text:
In Pivotal Ruling, Supreme Court Removes Barrier to Protecting First Amendment
Sarah Parshall Perry / @SarahPPerry / March 09, 2021
Seven other justices join Justice Clarence Thomas in his opinion determining that a request for nominal damages satisfies the Constitution's "redressability element" for standing before the court when a plaintiff's claim is based on a violation of a legal right. (Photo: Tasos Katopodis/Getty Images)
Sarah Parshall Perry / @SarahPPerry
Sarah Parshall Perry is a legal fellow in the Edwin Meese III Center for Legal and Judicial Studies at The Heritage Foundation.
A near-unanimous Supreme Court decided Monday that two Christian students have the right to sue the Georgia college that violated their free speech rights in the past.
The impact of the ruling is far greater than what at first sounds like a run-of-the-mill decision on a procedural issue that might prompt non-lawyers to take a nap.
The case is a significant win for the First Amendment and protection of religious speech on the campus of a public college. It portends more thoughtful administration of speech policies on campuses.
And the near unanimity of opinion by nine Supreme Court justices with vastly different ideological bents also may bode well for the high court's future interpretations of the importance of constitutional rights.
The case, Uzuegbunam v. Preczewski, involves two former students of Georgia Gwinnett College who, while enrolled there, sought to exercise their religious liberty and publicly share their faith with other students by handing out religious literature on campus.
However, college officials decided to enforce a policy that barred students from speaking to fellow students about their Christian religious beliefs in a public square. Later, officials barred the students' speech in a campus "free speech zone" even after the students had applied for and received the appropriate permit as requested by the school.
An analysis of the case reveals how hostile Georgia Gwinnett College was to religious speech.
After other students complained about Chike Uzuegbunam's sharing of his faith in the school's "free speech zone," campus police told Uzuegbunam that his speech violated campus policy because it "disturb(ed) the peace and/or comfort" of others.
Uzuegbunam and the other student filed lawsuits seeking injunctive relief and nominal damages. After the college eventually abandoned its speech policy, the school moved to dismiss the suits as moot.
But the students pressed forward, arguing that their claims were not moot because they sought to remedy a violation of their First Amendment rights through a request for nominal damages.
In a short opinion written by Justice Clarence Thomas and joined by seven other justices, the Supreme Court ruled that a request for nominal damages satisfies Article 3's "redressability element" necessary for standing before the court when a plaintiff's claim is based on a past violation of a legal right.
The court specified that even a claim for small or largely symbolic damages—here, for the violation of a constitutional right—is enough for a plaintiff to sue, assuming all other elements of standing are met.
In agreeing with the former students, the court looked to forms of relief generally awarded in common law to determine whether nominal damages can redress a past injury.
Significantly, the court acknowledged that the rule allowing nominal damages for a violation of a legal right is "decisively settled" and was established specifically to prevent favoring "economic rights over important, but not easily quantifiable, nonpecuniary rights."
During oral arguments in January, Justice Amy Coney Barrett asked the attorney for the Justice Department whether the case would have much broader implications than protecting free speech.
Specifically, Barrett asked whether the outcome of a dispute over New York City gun laws, a case that the court dismissed as moot after the city had changed its policy, would have been different if the challengers had added a claim for nominal damages for the violation of their constitutional rights.
The Justice Department attorney answered that the outcome indeed would have been different.
Chief Justice John Roberts was the lone dissenting vote, writing that the majority risked a "radical extension of judicial power."
Roberts' decision was unusual, not only because it was his first solo dissent since joining the court in 2005, but because of his strong language in rebuking the majority.
Roberts stressed that because the disputed speech restrictions no longer exist at Georgia Gwinnett College, and because the students had not asked for "actual damages" (monetary relief), the case no longer was a live controversy and therefore moot.
The chief justice suggested that the majority's ruling risked forcing federal courts to "give advisory opinions whenever a plaintiff tacks on a request for a dollar."
But Thomas countered that if the high court would recognize a dollar in economic injury for wasted bus fare in traveling to the free speech zone, it must certainly recognize a dollar for a completed violation of a constitutional right.
Holding that every violation of a right imports damage, Thomas wrote that nominal damages are sufficient to redress an injury even if that injury—like the violation of First Amendment rights—cannot be reduced to or quantified in simple economic terms.
The Supreme Court's decision in this case is a reminder that censorship has consequences and that redressing the harm from violating constitutional rights, though sometimes hard to quantify, is just as important as redressing the harm from violations of economic rights.
A college's preemptive silencing of a student's speech—even if it later changes that policy to pump the brakes on litigation—may not be enough to keep the matter out of court.
School administrators hopefully will think twice before promulgating and implementing campus speech codes and other policies that can adversely affect their students' constitutional rights.
Have an opinion about this article? To sound off, please email [email protected] and we will consider publishing your remarks in our regular "We Hear You" feature.


text:
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Q&A With Political Scientist Ayse Kaya
January 22nd, 2016 Mark Anskis
A scholar of globalization and international political economy, Assistant Professor of Political Science Ayse Kaya has always been curious about how states' economic power relates to their political power in multilateral economic institutions, such as the International Monetary Fund (IMF) and the World Bank.
While researching the topic, Kaya discovered that, contrary to popular belief, political power was not simply reflected in economic power.
"My discipline – international relations – routinely studies 'power' and 'institutions,' but had not adequately explored the intersection," she says. "We have perspectives that eloquently argue how institutions tame and discipline power to pave the way for cooperation among states. Hence, their focus is on institutions. On the other hand, we have perspectives that see institutions as servants of powerful actors."
This curiosity led Kaya to explore the topic in-depth in the new book Power and Global Economic Institutions (Cambridge University Press, 2015). Below, Kaya discusses the book, her findings, and how she plans to incorporate her findings into her teaching.
Q: What is the main takeaway of your new book?
A: Very simply, multilateral institutions adjust the importance of the distribution of inter-state economic power. Like a volume dial, in some institutional settings, you hear the differences in states' economic power loudly; in other cases, the effects of that economic distribution are relatively more muted.
Q: What implications does this have for today's society?
A: For one, this argument has implications on how we interpret the economic rise of China within a system that has been created and led by the United States. China's increasing economic prowess will be more important for some institutional settings than others. In other words, it is not a simple story of "China's economic rise becomes enhanced political power for China" in all multilateral institutions. As an example, if we look closely, we will see that China's economic rise has different implications for the World Bank (which focuses on economic development) versus the IMF (which focuses on monetary stability), even though these two are known as the "twin" international financial institutions.
Q: What is your assessment of the current multilateral economic system?
A: Political scientists like pointing out that institutions are sticky, so in this regard, I don't think the multilateral institutions face immediate existential threats. But, they do face consequential fragilities that need to be urgently addressed. As a quick example, both the economically rising states, such as China, and the institutionally dominant states, such as the United States, tend to exhibit greater interest in using some of these institutions as platforms to induce alterations in the other side's behavior, while shielding their own policies from change. This is a source of tension between the key shareholders of the institutions.
Historically, American leaders have taken a far-sighted view of institutions. During and after World War II, American policymakers led the creation of the United Nations, the World Bank, and the IMF to stabilize the system and prevent war. Americans have enormously benefited from this predictable rule-based order. When we hear U.S. politicians here scapegoating international institutions, it threatens to pull down the entire house at a moment when new great powers like China are entering the picture. Instead, we should look for opportunities to strengthen and update them.
Q: How will you incorporate your findings into your teaching?
A: Since I teach on global governance and international political economy, there will be an easy synergy between the book and the content of my teaching. I think the text will be helpful in giving students of international relations a historical background on key multilateral economic institutions – the IMF, the World Bank, and on the international trading system – as well as in having them deepen their understanding of these institutions' key functions and purposes. It also contains theoretical discussions on how different perspectives in international relations think about the impact of power on institutions. Finally, it discusses issues of widespread interest to Swarthmore students, such as the position of weak states in these institutions, which are often vulnerable to being coerced.
Economist Syon Bhanot Recognized for Government Innovation Efforts
Category: Faculty-Student Collaboration
The City of Philadelphia recognized Bhanot's efforts—powered in part by Swarthmore students—to use social science to positively influence public policy.
Students, Faculty to Bring Plans for Social Action to Clinton Global Initiative University
Joining more than 1,000 students and policy experts from around the country is Seimi Park '20, who aims to increase diversity and objectivity in journalism.
Submissions Welcome
The Communications Office invites all members of the Swarthmore community to share videos, photos, and story ideas for the College's website. Have you seen an alum in the news? Please let us know by writing news@swarthmore.edu.

redpajama_set_name: RedPajamaCommonCrawl


We wrote nine months ago about the projected shortage of hotels in the city centre due to high demand, and about the new developments that were underway at the time.
Since then we've seen a huge number of new announcements to meet accommodation demand - in the last month there's been about one per week!
Precinct Properties and IHG's announcement about the One Queen Street Hotel brings the number of new hotel rooms in live or planned developments to over 2,000.
Here's a recap of the recent announcements and all of the city centre hotels in the pipeline at the moment.
1 Queen Street (at corner of Quay Street) - 244 rooms and suites over 12 stories of a 22-level building created by the redevelopment of the existing HSBC Building. A rooftop bar and hospitality will spread across 500sqm on the 21st floor. The hotel is being developed by Precinct Properties as a complement to their Commercial Bay development on the same block and will be operated by IHG.
Artists impression of One Queen Street Hotel viewed from Waitemata Harbour. Image: Precinct Properties.
Corner Wellesley and Nelson Streets – 180 – 200 rooms with a gym, rooftop bar and separate restaurant and bar. To replace the old Datacom House on the site opposite the soon to open NZICC. View website.
51 Albert St – 225 rooms and 24 private residences over 41 stories. The development will incorporate the historic facades of Macdonald Halligan Motors Company on Albert Street and St Patrick's Square. View website.
Artist's impression of Hotel Indigo at 51 Albert Street. Image: 94 Feet.
500 Queen Street (so outside the Heart of the City boundary) – 96 rooms over eight stories, with a restaurant, conference room and fitness centre. The existing building on the site is being retrofitted as a hotel.
Corner Albert and Wolfe Streets - developers are in the concept planning stage for a substantial multi-level commercial project on the sites of the heritage Yates Building and the old carparking building on Wolfe Street. Plans include retail at the ground levels on Albert, Federal and Wolfe Streets, with the potential for a hotel and apartments above ground level.
Corner Gore and Galway Streets – 99 rooms and 5 luxury suites over 10 stories, with ground floor laneways connecting to neighbouring heritage buildings, retail and food and beverage outlets. View website.
67 Customs St East - 130 rooms, with a rooftop bar, streetfront café, restaurant and a floor of wellness facilities. View website.
100 Hobson Street - 303 rooms and suites above a retail and dining laneway with airbridge connectivity to the NZICC and main SKYCITY site. View website.
141 Halsey Street - 195 rooms plus food and beverage outlets, event spaces, spa, fitness centre and a 25 metre pool.
147 - 149 Victoria Street West - 48 serviced apartment suites, 55 residential apartments over 9 stories, with 8 retail and office units. Groundworks are underway.
10 – 12 Commerce Street - 35 hotel suites over floors 1 - 6 of a 57-level apartment tower. View website.
Corner Wyndham and Albert Streets – A 200 room Even hotel on the upper levels and a 290 room Holiday Inn Express on the lower levels of a 37 level tower. Demolition is underway.
Corner Elliot and Albert Streets - 300 rooms over four levels of a 52 storey tower.


Dow, James L. September 18, 1923.
This newspaper is part of the collection entitled: Texas Digital Newspaper Program and one other and was provided by Archer Public Library to The Portal to Texas History, a digital repository hosted by the UNT Libraries. More information about this issue can be viewed below.
Dow, James L. The Lubbock Daily Avalanche (Lubbock, Texas), Vol. 1, No. 277, Ed. 1 Tuesday, September 18, 1923, newspaper, September 18, 1923; Lubbock, Texas. (https://texashistory.unt.edu/ark:/67531/metapth658452/: accessed April 22, 2019), University of North Texas Libraries, The Portal to Texas History, https://texashistory.unt.edu; crediting Archer Public Library.


text:
\section{Introduction}
\label{S:1}
  In the past few years, Convolutional Neural Networks (CNN) have significantly prompted the development of many computer vision tasks \cite{he2016deep,long2015fully,ren2016faster,Bochkovskiy2020yolov4,liu2016ssd,lin2017focal}. However, the training of such models with millions of parameters requires a massive amount of labeled training data, such as MS COCO\cite{lin2014microsoft}, CityScapes\cite{cordts2016cityscapes} and ImageNet\cite{russakovsky2015imagenet} to achieve state-of-the-art results. At the same time, traditional object detection model is based on the condition of independent identical distribution. Due to the changing of scene, light, imaging angle and instance texture, object detection model do not satisfy the condition. It is obvious that the creation of such massive datasets has become one of the bottlenecks of these approaches, the datasets are accurately annotated, very expensive, time-consuming, and error-prone.

\begin{figure}[]
\centering
\includegraphics[height=0.25\linewidth]{fig1.jpg}
\caption{Intelligent robot for automatic loading and unloading, the first robot is Parcel Robot from Germany, the second robot is PIQR from Netherlands, the last is ours robot.}
\label{fig:1}
\end{figure}
With the development of the e-commerce logistics industry, logistics transfer is becoming busier and busier. The efficiency about the goods in and out of the warehouse is more and more important for the logistics transfer. This promote the development of automatic loading and unloading intelligent robots as shown in Figure.\ref{fig:1}. One of the most critical techniques for these intelligent robots is the detection of dense cartons. We have built a carton dataset SCD\cite{yang2021SCD} for carton detection, but the goods are diverse in different spots. So the carton datasets from different scenes are inconsistent in carton logo, color, texture, etc as shown in Figure.\ref{fig:2}, which means detectors trained by SCD can not satisfy the assumption of independent and identical distribution.

  To tackle the above issue, many researchers resort to data augmentation strategies to increase data coverage by utilizing available training data. Common data augmentation methods include random cropping, color jittering, random deformation, etc. However, these methods can only introduce limited data variation, so they improve the performance slightly. Recently, several works\cite{dwibedi2017cut,liu2020novel,tripathi2019learning} propose crop-and-paste data augmentation schemes for object detection \cite{ren2016faster,liu2016ssd,lin2017focal}, which is cropping some object foregrounds and pasting them into the target scene. However, these methods need to collect lots of object foregrounds with different poses, it is expensive and time-consuming. And the combination relationship of object foregrounds and backgrounds is complex. As a result, the augmentation samples may look unrealistic and hinder the model learning.

  Here, we propose a novel dataset synthesis method of replacing the foreground instance texture with the instance texture from the new scene to augment the datasets, the augmentation samples may look more realistic. And it is convenient to compare with the methods in Ref.\cite{dwibedi2017cut,liu2020novel,tripathi2019learning}, because our method do not need to collect variety of pictures for each kind of carton with different poses. As shown in Fig.\ref{fig:3}, our method involves four key stages:(1) label the images with our rules; (2) segment different surfaces of the carton to realize texture decoupling; (3) construct a complete contour for the surface of the carton, because the contour of the instance texture from target scene is complete; (4) synthesize images with Gaussian fusion method. We perform foreground instance texture replacement in CPLC scene, and all experiments are verified on models including Faster R-CNN\cite{ren2016faster} and RetinaNet\cite{lin2017focal}.

\begin{figure}[]
\centering
\subfloat[]{\label{Fig:2a
\includegraphics[width=0.45\linewidth]{fig2a.png}}
\subfloat[]{\label{Fig:2b
\includegraphics[width=0.45\linewidth]{fig2b.png}}

\subfloat[]{\label{Fig:2c
\includegraphics[width=0.45\linewidth]{fig2c.png}}
\subfloat[]{\label{Fig:2d
\includegraphics[width=0.45\linewidth]{fig2d.png}}
\caption{(a) The images come from a comprehensive pharmaceutical logistics company(CPLC); (b) The images come from an e-commerce logistics company(ECLC); (c) The images come from a fruit market(FM); (d) The foreground texture dataset from FM.  In the figure (a), (b) , (c), the stacking and palletizing structures of all cartons are similar in all scenarios.}
\label{fig:2}
\end{figure}

  Our method has three main contributions: (1) a novel dataset synthesis method of replacing the foreground instance texture to generate the target dataset with the high homogeneity of the carton palletizing form, as shown in Figure \ref{fig:2}; (2) a new surfaces segmentation algorithm for texture decoupling.  (3) a new contour reconstruction algorithm based on the parallelogram rule to reconstruct the incomplete contours.

\section{Related Work}
\label{S:2}
\subsection{Object detection}
\label{subS:2.1}
   In the past few years, with the development of Convolutional Neural Networks, object detection has been applied in many scenes, such as security, autonomous driving, defect detection. The CNN-based detection models are mainly classified into single-stage detectors\cite{lin2017focal,liu2018receptive,liu2016ssd,Bochkovskiy2020yolov4,wu2019iou} and two-stage detectors\cite{ren2016faster,girshick2014rich,lin2017feature}. Faster R-CNN\cite{ren2016faster}, acted as the mainstream two-stage detector, firstly uses RPN to generate region proposals which are fed into the FPN to conduct accurate localization and classification tasks. And some works such as Cascade R-CNN\cite{cai2018cascade}, Libra R-CNN\cite{pang2019libRCNN}, Mask R-CNN\cite{he2017maskrcnn} have inspired by Faster R-CNN. Although the two-stage detection models can achieve considerable performance, its efficiency is poor because of the complex multi-stage processing. To solve this problem, researchers have proposed many single-stage detection models such as YOLO\cite{redmon2016you}, YOLO V4\cite{Bochkovskiy2020yolov4}, SSD\cite{liu2016ssd} and RetinaNet\cite{lin2017focal}. SSD\cite{liu2016ssd} not only drew on the idea of RPN\cite{ren2016faster} but also adopted feature pyramid to detect multi-scale objects. And RetinaNet\cite{lin2017focal} proposed the focal loss to solve imbalance between the easy examples and hard examples. We use RetinaNet\cite{lin2017focal} and Faster R-CNN\cite{ren2016faster} as the baselines to demonstrate the effectiveness.

\subsection{Data Augmentations}
\label{subS:2.2}
  Data augmentations have played an key role in achieving state-of-the-art results for many computer vision tasks such as image classification\cite{he2016deep,tan2019efficientnet,shao2020label,park2019convolutional} on ImageNet\cite{krizhevsky2017imagenet}, object detection\cite{ren2016faster,lin2017focal,liu2016ssd,zhang2019feature} on MS COCO\cite{lin2014microsoft}. The simplest strategy for solving overfitting is to train CNN-based models on larger scale of data. Many data augmentation strategies have been proposed to augment the existing datasets such as random crop\cite{krizhevsky2017imagenet,szegedy2015going}, color jittering\cite{szegedy2015going}, perspective transformation\cite{huang2019faster}, Auto/RandAugment\cite{cubuk2018autoaugment,cubuk2020randaugment}, and random expansion\cite{ghiasi2020simple,liu2016ssd}. These augmentation strategies are originally designed for image classification\cite{krizhevsky2017imagenet} and are mainly used for improving the classification models' invariance to data transformations in image classification task.

\subsection{Copy-Paste Augmentation}
\label{subS:2.3}
  The methods based on the foreground and background decoupling and recombination is mostly used in the field of dataset expansion and dataset generation to improve the generalization of the model. For example, McLaughlin N et al.\cite{mclaughlin2015data} proposed to extract the foreground of the target dataset and then fuse with the street background to reduce the bias of the datasets. Georgakis G et al.\cite{georgakis2017synthesizing} used the depth information of the scene and combined with foreground examples to explore semantic fusion in a given background image. Dwibedi D et al.\cite{dwibedi2017cut} used a variety of synthetic methods to ignore these subtle pixel artifacts and focus only on the object appearance during data generating. But this method has defects because it can not cover all possible synthetic methods. Tripathi S et al.\cite{tripathi2019learning} used adversarial learning to construct a task-driven synthesis network for foreground instance and background image synthesis. Liu S et al.\cite{liu2020novel} used the foreground instances and background images to generate pedestrian data and then cycGAN \cite{zhu2017unpaired} is utilized for style transfer to achieve pedestrian detection in different scenes. At present, the methods of the foreground instance and background image fusion mainly focus on the contextual semantic relationship of the foreground instance and background image\cite{mclaughlin2015data,georgakis2017synthesizing,gupta2016synthetic,tripathi2019learning}. In addition, these methods in Ref.\cite{dwibedi2017cut,liu2020novel,tripathi2019learning,ghiasi2020simple} focus on how to ignore these subtle pixel artifacts during the synthesis process. Compared with the methods in Ref.\cite{dwibedi2017cut,liu2020novel,tripathi2019learning,ghiasi2020simple}, the difference of our method is that the scale and position of instance in the background image remains unchanged. The carton instance is composed of multiple surfaces, so different combinations of the same surface can generate a new instance with different perspective and poses. Compared with the methods in Ref. \cite{dwibedi2017cut,liu2020novel,tripathi2019learning,ghiasi2020simple}, our method does not need to collect a large number of foreground instances with different perspectives and poses.
\section{Method}
\label{S:3}
\subsection{Approach overview}
\label{subS:3.1}

\begin{figure}[]
\centering
\includegraphics[height=0.5\linewidth]{fig3.jpg}
\caption{We propose a carton datasets synthesis method for object
detection with different perspectives and poses. Firstly, we have built a Stacked Carton Datasets(SCD)\cite{yang2021SCD}; secondly, we label the images with our labeling rules; thirdly, we perform surfaces segmentation algorithm to get different surfaces; then we use the contour reconstruction algorithm to construct a complete quadrilateral contour in part 4; next we use Gaussian filtering algorithm for foreground texture replacement and image synthesis; finally, we use the generated data to train the detection model.}
\label{fig:3}
\end{figure}

  We propose a simple approach to rapidly collect data for target domain with less time consumption and no human annotation. And the key idea of our approach is keeping the contour of the carton from source domain unchanging to replace their foreground texture with the target texture. This approach can capture all visual diversity of an instance with different views, scales, and orientation. And our approach can maintain the contextual semantic relationship of the image to make the synthesized image more realistic. The architecture of our carton datasets synthesis method is illustrated in Fig.\ref{fig:3}. And there are six steps of our method:

  (1) \textbf{Collect source images:} We have built a Stacked Carton Dataset(SCD)\cite{yang2021SCD} for carton detection task in the warehousing and logistics industry.

  (2) \textbf{Labeling process:} In the image plane, the carton may contain one, two, or three visible surfaces depending on the shooting angle and each surface has different texture. According to the traditional labeling rules, different surfaces cannot be distinguished. So we have designed new labeling rules(as shown in Sec.\ref{subS:3.2}) to help surface segmentation.

  (3) \textbf{Surfaces segmentation algorithm:} As shown in Fig.\ref{fig:label}, each surface of the carton is a closed polygon such as: 2-3-4-5, 4-6-7-8-5 and 1-2-5-8-9. So the surface segmentation is to find all the closed polygon without any overlap. Here, we consider the labeled point as a city, so the surface segmentation can be solved by the method of TSP\cite{gavish1978travelling}. Our algorithm consists of three steps(see Sec.\ref{subS:3.3} for detailed information) as shown in third module in Fig.\ref{fig:3}. The first step is data processing, the second step is calculating cost matrix as a directed graph and the third step is to get all closed polygons of the carton with TSP algorithm.

  (4) \textbf{Contour reconstruction algorithm:} The contour of the foreground texture from target datasets is complete as shown in Fig.\ref{Fig:2d}. So when the contour of the surface from source datasets is incomplete, we should construct a complete contour. And we construct a parallelogram as the complete contour for the surface(see Sec.\ref{subS:3.4} for detailed information).

  (5) \textbf{Image synthesis:} Gaussian fusion method is used to generate images and the random nosie as negative texture example is used to make the detection model focus only on the object appearance(see Sec.\ref{subS:3.5} for detailed information).

  (6) \textbf{Training:} We use the generated images and the source images to train the detection models.

\subsection{Labeling method}
\label{subS:3.2}
  SCD\cite{yang2021SCD} mainly focus on the task of carton detection in the logistics industry. The images in SCD are collected from 3 scenarios of different locations. And each scenario contains a large number of neatly stacked cartons as shown in Figure \ref{fig:2}.

  \textbf{Data collection:} SCD mainly collects carton images in the loading and unloading dock scenes. Because the cameras of the palletizing robots is approximately parallel to the surface of the goods, the imaging plane is approximately parallel to the surface of the goods during data collection(as shown in Figure \ref{fig:2}). And the shooting distance is within 5 m.

\begin{figure}[htbp]
\centering
\includegraphics[height=0.3\linewidth]{label.png}
\caption{Labeling method. The number represents labeled point and the arrows represents the labeling process. \textbf{Common lines:} the line between two visible surfaces, such as line 5-2, 5-4, 5-8. \textbf{Three-line points}: the points which are intersected by three visible lines such as point 5, 2, 4, 8; \textbf{Two-line points}: the points which are intersected by two visible lines such as point 1, 3, 6, 7, 9.}
\label{fig:label}
\end{figure}

  \textbf{Labeling rules:} SCD utilizes LabelMe\cite{russell2008labelme} for labeling. Besides, to help the robot pick up the goods, one auxiliary labling rule is proposed. \textbf{Occlusion/All:} it is labeled as "Occlusion" when all the surfaces of the carton are occluded, and "All" when at least one surface of the carton is not occluded. To segment different surfaces of the carton, we have designed a new labeling method as shown in Figure \ref{fig:label} and the \textbf{labeling rules} includes: (1) only the two-line points can be selected as the start points; (2) the points should be labeled in a clockwise direction; (3) the common lines should be repeatedly labeled twice.

\subsection{Surfaces segmentation algorithm}
\label{subS:3.3}
  \textbf{Data procssing:} Based on our labeling method, it is easy to judge whether the point is a faceted point according to definition \ref{df1}. But during labeling process, the coordinates of the same faceted points have errors when labeling according to the third labeling rule. Thus, we judge the point is the same faceted points in labeled points set $P^S$(as shown Figure \ref{fig:label}) according to Eq.\ref{eq1}(3 to 11 line in Algorithm\ref{alg:r2p}). Then the average of the coordinate about the same faceted points is used to replace the values of the same faceted points in $P^S$ to get a new point set as $P^{new}$(17 to 18 line in Algorithm\ref{alg:r2p}). Finally, we get a point set $P^{t}$ without repeated points from $P^{new}$(12 to 16 line in Algorithm\ref{alg:r2p}) to calculate the cost matrix.

\newtheorem{definition}{\textbf{Definition}}
\begin{definition}
The three-line points are faceted points as shown in Fig.\ref{fig:label}. According to the labeling rules in section \ref{subS:3.2}, the points which are repeated more than 1 times in the labeled points are faceted points.
\label{df1}
\end{definition}

\begin{equation}
\label{eq1}
{D(p_i,p_j)}=\sqrt{(p_{i_x}-p_{j_x})^2+(p_{i_y}-p_{j_y})^2}\leq\psi
\end{equation}
  In Eq.\ref{eq1}, $p_i,p_j\in P^S$, and $(p_{i_x},p_{i_y})$ is the coordinate of $p_i$. $\psi$ is a hyperparameter to determine whether two points are the same faceted point,$\psi=25$ here.

  \begin{algorithm}[]
    \caption{Surfaces segmentation algorithm }
    \LinesNumbered
    \label{alg:r2p}
    \KwIn{$P^S=\{p_i\}_{i=0}^n$: the points list of the instance from labeling points;}
    \KwOut{$P_{result}=\{p_i\}_{i=0}^2$ :the points of surfaces in the carton;}
    \textbf{Initialize:} {$\psi=25$;\quad$index=[[],[]...]$: the Array which save the indexes of points with same coordinate in $P^s$;\quad $P^t=[]$:the Array which do not have repeated points.\\
    }
    \textbf{\#Data procssing:}\\
    \For{$i=0$ to n }
      {
       $index{[i]}\gets \{i\}$\\
       \For{$j=0$ to n}
        {
         \text{Calculate $D(P^S[i],P^S[j])$ by Eq.(\ref{eq1})} \\
         \If{$D(P^S[i],P^S[j])\le \psi$}
            {  \eIf{$i$ is $j$}{continue}{$index{[i]}\gets index{[i]}+\{j\}$}
            }
        }
      }
    $index_{rm} \gets RemoveDuplicateData(index)$\\
    $K \gets Length(index_{rm})$ \\
    \For {$i=0$ to $K$}
     {
       $N \gets Length(index_{remove}[i])$\\
       $P^t[i] \gets \frac{\sum_{k=0}^NP^S[index_{rm}[i][k]]}{N}$\\
       \For{$j=0$ to $N$}
       {
         $P^{new}[index_{rm}[i][j]]\gets P^t[j]$
       }
     }

   \textbf{\#Computational cost matrix $V$:} \\
   \For{ $i=0$ to $K$ }
     {
       \For{ $j=0$ to $K$ }
       {
         \eIf{$i$ is $j$}
         {
           continue
         }{
           calculate $V_{[i][j]}$ by Eq.(\ref{eq2}) with $P^t[i],P^t[j]$ and $P^{new}$
         }
        }
      }
   \textbf{\#The TSP solution:} \\
     $D_s=[]$ \\
     \For{$k=0$ to $K$}
      {
           $N \gets Length(index_{rm}[k])$\\
           \eIf{$N >1$}
           {
             continue
           }{
               $p_{init} \gets P^t[k]$\\
               $\overline{P}_{/k}\gets P^t-\{P^t[k]\}$\\
               \text{get shortest closed polygons as} $D_{s}{[i]}$ by Eq.(\ref{eq3}) with $V, \overline{P}_{/k}$ and $p_{init}$
            }
      }
      ${P_{result}\gets RemoveDuplicateSurface(D_s)}$
\end{algorithm}

  \textbf{Calculating cost matrix:} For Traveling Salesman Problem(TSP), it is important to get a cost matrix about each city. In our method, each labeled point is regarded as a city. We suppose that the $G(P^t,V)$ is a directed graph, $V=[V_{i,j}]_{k\times k}$ is the cost matrix between each point in $P^t$ which is calculated by Eq.\ref{eq2}(20 to 25 line in Algorithm\ref{alg:r2p}). In Eq.\ref{eq2}, the symbol \textbf{$\rightarrow$} means the index of point $p_i$ in $P^{new}$ plus 1 is equal to the index of $p_j$ in $P^{new}$. It means that $p_i$ to $p_j$ is connected and assigned the value 1, otherwise it is not connected and the value is infinite represented by $Inf$.

\begin{equation}
\label{eq2}
{V_{i,j}}={\left\{\begin{aligned}
                    & 1, p_i \rightarrow p_j\\
                    & Inf\\
                   \end{aligned}
            \right.}
        \qquad\textbf{s.t}{\left\{\begin{aligned}
        & p_i,p_j\in P^t\\
        & P^t \subseteq P^{new}\\
             \end{aligned}
              \right.}
\end{equation}

  \textbf{TSP algorithm:}
  The surfaces segmentation is equivalent to find all closed polygons without overlap in $G(P^t, V)$, and we can use the TSP\cite{gavish1978travelling} method to find the closed polygons. Firstly, we set two-line point as an initial point $p_{init}$(28 to 33 line in Algorithm\ref{alg:r2p}). Secondly we remove the initial point in $P^{t}$ and replace it with $\overline{P}$(34 line in Algorithm\ref{alg:r2p}). Then the path back to $p_{init}$ in $\overline{P}$ calculated with the least cost by Eq.\ref{eq3} is the segmented surface. Finally, each point of two-line points as initial point is used to get the surfaces and remove the duplicate surfaces(28 to 36 line in Algorithm\ref{alg:r2p}).

\begin{equation}
\label{eq3}
{F(p_{init},G(\overline{P},V))}=\min{F(p_k,G(\overline{P}_{/k},V))+V_{init,k}}
\quad \text{s.t} \left\{\begin{aligned}
       &  p_{init}\in P^t\\
       &  p_k\in \overline{P}\\
       &  \overline{P}_{/k}=\overline{P}-\{p_k\}\\

       & \sum_{i=0}^{|P^t|}V_{i,j}\leq 3\\
       &  \sum_{j=0}^{|P^t|}V_{i,j}\leq 3\\

       \end{aligned}
       \right.
\end{equation}
In Eq.\ref{eq3}, $\overline{P}_{/k}=\overline{P}-\{p_k\}$ is the unsearched point set. $\sum_{i=0}^{|P^t|}V_{i,j}\leq 3$ and $\sum_{j=0}^{|P^t|}V_{i,j}\leq 3$ are the conditions that any point in $P^t$ is connected with other points at most 3 times. And when {$V_{k,init}=1$}, the $F(p_{k},G(\overline{P},V))$ ends up and the path is a closed polygon. Finally the path with least cost is a surface of the carton.

\subsection{Contour reconstruction algorithm}
\label{subS:3.4}
\textbf{Theoretical analysis:} As we know, for an incomplete contour of surface, it can be obtained by occlusion and truncation with many parallelograms, and for parallelograms with too large scale, it is easy to cause the synthesized image to be not realistic. So we add an additional condition with smallest area when we construct a complete contour. Assuming that the incomplete contour of the surface is $X$ and the parallelogram contour is $Y$, the goal is to find a optimal parallelogram $Y^{\ast}$ with our conditions in contour sets ${\Omega}^{R^2}$, as shown in Eq.\ref{eq5}.

\begin{equation}
\label{eq5}
{Y^{\ast}}=\mathop{\arg\max}_{Y\in {\Omega}^{R^2}}P(Y|X)
\quad \text{s.t} \left\{\begin{aligned}
        & S_{Y^{\ast}}=\min S_Y\\
        & K_{A}=K_{C} \And K_{B}=K_{D} \\
       \end{aligned}
       \right.
\end{equation}
In Eq.\ref{eq5}, $S_{Y^*}=\min S_Y$ represents the complete contour with the smallest area. $K$ is the slope of the line, $A,B,C$,and $D$ are the edge of parallelogram. $K_{A}=K_{C}$ and $K_{B}=K_{D}$ are the conditions of the parallelogram rule to construct a parallelogram.

Because of the perspective transformation principle, some surfaces with complete contours in $X$ may not satisfy the parallelogram contour. In order to maintain the perspective transformation relationship of the complete contour as much as possible, we compare the area of the original complete contour(it is assumed that the contour represented by 4 points is a complete contour) with the area of the parallelogram constructed by Eq.\ref{eq5} to get the final contour. The final contour $Y_{final}$ is calculated in Eq.\ref{eq6}.
\begin{equation}
\label{eq6}
{Y_{final}}=\left\{\begin{aligned}
                    & y ,\qquad\textbf{s.t} {\left\{\begin{aligned}
                              {\gamma<\frac{area(y)}{area(Y^*)} }\\
                              len(y) = 4\\
                              \end{aligned}
                              \right.}\\
                    & Y^*,\qquad\qquad\qquad others\\
                   \end{aligned}
            \right.
\end{equation}
In Eq.\ref{eq6}, $y$ represents the original contour. The $len(y)=4$ is the condition that the original contour is represented by 4 points. $area(*)$ is the area of each contour. $\gamma$ is the hyperparameter to decide the final contour and $\gamma=2/3$ is used in our experiments .

\textbf{Parallelogram reconstruction:} As we all known, the parallelograms satisfy the convex polygon condition(as shown in definition\ref{df2}). So during constructing a parallelogram, we select a line $A$($ap_{i_x}+bp_{i_y}+c=0$) from the edge of incomplete contour as the constructed line(as shown in definition\ref{df3}). And let $P_{surface}=\{p_0,p_1,…,p_n \}$ denote the incomplete contour. When $A$ and it's adjacent edge $B$ in $P_{surface}$ both satisfy the convex polygon condition, we construct a parallelogram with $A$ and $B$. Firstly, we get the point $P_A$ with farthest distance to $A$, and $P_B$ with farthest distance to $B$ in $P_{surface}$. Then we calculate the slope of $A$ and $B$ as $K_A$, $K_B$ and construct the other edges($C$ and $D$) of parallelogram with ($K_A$,$P_A$) and ($K_B$,$P_B$)(22 to 25 line in algorithm\ref{alg:r3p}). Finally, we calculate the point of intersection of each edge in $A,B,C,D$ to get the parallelogram(26 to 29 line in algorithm\ref{alg:r3p}).
\begin{definition}
 Convex polygon condition: All points in the parallelogram must be on the side of any edge.
\label{df2}
\end{definition}
\begin{definition}
 Constructed line: when constructing a parallelogram, the selected line which is the edge of the parallelogram is a constructed line. It is judged by Eq.\ref{eq7} and Eq.\ref{eq8}. And when $|\beta|$ equals to $|m-n|$, the selected line is a constructed line.
\label{df3}
\end{definition}
\begin{equation}
\label{eq7}
\beta = \sum_{i=0}^n\beta_i\\
\quad \text{s.t} \left\{\begin{aligned}
     & p_i \in P_{surface}\\
      & \beta_i={\left\{\begin{aligned}
      & 1 , \qquad if: ap_{i_x}+bp_{i_y}+c>0\\
      & 0 , \qquad if: ap_{i_x}+bp_{i_y}+c=0\\
      & -1, \qquad\qquad\qquad others\\
      \end{aligned}
       \right.}\\
       \end{aligned}
       \right.
\end{equation}

\begin{equation}
\label{eq8}
m=\sum_{i=0}^{n}g_i\
\quad \text{s.t}\quad
      g_i={\left\{\begin{aligned}
      & 1,\qquad if: \beta_i=0\\
      & 0,\qquad others
       \end{aligned}
       \right.
      }\\
\end{equation}
In Eq.\ref{eq7} and Eq.\ref{eq8}, $m$ is the number of points in the constructed line. $(p_{i_x},p_{i_y})$ is the coordinate of point $p_i$ in $P_{surface}$.

\begin{algorithm}
\caption{single visible surface reconstruction}
\LinesNumbered
\label{alg:r3p}
\KwIn{$P_{surface}=\{p_i\}_{i=0}^n$: incomplete contour points; \quad$Label$: label information }
\KwOut{the complete contour points of surface: $P_{c}=\{p_i\}_{i=0}^3$}
\textbf{Initialize:}{\quad$FLAG=False$: True means the constructed line and it's adjacent edge both satisfy the convex condition;\quad$(A,B,C)$ is the parameters of line $Ax+By+C=0$.\\}
\eIf{ $Label$ is $all$}
{
 $P_c \gets P_{surface}$\\
}
{
 \For{$i=0$ to n}
 {
  calculate the parameters  $(A_1,B_1,C_1)$ of the $line_1$ with $P_{surface}[i],P_{surface}[i+1]$\\
  calculate the parameters  $(A_2,B_2,C_2)$ of the $line_2$ with $P_{surface}[i+1],P_{surface}[i+2]$\\
   \For{$k=0$ to $n$}
   {
    $\beta_1[k],g_1[k]$ $\gets$ calculated by Eq.(\ref{eq7}) and Eq.(\ref{eq8}) with $line_1$ and $P_{surface}[k]$\\
    $\beta_2[k],g_2[k]$ $\gets$ calculated by Eq.(\ref{eq7}) and Eq.(\ref{eq8}) with $line_2$ and $P_{surface}[k]$\\
   }
  $\beta_{line1} \gets \sum_{i=0}^n\beta_1[i]$\\
  $m_{line1} \gets \sum_{i=0}^ng_1[i]$\\
  $\beta_{line2} \gets \sum_{i=0}^n\beta_2[i]$\\
  $m_{line2} \gets \sum_{i=0}^ng_2[i]$\\
  \eIf{$|\beta_{line1}|$ is $|n-m_{line1}|$ and $|\beta_{line2}|$ is $|n-m_{line2}|$}{
  $FLAG \gets True$ }{$FLAG \gets False$}
  $K_1 \gets \frac{P_{surface}[i]_y-P_{surface}[i+1]_y}{P_{surface}[i]_x-P_{surface}[i+1]_x}$ \qquad\#slope of line1\\
  $K_2 \gets \frac{P_{surface}[i+1]_y-P_{surface}[i+2]_y}{P_{surface}[i+1]_x-P_{surface}[i+2]_x}$\qquad\#slope of line2\\
  \eIf{FLAG is True}
   {
   $P_{1}$ is the point with farthest distance to line1 in $P_{surface}$\\
   $P_{2}$ is the point with farthest distance to line2 in $P_{surface}$\\
  calculate the parameters  $(A3,B3,C3)$ of the $line_3$ with $K_1$ and $P_{1}$\\
  calculate the parameters  $(A4,B4,C4)$ of the $line_4$ with $K_2$ and $P_{2}$\\
  $Point[0]\gets P_{surface}[i+1]$\\
  $Point[1]\gets CalculatePointofIntersection(line_3,line_2)$\\
  $Point[2]\gets CalculatePointofIntersection(line_3,line_4)$\\
  $Point[3]\gets CalculatePointofIntersection(line_1,line_4)$\\
  $P_{list[i]}\gets Point$\\
  $S_{[i]} \gets area(Point)$ \\
   }
{
   $P_{list[i]} \gets Null$\\
   $S_{[i]} \gets Infinity$ \\
}
}
\eIf{$n$ is not $4$}
{$P_c$ $\gets$ Get the complete contour by Eq.(\ref{eq5}) with $P_{list}$ and $S$}
{$P_c$ $\gets$ Get the complete contour by Eq.(\ref{eq5}) and Eq.(\ref{eq6}) with $P_{list}$ and $S$}
}
\end{algorithm}

\textbf{Parallelogram  reconstruction strategy:}
Due to the perspective transformation of imaging, the cartons appear in three kinds of appearance in the image including single visible surface, two visible surfaces and three visible surfaces. The contour reconstruction methods for these three kinds of appearance are as follows:

(1) {single visible surface reconstruction:}  When there is no occlusion as \textbf{All} according to the labeling rules, the contour keeps unchanged. When the carton is labeled as \textbf{Occlusion}, we select each edge in $P_{surface}$ as the constructed line to construct a parallelogram and we choose the parallelogram with smallest area as the contour of the $P_{surface}$(as shown in algorithm\ref{alg:r3p}).

(2) {two visible surfaces:} Because there is a common line, when we construct a parallelogram, we only use the common line as constructed line to construct parallelograms for each surface. And we also choose the parallelogram with smallest area as the contour of the $P_{surface}$. Finally, for the parallelogram of each surface, we should adjust it's scale, so that the coordinate of the common line in each surface is equal in image.

(3) {three visible surfaces:} Because there are two common lines in each surface, when we construct a parallelogram, the common lines as constructed line and its adjacent edge are used to construct a parallelogram for each surface. Finally, for the parallelogram of each surface, we also should adjust its scale, so that the coordinate of the common line in each surface is equal in image.

\subsection{Image synthesis}
\label{subS:3.5}
\textbf{Foreground texture datasets:} We have collected different kinds of cartons which have only one surface as the foreground texture datasets(as shown in Fig.\ref{Fig:2d}). Because the carton is composed of multiple surfaces and each surface has a specific combination relationship(such as the direction of the surface with each other) with each other, as shown in Figure \ref{Fig6b}. The method shown in Figure \ref{fig6} is used to label the foreground instance to ensure the true relationship with each other. And we build a subset of data in the foreground texture datasets according to the number of visible surface.

\begin{figure}[]
\centering
\subfloat[]{\label{Fig6a
\includegraphics[width=0.45\linewidth]{fig6a.jpg}}
\subfloat[]{\label{Fig6b
\includegraphics[width=0.3\linewidth]{fig6b.jpg}}
\caption{The example of foreground texture labeling rules. (a) is the labeling rules of the two visible surfaces, we label the instance according to arrow and starting to common line; (b) is the labeling rules of the three visible surfaces, we also label the instance according to arrow. The image in the first row and first column is vertical view; the image in the second row and first column is the front view, and the image in the second row and last column is end view. As we can see in the first row and last column, the combination relationship of the surface is fixed with each other in the carton instance.}
\label{fig6}
\end{figure}

\textbf{Image synthesis method:} During performing the image synthesis algorithm, Perspective transformation principle is used to adaptively deform the foreground instance to ensure the linearity of the edges. The coordinates of the foreground instance are $P_{pre}=\{p_i\}_{i=0}^3$, and the coordinates of the reconstructed contour are $P_{back}=\{p_j\}_{j=0}^3$, the perspective principle is as follows:
\begin{equation}
\label{eq9}
\left[
  \begin{array}{c}
    P_{pre}\\
    1\\
  \end{array}
\right]=M\times\left[\begin{array}{c}
    P_{back}\\
    1\\
  \end{array}
  \right]=\left[
  \begin{array}{ccc}
    a_{00} &a_{01} & a_{02}\\
    a_{10} &a_{11} & a_{12}\\
    a_{20} &a_{21} & a_{22}\\
  \end{array}
\right]\left[
  \begin{array}{c}
    P_{back}\\
    1\\
  \end{array}
\right]
\end{equation}
 $M$ can be solved when $P_{pre}$ and $P_{back}$ are fixed. Then, $M$ is used to generate images by Eq.\ref{eq10}. In Eq.\ref{eq10}, $I$ is the original image from source datasets, $I_{pre}$ is the foreground instance texture from target datasets, $I_{back}$ is the mask of the reconstructed contour, $I_x$ is the mask of the original instance in $I$ and $I_{synthetic}$ is the synthetic image. The $\odot$ represents the image fusion operation \cite{dwibedi2017cut} as shown in the fifth module in Figure \ref{fig:3}. And $\oplus$ represents the pixel-level image merging operation.
\begin{equation}
\label{eq10}
I_{synthetic}=(M\times I_{pre}\odot I_x)\oplus((\textbf{1}-I_x)\odot I)\\
\end{equation}

 Because the carton is composed of multiple surfaces in the image, the texture from foreground texture datasets needs to be selected according to the relationship of the surface in $I$. For single visible surface, $I_{pre}$ is randomly selected. And for the two visible surfaces and three visible surfaces, we firstly select the subset of data corresponding to the number of surfaces, then we randomly select the foreground instance textures to generate image.

\textbf{Subtle pixel artifacts:} In the process of image synthesis, because of the brightness difference between $I_{pre}$ and $I$, there must be some artificial noise in $I_{synthetic}$, such as the subtle pixel artifacts at the edge of the synthesized instance\cite{dwibedi2017cut}. In order to reduce the influence of subtle pixel artifacts, we use Gaussian fusion method for foreground texture fusion. And the method in Ref.\cite{tripathi2019learning} is used to replace the instance texture with a random noise texture with a probability of 0.2 (as shown in Figure \ref{fig:3}) to make the detection model focus only on the object appearance.

\section{Experiments}
\label{S:4}
This chapter mainly explores the application of surfaces segmentation algorithms and contour construction algorithms in the field of data expansion. We verify the effectiveness of our method on the object detectors such as RetinaNet\cite{lin2017focal} and Faster R-CNN\cite{ren2016faster}. All experiments are based on PyTorch and conducted on 2 GTX1080Ti.

\subsection{Experimental Settings}
\label{S:4.1}
\textbf{Datasets:} The information of the SCD is shown in Table \ref{SCDinformation}. We sampled 520 images from CPLC as the carton stacking skeleton data and used the method in chapter \ref{subS:3.2} to label them. Then, we randomly selected 269 single-sided instances, 51 double-sided instances, and 23 three-sided instances from FM as the substitute texture for foreground. In addition, 149 single-sided instances, 27 double-sided instances, and 30 three-sided instances are randomly sampled from ECLC for texture replacement. All experiments take CPLC as base dataset which is split into 3589 images for training and 500 images for testing.

\begin{table}[htbp]
    \vspace{10pt}
    \centering
    \begin{tabular}{c c c c c c c }
        \hline
        \multirow{2}*{\text{Scene}} &\multirow{2}*{\text{Train No.}} &\multirow{2}*{\text{Test No.}} &\multirow{2}*{\text{Stacked skeleton No.}}& \multicolumn{3}{c}{foreground texture datasets}\\
         ~&  ~ & ~ & ~ & Single sides & Two sides & three sides\\
        \hline
        \text{CPLC} & 3589 & 500 & 520 & 0 & 0 & 0\\

        \text{ECLC} & 1722 & 500 & 0 & 149 & 27 & 30\\

        \text{FM} & 0 & 492 & 0 & 269 & 51 & 23 \\
        \hline
    \end{tabular}
    \caption{Data distribution of the SCD datasets in different scenarios and distribution of the texture datasets}
    \label{SCDinformation}
\end{table}
\textbf{Evaluation metric:} We adopt the same performance measurement as the MS COCO Challenge \cite{lin2014microsoft} to report the experimental results including the calculation of mean Average Precision (mAP) for a specific value of the IoU threshold to determine true positives and false positives. The main performance measurement used in this benchmark is shown by AP, which is averaging mAP across the different value of IoU thresholds, i.e. IoU = {.5, .6, .7, · · ·, .9}.

 \textbf{Implementation details:} All experiments are implemented on basis of PyTorch\cite{paszke2019pytorch} and MMDetection \cite{chen2019mmdetection}. We utilize ResNet-18 in RetinaNet\cite{lin2017focal} as backbones which are pre-trained on ImageNet. A mini-batch of 4 images per GPU is used during training RetinaNet\cite{lin2017focal} and Faster R-CNN\cite{ren2016faster}, thus making a total mini-batch of 8 images on 2 GPUs. The synchronized Stochastic Gradient Descent (SGD) is used for model optimization. The weight decay of 0.0001 and the momentum of 0.9 are adopted. A linear scaling rule\cite{goyal2017linear} is carried out to set the learning rate during training (0.005 in RetinaNet and 0.01 in Faster R-CNN). And a linear warm-up strategy is adopted in the first 500 iterations. Besides that the learning rate changes linearly with mini-batch, the flip ratio is 0.5 and the image scale is [600, 1000] in all experiments. And other settings are consistent with the default settings of MMDetection\cite{chen2019mmdetection}.

 \begin{figure}[]
\centering
\includegraphics[height=0.35\linewidth]{fig7.jpg}
\caption{The results of surfaces segmentation algorithm and contour reconstruction algorithm. The first line is the original picture. The second line is the result of surfaces segmentation algorithm when $\psi=25$. If the carton has one surface, it is represented by blue, the two surfaces are represented by blue and green, and the three surfaces are represented by blue, green, and red for different surfaces. The third row is the result of the contour reconstruction algorithm, and the contour of different colors represent the complete contour.}
\label{fig:7}
\end{figure}
 \subsection{Qualitative Analysis}
\label{S:4.2}
\textbf{The results of surfaces segmentation algorithm:} We selects 520 images as the carton stacking skeleton datasets from CPLC and then surfaces segmentation algorithm are conducted to extract the segmentation surfaces of the skeleton(see second row of Fig.\ref{fig:7}). To search for the best result of the surfaces segmentation algorithm, sampling statistics are employed to achieve the optimal hyperparameter $\psi$. For each group of $\psi$, we randomly select 10 pictures for surfaces segmentation algorithm. Then we compute the percentage of incorrectly parameter with the smallest average error. Table \ref{paramterfacted} reports that the best result is achieved with $\psi=25$ while Fig.\ref{fig:8} shows the corresponding visualization.

\begin{table}[htbp]
    \vspace{10pt}
    \centering
    \begin{tabular}{c c c c c c c }
        \hline
         ~ &1 &2 &3 &4 &5 &mean\\
        \hline
        $\psi=10$ &3.52\% &2.8\% &0 &9.41\% &8.33\% &4.81\%\\
        $\psi=15$ &1.41\% &2.8\% &0 &1.34\% &5.00\% &2.11\%\\
        $\psi=20$ &0      &0     &0 &0      &0.08\% &0.016\%\\
        $\psi=25$ &0      &0     &0 &0      &0      &0\\
        \hline
    \end{tabular}
    \caption{Statistics of error rate of the faceted results under different hyperparameters $\psi$.}
    \label{paramterfacted}
\end{table}
\begin{figure}[htbp]
\centering
\includegraphics[height=0.45\linewidth]{fig8.jpg}
\caption{The result of surfaces segmentation algorithm under different hyperparameters $\psi$. If the carton has one surface, it is represented by blue, two surfaces are represented by blue and green, and three surfaces are represented by blue, green, and red for different surface. The red square is the wrong result of the surfaces segmentation algorithm.}
\label{fig:8}
\end{figure}

\textbf{The results of contour reconstruction algorithm:} Because of the occlusion and truncation, the contours of the instances are incomplete. With the method in Chapter \ref{subS:3.4}, we construct the complete contours shown in Fig.\ref{fig:7}. We also use sampling statistics for evaluation to get the optimal value of the $\gamma$. For each group of $\gamma$, we randomly select 10 pictures from the carton stacking skeleton datasets for surfaces segmentation algorithm, then we perform contour construction. Finally, we compute the percentage of unreasonable samples (the examples that are occluded but not reconstructed by our method, or the examples are reconstructed but unreasonable by comparison with the original complete contour) in the total instances. Table \ref{constrct} reports that the smallest average error is achieved when setting $\gamma$ as $\frac{2}{3}$ while Fig.\ref{Fig9a} shows the corresponding visualization. The same procedure are conducted in FM to search the optimal value of $\gamma$. The generated images are mixed with the training sets of CPLC and then fed into RetinaNet for training. As shown in Fig.\ref{Fig9b}, the fine-tuning detector is used to test data in different scenarios and the best performance is achieved with $\gamma =\frac{2}{3}$.

\begin{table}[htbp]

    \centering
    \begin{tabular}{c c c c c c c }
        \hline
         ~ &1 &2 &3 &4 &5 &mean\\
        \hline
        $\gamma=\frac{1}{4}$ &9.86\% &10.19\% &8.72\% &8.24\% &10.83\% &9.57\% \\
        $\gamma=\frac{1}{3}$ &9.86\% &9.26\%  &8.72\% &8.24\% &10.00\% &9.22\% \\
        $\gamma=\frac{1}{2}$ &9.86\% &9.26\%  &8.72\% &8.24\% &10.00\% &9.16\% \\
        $\gamma=\frac{2}{3}$ &7.04\% &8.33\%  &6.71\% &8.24\% &9.17\%  &7.90\% \\
        $\gamma=\frac{3}{4}$ &7.75\% &9.26\%  &7.38\% &8.24\% &9.17\%  &8.36\% \\
        \hline
    \end{tabular}
    \caption{The ratio of unreasonable contours generated by different values of the $\gamma$ to the total number of sample instances}
    \label{constrct}
\end{table}

\textbf{Image synthesis:} After contour reconstruction, the Gaussian fusion method is used to generate images. In the processing of image synthesis, the instances from foreground instance texture datasets are randomly selected up to the same number of surfaces in the skeleton picture. And then we replace the instance texture in the skeleton picture with the texture from foreground texture datasets. The method in reference \cite{tripathi2019learning} is used to solve the influence of the subtle pixel artifacts, as  shown in \ref{fig:10}.
\begin{figure}[]
\centering
\subfloat[]{\label{Fig9a
\includegraphics[width=0.45\linewidth]{fig9.jpg}}
\subfloat[]{\label{Fig9b
\includegraphics[width=0.45\linewidth]{fig13.png}}
\caption{(a) the results of the contour reconstruction under different $\gamma$. The yellow ellipse indicates unreasonable contour reconstruction. (b) 6000 images are generated for each $\gamma$ when FM is the foreground texture datasets. Then the generated images are mixed with the train datasets which contains 3589 images in CPLC to train the RetinaNet\cite{lin2017focal} detection model.}
\label{fig:9}
\end{figure}

\subsection{Main Results}
\label{S:4.3}
\textbf{Baselines on carton datasets:} To establish baselines, both RetinaNet\cite{lin2017focal} and Faster R-CNN\cite{ren2016faster} equipped with ResNet18 are employed to fine-tuning on training set of CPLC and test respectively on 500 images from ECLC and 492 images from FM. The overall results are reported in Table \ref{SGCMP_E} which shows that huge domain shift exists from CPCL to FM and ECLC, up to $23\% \sim 26.8\%$ in AP.

\textbf{Foreground texture datasets from FM:} 343 carton instances with special foreground texture from FM are plugged into 520 skeletons extracted from CPLC. Then we replace the texture of each instance in CPLC skeleton datasets with a random noise texture with a probability of 0.2. Finally, we used the Gaussian fusion method to generate 6000 images and mix with CPLC datasets as \text{CPLC+G}\_{FM}. For comparison, we use the data augmentation method to expand the original CPLC dataset(such as random scaling, random flipping, random cutting, etc) to 6000 images (as shown in Figure \ref{fig:11}) as AUG which are subsequently mixed with CPLC to the same scale. The comparative experiments are shown in Table \ref{SGCMP_E}.
\begin{figure}[]
\centering
\subfloat[]{\label{fig:10
\includegraphics[width=0.45\linewidth]{fig10.jpg}}
\subfloat[]{\label{fig:11
\includegraphics[width=0.45\linewidth]{fig11.jpg}}
\caption{(a) Image synthesis renderings, the first column is the original image and the rest are the generated images. (b) Data augmentation for CPLC with random scaling, random flipping, random cutting etc.}
\end{figure}

\begin{table}[htbp]
\vspace{12pt}
\centering
\begin{tabular}{c c c c c c c}
\hline
{datasets} & {train No.} & {model} & {test} & $AP$ & $AP_{50}$ & $AP_{80}$\\
\hline
\multirow{3}*{\text{Baseline: CPLC}} & \multirow{3}*{3589} & \multirow{9}*{\text{RetinaNet{\cite{lin2017focal}}}} & \text{CPLC} & \text{0.814} & \text{0.970} & \text{0.877}\\
~ & ~ & ~& \text{FM} & \text{0.575} & \text{0.810} & \text{0.569}\\
~ & ~ & ~& \text{ECLC} & \text{0.546} & \text{0.798} & \text{0.520} \\

\multirow{3}*{\text{CPLC+AUG}} & \multirow{3}*{9589} & ~ & \text{CPLC} & \text{0.829} & \text{0.969} & \text{0.8895}\\
~ & ~ & ~& \text{FM} & \text{0.588} & \text{0.812} & \text{0.589}\\
~ & ~ & ~& \text{ECLC} & \text{0.552} & \text{0.791} & \text{0.533} \\
\multirow{3}*{\text{CPLC+G\_FM}} & \multirow{3}*{9589} & ~ & \textbf{CPLC} & \textbf{0.834} & \textbf{0.973} & \textbf{0.896} \\
~ & ~ & ~& \textbf{FM} & \textbf{0.640} & \textbf{0.852} & \textbf{0.650} \\
~ & ~ & ~& \textbf{ECLC} & \textbf{0.572} & \textbf{0.810} & \textbf{0.568} \\
\hline
\multirow{3}*{\text{Baseline: CPLC}} & \multirow{3}*{3589} & \multirow{9}*{\text{Faster R-CNN\cite{ren2016faster}}}& \text{CPLC}  & \text{0.837} & \text{0.976} & \text{0.902}\\
~ & ~ & ~& \text{FM} & \text{0.607} & \text{0.811} & \text{0.624}\\
~ & ~ & ~& \text{ECLC} & \text{0.577} & \text{0.813} & \text{0.580} \\
\multirow{3}*{\text{CPLC+AUG}} & \multirow{3}*{9589} & ~ & \text{CPLC}  & \text{0.846} & \text{0.976} & \text{0.910}\\
~ & ~ & ~& \text{FM} & \text{0.616} & \text{0.815} & \text{0.625}\\
~ & ~ & ~& \text{ECLC} & \text{0.573} & \text{0.792} & \text{0.573} \\

\multirow{3}*{\text{CPLC+G\_FM}} & \multirow{3}*{9589} & ~ & \textbf{CPLC} & \textbf{0.852} & \textbf{0.977} & \textbf{0.914} \\
~ & ~ & ~& \textbf{FM} & \textbf{0.675} & \textbf{0.855} & \textbf{0.700} \\
~ & ~ & ~& \textbf{ECLC} & \textbf{0.593} & \textbf{0.806} & \textbf{0.588} \\
\hline
\end{tabular}
\caption{The performance of the method in this paper to generate FM foreground texture images on the detection model.}
\label{SGCMP_E}
\end{table}

As shown in Table \ref{SGCMP_E}, when RetinaNet and Faster R-CNN are trained on \text{CPLC+G\_FM} and tested on CPLC, the AP can be improved by 2\% and 1.5\% by comparison with training on CPLC. In addition, compared with only training on \text{CPLC+AUG}, the performance of our method can improve AP by 0.5\% and 0.6\%. This demonstrates that our method is better than the traditional data augmentation strategy. And when RetinaNet and Faster-RCNN are tested on FM, the AP increases by 6.5\% and 6.8\% by comparison with training on CPLC. When testing on ECLC, the AP also can be improved by 2.6\% and 1.6\% by comparison with training on CPLC. This demonstrates that our method can greatly ease the domain shift. Finally, RetinaNet and Faster R-CNN are tested on FM and ECLC, the performance of our method can improve AP by 5.2\% and 5.9\% on FM test set and 2\% and 2\% on ECLC test set, compared with training on \text{CPLC+AUG}. This demonstrates that our method is not only better than the traditional data augmentation strategy for data augmentation, but also can effectively ameliorate the domain shift.

\begin{table}[htbp]
\vspace{12pt}
\centering
\begin{tabular}{c c c c c c c}
\hline
{datasets} & {train No.} & {model} & {test} & $AP$ & $AP_{50}$ & $AP_{80}$\\
\hline
\multirow{3}*{\text{Baseline: CPLC}} & \multirow{3}*{3589} & \multirow{9}*{\text{RetinaNet{\cite{lin2017focal}}}} & \text{CPLC} & \text{0.814} & \text{0.970} & \text{0.877}\\
~ & ~ & ~& \text{FM} & \text{0.575} & \text{0.810} & \text{0.569}\\
~ & ~ & ~& \text{ECLC} & \text{0.546} & \text{0.798} & \text{0.520} \\

\multirow{3}*{\text{CPLC+AUG}} & \multirow{3}*{9589} & ~ & \text{CPLC} & \text{0.829} & \text{0.969} & \text{0.8895}\\
~ & ~ & ~& \text{FM} & \text{0.588} & \text{0.812} & \text{0.589}\\
~ & ~ & ~& \text{ECLC} & \text{0.552} & \text{0.791} & \text{0.533} \\

\multirow{3}*{\text{CPLC+G\_ECLC}} & \multirow{3}*{9589} & ~ & \textbf{CPLC} & \textbf{0.831} & \textbf{0.972} & \textbf{0.894} \\
~ & ~ & ~& \textbf{FM} & \textbf{0.595} & \textbf{0.820} & \textbf{0.598} \\
~ & ~ & ~& \textbf{ECLC} & \textbf{0.589} & \textbf{0.812} & \textbf{0.575} \\
\hline
\multirow{3}*{\text{Baseline: CPLC}} & \multirow{3}*{3589} & \multirow{9}*{\text{Faster R-CNN\cite{ren2016faster}}}& \text{CPLC}  & \text{0.837} & \text{0.976} & \text{0.902}\\
~ & ~ & ~& \text{FM} & \text{0.607} & \text{0.811} & \text{0.624}\\
~ & ~ & ~& \text{ECLC} & \text{0.577} & \text{0.813} & \text{0.580} \\

\multirow{3}*{\text{CPLC+AUG}} & \multirow{3}*{9589} & ~ & \text{CPLC}  & \text{0.846} & \text{0.976} & \text{0.910}\\
~ & ~ & ~& \text{FM} & \text{0.616} & \text{0.815} & \text{0.625}\\
~ & ~ & ~& \text{ECLC} & \text{0.573} & \text{0.792} & \text{0.573} \\

\multirow{3}*{\text{CPLC+G\_ECLC}} & \multirow{3}*{9589} & ~ & \textbf{CPLC} & \textbf{0.846} & \textbf{0.975} & \textbf{0.909} \\
~ & ~ & ~& \textbf{FM} & \textbf{0.624} & \textbf{0.830} & \textbf{0.635} \\
~ & ~ & ~& \textbf{ECLC} & \textbf{0.611} & \textbf{0.818} & \textbf{0.627} \\
\hline
\end{tabular}
\caption{The performance of the method in this paper to generate ECLC foreground texture data on the detection model which compared with data augmentation methods}
\label{JDCMP_E}
\end{table}

\textbf{Foreground texture datasets from ECLC:} The foreground instances in ECLC which contain 206 instances is used as foreground texture datasets, the skeleton in CPLC as the template datasets. And the parameters of all algorithms are kept the same as before. The Gaussian fusion method is used to generate 6000 images, which are mixed with CPLC datasets as \text{CPLC+G\_ECLC}, the results are shown in Table \ref{JDCMP_E}.

It can be seen in Table \ref{JDCMP_E} that the AP of the \text{CPLC+G\_ECLC} is improved by 1.7\% on CPLC by comparison with the baseline on the RetinaNet.. And the AP of RetinaNet training on \text{CPLC+G\_ECLC} is improved by 2\% on the FM, 4.3\% on ECLC by comparison with training Baseline. At the same time, compared with the \text{CPLC+AUG}, the \text{CPLC+G\_ECLC} has a slightly improvement of only 0.2\% on the CPLC, 0.7\% on FM, but it is improved by 3.7\% on ECLC. In the Faster R-CNN, our method improve by 0.9\% on CPLC, 1.7\% on FM, and 3.4\% on ECLC by comparison with baseline. Our method outperforms the \text{CPLC+AUG} with 0.8\% on FM and 3.8\% on test sets of CPLC. This further demonstrates that our method is not only better than the traditional data augmentation strategy for data augmentation, but also can effectively solve the domain shift. Because the number of foreground texture datasets from ECLC is smaller than that from FM, it causes the performance of ECLC to be lower than that of FM.

According to the results in Table \ref{SGCMP_E} and \ref{JDCMP_E}, our method achieves great successes in both detectors and different scenarios. The reason is that the method achieves texture feature alignment at the instance level. Assuming that the data generated by our method is the source domain $D_s=\{x_i,y_i\}_{i=0}^N$, the unlabeled data set in the test set is $D_T=\{x_i\}_{i=0}^M$. The object detection problem can be viewed as learning the posterior $P(C,B|I)$, where $I$ is the image representation, $B$ is the bounding-box of an object and $C\in\{1, . . . ,K\}$ the category of the object ($K$ is the total number of categories, $K=1$ in this paper). Let us denote the joint distribution of training samples for object detection as $P(C,B,I)$. Because the conditional probabilities in the source domain and target domain are the same as $P_S(C, B|I)=P_T(C, B|I)$. And as we all know, $P_S(I)\ne P_T(I)$, in order to make the source domain and target domain have the same or similar joint probability distribution, the image-level feature alignment is required to make $P_S(I)\approx P_T(I)$. The method in this paper forces $P_S(I)\approx P_T(I)$ from foreground instance texture.
\begin{figure}[]
\centering
\subfloat[]{\label{Fig15a
\includegraphics[width=0.45\linewidth]{fig15a.png}}
\subfloat[]{\label{Fig15b
\includegraphics[width=0.45\linewidth]{fig15b.png}}\\
\subfloat[]{\label{Fig15c
\includegraphics[width=0.45\linewidth]{fig15c.png}}
\caption{The influence of the probability on RetinaNet that the noise texture is the foreground instance texture. FM is used as the foreground texture datasets to generate 3589 images and directly train the RetinaNet\cite{lin2017focal} model. (a) is the result of testing on CPLC,(b) is the result of testing on FM,(c) is the result of testing on ECLC. The best probability is 0.2 .}
\label{fig:15}
\end{figure}

\begin{figure}[]
\centering
\includegraphics[width=0.65\linewidth]{fig14.png}
\caption{The influence of the amount of generated images on detection model. FM is the foreground texture datasets to generate different numbers of training sets as G\_FM. And the G\_FM is mixed with CPLC to train the detection model and test them on the FM test sets.}
\label{fig:14}
\end{figure}

\subsection{Ablation Studies}
\textbf{The influence of the probability on RetinaNet that the noise texture is the foreground texture:} To explore the impact of random noise for the detectors, the foreground texture dataset in FM is used as a benchmark to generate 3589 images under different probabilities of random noise to train the RetinaNet\cite{lin2017focal} and test on the CPLC, FM, and ECLC test sets. According to the results in Figure \ref{fig:15}, the performance of the model is the best on CPLC and ECLC test sets when the probability of the random noise is 0.2, which means that the random noise can effectively improve the stability of the model and effectively suppress the influence of artificial noise on the model and make the detectors focus only on the object appearance.

\textbf{The influence of the amount of generated images on detectors:} To explore the influence of the number of the generated images on the detectors, FM is used as the foreground instance texture datasets and the random noise probability is set as 0.2 to generate different numbers of images. Then they are mixed with the CPLC training datasets to train the RetinaNet \cite{lin2017focal} and Faster R-CNN \cite{ren2016faster} and tested on FM. As shown in Figure \ref{fig:14}, the performance of the models increases as the number of the generated images. Compared with the baseline in Table \ref{SGCMP_E}, the performance of our methods is improved by 6.8\% on RetinaNet and 7.1\% on Faster-RCNN when the number of generated images is 8000. However, the performance of the RetinaNet and Faster R-CNN increase slowly when the number of the generated images exceeds 6000. Because the influence of subtle pixel artifacts, our method is saturated when the number of generated images is more than 6000.
\begin{figure}[]
\centering
\subfloat[]{\label{Fig12a
\includegraphics[width=0.45\linewidth]{fig12a.png}}
\subfloat[]{\label{Fig12c
\includegraphics[width=0.45\linewidth]{fig12c.png}}
\caption{The influence of epochs in RetinaNet \cite{lin2017focal}, (a) the result of testing on the ECLC test sets, (b) the result of testing on FM test sets. It can be seen that our method has the best performance in all test sets, and the performance of our method is greatest on the FM test sets.}
\label{fig:12}
\end{figure}

\textbf{The influence of training epochs in RetinaNet:} We conduct experiments with respect to the number of training epochs on the RetinaNet(as shown in Figure \ref{fig:12}). The \text{G\_FM} datasets in Chapter \ref{S:4.3} are mixed with CPLC datasets as the training datasets named \text{CPLC+G\_FM}, and test on FM and ECLC test sets. The performance of our method on FM test set by comparison with the Baseline is improved to 8.4\% when the number of training epochs is 36. And it proves that our method can ease the domain shift. Because the number of epochs increases, the model learns more basic features.

\section{Conclusions}
\label{S:5}
A new datasets synthesis method based on datasets SCD for object detection is proposed in this paper. The method  can effectively solve the domain shift. The experiment results show that the surfaces segmentation algorithm can effectively decompose the surfaces of the cartons and the contour reconstruction algorithm can effectively reconstruct a complete contour for the incomplete contour of the carton instance.

Our method is not limited to the application of the carton data set and also applicable to the task of any rectangular instance. In the future, we will explore the application of this framework in one-shot learning,  simpler surfaces segmentation algorithms and how to reduce the harm of the subtle pixel artifacts to the model.

\section{Acknowledgements}
\label{S:6}
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

\bibliographystyle{elsarticle-num-names}

redpajama_set_name: RedPajamaArXiv


text:
hare, eugene (24)
cleveland state university news bureau (10)
rieter, bill (photographer) (9)
cleveland state university. office of information services. (2)
guzman, melanie (2)
humel, chuck (photographer) (2)
deevers, lance (graphic designer) (1)
dick, ron (graphic designer) (1)
the river burns (1)
1992-04; 1992-08 (5)
2 x 2 in. (20)
8 x 10 in. (4)
8.5 x 11 in. (4)
5.5 x 8.5 in. (2)
theatrical productions (43)
montezuma (38)
albertine in five times (5)
Contributors: Cap, David (lighting designer)
Albertine in Five Times
Theatrical productions; Albertine in Five Times
Program for Cleveland State University's 1992 production of "Albertine in Five Times."
July 31, 1992 press release for the return engagement of Cleveland State University's production of "Albertine in Five Times." The cast was identical except for the addition of Emmy Castle in place of Garryann Pekarcik for Albertine at age 60.
March 6, 1992 press release for the upcoming Cleveland State University production of "Albertine in Five Times."
Theatrical productions; Montezuma
Photo of a scene taken during Cleveland State University's 1992 production of "Montezuma."
Photo of Duane Noch as Hernan Cortez in the 1992 world premier of Cleveland State University's "Montezuma."
Photo of Jonathan Lopez in the title role of Montezuma taken during Cleveland State University's 1992 production of the same name.
Photo of the Aztec shield used in the 1992 world premier of Cleveland State University's "Montezuma."
Photo of Jonathan Lopez in the title role of "Montezuma" taken during Cleveland State University's 1992 production of the same name.


I am not sure why we are so hyped up about minus nine degree temperatures. I remember that the Polar Vortex winter of 2014 had many many days with colder temperatures. I dunno what the wind chill was and I don't really care. It WAS windy today. I made a quick trip over to the Plum and I pulled my smartwool leggings on over my tights (underneath my long wool skirt). I was warm enough and it was only about minus four at that time anyway.
But hype? Hmmm… So the schools were closed Monday, today, and tomorrow. That's not unusual. My company told Planet Ann Arbor folks to telecommute Monday, today, and tomorrow. It wasn't an order exactly, just a strong suggestion. We all have badges that'll let us in the door so we can get in whenever we want.
Trash/recycling is delayed two days. That means we will put out our trash for an unprecedented *Sunday* pickup this week. I wonder what the neighbors will think when I don't put my trash out for its usual Friday pickup. I know LofP will be paying attention to what Garbage Woman does. Garbage pickup is not like it used to be when Bad Boyfriend worked for Soo Sanitation and had to jump off the truck to physically pick up garbage cans to throw them into the back of the truck. Nowadays our truck drivers stay in the truck and use a big mechanical arm to pick up the carts. So I'm not sure why the trucks can't go out. But I am not complaining.
I can better understand why there was no mail delivery today. This wasn't random like back in the polar vortex winter when sometimes the mail simply didn't show up. We were *told* that there would be no mail in our zip code. I am okay with this. Postal mail is not how most important documents are dispatched any more and I am happy that postal delivery folks do not have to deliver a bunch of junk mail on foot in subzero temperatures. I do hope they are being PAID.
I was actually hot in my house this afternoon even though the thermostat was set for 67. It got warmer with our Hotlantia/Yooperland friends who stopped by for lasagne and MoonX whine. Black pinot noir. Our lights were pink/magenta when I took the pic. They changed color throughout the night.
This entry was posted on Wednesday, January 30th, 2019 at 9:49 pm	and is filed under Uncategorized. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.
66 Fahrenheit is fine…on the inside–probably younger.
These all sound like commonsense decisions to me. Safety is paramount when it's that cold because things can go bad quickly. According to the Kentucky governor, we are getting "soft" though. Here, where we don't have a polar vortex, the news is full of MEASLES. And then again, MEASLES. MEASLES. I read somewhere that we could get a polar vortex, although it would be puny. We're at the same latitude as someplace in Michigan with -60, but our temperatures are moderated by the Pacific Ocean.


A total of 1.8 million faulty Takata airbags still have to be replaced in vehicles used in Australia, the consumer watchdog says, with the recall likely to run until 2020.
Ten NSW space start-up companies have a chance to take part in an intensive bootcamp learning experience in the United States.
Australian start-up satellite technology company Fleet Space Technologies has opened its first nanosatellite ground station control centre in Adelaide.
Western Australian energy solutions provider Power Ledger has inked an agreement with the Yolk Property Group to use its blockchain technology for solar energy trading between residents of the near-complete Evermore residential development.
Industries in Western Australia are benefitting from the recent launch of new Japanese satellites, a newly released research report claims.
An initiative aimed at developing an enhanced audio-visual experience for motor vehicles and to capitalise on the latest advances in connectivity and multimedia technologies has been launched by the International Telecommunication Union.
The first placements of PhDs, including women graduates, with business and industry have commenced under a partnership between the Australian Mathematical Sciences Institute's APR.Intern program and the recently established ARC Centre for Personalised Therapeutics Technologies.
Hey spaceman, is that some junk in your trunk, is your passive radar just actively happy to see me? Curtin University Astronomers have the answer.
The CSIRO has succeeded in bringing about a significant reduction in the population of a species of mosquito that spreads diseases like dengue and Zika by sterilising males and releasing them in a test region between November 2017 and June this year.
An Australian performance standard for residential and small-commercial battery systems connected to solar PV systems is under development.


text:
Q: Child theme installed & working, but custom css not working I've tried everything I can think of to fix this issue, but I am totally stuck.
I have created a child theme, and installed it on Wordpress. I know that my style.css is working, because during some testing to see what the issue was, I was able to change the name of the child theme and have that show up on my site. I believe that the issue is with the @import, but can't figure out how to fix.
Here's what my css looks like:
    /*
     Theme Name:     Accio Child
     Theme URI:      http://jowerstraining.com
     Description:    Accio Child Theme
     Template:       accio
    */

    @import url('../accio/style.css');
    @import url('../accio/css/animation.css');
    @import url('../accio/css/custom1.css');
    @import url('../accio/css/custom2.css');

The theme I'm using has several stylesheets, and that's where I think my problem is coming from. Any ideas on how to resolve this?
Building the site with MAMP so can't post link, sorry.

A: We had this same problem when a client wanted to use this theme. We decided to add this to our functions.php file and kickstart the child theme CSS that way:
/**
 * Add's custom styles
 *
 */

function theme_styles() {
    wp_enqueue_style( 'child-theme-styles', get_stylesheet_uri() );
}

add_action('wp_enqueue_scripts', 'theme_styles');

Note, we did have trouble at first because we were using get_stylesheet_directory_uri() which was returing the path to the accio theme folder, and not our child theme folder. We found that get_stylesheet_uri() gave us the correct path to our style.css file.

A: what jhoffmcd said worked for me as well, however I had to add ", 99" so my stylesheet loaded last.  Thanks jhoffmcd.
function theme_styles() {
    wp_enqueue_style( 'child-theme-styles', get_stylesheet_uri() );
}

add_action('wp_enqueue_scripts', 'theme_styles', 99);

A: Sometimes, the main style.css file located at the root of template directory is not enqueued for loading (but just used for the comments to give information about the theme).
You should check wether this file is actually loaded or check if the parent theme is using an another css file such as css/app.css for instance.
If it still not working, you have to check how the css file is enqueued by the parent theme. It should be something like this :
wp_enqueue_style( 'style-name', get_stylesheet_directory_uri() . 'css/app.css' );

Note that get_stylesheet_directory_uri() will return the child theme URI if it exists, but get_template_directory_uri() will always return the current theme directory URI and will never be overridden.

A: You can try to use wp_enqueue_style function http://codex.wordpress.org/Function_Reference/wp_enqueue_style

A: just add the code
@import url("../../yourtheme-child/css/custom.css");

to your custom.css inside you main theme.

redpajama_set_name: RedPajamaStackExchange


text:
Ray Starmann
Joe Ragonese
J. Stewart Cook
Posted on May 14, 2017 by Ray Starmann
Germany Confiscating Homes to Use for Migrants "A massive attack on property rights"
In an unprecedented move, Hamburg authorities confiscated six residential units in the Hamm district near the city center. A trustee appointed by the city is now renovating the properties and will rent them — against the will of the owner — to tenants chosen by the city. District spokeswoman Sorina Weiland said that all renovation costs will be billed to the owner of the properties.
Similar expropriation measures have been proposed in Berlin, the German capital, but abandoned because they were deemed unconstitutional.
Some Germans are asking what is next: Will authorities now limit the maximum amount of living space per person, and force those with large apartments to share them with strangers?
Authorities in Hamburg, the second-largest city in Germany, have begun confiscating private dwellings to ease a housing shortage — one that has been acutely exacerbated by Chancellor Angela Merkel's decision to allow more than two million migrants into the country in recent years.
City officials have been seizing commercial properties and converting them into migrant shelters since late 2015, when Merkel opened German borders to hundreds of thousands of migrants from Africa, Asia and the Middle East. Now, however, the city is expropriating residential property units owned by private citizens.
In an unprecedented move, Hamburg authorities recently confiscated six residential units in the Hamm district near the city center. The units, which are owned by a private landlord, are in need of repair and have been vacant since 2012. A trustee appointed by the city is now renovating the properties and will rent them — against the will of the owner — to tenants chosen by the city. District spokeswoman Sorina Weiland said that all renovation costs will be billed to the owner of the properties.
The expropriation is authorized by the Hamburg Housing Protection Act (Hamburger Wohnraumschutzgesetz), a 1982 law that was updated by the city's Socialist government in May 2013 to enable the city to seize any residential property unit that has been vacant for more than four months.
The forced lease, the first of its kind in Germany, is said to be aimed at pressuring the owners of other vacant residences in the city to make them available for rent. Of the 700,000 rental units in Hamburg, somewhere between 1,000 and 5,000 (less than one percent) are believed to be vacant, according an estimate by the Hamburg Senate.
Socialists and Greens in Hamburg recently established a "hotline" where local residents can report vacant properties. Activists have also created a website — Leerstandsmelder (Vacancy Detector) — to identify unoccupied real estate in Hamburg and other German cities.
It remains unclear why the landlord in Hamm left his apartments vacant for more than five years. Some have posited that, given the location of the properties, the renovation costs may have been too high and probably would not have been offset by the rental income.
Others are blaming city officials for not approving more building permits to allow for the construction of new residential units. A study conducted in 2012 — well before the migrant crisis reached epic proportions — forecast that by 2017, Hamburg would have a deficit of at least 50,000 rental properties.
In 2016, however, only 2,433 new residential units came onto the market, while only 2,290 new building permits were approved, according to statistics provided by the City of Hamburg. These numbers were up slightly from 2,192 new units and 2,041 new permit approvals in 2015.
In 2012, Hamburg's Socialist government presented a plan to build 6,000 new residential units per year. The plan never materialized, however, because prospective builders were constricted by government-imposed rental caps which would have made it impossible for them to even recover their construction costs.
Since then, the city has turned to seizing private property to resolve its self-inflicted housing crisis.
On October 1, 2015, the Hamburg Parliament (Hamburgische Bürgerschaft) approved a new law that allows the city to seize vacant commercial real estate (office buildings and land) and use it to house migrants.
City officials said the measure was necessary because, at the time, more than 400 new migrants were arriving in Hamburg each day and all the existing refugee shelters were full. They said that because the owners of vacant real estate refused to make their property available to the city on a voluntary basis, the city should be given the right to take it by force.
The measure was applauded by those on the left of the political spectrum. "We are doing everything we can to ensure that the refugees are not homeless during the coming winter," said Senator Till Steffen of the Green Party. "For this reason, we need to use vacant commercial properties."
Others have argued that efforts by the state to seize private property are autocratic and reek of Communism. "The proposed confiscation of private land and buildings is a massive attack on the property rights of the citizens of Hamburg," said André Trepoll of the center-right Christian Democratic Union (CDU). "It amounts to an expropriation by the state." He said the proposed measure is a "law of intimidation" that amounts to a "political dam-break with far-reaching implications." He added: "The ends do not justify any and all means."
Katja Suding, the leader of the Free Democrats (FDP) in Hamburg, said that the proposed law is an "unacceptable crossing of red lines… Such coercive measures will only fuel resentment against refugees."
In November 2015, lawmakers in Berlin considered emergency legislation that would have allowed local authorities to seize private residences to accommodate asylum seekers. The proposal would have authorized police forcibly to enter private homes and apartments without a warrant to determine their suitability as housing for refugees and migrants.
The legislation, proposed by Berlin Mayor Michael Müller of the center-left Social Democrats (SPD), would have amended Section 36 of Berlin's Public Order and Safety Law (Allgemeine Gesetz zum Schutz der öffentlichen Sicherheit und Ordnung, ASOG), which currently allows police to enter private residences only in extreme instances, to "avert acute threats," that is, to fight serious crime. Müller wanted to expand the scope for warrantless inspections to include "preventing homelessness."
The proposal was kept secret from the public until the leader of the Free Democrats (FDP) in Berlin, Sebastian Czaja, warned the measure would violate the German constitution. He said:
"The plans of the Berlin Senate to requisition residential and commercial property without the consent of the owner to accommodate refugees is an open breach of the constitution. The attempt by the Senate to undermine the constitutional right to property and the inviolability of the home must be resolutely opposed."
Since then, both the mayor's office and the Senate appear to have abandoned their plans.
Following an investigation, Gunnar Schupelius, a columnist with the Berlin newspaper BZ, wrote:
"A strange report made the rounds at the weekend: The Senate would authorize the police to enter private homes to house refugees, even against the will of the owner. I thought it was only satire, then a misunderstanding, because the Basic Law, Article 13, states: 'The home is inviolable.'
"So I went on a search for the source of this strange report and found it. There is a 'proposal' which the Senate Chancellery (Senatskanzlei) has apparently circulated among the senators. The Senate Chancellery is another name for the mayor's office. The permanent secretary is Björn Böhning (SPD)…
"The proposal is clear: The police can enter private property without a court order in order to search for housing for refugees when these are threatened with homelessness. You can do that 'without the consent of the owner.' And not only should the police be allowed to do this, but also the regulatory agencies.
"This delicate 'proposal' attracted little public attention. Only Berlin FDP General Secretary Sebastian Czaja spoke up and warned of an 'open preparation for breach of the constitution.' Internally, there should have been protests. The 'proposal' suddenly disappeared from the table. Is it completely gone or will it return?"
It remains unclear why no one has challenged the constitutionality of Hamburg's expropriation law.
Meanwhile, some Germans are asking what is next: Will authorities now limit the maximum amount of living space per person, and force those with large apartments to share them with strangers?
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Mitchell Hamline Alumni Board elects new members
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Category: Alumni, Alumni Spotlight
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