Board index » Music
All Purpose DEATH METAL Thread
[ 1051 posts ] Go to page Previous 1 ... 48, 49, 50, 51, 52, 53 Next
Re: All Purpose DEATH METAL Thread
opaline wrote:
Have you heard Gorefest's 2007 "Rise To Ruin" album Sol? It's pretty decent as well.
It's weird to think that for a brief moment in time Gorefest were Nuclear Blast's biggest selling band.
They were big in Germany and Holland apparently but nowhere else. I don't recall anyone down here every talking about them in the mid 90's.
Just Browning. It's another of those stupid legal battles where the rest of (the then line-up of) Nocturnus went to court over use of the name and Browning was locked out. He reformed the original line-up as After Death and this is a continuation of that band. Clearly he's riffing on the concept behind The Key, certainly aesthetically. Not sure why he's bothering, The Key and (to a lesser extent) Thresholds were the only things from either band worth hearing.
Oh, another one of those fucking situations eh?
Bloody hell! There have been at least a handful of those in the last few years I think. It sends out confusing signals to fans and turns them off.
Queensryche managed to get away with it because they were able to retain the name, but the Entombed situation was a really sad story.
Cuchulainn wrote:
After the demos Nocturnus were shite. The Incubus demo shits all over Nocturnus.
I really like the Incubus demo too but "The Key" & the self-titled Nocturnus single were also worth checking out. None are essential listening though.
Stat_Rad wrote:
It didn't hurt that the couple of studio albums with Todd La Torre were simply better releases than anything the band had done since the mid-90's. I still don't have much time for those two records though. 1994's "Promised Land" was the last Queensrÿche album I genuinely enjoyed.
Condition Human is their best since Promised Land imo, but yes they aren't the band they used to be.
Promised Land was the last QR album with the classic lineup worth bothering with.
BlackEyedMetal
Location: Somewhere in England
Someone please try and sell me on the idea of DM. I can't promise to like it but I will listen to classic album recs.
blacklorre
Cant remember the first DM band I heard! First thrash was deffo Metallica. Surely I should remeber something as important as that! Scream Bloody Gore comes to mind. And Deicide. (I remember it was Morbid Angel)
The very first death metal bands I had heard must have been Death and Morbid Angel I suppose. I remember listening to ''Rapture'' and asking my dad what that word meant.
For me it was definitely Death's "Leprosy".
Location: Béal Átha na Slua
The death metal album. First one I heard too, it's never been bettered.
http://cow.org/csi/
I'd take plenty of other 80s death metal albums over Leprosy such as Bestial Devastation/Morbid Visions, Bloody Vengeance, Slowly We Rot, Severed Survival, Seven Churches, Altars of Madness and Consuming Impulse.
Madness. My views on pre-split Autopsy are well known by now, but I'd say that Morbid Angel, Obituary and Pestilence each blew those respective albums away with their follow-ups (yes, I know they were all '90s releases) and putting those first two Sepultura releases in the same league as any of those albums is just insane. Seven Churches is alright, but never impressed me much.
Blessed Are the Sick is alright, but the production doesn't do the songwriting justice not to mention it just doesn't sound as coherent as AoM.
I could never get into Cause of Death as I find it rather...plodding whereas the debut just comes off a bit more vicious to me.
Testimony of the Ancients works as background music. It was the first death metal CD that I had bought, but I really got bored of it after a couple of years. To me it's just a bit too restrained sounding, missing the powerful riffwork of MM and CI, even if I still love the guitar leads on that record. Also Mameli is no van Drunen but whatever.
It's all subjective obviously, but to me there's no comparison between those albums Blessed..., Cause... and Testimony... are the sound of bands who've finetuned their craft to its zenith. The preceding albums are all great of course, but are overshadowed by their follow-ups. My only issue with any of those three would be the interludes/intros on Testimony..., they're just annoying.
BlackEyedMetal wrote:
Posessed - Seven Churches
Morbid Angel - Alters Of Madness
Entombed - Left Hand Path
Dismember - Like An Everflowing Stream
Incantation - Onward to Golgotha
I'd go for them as a basic 5 to try.
I suppose it would help to know what you like at the moment, to see if there was an obvious 'route' into the genre.
Dr Yail Bloor
I think if you have to be sold on anything it probably isn't for you
Mo Mo Gaddafi wrote:
His massive cock pleasured me immensely.
Dr Yail Bloor wrote:
Whilst I get what your saying, peoples tastes change over time.
I never used to be big into it, but then the more I got into black metal and more extreme stuff the more I could tolerate it, and even then it was only the ones with really strong thrashy blast beats like the Swedish stuff. But then I kind of got into Punk, and retrospectively got into some of the earlier death metal bands like Seven Churches and Scream Bloody Gore. And the thing was with every new band I got into, the more familiar I got with the genre and it's conventions, the more I found other new bands. It took a long time before I got into Incantation for example, but one day I just downloaded their first three albums, put it on my running playlist and it clicked. I think sometimes you just need that one album to hook you. For me albums like Winterwolfs Cycle of the Werewolf and The Chasm's Farseeing The Paranormal Abysm were just as important to me getting into the genre as Alters of Madness and From Beyond were. Something clicked with those albums which opened me up to other stuff. And then on the other hand, when I first heard Soulside Journey I wasn't into it at all, but once I'd get my head around Carnage and Dismember and the like, going back to Soulside made a lot more sense. I think a lot of music is built on frames of reference and building an understanding of the 'language' of the genre, and how it should be listened to.
Good point. For me certain albums don't click until I try them years again after having them heard for the first time. I recall hearing Seven Churches for the first time and I wasn't blown away by it. I remember hearing None So Vile for the first time and thought it was too br00tal for my taste. Nowadays I like both albums a lot.
Annihislater wrote:
Yeah my quote was a bit dickish.
SLAYYYYYYYEEEERRRRRRR!
I mostly agree with this.
Let's put it this way, when I first heard death metal I didn't love it, but I was very intrigued and wanted to hear more to 'understand' it.
I agree with Anni's point that people's change over time, but I think often the reason they never got into something is because they weren't exposed to the right thing at the right time etc.
On a related note, I'm also a firm believer that people who claim that they really into something then 'got over it' were probably never really into it at all or it was just a phase they went through.
I had a guy say to me the other day that he used to watch a lot of 'art films' at university but he 'got over it'. In reality he watched a few handfuls of films during his film studies courses and probably enjoyed the novelty of it but never really understood what he was watching.
Most of the guys I went to school with who were into metal and are now 'over it' are in the same boat. They liked it for a few years in their teens and early 20's, never bought many albums, never watched the scene too closely etc.
I know people are very resistant to that kind of opinion/interpretation of tastes/interests, but I don't care. I'm not trying to be politically correct. I honestly think that most people who make those claims were just never really into the things they claimed to be into that seriously.
Users browsing this forum: MSN [Bot] and 12 guests


Gift vouchers must be redeemed in person in the restaurant or bar. Gift vouchers expire 365 days after the day of purchase and must be used within that time frame. Vouchers cannot be exchanged for cash, and change will not be given if it is exchanged for goods less than its face value. No refunds are available. Gift vouchers cannot be used to purchase further gift vouchers.
Starting every Monday and Tuesday evening from the 2nd of October until the 28th of February 2018. Spend over £5 on alcoholic drinks in one transaction and receive a free voucher for one fish & chips mini snack cone. Offer applies to drinks ordered at the bar only. Does not apply to restaurant menu or people dining in the restaurant. Minimum spend £5. Only alcoholic drinks will qualify towards the £5 minimum spend. One use per person. Offer available Monday & Tuesday from 18:00. Only valid between 02.10.17 to 28.02.18 only. Offer may be withdrawn at any time. Offers cannot be used in conjunction with each other.
Party organisers please be aware that you are responsible for the payment of your bill in full. We do not split bills for parties larger than 8 people. The amount paid as a deposit will be deducted from the total of the final bill. We do accept multiple payments from the final bill but only if the party organiser is present.
Please note: Your booking ensures that a table will be available to you for a maximum of 2 hours and 15 minutes during our busy times.
Please speak to our staff about the ingredients in your meal, when making your order. Our menu descriptions do not list all of the ingredients included in the dish. An information pack is available listing all the allergens, please ask your waiter. We cannot guarantee allergen-free food due to the presence of the allergens on the premises. Special Gluten-free and Dairy-free dishes are available, please ask your waiter. No exchanges or refunds can be given once your meal has been ordered.
Due to market conditions beyond our control, dishes and prices may need to change from time to time. All dishes are subject to availability and we reserve the right to remove or amend any dishes or prices without prior notice.
All cash tips go straight to your waiter. For credit card & service charge tips, a small charge of 10% is deducted to cover the costs of getting these taxed & paid to each individual.
Please be aware that we are unable to reserve tables by the window or outside.
Any amendments will incur extra chargesand are subject to managements discretion.


Hotel Balbin is located in the center of Prague, only 300 meters from Wenceslas Square. Hotel offers accommodation in 2-bedded to 4-bedded rooms and in 2 suites. Some rooms are available to connect to wi-fi. The guesthouse has a nonstop reception desk and a lift.
Renovated three star pension Mánes is situated in the center of Prague in the area called Královské Vinohrady. Nearby you can find a lot of shops, bars, restaurants and café bars which emphasize the atmosphere of our old-Prague localization.
Pension is situated near Riegerový sady and only 5 minutes walking from Wenceslav Square. Every room has stylish equipment where separated toilet, shower and in-room TV is obviosity. During your accommodation you can put your valuable stuff on reception to safe keeping for free and you can also use our free Internet access (WiFi).
I will be pleased to welcome you in this historical apartment situated in heart of Prague in Staroměstské náměstí. I hope it will be unforgetable time spent here.
Hotel Arkada offers rooms with free Wi-Fi in the heart of Královské Vinohrady, only a short walk from Wenceslas Square. It features a 24-hour front desk.
The rooms in this restored Art Nouveau building have a flat-screen TV, a minibar, and a work desk.
Arkada is a 10-minute walk from the Dvořák Museum. The key sights of the center of Prague, including the historic quarter Square and Franz Kafka's Birthplace, are a 20-minute walk from the hotel.
Arkada Hotel has a tour desk and offers an airport shuttle service upon request. Prague's Main Station is 930 yards away, and the Náměstí Míru underground (subway) stop is 550 yards away.
Prague Hotel Tosca, from the category pleasant family Prague hotels, is situated in the centre of Královské Vinohrady, offering comfortable acccommodation in Prague in 38 rooms located in a five-floor building with a lift. The hotel is located in closest proximity to the centre of Prague by Náměstí Míru (Peace Square). Its excellent location, transportation availability and parking possibility makes the hotel an ideal place for Prague accommodating both individual tourists and groups.
Royal Plaza Hotel is located in the right center of Prague, Vinohrady. Hotel offers accommodation in 2-bedded to 4-bedded rooms. The entire building is available to connect to free wifi. Hotel has nonstop front desk.
Hotel Lunik Praha, from category 3 star hotels in Prague, is situated in a quiet part of the historical Prague centre. Close to the Wenceslas Square or the metro station is very close to the hotel to take you around the city. The hotel´s building has 5 floors and was newly reconstructed in year 2005. There are lots of shops, restaurants, bars around.


The Accounting Division ensures that all financial transactions are accurately reported in accordance with Generally Accepted Accounting Principles (GAAP) and the Government Accounting Standards Board (GASB).
The City's Federal Employer ID number is 59-6000409. The City's Sales Tax Exemption Certificate number is 85-8012514520C-3.


"Everything's his or will be." AMC has released new teasers for season seven of The Walking Dead.
The new season will pick up right where season six left off, with Negan (Jeffrey Dean Morgan) about to beat the life out of a main character. The cast also includes Andrew Lincoln, Norman Reedus, Steven Yeun, Lennie James, Chandler Riggs, and Melissa McBride.
Season seven of The Walking Dead premieres on October 23rd at 9 p.m. ET/PT.
What do you think? Are you a fan of The Walking Dead? Who do you think Negan hits?


ABC anchor George Stephanopoulos had connections to the Clinton Foundation that go back much further than he has admitted.
Stephanopoulos has conceded making donations going back to 2011, but new reports demonstrate that he has been heavily involved with the Clinton Foundation since at least 2006.
'Clinton' Cash author Peter Schweizer discusses the deepening scandal with Fox News host Sean Hannity.
ABC News has taken no disciplinary action against the journalistic malfeasance of their head anchor. Schweizer asserts that the Stephanopoulos ethics violations are "far more severe" than the embellishments made by NBC anchor Brian Williams who was suspended for six months earlier this year by his network for abusing the public trust.


text:
ABOUT BBC MIGRATION & EDUCATION CONSULTANTS
BBC Migration & Education Consultants is a Premium Overseas Study and Immigration Advisor. Our chief Mr. B B Charania is an offshore Migration Agent (3012232). BBC is as a private company that offers a gamut of professional Services to students who aspire to study overseas and also we represent skilled professionals who wish to seek work/immigration visa.
BBC Migration & Education Consultants is considered to be one of the most comprehensive and experienced visa consultants in India for studies and immigration abroad.
Do it Once, Do it Right! If you wish to Study or Work Abroad in the least possible time for the least possible expense, it is crucial to present the best possible case in the first time. The best way to present the best case possible is having an experienced Advisor represent your case. BBC Migration Consultants has aced Study & Immigration Visas to Australia, New Zealand, Canada, UK, Denmark and Germany. As a result, we have 99% of success rate!
Mr. B B Charania , Founder
BBC Migration & Education Consultant's founder Mr. B B Charania has 10+ years of work experience as Study & Immigration Consultant. He has wealth of knowledge for immigration to Australia, Canada, New Zealand & UK.
He is available for career guidance and counselling by prior appointment.
Karim B Charania, Consultant
Karim B Charania of KBC Visas is Licensed Immigration Advisor for Australia and New Zealand. He is our in-house Immigration Specialist. He expertise on wide range of visas and has a proven track record of 99% has made him one of the most sought after names in the industry.
Helping People Migrate
BBC Migration & Education Consultants helps you to bridge the gap and guide you through the visa confusion. At BBC two approaches are very important understanding your profile and giving professional legal advice with unmatched personal attention to each one of our clients in obtaining their visas.
It's Your Future! BBC Migration & Education Consultants has focused exclusively on Overseas Studies and Immigration visas. As a result 99% of the application we have processed are approved. If you want to study or work in Australia, New Zealand, UK, Canada or Europe in the least possible time for the least possible expense. It is crucial to present the best possible case - the first time. The best way to present the best case possible is having an experienced adviser at BBC represent you!
Code of Conduct/License Professional - As a licenced professional we abide to the code of conduct


Breathers - CST The Composites Store, Inc.
Econoweave 44, 4 oz. Breather/Bleeder Strip, 2" x 12 ft. pkg.
Econoweave 44, 4 oz. Breather/Bleeder Strip, 2", 100 yds./rl.
Econoweave 44, 4 oz. Breather/Bleeder, 15", 6 yd. pkg.
Econoweave 44, 4 oz. Breather/Bleeder, 15", 10-24 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 15", 25-99 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 15", 100 yds./rl.
Econoweave 44, 4 oz. Breather/Bleeder, 30", 1 yd. pkg.
Econoweave 44, 4 oz. Breather/Bleeder, 30", 10-24 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 30", 25-99 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 30", 100 yds./rl.
Econoweave 44, 4 oz. Breather/Bleeder, 60", 1 yd. pkg.
Econoweave 44, 4 oz. Breather/Bleeder, 60", 10-24 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 60", 25-99 yds.
Econoweave 44, 4 oz. Breather/Bleeder, 60", 100 yds./rl.


Just a slightly formalised list for keeping track of my blog post ideas.
It has a space for the idea, and then three check boxes for marking when the post has been written, when images have been organised and when it's finally uploaded / posted.
At the top are five prompts that I find can help with generating new ideas.
The PDF contains four pages in A5 size and are meant to be printed back to back (there's a margin allowance for punching or binding). The first set of two pages are a back to back set with the prompts at the top of the first page. The second set of pages are a back to back set without the prompts.


text:
Q: Suddenly my HDMI 2nd monitor stopped being detected today, and VGA 1st monitor is stuck at 640 X 480 I am hoping to find a solution to this very annoying issue. I turned on my PC today and noticed that only my VGA monitor was detected, which is odd because just last night both monitors were working normal. The other issue is that the monitor that does work will only allow me to have 640 X 480 resolution.
Things I have tried:

*

*Switching from Nvidia drivers to Xorg.

*Purging and reinstalling Nvidia

*Rebooting several times

*Adding monitors in the terminal with Xrandr (When I do this it gives me "Failed to get size of gamma for output defaults")
Yesterday I was tinkering around a bit with Wine and PlayOnLinux, but I am not sure if that is what caused this issue. When I go to the monitor settings it only shows my VGA monitor, and will not let me change the resolution. I was originally allowed to change the resolutions of the HDMI and VGA monitors with no problem.
Let me know what other information I can provide to help fix this issue.
Following the advice of a commenter I tried dmsg | grep -i nvidia and got this output:
[    2.522071] nvidia: module license 'NVIDIA' taints kernel.
[    2.530529] nvidia: module verification failed: signature and/or required key missing - tainting kernel
[    2.537250] nvidia-nvlink: Nvlink Core is being initialized, major device number 247
[    2.537639] NVRM: loading NVIDIA UNIX x86_64 Kernel Module  384.98  Thu Oct 26 15:16:01 PDT 2017 (using threaded interrupts)
[    2.539046] nvidia-modeset: Loading NVIDIA Kernel Mode Setting Driver for UNIX platforms  384.98  Thu Oct 26 14:41:13 PDT 2017
[    2.539722] [drm] [nvidia-drm] [GPU ID 0x00000100] Loading driver
[   13.660397] input: HDA NVidia HDMI/DP,pcm=3 as /devices/pci0000:00/0000:00:02.0/0000:01:00.1/sound/card1/input5
[   13.660463] input: HDA NVidia HDMI/DP,pcm=7 as /devices/pci0000:00/0000:00:02.0/0000:01:00.1/sound/card1/input6
[   13.660530] input: HDA NVidia HDMI/DP,pcm=8 as /devices/pci0000:00/0000:00:02.0/0000:01:00.1/sound/card1/input7

After entering lspci -v | grep -i nvidia I get:
01:00.0 VGA compatible controller: NVIDIA Corporation GK107 [GeForce GT 640] (rev a1) (prog-if 00 [VGA controller])
    Kernel driver in use: nvidia
    Kernel modules: nvidiafb, nouveau, nvidia_384_drm, nvidia_384
01:00.1 Audio device: NVIDIA Corporation GK107 HDMI Audio Controller (rev a1)


text:
Trouvez nos avocats
Médias & divertissement
Industrie de la finance
Personnel, emploi et avantages sociaux
Contentieux & règlement des litiges
Capitaux privés
Restructuration & insolvabilité
Rémunération des dirigeants, planification fiscale et patrimoniale
Propriété intellectuelle, technologies et données
Équipes régionales
Région Nordique
Voir toutes nos expertises
Présence dans le monde
Encourager l'innovation
UN CABINET RESPONSABLE
9 June 2020 Reed Smith Client Alerts
Congress passes the Paycheck Protection Program Flexibility Act of 2020
Page d'accueil Perspectives Congress passes the Paycheck Protection Program Flexibility Act of 2020
On June 5, 2020, President Trump signed into law a Paycheck Protection Program (PPP) reform bill that was first drafted in the U.S. House of Representatives.
The new law, entitled the Paycheck Protection Program Flexibility Act of 2020 (PPPFA), includes a number of long-sought-after changes to the PPP. Among the most anticipated changes are a reduction of the 75 percent rule for spending on payroll costs and an extension of the "covered period" for forgiveness purposes.
Below is a summary of the key features of the Act. We caution that this summary is not exhaustive, but it should nevertheless provide an overview of the Act's most noteworthy changes to the PPP. For more information regarding these new changes or for general details about the PPP, please reach out to any of the below authors.
Auteurs: Robert K. Stalzer Aryeh M. Younger
Required spending on payroll costs changes from 75 percent to 60 percent. A borrower is no longer required to spend at least 75 percent of its PPP loan proceeds on payroll costs in order to be eligible for full loan forgiveness. The Act reduces the 75 percent requirement for spending on payroll costs to 60 percent. According to the Small Business Administration (SBA) and the U.S. Treasury Department, forthcoming rules and guidance will clarify that if a borrower uses less than 60 percent of the loan amount for payroll costs, the borrower may still be eligible for partial loan forgiveness.
Extension of the covered period. The Act amends the definition of the covered period, the period during which a borrower's payroll and permitted non-payroll costs must be incurred or paid for purposes of loan forgiveness. The covered period is now defined as the earlier of 24 weeks after the date of a loan's origination or December 31, 2020. Additionally, a borrower that received a PPP loan prior to this change is permitted to elect the original eight-week covered period instead of the new 24-week covered period.
Changes to a borrower's number of full-time equivalent employees (FTE) will not necessarily result in a reduction to the borrower's forgiveness amount. A reduction in a borrower's FTE will not lead to a reduction in the loan forgiveness amount so long as the borrower can document that it (1) is unable to rehire the same employees who were working as of February 15, 2020; (2) is unable to hire similarly qualified employees for unfilled positions on or before December 31, 20201; or (3) is unable to return to the same level of business activity at which it was operating on or before February 15, 2020, because of the borrower's compliance with the federal government's safety requirements relating to sanitation, social distancing, or any other worker or customer safety mandate in connection with COVID-19 (the rules or guidance must have been issued by the Centers for Disease Control and Prevention, the Department of Health and Human Services, or the Occupational Safety and Health Administration).
Ten-month window to apply for forgiveness. The borrower has 10 months after the last day of its covered period to apply for forgiveness before the borrower starts making payments on the PPP loan.
Extension of the safe harbor date. The Act extends the safe harbor date for rehiring employees from June 30, 2020, to December 31, 2020.
Extension of the loan deferment period. The period for deferment of loan payments has been extended to when the amount of forgiveness is determined and remitted to the lender. This is a change from the previous law, which mandated deferments of not less than six months and not more than one year.
An employer whose PPP loan has been forgiven is no longer excluded from deferring its share of payroll costs. The CARES Act includes a provision that allows employers to defer payment of their share of payroll taxes until 2021. Employers must then pay 50 percent of such taxes in 2021 and the other 50 percent in 2022. The law previously contained an exclusion for employers who receive forgivable PPP loans – these employers could not defer paying their shares of payroll taxes. The Act now eliminates this exclusion by permitting all employers to defer the payment of their share of payroll taxes regardless of whether they have received forgiveness for PPP loans.
Maturity date is increased to a minimum of five years. The minimum maturity date for PPP loans has been increased from two years to five years. Although the CARES Act states that the maximum maturity date for PPP loans is 10 years, the SBA's first interim final rule mandated that all PPP loans would have a maturity date of two years. The Act increases the maturity date for PPP loans to a minimum of five years, while retaining the maximum maturity date of 10 years. The interest rate of 1 percent will remain the same.
December 31, 2020, is a safe harbor date for borrowers whose covered periods expire prior to that date.
Compétences: Droit des sociétés Industrie de la finance
Bureaux: Houston Washington, D.C. New York Philadelphie
Thème: CARES Act Resource Center Coronavirus (COVID-19) Resource Center
Ce qui pourrait également vous intéresser
Preparing for the end of the COVID-19 public health emergency
HRSA asks for comment on provider relief fund and ARP rural reporting requirements
Avis juridiques
Déclaration relative à l'esclavage moderne et au trafic d'êtres humains
© 2023 Reed Smith LLP. Tous droits réservés.


3 Ways to save money for your ticket from Oberau to Lebach?
The most wanted dates and times of departure to travel to Lebach are almost always more expensive.
Our booking system will always propose you the cheapest prices for your rail tickets .
Advance rail tickets from Oberau to Lebach are the cheapest train tickets. You may book up to 3 months in advance and up to 6 months for Eurostar.
Do you need help to buy your rail ticket to Lebach?
Questions , remarks about your rail trip Oberau to Lebach?


text:
Climate Change Connection > 2016 > October
Oilsands under the spotlight in Leo DiCaprio's new climate change doc
Oct 27, 2016 – CBC News –
The new documentary "Before the Flood" had its world premiere at the Toronto International Film Festival. It will be screened in 171 countries, in 45 languages, when it debuts on the National Geographic Channel on Sunday.
Police issue 99 trespass citations during pipeline protest on Parliament Hill
People were issued citations by the RCMP for trespassing after climbing over police barricades near the foot of the Peace Tower. They were protesting the proposed expansion of Kinder Morgan's Trans Mountain pipeline.
Mini-home builders pushing for major bylaw amendments
Oct 21, 2016 – Winnipeg Free Press –
The owners of the city's only custom builder of tiny houses have a big problem — they haven't been able to convince Winnipeg officials to amend city bylaws to allow for mini homes to be built on individual lots in the city.
'Monumental' deal to cut HFCs, fastest growing greenhouse gases
Oct 15, 2016 – BBC News –
Delegates meeting in Rwanda accepted a complex amendment to the Montreal Protocol that will see richer countries cut back their hydrofluorocarbon (HFC) use from 2019. HFCs are widely used in fridges, air conditioning and aerosol sprays.
Before the Flood: Leonardo DiCaprio hopes his new film will inspire climate action
Oct 14, 2016 – The Guardian –
Documentary that sees the actor touring the world to see global warming's impact first-hand has its UK premiere in London on Friday
Electric cars set to pass 2m landmark globally by end of 2016
China leads EU and US in market size, with Nissan Leaf taking the top spot for best-selling model followed by Tesla's Model S and two Chinese-made models
Coming carbon pricing scheme shouldn't alienate farmers, researcher says
As the federal government gets ready to roll out a national carbon pricing plan, Ian Mauro, professor of geography at the University of Winnipeg, said shifting attitudes on meat consumption and carbon reduction models need to be sensitive to life on the Prairies.
BC company claims 'Net Zero' answer to reserve housing energy woes
Oct 6, 2016 – The Tyee –
For many reserves, the cost of replacement housing is too high to permit anything but a triaging of the worst of the worst cases, while backlogs of needed repairs and replacements continue to grow. Ottawa says it is taking steps to address all these problems. Now a BC company is trying to show it has the house to address all three at once.
Pallister pulls plug on NDP's carbon cap-and-trade plan
Oct 4, 2016 – Winnipeg Free Press –
OTTAWA — Manitoba Premier Brian Pallister is scrapping plans for a provincial cap-and-trade system on large carbon emitters in favour of a Manitoba-made solution to put a price on carbon.
Justin Trudeau gives provinces until 2018 to adopt carbon price plan
Oct 3, 2016 – CBC News –
Prime Minister Justin Trudeau has given provinces until 2018 to adopt a carbon pricing scheme, or the federal government will step in and impose a price.


While last month's Advocacy Adviser column focused on the ASHRAE standards-maintenance process, this month's column will go further into the ASHRAE public review process.
Typically, public reviews are open for 30-45 days to provide the public ample time to review the proposed changes and make comments. All comments must be submitted electronically via the ASHRAE website so that they can be managed by the Standing Standards Project Committee (SSPC). Public review comments received during an open public review are reported to all members of the SSPC. An effort to resolve all negative public review comments are made by the SSPC and each negative commenter is advised of the disposition of the objections and reasons. After consideration of comments or because of new information, the SSPC may make changes to the draft. If the committee determines that a full subsequent public review is required because of these changes, the public review process is initiated again regarding the changes.
Once the public comments are resolved and the committee is satisfied with the proposed change, the committee will recommend to the ASHRAE board of directors publishing the proposal as an addendum to the current standard. For those standards that are co-published by the American Society for Health Care Engineering (ASHE), approval also must be obtained from the ASHE board.
Once approvals are obtained, ASHRAE recommends the proposal to the American National Standards Institute (ANSI), which verifies that the public consensus process was followed and, if approved by ANSI, the addendum is published and posted on ASHRAE's website.
Access the ASHRAE Online Standards Actions & Public Review Drafts web page and the ASHRAE Standards Addenda web page to engage in ASHRAE's public review process.
Also this week: Joint Commission focuses on strategies to reduce OR noise.
Did you know changes to ASHRAE standards can be proposed at any time throughout the year?
Proposed addenda to Standard 170-2017, Ventilation of Health Care Facilities seek to change its scope and clarify its purpose.


Demonia Sweeter Than Eva Trinity Boots cuz you got em' hooked on these baddies. These badass knee-high boots have multiple metal badazz buckles all da way up yer leg, epic platforms and back zipper closure.
WLNHMGO Tongue Twizt Trinity B..
XTUWWGT Low Trinity Boots Fash..