text:
Reformist
Lassa fever is a national tragedy
April 28, 2016 reformist Leave a comment
HEALTH experts in Nigeria have said that the recurrent Lassa fever outbreak in Nigeria is akin to a national embarrassment and that it will be no exagerration to describe it as a national tragedy. Expressing dissatisfaction, the Chairman, Lassa Fever Control Committee in Nigeria, Prof. Oyewale Tomori, who is President of the Nigeria Academic of Science, NAS, observed that Lassa fever that has been claiming lives over the past 47 years without concrete strategies to bring it under control, can only be referred to as a continuous national tragedy. Prof. Oyewale Tomori Prof. Oyewale Tomori Tomori, who spoke on Monday at a symposium on Lassa fever organised by the Nigerian Institute of Medical Research, NIMR, Yaba, Lagos, said the break down in disease surveillance did not make the country notice that Lassa fever has consistently brought sorrow, pains and agony to several homes. "If you are not hearing of new cases, it is not that the disease is not occurring, but because our disease surveillance is not up to par. "We deceive ourselves that it's a seasonal disease, but the fact remains that it occurs throughout the year. At least three people are diagnosed of Lassa fever daily somewhere in the country." Further, Tomori said: "We have little value for life, until more than 100 people die, it is not an emergency, For many years Lassa fever has been with us but we don't take it serious. In other parts of the world, when a single person dies of a disease, it is a national emergency. For how long are we going to continue to call tragedy an embarrassment? Surveillance "We abandon disease surveillance and control activities; there was a time people worked together, the laboratories at Ibadan, the Ministry of Health, NIMR, joined forces to protect the country. In the 60's to 80's at Ibadan, we produced every reagent we needed in the country. We did not depend on importation. But now we are lazy and everybody wants to make money from importation." In his explanation, he said six to seven laboratories in the country cannot give proper results. "This is due to lack of support. The only few that function in the country have the support of partner agencies and backing from abroad," he noted. On his part, President, Bioresources Development Group, Prof. Maurice Iwu, said that the Lassa fever issue was more than an embarras- sment because the country has the personnel required,knowledge of the Lassa fever and how to prevent it, but the disease still claims lives. Iwu said unless the country adopts the approach used during the Ebola outbreak, many more people would fall victim. "As long as Lassa fever is anywhere in the country, as long as we have restaurants that don't keep good hygiene, as long as we have houses that are co-infested with rat and horse, as long as we have dirty environment, we are all vulnerable. "The only thing we can do is keep track of the virus, and from time to time do research. Our universities should make sure that 80 percent of their research is localized to treat our own diseases, issues and viruses we live with," he said. In his contribution, the Director Gen-eral, NIMR, Prof. In-nocent Ujah, said the national response to the 47-year-old epidemic in Nigeria has remained largely poor and there appears to be no strategic plan to allocate fund for Lassa and other viral Haemorrhagic fevers research, surveillance and appropriation response. Ujah mentioned lack of manpower and well equipped laboratories as part of challenges hindering the institution from recording success. "Public health education and awareness are usually far in between and there are no comprehensive preventive measures to contain the disease. "NIMR has only one medical virologist and the laboratories are not well equipped for Lassa fever and other Haemorrhagic fever research," he remarked.
READ ALSO Federal Teaching Hospital Abakaliki (FETHA), after late Alex Ekwueme.
« I caught my girlfriend masturbating
FG plans big for SMART Nigeria »
Need A Website For Your Business?
Visit CFEN
How Gov. Obiano Banned Illegal Levies
Over. 60 Lodgers, Staff escape death as Hotel collapse in Abakaliki
Police parades over 20 armed robbery suspects, kidnappers in Enugu
Anmbra Market Crisis: Anakwe, Ex-OMMATU Leader Asks Obiano to Intervene
Court remands Prince Afam Ude in Prison for Conspiracy of felony, Murder
FUTHA debunks allegation of detaining 22 nursing mothers
BNG blasts Arewa Youths, Nigeria occupational forces —–we are waiting for proper action
Anambra: One Injured, Two Detained As Tipper Drivers Clash With LG Boys
UNN makes another breakthrough, produces electric car
Ebonyi communal war: death toll rises to 14
Newsscroll
MOUAU Graduates 4,958, Gives Employment To Best Graduating student
New graduates emerges and the best gets automatic employment. Find out more.
Ugwuanyi turns Enugu safe haven for investment – Prof Soludo
What did Ugwuanyi do this time that warranted the comment from Prof. Soludo? Find out by visiting the page.
APGA responsible for death of 2000 Biafran Agitators–IPOB
APGA responsible for death of 2000 Biafran Agitators–IPOB By Larry Onwutra The Indigenous People of Biafra (IPOB) and its leadership worldwide have described those gazing at self determination and referendum like a call for war as uncivilized and uneducated people. The media spokesman of the IPOB Emma Powerful yesterday said in a press statement that the antics of the All [...]
Ugwuanyi turns Enugu safe haven for investment – Prof Soludo The former Governor of the Central Bank of Nigeria (CBN), Prof. Chukwuma Soludo has applauded Governor Ifeanyi Ugwuanyi's giant strides in Enugu State, saying that his feats in securing the state from vices such as kidnapping, have positioned it as a safe haven for investment and living. Prof. Soludo who [...]
The Vice-Chancellor of University of Nigeria Nsukka (UNN) Prof. Benjamin Chukwuma
The Vice-Chancellor of University of Nigeria Nsukka (UNN) Prof. Benjamin Chukwuma Ozumba (3rd left) been flanked by left DVC Prof. James Ogbonna, Prof. Charles Igwe and from (1st right) the registrar Barr. Igbokwe and Prof. Smart Uchegbu of UNN Enugu Campus during the press briefing on the 47th convocation ceremony held at the Enugu Campus. Photo by Sunday Ogbuabo
MOUAU Graduates 4,958, Gives Employment To Best Graduating student From Ify Ashi Umuahia Michael Okpara University Of Agriculture Umudike (MOUAU) Umuahia, Abia State in its 8th convocation ceremony yesterday graduated a total of 4,958 with a student in statistics Nworu Charles Chinedu coming out the best graduating students with an aggregate grade of 4.91. The Vice Chancellor professor Francis Ogbonnaya Otunta reading [...]
Abia Business Man Recount Difficulties In Doing Business In South East
Abia Business Man Recount Difficulties In Doing Business In South East By Ify Ashi Umuahia The Chief executive, Obioma Enterprises Nigeria Limited, Chief Obioma Okorie has lamented over the hardship faced by business men in doing business in Nigeria and worst in South East. Chief Okorie said Nigeria now is out of recession but in reality the impact is not felt [...]
Catholic Group offered free Medical care to Abia Students
Catholic Group offered free Medical care to Abia Students By Ify Ashi Umuahia Madonna Catholic hospital Umuahia Abia state in collaboration with a Non Governmental Organization Back to the Hero's Past (BATHEP) and a Corp member Ifeoma Nweke Who served in Madonna Catholic hospital, Umuahia yesterday offered Free eye medical care to secondary school students and their teachers. Speaking to [...]
Enugu Govt Approves N1.5b for Rehabilitation of 14 Rural and Urban Roads
Enugu Govt Approves N1.5b for Rehabilitation of 14 Rural and Urban Roads The Enugu State Executive Council has approved the sum of N1,532,148,154.76 for the construction of 14 critical roads in both the Enugu metropolis and some rural communities. This fresh massive approval came on the heels of the flag off of construction and rehabilitation of four other critical roads [...]
IPOB accused Buhari's Govt of frustrating Court cases against Biafra agitators
IPOB accused Buhari's Govt of frustrating cases against Biafra agitators The Indigenous People of Biafra (IPOB) worldwide under the command and leadership of Mazi Nnamdi Kanu the prophet of our time, has drawn the attention of the civilized world to the endless, continued adjournment and postponement of court cases involving IPOB in every Nigerian court including ECOWAS Court sitting in [...]
Powered by Reformist Media Limited and Managed By - CFEN-08037816296.


I'm always the go to class mate when it comes to help studying or figuring out the lesson. This hasn't changed even when getting to college. I can help with just about any subject but feel more comfortable with those below.
I am getting into the tutoring game since I feel that I could help out fellow students in their classes as well as help me keep this in application instead of in metaphorical storage.


Sorry, we do not have enough "Shuttle Bus B" in stock to fulfill your order right now. Please try again in 10 minutes or edit your cart and try again. We apologize for any inconvenience caused.
View cart "Shuttle Bus B" has been added to your cart.


text:
Себре́рос () — муниципалитет в Испании, входит в провинцию Авила в составе автономного сообщества Кастилия-и -Леон. Муниципалитет находится в составе района (комарки) Бургохондо-Себрерос-Эль-Тьембло. Занимает площадь 137 км². Население — 3444 человека (на 2010 год). Расстояние до административного центра провинции — 45 км.

Население

Примечания

Муниципалитеты Авилы

redpajama_set_name: RedPajamaWikipedia


text:
\section{Introduction}
\IEEEPARstart{A}{erial} object detection has received much attention in recent years, due to its important role in land and resources survey, geographic mapping, and other fields \cite{8113128}. However, existing state-of-the-art aerial object detectors usually require a large amount of training data with bounding-box annotations for model training to reach the desired performance, which is typically expensive to obtain \cite{hua2020relation}. Active learning~(AL) is a machine learning technique that selectively queries the informative unlabeled examples from the oracle for annotation to reduce the labeling cost. It has been successfully applied to the generic object detection problem for data-efficient learning \cite{kao2018localization, tang2021qbox, desai2020towards, roy2018deep}. However, existing generic active learning methods can hardly be applied to the remote sensing images. As shown in Figure \ref{intro}, the objects in aerial images (i.e., remote sensing data) are usually small, blurred, and densely distributed in the complex background. Such characteristics are not well-considered by the existing methods, thus they may lead to unsatisfied performances. For these reasons, we strive to design an effective active learning method for aerial object detection that sufficiently takes into account the challenges of remote sensing images.

There are basically two aspects needed to be considered in active object detection, i.e., \textbf{query strategy} and \textbf{query type}.
The former investigates the measurements of the informativeness of the data, and the latter designs an efficient manner to acquire knowledge from the oracle.

For the query strategy, most existing methods try to evaluate the uncertainty criteria of the unlabeled data. However, these common criteria neglect one of the most notable problems of aerial data -- the severe class imbalance problem \cite{li2017cost}. As a result, query by uncertainty may further intensify the imbalance problem and bring challenges to the model training. Since the uncertain patterns are more likely to come from uncertain classes (the classes with rare samples), class preference should be considered in the active selection for the aerial object detection task. However, implementing such a criterion can be highly challenging according to the active learning literature~\cite{ning2022active}, since it is hard to accurately predict the class labels for the unlabeled images with only limited training data.

\begin{figure}[!t]
\centering
\includegraphics[width=0.45\textwidth]{intro.jpg}\caption{Comparison of generic object detection and aerial object detection. (a) generic object detection: objects in general scenes are usually sparsely distributed and easy to identify. (b) aerial object detection: objects in aerial images (i.e., remote sensing data) are usually small, blurred, and densely distributed in the complex background.}
\label{intro}
\end{figure}

For the query type, existing solutions can be roughly divided into the following two categories, image-based as shown in Figure~\ref{query type} (a) and object-based methods as shown in Figure~\ref{query type} (b). Image-based methods estimate the uncertainty of the whole image and require the bounding-box annotation of all the objects from the image \cite{xu2021adaptive,xu2020using}. Although they are adept at capturing the comprehensive contextual information of the image, these approaches suffer from inefficient and redundant labeling problems for aerial object detection. Because an aerial image usually has plenty of similar objects with information redundancy, annotating all of them may lead to a waste of labeling costs. Object-based sampling methods have recently been studied for object detection \cite{tang2021qbox,desai2020towards}. They query a specific object (i.e., bounding-box) rather than the whole image for more fine-grained and cost-effective supervision, which are more suitable to the aerial data. However, these approaches also confront limitations. On the one hand, they ignore the contextual information of the objects, i.e., they only consider the uncertainty of the object, but neglect the spatial information and the semantic structure of the image. On the other hand, they may introduce training noise. Because each image is partially annotated, i.e., only the supervision of certain objects can be obtained, the remaining unknown regions are usually treated as background, which can mislead the model due to the latent foregrounds.

To address the problems raised above, in this paper, we present a novel active learning method for aerial object detection, Mixed Uncertainty Sampling with Class Distribution Balancing~(MUS-CDB). Specifically, for the query type, we propose an object-based mixed uncertainty sampling method to label the most informative and representative objects. Different from the existing works, our method addresses the limitations of both conventional object-based and image-based methods as analyzed above, i.e., the redundant and myopic information querying, by considering both object-level and image-level uncertain information. For the query strategy, we propose a hybrid selection criteria, which considers both uncertainty and class distribution balancing. The former identifies the objects that are most helpful to improve the performance of current detection model, while the latter tries to improve the class distribution balance of training samples to further enhance the model's capability on rare classes. Finally, in order to fully utilize the weakly supervised data, i.e., partially labeled images, we propose an effective training scheme associated with a loss function, which can effectively mine the latent knowledge in the unlabeled image regions that have not been queried as positive samples.  Extensive experiments are conducted on DOTA-v1.0\cite{xia2018dota} and DOTA-v2.0\cite{ding2021object}, and the results show that the proposed method can significantly outperform the conventional image-based and object-based active learning methods for aerial object detection.

We summarize our contributions as the following:
\begin{enumerate}
\item{We propose an object-based active learning method for aerial object detection, which considers the characteristics of remote sensing data. It incorporates both uncertainty and class balancing in active select informative and representative object samples.}
\item{We propose a training scheme associated with a loss function for object-based querying with partially labeled data. It sufficiently and robustly exploits the queried information from partial labels and can effectively improve the model's capability.}
\item{Extensive experiments on DOTA-v1.0 and DOTA-v2.0 validate the effectiveness and practicability of the proposed method, which significantly outperforms the conventional image-based and object-based active learning methods for aerial object detection.}
\end{enumerate}

The rest of this article is structured as follows. In Section~\ref{related-work}, We discuss the related work. In Section~\ref{Methodology}, we introduce the proposed method in detail. The experimental results are presented in Section~\ref{Experiments}. Section~\ref{Conclusion} presents the conclusion.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.9\textwidth]{head.jpg}
\caption{Illustration of different types of AL methods. (a) Image-based sampling methods fully annotate the whole image, which may result in the inefficient and redundant annotation. (b) Object-based sampling methods select objects for which the model is most uncertain.}
\label{query type}
\end{figure*}

\section{RELATED WORK}  \label{related-work}
\subsection{Aerial Object Detection}
Aerial object detection is an important task in computer vision \cite{8113128}. Compared with the conventional object detection task, aerial object detection is more challenging for the following reasons. Firstly, the objects in remote sensing images have various orientations and aspect ratios. The detector often needs to predict the angle of the bounding box in order to locate the object accurately. Secondly, the objects in remote sensing images are usually small, and their visual features are easily affected by complex imaging processes, noise, and occlusion. Many works have been devoted to solving these specific problems of aerial object detection.

To mine the direction information, some methods use multiple anchors with different angles, scales, and aspect ratios for regression tasks  \cite{ma2018arbitrary, zhang2018toward}. However, these methods are usually computationally expensive. Another group of methods uses only horizontal anchors to detect rotated objects to improve efficiency. For example, Ding et al. \cite{ding2019learning} propose RoI Transformer to convert Horizontal RoIs (HRoIs) into RRoIs, which can effectively reduce the number of anchors. DRN~\cite{pan2020dynamic} performs orientated object detection through dynamic feature selection and optimization. Wei et al. \cite{wei2022learning} propose a calibrated-guidance (CG) scheme to enhance channel communications. CSL \cite{yang2020arbitrary} treats angle prediction as a classification task to avoid discontinuous boundary problems. ReDet \cite{han2021redet} is committed to improving the model's feature representation by modifying the backbone to generate rotation-equivariant features, and employing RiRoI Align in the detection head to extract rotation-invariant features.

For the challenge of small objects, Qiu et al. \cite{qiu2019a2rmnet} propose an adaptive aspect ratio network. It on the one hand assigns different weights to the feature maps, and on the other hand predicts proper ratio aspects for different objects. Li et al.~\cite{li2019deep} design a perceptual generative adversarial network to enhance feature response, which converts the features of small targets into large ones with homogeneous attributes. Zheng et al.~\cite{zheng2020hynet} propose a hyperscale detector to learn scale-invariant representations of objects. Liang et al. \cite{liang2021anchor} present a dynamic enhancement anchor network (DEA-Net), it contains a sample generator to augment the training data, and a discriminator to distinguish the samples between an anchor-based unit and an anchor-free unit to help train the sample generator. Liu et al.~\cite{liu2021abnet} design an enhanced effective channel attention (EECA) mechanism, an adaptive feature pyramid network (AFPN), and a context enhancement module (CEM) in an adaptive balanced network (ABNet) in order to capture more discriminative features.

Most of the existing methods are based on the assumption that a large number of labeled data for model training is usually unavailable in real applications due to the prohibitive labeling cost of annotating remote sensing images.

\subsection{Active Learning and Its Applications in Object Detection}
 Active learning has received more and more attention in recent years due to its potential to reduce the labeling cost~\cite{settles2009active}. It selectively queries the labels of the most informative unlabeled examples from the oracle. It has been successfully applied to many important tasks in computer vision, such as classification~\cite{sinha2019variational, ebrahimi2020minimax, beluch2018power}, detection~\cite{roy2018deep, aghdam2019active, yoo2019learning} and segmentation~\cite{cai2021revisiting, casanova2020reinforced, kasarla2019region}. There are two settings in active learning literature, one is the membership query, and the other is the pool-based setting. Among them, the membership query method directly generates informative samples from the feature space for querying  \cite{mahapatra2018efficient, mayer2020adversarial, zhu2017generative}, while the pool-based active learning methods assume that there is a large pool of unlabeled data for sampling. They try to select the most informative examples from the pool for querying. In this paper, we mainly study the latter setting.

Active learning mainly studies effective query strategies and query types. For the former research topic, many selection criteria are proposed to evaluate the informativeness of the unlabeled data from different aspects. Existing methods can mainly be divided into the uncertainty-based approaches~\cite{yan2018cost, kirsch2019batchbald}, representativeness-based approaches~\cite{sener2017active, sinha2019variational} and hybrid approaches~\cite{huang2010active, tang2019self}. Uncertainty-based methods prefer the examples with high prediction uncertainty of the model. For example, Yoo et al.~\cite{yoo2019learning} add a loss prediction branch to the neural network to predict the loss of unlabeled samples. The data with large predicted loss will be queried from the oracle. Roy et al. \cite{roy2018deep} estimate the uncertainty by the difference between the convolutional layers of the object detector backbone. The data with high divergence are preferred in active selection.  Kao et al. \cite{kao2018localization} propose ``localization tightness" and ``localization stability" criteria. They measure the overlap ratio between region proposals and final predictions, and the prediction changes of the original and corrupted images to evaluate the uncertainty.
Representativeness-based methods prefer data that can well represent the latent data distribution. Sener et al. \cite{sener2017active} propose a coreset method to query the data that can cover the whole dataset with a minimum radius. Sinha et al. \cite{sinha2019variational} train a variational autoencoder (VAE) and an adversarial network to classify the unlabeled and labeled data, then select the data which is predicted as the unlabeled one. For the hybrid methods, which consider multiple criteria simultaneously, Ash et al. \cite{ash2019deep} propose a hybrid AL method that clusters the gradient of the target model's final output layer as the feature of the unlabeled samples that contain the uncertainty information. Sharat et al. \cite{agarwal2020contextual} use the probability vector predicted by CNN to select objects located in different backgrounds.

For the query type, many methods are proposed to improve the querying efficiency by an effective query type, i.e., taking an object rather than the image as the basic unit for querying~\cite{wang2018towards, tang2021qbox, desai2020towards}. For example, Tang et al.~\cite{tang2021qbox} consider the partial transfer object detection task, and query the source objects which are informative and transferrable to the target domain. Laielli et al. \cite{laielli2021region} propose a region-level sampling method, which calculates the score of each region according to an accumulation of the informativeness and similarity of each query-neighbor pairing within a region and finally selects the image region with the highest score to send to the experts for annotation. Xie et al. \cite{xie2022towards} propose a region-based active learning approach for semantic segmentation with the existence of domain shift. The authors evaluate the informativeness by the category diversity of pixels within a region, and the classification uncertainty. Liang et al. \cite{liang2022exploring} propose a sampling method combining spatial and temporal diversity to label the most informative frames and objects according to the multimodal information provided by the AV dataset. Most of the existing works consider the common learning tasks, but they are less applicable to aerial object detection due to the neglect of the challenges of this task.

\begin{figure*}[!t]
\centering
\includegraphics[width=.8\textwidth]{framework.jpg}\caption{Overall framework of the proposed method. The active learning sampling strategy consists of two modules: Image-Guided Uncertainty Sampling (IGUS) and Distribution-Guided Class Balancing (DGCB). The IGUS module combines the image uncertainty and object uncertainty to obtain the final object score. The DGCB module uses the class distribution of the labeled objects pool to constrain the class distribution of the sampling results. We take the model prediction object as the basic sampling unit and send it to experts for labeling. ReDet is a remote sensing object detector.}
\label{framework}
\end{figure*}

\section{The Methodology}\label{Methodology}

\subsection{Problem Definition}
In the active learning for aerial object detection task, there is a small set of fully labeled images
\begin{equation}
\mathcal{D}_L = \{(X_i^L, \mathcal{Y}_i^L)\}_{i=1}^{N_L},
\end{equation}
to initialize the model. To be specific,
\begin{equation}
\mathcal{Y}_i^L=\{(\textbf{c}_{ij}^L, \textbf{b}_{ij}^L)\}_{j=1}^{n_i},
\end{equation}
where $\textbf{c}_{ij}^L$ is the one-hot class label belonging to one of the $C$ known classes in label space and $\textbf{b}_{ij}^L$ is the bounding box label. Meanwhile, there is also a large set of unlabeled images,
\begin{equation}
\mathcal{D}_U = \{(X_i^U)\}_{i=1}^{N_U}
\end{equation}
for data selection, where $N_U \gg N_L$. Our goal is to train a well-performed detector $f$ with the least queries from $\mathcal{D}_U$. Denote by
\begin{equation}
\mathcal{P}_U = \{(X_i^U, \mathcal{\hat{Y}}_i^U)\}_{i=1}^{N_U},
\end{equation}
the model prediction on the unlabeled data where
\begin{equation}
\mathcal{\hat{Y}}_i^U = \{(\mathbf{\hat{c}}_{ij}^U, \mathbf{\hat{b}}_{ij}^U)\}_{j=1}^{\hat n_i}.
\end{equation}
To be specific,
$\mathbf{\hat{c}}_{ij}^U, \mathbf{\hat{b}}_{ij}^U$
represent the model probability prediction of each category, i.e.,
\begin{equation}
\mathbf{\hat{c}}_{ij}^U = \{\hat{c}_{ijk}^U\}_{k=1}^C
\end{equation}
and bounding box position, respectively. Given a sampling function $\varphi$, which takes $\mathcal{P}_U$ as input, to select the informative data for labeling. For the proposed object-based active learning method, we rate each predicted bounding box in the unlabeled images set $\mathcal{D}_U$, and select the top $N$ informative predicted bounding boxes for querying. Once labeled, these bounding boxes are added to the partially labeled set
\begin{equation}
\mathcal{D}_P = \{(X_i^P, \mathcal{Y}_i^P)\}_{i=1}^{N_P}
\end{equation}
to improve the object detector, along with the initial labeled set $\mathcal{D}_L$.

In this work, we design two modules to perform a cost-effective selection for objects, i.e., the Image-guided Uncertainty Sampling module (IGUS) and the Distributed-guided Class Balancing module (DGCB). Next, we will introduce the proposed query strategy in detail, and elaborate on the training scheme to fully utilize the partial labels.

\subsection{Mixed Uncertainty Sampling}
As mentioned before, existing object-based sampling methods mainly consider the information of the prediction box itself, i.e., category uncertainty or regression uncertainty, but neglect the spatial information and the semantic structure of the image. To tackle this problem, we propose to consider both uncertainty of the image and the object for more comprehensive data evaluation, which incorporates both global and local information.

As for the image uncertainty, we believe that if there are many predicted objects with high uncertainty in an image, it is not well-learned by the model. Thus, this image should be preferred in active selection. To this end, we evaluate and aggregate the uncertainty values of the most confident model predictions (i.e., objects whose predicted class confidence is greater than a specific threshold) to indicate the uncertainty value of the whole image. This process can be regarded as a fusion of the spatial and semantic structure information of the image. Specifically, the image uncertainty $\varphi_i^I$ for a given image $X_i^U$ is formulated as
\begin{equation}
\begin{aligned}
   \varphi_i^I = 1 -  \frac{1}{|\mathcal{S}^{\theta}_i|}\sum_{j \in \mathcal{S}^{\theta}_i} \max \mathbf{\hat{c}}_{ij}^U \,,
\end{aligned}
\label{eq:image}
\end{equation}
where
\begin{equation}
    \begin{aligned}
       \mathcal{S}^{\theta}_i = \{ j | \max \mathbf{\hat{c}}_{ij}^U>\theta , \forall j = 1,\dots,\hat n_i \}\,,
    \end{aligned}
    \label{eq:set}
\end{equation}
and $|\cdot|$ represents the number of elements in the set and $\theta$ is the threshold. Here we use the parameter $\theta$ to impose preference on the images with distinct information in the active selection phase. It is conceivable that the images with many similar objects are more likely to receive a small $\varphi_i^I$ due to the sufficient supervision of this pattern. On the contrary, the examples with higher values of $\varphi_i^I$ are more likely to contain knowledge of the rare patterns, thus are more informative. One phenomenon to imply this motivation is that, the distribution of different categories of remote sensing images has certain regularity. Some head categories, such as small vehicle and large vehicle, are densely distributed and very similar to each other; Some medium categories, such as bridge, are sparsely distributed in the picture, and the background of the picture is blurry and difficult to distinguish; Tail categories, such as helicopter, have a small number and poor performance. These images with similar and densely distributed objects on the one hand may introduce information redundancy, on the other hand may intensify the class imbalance problem in model learning. For these reasons, we should avoid querying these images.

To further consider the instance-level information in our object-based querying, we employ entropy to evaluate the uncertainty of each predicted bounding box. Specifically, the object uncertainty is calculated as follows
\begin{equation}
\begin{aligned}
   \varphi_{ij}^O = -\sum_{k=1}^C \mathbb P(\hat c_{ijk}^U|X_i^U) \log \mathbb P(\hat c_{ijk}^U|X_i^U)\,,
\end{aligned}
\label{eq:01}
\end{equation}
where $\mathbb P(\hat c_{ijk}^U|X_i^U)$ is the predicted probability of the $j^{\operatorname{th}}$ bounding box in the image $X_i^U$ on class $k$.

Although the uncertainty measurement of the objects is straightforward, we note that this criterion is combined with the image uncertainty and class balancing to conduct active selection. The overall query strategy well considers the spatial information and the semantic structure of the image, as well as the instance-level uncertainty. We will show the effectiveness of the selection criteria empirically in the experiments.

\subsection{Class Distribution Balancing}
Many remote sensing datasets suffer from the problem of class imbalance \cite{xia2018dota}, as some categories are naturally rare. This phenomenon will significantly jeopardize the model performance, especially for the minority classes. To tackle this problem, we propose a selection criterion to emphasize the categories with low occurrence frequency in active querying.
Specifically, we first identify the minority classes by counting the objects of each class on the labeled set. Denote by $a_k$ the number of objects in class $k$, $\forall k=1,\dots,C$. We would like to query more objects in the class with a small $a_k$, e.g., by imposing a preference $\zeta_k$, which is inversely proportional to the $a_k$, on each class during the selection phase
\begin{equation}
    \begin{aligned}
        \zeta_k = \frac{exp(\beta_k)}{\sum\limits_k exp(\beta_k)}\,,
    \end{aligned}
    \label{eq:distribution}
    \end{equation}
    where
\begin{equation}
\begin{aligned}
    \beta_k = 1-\frac{a_k}{\sum\limits_{k}a_k}\,.
\end{aligned}
\end{equation}

\begin{figure}[!t]
\centering
\includegraphics[width=0.5\textwidth]{CB.jpg}
\caption{The procedure of the proposed Distribution-Guided Class Balancing module (DGCB). We reverse the class distribution of the labeled samples and perform a softmax operation to obtain the distribution that the class should satisfy when sampling.}
\label{growth}
\end{figure}

Finally, we summarize our active learning method as the following. We first score each predicted object by
\begin{equation}
    \begin{aligned}
    &\varphi_{ij} = \varphi_i^I \cdot \varphi_{ij}^O\,, \\
    \forall i=1,&\dots,N^U,\; \forall j=1,\dots,\hat n_i\,.
    \end{aligned}
    \label{object_score}
\end{equation}
Then, we allocate the labeling budget for each class according to the $\zeta_k^T$, and query the annotation of the top-rated candidate objects. If the budget of a specific class has run out, we drop the rest objects which are predicted as this class.

\subsection{Dealing with Partial Labeled Data}
In order to fully utilize the weakly supervised data, i.e., partially labeled images, we devise an effective training strategy. For the fully labeled datasets, we follow the default training scheme of the ReDet model to update the detector. For the partially labeled datasets, since the labeling of the images is incomplete, we cannot directly employ the vanilla training scheme, because it will treat the unlabeled region as background. To tackle this problem, we propose a partial training loss to effectively mine the latent knowledge in the unsupervised image regions that have not been queried.

Specifically, for the loss of RPN head $\mathcal{L}_{rpn}$, we take the same form as in the vanilla ReDet model\cite{han2021redet}. For the loss of bounding-box head $\mathcal{L}_{bbox}$, inspired by the state-of-the-art semi-supervised learning method \cite{xu2021end}, in which the unlabeled data is usually exploited by assigning pseudo-labels and its loss is calculated with a relatively small weight. Because the pseudo-labels may be incorrect, down-weighting the loss is equivalent to introducing a noise prior to the model for robust learning. Following this principle, we employ an adaptive weight to each object when calculating the loss, i.e., if the model prediction is confident, the risk that the pseudo-label is noisy is relatively low, thus the loss weight should be higher for this object. Formally, the $\mathcal{L}_{bbox}$ is defined as follows

\begin{equation}
\begin{aligned}
    \mathcal{L}_{bbox} = &\lambda_{cls}\sum_{j=1}^{W}\sum_{k=1}^{C+1}\mathbb I_{i}^{weak}\omega[-\mathbf{{o}_{ijk}}\log (\mathbf{\hat{o}_{ijk}})] + \\
     &\lambda_{cls}\sum_{j=1}^{W}\sum_{k=1}^{C+1}\mathbb I_{i}^{fully}[-\mathbf{{o}_{ijk}}\log (\mathbf{\hat{o}_{ijk}})] + \\
    & \lambda_{reg}\sum_{j=1}^{W}\sum_{u=1}^{5}\mathbb I_{ij}^{obj}\ell_{s}(\hat{v}_{iju}-v_{iju}) \,,
\end{aligned}
\label{eq:lose}
\end{equation}
where

\begin{equation}
\omega = \left\{
	\begin{aligned}
	1 \quad \mathbb I_{ij}^{obj} = 1\\
	\mu \quad \mathbb I_{ij}^{obj} = 0
	\end{aligned}
	\right
	.
\label{eq:w}
\end{equation}

and

\begin{equation}
\ell_{s}(x) = \begin{cases}
    0.5(x)^2, \quad &if |x|<1\\
    |x|-0.5, \quad &otherwise
    \end{cases}
    .
\end{equation}

Here, $\lambda_{cls}$ and $\lambda_{reg}$ are the tradeoff parameters, $i$ and $j$ are the indexes of images and region proposals, respectively. $W$ indicates the number of proposals involved in bounding-box head in training. $\mathbb I_{i}^{weak}$ and $\mathbb I_{i}^{fully}$ are indicator functions. They equal $1$ when the image $i$ is a partially labeled image or a fully labeled image, respectively. $\mathbb I_{ij}^{obj}$ indicates whether the proposal contains a foreground object. $\omega$ is introduced to down-weighting the background objects for robust learning.

The $\mathcal{L}_{bbox}$ contains the classification loss (the first two terms) and the box regression loss (the last term). For the classification loss, the model predicts a discrete probability distribution over $C$ categories plus the background for each proposal, i.e., $\mathbf{\hat{o}_{ijk}}, \forall k=1, \dots, C+1$. For the regression loss, it is defined over the bounding-box coordinate offsets plus the angle, i.e., $\hat{v}_{iju},\,\forall u=1,\dots,5$. Further, the smooth L1 regularization $\ell_s$ is employed to stabilize the training.
\subsection{The Overall Framework}
We summarize our methods in Algorithm~\ref{alg}. Firstly, we use the labeled dataset to train the detector $f$, and the prediction results of $f$ on the unlabeled dataset are filtered to form the candidate objects to be sampled (i.e., excluding the prediction objects that highly overlap with the ground truth object). Secondly, we employ the IGUS module to calculate the informativeness of the predicted objects. Thirdly, we utilize the DGCB module to calculate the category sampling distribution $\zeta_k^T$, and initialize the annotation budges allocation vector $a$ according to $\zeta_k^T$. Finally, according to the annotation cost of each category shown in $a$, we annotate the candidate objects in the order of information content from high to low within the limit.
\begin{algorithm}[t]
    \renewcommand{\algorithmicrequire}{\textbf{Input:}}
    \renewcommand{\algorithmicensure}{\textbf{Output:}}
    \caption{Our proposed MUS-CDB}
    \label{alg}
    \begin{algorithmic}[1]
        \Require labeled data set $D_L$, unlabeled data set $D_U$, queried set $D_P$, annotation budget $N$, detection model $f$.
        \State Train the detection model $f$ with $D_L$.
        \State $\mathcal P^U \leftarrow \{(X_i^U, f(X_i^U) \,|\, \forall i=1,...,N_U\}$.
        \State Remove the objects in $\mathcal P^U$ which is highly overlap  with the labeled object.
        \State $\mathbf \zeta \gets \{\zeta_k\,|\, \forall k=1,\dots,C\}$.
        \Comment{by Equ.~\eqref{eq:distribution}}
        \State $\Phi \gets \{\varphi_{ij}\,|\,\forall i=1,\dots,N^U,\; \forall j=1,\dots,\hat n_i\}$.
        \Comment{By Equ.~\eqref{object_score}}
        \State $\mathbf {a} \gets \{a_k \,| \,a_k=N\cdot \zeta_k,\, \forall k=1,\dots,C\}$.  \Comment{calculate the labeling budget for each class}
        \State $\Phi \gets \operatorname{sort}(\Phi)$. \Comment{sort $\Phi$ in descending order}
        \While{$N > 0$}
            \State $i, j \gets \{i,j \,|\,\varphi_{ij}=\operatorname{pop}(\Phi)\} $.
            \State $c \gets \text{argmax}\{ \hat{\mathbf c}_{ij}^U\}$.
            \If{$a_c>0$}
                \State $D_P \gets D_P \cup \operatorname{label}(\varphi_{ij})$.  \Comment{label the $\varphi_{ij}$ instance according to the labeling rule}
                \State $a_c \gets a_c - 1$.
                \State $N \gets N - 1$.
            \EndIf
        \EndWhile
        \Ensure the updated queried set $D_P$
    \end{algorithmic}
\end{algorithm}

\begin{table*}[bp]
 \begin{center}
  \caption{Mean average precision of different AL methods on DOTA-v1.0 \cite{xia2018dota} test set and DOTA-v2.0 \cite{ding2021object} validation set with different numbers of queried objects. Each number is displayed in percentage, and numbers in bold are the best results per column. * denotes the AL method that follows the object-based query type.}
  \label{table:results1}
  \setlength{\tabcolsep}{8pt}{
  \begin{tabular}{c|ccccc|ccccc}
   \hline\noalign{\smallskip}
   Dataset & \multicolumn{5}{c|}{DOTA-v1.0} & \multicolumn{5}{c}{DOTA-V2.0} \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   AL cycle & Cycle-0 & Cycle-1 & Cycle-2 & Cycle-3 & Cycle-4 & Cycle-0 & Cycle-1 & Cycle-2 & Cycle-3 & Cycle-4 \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   Random & 53.9 & 58.1 & 60.3 & 61.8 & 64.4 & 22.8 & 24.6 & 25.1 & 25.3 & 26.2 \\
   Coreset\cite{sener2017active} & 53.9 & 55.8 & 59.6 & 62.1 & 63.6 & 22.8 & 23.5 & 23.8 & 25.1 & 26.6 \\
   Localization Stability\cite{kao2018localization} & 53.9 & 61.6 & 63.7 & 66.8 & 66.9 & 22.8 & 27.1 & 30.9 & 31.1 & 33.5  \\
   Entropy\cite{settles2009active} & 53.9 & 64.2 & 65.7 & 67.2 & 67.4 & 22.8 & 28.3 & 30.9 & 31.7 & 35.0 \\
   Qbox$^{*}$\cite{tang2021qbox} & 53.9 & 58.7 & 61.4 & 64.1 & 65.2 & 22.8 & 24.1 & 27.8 & 28.8 & 31.2 \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
    \bf{IGUS$^{*}$} & 53.9 & 64.6 & 66.3 & 68.3 & 69.1 & 22.8 & 33.0 & 35.8 & 38.0 & 39.5 \\
     \bf{DGCB$^{*}$} & 53.9 & \bf{65.2} & 66.9 & \bf{69.2} & 69.2 & 22.8 & 33.2 & 36.1 & 39.1 & 40.6 \\
   \bf{MUS-CDB$^{*}$} & 53.9 & 64.9 & \bf{67.3} & \bf{69.2} & \bf{70.1} & 22.8 & \bf{34.1} & \bf{37.2} & \bf{40.2} & \bf{40.9}\\
   \noalign{\smallskip}
   \hline
  \end{tabular}}
 \end{center}
\end{table*}

\begin{table*}[h]
 \begin{center}
  \caption{Average category-wise AP for each method on DOTA-v1.0 \cite{xia2018dota} test set after 5 iterations of active learning. Each number shown in the table is displayed in percentage. The optimal and second optimal results in each column are highlighted in bold, and the second optimal results are underlined to distinguish them. * denotes that the AL method follows the object-based query type.}
  \label{table:cateAP1}
  \setlength{\tabcolsep}{5pt}{
  \begin{tabular}{c|ccc|cccccc|cccccc}
   \hline\noalign{\smallskip}
     & \multicolumn{3}{c|}{Common} & \multicolumn{6}{c|}{Middle} & \multicolumn{6}{c}{Rare} \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   category & SV & LV & SH & PL & BR & TC & ST & HA & SP & BD & GTF & BC & SBF & RA & HC \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   Random & \bf{72.0} & 68.4 & 78.9 & 88.3 & 35.0 & \bf{\underline{90.8}} & 76.8 & \bf{54.6} & 56.0 & 58.3 & 49.9 & 68.9 & 38.0 & 54.6 & 10.6 \\
   Coreset\cite{sener2017active} & 71.5 & 66.1 & \bf{81.0} & 88.1 & 31.8 & \bf{\underline{90.8}} & 75.3 & \bf{\underline{54.2}} & 51.2 & 52.5 & 47.5 & 65.6 & 33.7 & 51.1 & 24.6\\
   Localization Stability\cite{kao2018localization} & \bf{72.0} & 68.0 & 77.9 & \bf{88.8} & \bf{42.4} & \bf{\underline{90.8}} & 76.3 & 48.4 & 54.1 & 66.6 & 59.8 & \bf{\underline{73.9}} & 41.6 & 55.3 & 23.0\\
   Entropy\cite{settles2009active} & 70.7 & 67.2 & 77.7 & \bf{\underline{88.6}} & 32.5 & \bf{90.9} & 74.5 & 45.6 & 53.4 & \bf{71.5} & \bf{\underline{62.9}} & \bf{76.7} & 50.5 & 54.9 & \bf{37.6}  \\
   Qbox$^{*}$\cite{tang2021qbox} & \bf{72.0} & \bf{\underline{69.5}} & 78.1 & 87.7 & 32.3 & 90.7 & 74.6 & 46.6 & 52.9 & 63.2 & 58.1 & 67.6 & 42.8 & 50.7 & 23.3\\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   \bf{IGUS$^{*}$} & \bf{\underline{71.7}} & \bf{69.6} & 81.1 & 88.3 & 37.2 & 90.7 & 77.4 & 53.1 & 54.9 & 68.9 & 62.1 & 72.4 & \bf{\underline{50.9}} & 55.7 & 32.8  \\
   \bf{DGCB$^{*}$} & 71.6 & 69.2 & \bf{\underline{79.5}} & 88.5 & 36.8 & 90.7 & \bf{\underline{78.2}} & 54.0 & \bf{59.1} & 69.4 & 62.6 & 72.1 & 50.7 & \bf{57.4} & \bf{\underline{33.2}}  \\
   \bf{MUS-CDB$^{*}$} & \bf{\underline{71.7}} & 69.2 & \bf{\underline{79.5}} & \bf{88.8} & \bf{\underline{38.8}} & 90.7 & \bf{78.5} & 53.3 & \bf{\underline{57.3}} & \bf{\underline{70.8}} & \bf{63.4} & \bf{\underline{73.9}} & \bf{51.2} & \bf{55.9} & 33.1  \\
   \noalign{\smallskip}
   \hline
  \end{tabular}}
 \end{center}
\end{table*}

\begin{table*}[h]
 \begin{center}
  \caption{Average category-wise AP for each method on DOTA-v2.0 \cite{ding2021object} validation set after 5 iterations of active learning. Each number shown in the table is displayed in percentage. The optimal and second optimal results in each column are highlighted in bold, and the second optimal results are underlined to distinguish them. * denotes that the AL method follows the object-based query type.}
  \label{table:cateAP2}
  \setlength{\tabcolsep}{5pt}{
  \begin{tabular}{c|ccc|cccccc|cccccccc}
   \hline\noalign{\smallskip}
     & \multicolumn{3}{c|}{Common} & \multicolumn{6}{c|}{Middle} & \multicolumn{8}{c}{Rare} \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   category & SV & LV & SH & PL & BR & TC & ST & HA & SP & BD & GTF & BC & SBF & RA & HC & CC & AP \\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   Random & 29.5 & 37.5 & 59.2 & 72.5 & 2.4 & 74.4 & 47.5 & 11.7 & 23.5 & 18.3 & 19.2 & 5.0 & 8.3 & 35.3 & 0.0 & 0.0 & 2.1\\
   Coreset\cite{sener2017active} & 29.1 & 36.3 & 63.3 & 71.9 & 3.9 & 74.4 & 47.9 & \bf{17.8} & 20.4 & 8.5 & 15.5 & 2.2 & 9.1 & 38.2 & 0.0 & 0.0 & 0.0 \\
   Localization Stability\cite{kao2018localization} & 28.8 & 33.3 & 59.2 & \bf{76.9} & \bf{20.0} & 75.8 & 48.8 & 13.2 & 21.2 & 33.6 & 29.7 & 16.2 & 13.3 & 41.5 & 4.3 & 0.0 & 7.5\\
   Entropy\cite{settles2009active} & 29.6 & 37.3 & 58.7 & \bf{\underline{75.6}} & 6.2 & 78.6 & 47.9 & 8.9 & 20.8 & 36.9 & 29.9 & 28.1 & 12.7 & 37.5 & \bf{24.0} & 0.0 & 2.6 \\
   Qbox$^{*}$\cite{tang2021qbox} & \bf{31.7} & \bf{44.3} & \bf{64.6} & 70.9 & 3.4 & 77.9 & 47.9 & 9.1 & 22.3 & 17.2 & 32.2 & 14.0 & 11.8 & 37.9 & 0.0 & 0.0 & 0.1\\
   \noalign{\smallskip}
   \hline
   \noalign{\smallskip}
   \bf{IGUS$^{*}$} & 30.6 & 41.3 & 63.3 & 73.8 & 7.4 & 79.2 & 48.6 & 15.2 & \bf{25.8} & 42.4 & \bf{\underline{50.2}} & 37.1 & \bf{34.5} & \bf{45.3} & 3.6 & \bf{0.3} & \bf{\underline{10.3}}  \\
   \bf{DGCB$^{*}$} & 30.2 & \bf{\underline{41.4}} & 63.0 & 75.3 & 9.6 & \bf{81.4} & \bf{49.1} & \bf{\underline{17.7}} & \bf{25.8} & \bf{45.2} & 49.3 & \bf{42.2} & 31.2 & 42.5 & \bf{\underline{8.1}} & 0.0 & 6.9 \\
   \bf{MUS-CDB$^{*}$} & \bf{\underline{30.8}} & 40.9 & \bf{\underline{64.0}} & 74.9 & \bf{\underline{11.9}} & \bf{\underline{81.2}} & \bf{\underline{49.0}} & 16.3 & \bf{\underline{25.5}} & \bf{\underline{43.9}} & \bf{52.5} & \bf{\underline{41.6}} & \bf{\underline{32.8}} & \bf{\underline{45.2}} & 6.9 & \bf{\underline{0.04}} & \bf{13.5} \\
   \noalign{\smallskip}
   \hline
  \end{tabular}}
 \end{center}
\end{table*}

\section{Experiments}\label{Experiments}

\subsection{Experimental Settings}
\subsubsection{Dataset}
DOTA is the largest oriented object detection dataset in the field of aerial imagery. It is also the common benchmark in the aerial object detection task. We conduct experiments on DOTA-v1.0 \cite{xia2018dota} and DOTA-v2.0 \cite{ding2021object}. Specifically, DOTA-v1.0 contains 2,806 large-scale aerial images and 188,282 objects. DOTA-v2.0 collects more Google Earth, GF-2 Satellite and aerial images. There are 11,268 images and 1,793,658 objects in DOTA-v2.0. Each image is between 800 $\times$ 800 and 20000 $\times$ 20000 pixels in size. There are a total of 15 common classes in the DOTA-v1.0 containing plane (PL), baseball diamond (BD), bridge (BR), ground track field (GTF), small vehicle (SV), large vehicle (LV), ship (SH), tennis court (TC), basketball court (BC), storage tank (ST), soccer-ball field (SBF), roundabout (RA), harbor (HA), swimming pool (SP) and helicopter (HC). DOTA-v2.0 further adds the new categories, container crane (CC), airport (AP) and helipad (HP).

For DOTA-v1.0,  the training and testing sets follows the setup of the previous work \cite{han2021redet}. For DOTA-v2.0. we use the training set for training and the validation set for testing. We crop the original image to 1024 $\times$ 1024 patches with an overlap of 200. The random horizontal flip is employed in data augmentation.

\subsubsection{Comparison Methods}
We compare the proposed method with various types of active learning methods, including random selection (Random), uncertainty-based active learning methods (Entropy~\cite{settles2009active}), representation-based active learning methods (Coreset~\cite{sener2017active}), detection-specific active learning methods (Localization Stabiloty~\cite{kao2018localization}) and object-level active learning methods (Qbox~\cite{tang2021qbox}). In this paper, we introduce two baseline methods, image-based sampling and object-based query type. Both Qbox and our proposed method use an object-based query approach. Other methods use an image-based query approach. For Qbox, we take the model predicted bounding box as the candidate sampling object. The informativeness of each predicted bounding box is calculated using the inconsistency defined in Qbox, and send the predicted bounding box to the oracle for labeling in descending order. For entropy, we use average object entropy as image uncertainty.

\subsubsection{Performance Measurement}
We report the performance of different AL methods on the DOTA-v1.0 and DOTA-v2.0 datasets by calculating the Average Precision of each class at an IoU threshold of 0.5 and a score threshold of 0.3. Since both datasets, especially DOTA-v2.0 are evaluated in low data regimes (i.e., several tail categories have only a few labels in the first few active learning iterations), choosing 0.5 as the IoU threshold compared to 0.75 or 0.5 : 0.95 thresholds can better distinguish performance.

\subsubsection{Labeling Rules}
Image-based sampling methods and object-based sampling methods have different labeling rules. First, object-based sampling methods send the predicted bounding box as a unit to the oracle for labeling, and if there is only one unlabeled ground-truth object in the query bounding box, the label of that object is returned. If there are multiple unlabeled ground-truth objects in the query bounding box, calculate the IOU of each ground-truth object and the query bounding box, and return the label of the object with the largest IOU. After the queried bounding box obtains the object annotations, the annotation cost (i.e., the number of annotated objects) is increased by one. According to \cite{su2012crowdsourcing, chandra2020active, papadopoulos2016we}, Oracle takes 7.8s to find a target, 25.5s to draw a box, and only 1.6s to identify whether there is a target in an area, So if the query bounding box received by oracle does not contain an object, we do nothing. The image-based sampling method sends the queried image to the oracle for exhaustive annotation, and the number of objects in the image is the annotation cost of the image. Note that the cost calculation of the image-based sampling method follows the experimental setup of Qbox~\cite{tang2021qbox}.

\subsubsection{Implementation Details}
We adopt ReDet \cite{han2021redet} architecture with ReResNet50 pretrained in ImageNet \cite{krizhevsky2017imagenet} as backbone. All experiments are conducted on GeForce RTX 2080 Ti (11G). For DOTA-v1.0, We randomly select 5\% images (1052 images) from the training and validation sets as a small set of fully labeled images. The remaining images in the training and validation sets make up the unlabeled data set. In each round, 5000 objects are selected from the unlabeled set for querying. Then, the model with default pre-trained weights will be fine-tuned on the labeled datasets for 12 epochs  with a batch size of 2, and the initial learning rate is set to 2.5 $\times$ $10^{-3}$, using the SGD optimizer mentum of 0.9 and weight decay of 1 $\times$ $10^{-4}$. The learning rate is reduced by 0.1 at 8 and 11 epochs. For DOTA-v2.0, we randomly selected 500 images from the training set as the initially labeled images. The remaining images in the training sets are considered as the unlabeled pool. In each round,  1000 objects are selected from the unlabeled data set for querying. The parameter settings of model training are consistent with DOTA-v1.0. For active learning, the parameter settings involved in our sampling method are as follows. For the adaptive weight $\mu$ in Equ.~\eqref{eq:w}, we implement it as the background score predicted by the model for the negative proposals. In this way, we can effectively reduce the impact of the partially labeled data by down-weighting the loss of the false-negative objects. For the tradeoff parameters $\lambda_{cls}$ and $\lambda_{reg}$ in Equ.~\eqref{eq:lose}, we use the number of proposals and positive proposals in a mini-batch for normalization, respectively. Regarding the hyperparameter $\theta$ in Equ.~\ref{eq:image}, we set it to 0.08, and Table \ref{table:threshold} lists the performance change of our method under different values of this parameter.

\begin{figure}[!t]
\centering
\includegraphics[width=0.5\textwidth]{radar.jpg}
\caption{Radar charts for each category of objects in different datasets. (a) DOTA-v1.0 \cite{xia2018dota}. (b) DOTA-v2.0 \cite{ding2021object}. Different colored lines represent different active learning methods. The larger area enclosed by the outer line, the better performance of the corresponding method. The value in this graph denotes the average mAP over five iterations of active learning.}
\label{radar}
\end{figure}

\subsection{Performance}
\subsubsection{Performance Comparisons on Benchmark Datasets}
We report the performances of all AL methods on the DOTA-v1.0 and DOTA-v2.0 benchmarks to demonstrate that our method can better mine the most informative objects from the unlabeled data pool in remote sensing scenarios. The experimental results of the two datasets are shown in Table~\ref{table:results1}.

For DOTA-v1.0, it is evident from Table \ref{table:results1} that MUS-CDB significantly outperforms the other methods at each iteration of active learning. Specifically, in terms of mAP, when 5000, 10000, 15000, and 20000 objects were sampled, the MUS-CDB surpassed the random method by 6.8, 7, 7.4, and 5.7, and the sub-optimal method by 0.7, 1.6, 2 and 2.7, respectively. These performance boosts prove that our method can accurately identify informative objects.

For DOTA-v2.0, we can observe that MUS-CDB shows more impressive improvements over image-based and object-based sampling methods. Specifically, the results in Table \ref{table:results1} demonstrate that in terms of mAP, when 1000, 2000, 3000, and 4000 objects are sampled, the MUS-CDB surpassed the random method by 9.5, 12.1, 14.9, and 14.7, and the sub-optimal method by 5.8, 6.3, 8.5 and 5.9, respectively. The performance improvement on DOTA-v2.0 is more significant than that on DOTA-v1.0, which shows our approach can better guide low-performing models to sample highly informative objects based on only a small number of training samples. In addition, through Table \ref{table:results1}, we can also observe that our method only queries 1000 unlabeled objects in DOTA-v2.0 and can reach or even surpass the performance of other comparative methods sampling 4000 unlabeled objects. The results show that our proposed method MUS-CDB can save more than 75\% of the labeling cost to reach the same performance compared to the baselines and state-of-the-art active object detection methods.

According to Table \ref{table:results1}, Entropy and localization stability perform well in DOTA-v1.0 and DOTA-v2.0. On the contrary, coreset and random perform poorly as expected, which explains that the uncertainty-based methods are more effective than representation-based ones in chaotic scenes. The object-based sampling method Qbox does not perform well on both datasets. Because Qbox is originally designed for ordinary scenes, which is very different from remote sensing scenes, therefore, the sampling strategy of Qbox cannot identify the informativeness of objects in remote sensing images accurately, which also shows the necessity of designing novel active learning methods for aerial object detection tasks. The ablation method DGCB also performs well in both datasets. This result
reveals the importance of considering class balancing in active selection for remote sensing images.

\begin{figure*}[!t]
\centering
\includegraphics[width=0.55\textwidth]{frequency.jpg}
\caption{The average category frequency(\%) of DOTA-v1.0 \cite{xia2018dota} sampled by our AL methods in each round. Our proposed distribution-guided class balancing module (DGCB) can sample more middle and rare categories, and fewer common categories.}
\label{class}
\end{figure*}

\subsubsection{Category-wise AP}
Table \ref{table:cateAP2} and \ref{table:cateAP1} show the average category-wise AP of all the compared methods on DOTA-v1.0 and DOTA-v2.0, respectively. The optimal and second-best results in each column are highlighted in boldface, while the second-best result is additionally underlined.

From Table \ref{table:cateAP2} and \ref{table:cateAP1}, we can observe that our method MUS-CDB and its variants can usually achieve the best performance in most categories. Especially some categories that are not well-learned. This result indicates that our proposed query strategy can impartially improve the performance of each category. On the other side, Qbox and uncertainty-based active learning methods excessively query specific categories while ignoring the overall performance improvement. Specifically, Qbox focuses on the performance of bridge, tennis court, and storage tank. However, the category's performance in the rare categories has been neglected. Similarly, entropy only achieves the performance improvement of helicopter and container crane and pays little attention to other categories in the Middle and Rare categories (e.g., Plane, Swimming pool, and Ground track field). The performance superiority of our method over other methods in each category is visualized in Figure \ref{radar}.

\subsubsection{Statistics of Queried Class Distribution}
Figure \ref{class} shows the class frequency of the DOTA-v1.0 dataset and the average class frequency of training samples selected by different methods in each cycle of AL. As expected, we see that the actual distribution of each class in the DOTA-v1.0 is exactly long-tailed. In the face of this problem mentioned above, our method (gray column) will select many common categories without the DGCB module. With the addition of the DGCB module (blue column), our method can allocate more annotation budgets to some medium and rare categories. Especially baseball diamond, bridge, ground track field, basketball court, soccer-ball field, roundabout, harbor, swimming pool, and helicopter categories. The category distribution of the random sampling method tends to be consistent with the dataset's original category distribution; The yellow curve is the growth curve formed by our method and random method on different categories. Our method outperforms the random method in all categories except the small vehicle category. The performance improvement of our method is most evident in middle and rare classes (i.e., baseball diamond, bridge, ground track field, soccer-ball field, and helicopter). These data confirmed the diversity and balance of samples selected by our method.

\begin{table}[h]
\centering
\caption{Performance comparison on DOTA-v2.0 \cite{ding2021object} with different score thresholds in Equ.~\ref{eq:image} of image-guided uncertainty sampling module (IGUS).}
\scalebox{1.1}{
\begin{tabular}{c|ccccc}
\hline\noalign{\smallskip}
$\theta$& Cycle-0 & Cycle-1 & Cycle-2 & Cycle-3 & Cycle-4 \\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
0.05 & 22.8 & 30.8 & 36.7 & 39.4 & 40.6 \\
0.10 & 22.8 & \bf{34.1} & \bf{37.2} & \bf{40.2} & \bf{40.9} \\
0.15 & 22.8 & 32.7 & 37.1 & 39.4 & 40.4 \\
\noalign{\smallskip}
\hline
\end{tabular}}
\label{table:threshold}
\end{table}

\subsubsection{Score Threshold $\theta$}
$\theta$ is the parameter of module IGUS in Equ.~\eqref{eq:image}, which is used to impose preference on the images with distinct information in the active selection phase. According to the results in Table \ref{table:threshold}, it can be known that the optimal value of $\theta$ is obtained around 0.08. Therefore, we use $\theta$ = 0.10 for all our experiments. This can be explained by the fact that when $\theta$ is close to the lower bound of 0.05, more noise is involved in calculating the image uncertainty score. Therefore the final object information calculation is inaccurate. Conversely, if $\theta$ becomes larger, the performance degrades rapidly. This is because predicted bounding boxes containing rare patterns in the image are filtered because of low predicted scores and cannot participate in calculating of image information.

\begin{figure*}[!t]
\centering
\includegraphics[width=\textwidth]{vis.jpg}
\caption{Examples of the top-rated objects or images queried by MUS-CDB, Qbox \cite{tang2021qbox}, Random, Coreset \cite{sener2017active}, Entropy \cite{settles2009active} and Localization Stability \cite{kao2018localization} on the DOTA-v1.0 \cite{xia2018dota}. * denotes that the AL method follows the object-based query type. The visual comparison results in the first column and the second column show that our proposed method MUS-CDB can sample more valuable middle and rare classes than another object-based sampling method Qbox. In addition, compared with other image-based sampling methods, it can be found that MUS-CDB can effectively save sampling costs through object-based sampling, and then sampling more diverse objects.}
\label{vis}
\end{figure*}

\subsubsection{Visualization of the Queried Boxes}
In order to demonstrate whether MUS-CDB selects the expected objects for sampling, we visualize the top-ranked objects sampled by the object-based sampling method MUS-CDB versus Qbox and the top-ranked images sampled by the image-based sampling methods random, entropy, coreset, and localization stability. The visualization results are listed in Figure~\ref{vis}. There is a long tail phenomenon in the DOTA-v1.0 dataset, where small vehicle, large vehicle, and ship belong to the head category, and baseball diamond, ground track field, basketball court, soccer-ball field, roundabout, and helicopter belong to the tail category. According to Figure~\ref{vis}, we can see that our method MUS-CDB can preferentially sample informative examples of tail categories, such as helicopter, baseball diamond, ground track field, and soccer-ball field. However, the sampling results of the random method are consistent with the original category distribution of the DOTA-v2.0 dataset, manifested in over-sampling the low-informative head categories and intermediate categories such as plane, ship, small vehicle, and large vehicle. Although the collected images have rich land types, such as airports, ports, and cities, the distribution of the queried samples is too similar to the labeled dataset, which does not improve the model performance very much. As an object-based sampling method, Qbox oversamples head categories, such as small vehicle, large vehicle, and ship, and fails to sample tail categories. Although informative samples in the head category are collected, these samples do not improve the model performance much. Entropy can effectively find uncertain samples, but the image-based sampling method results in that most of the queried samples are in the same pattern, which leads to information redundancy. The images sampled by the coreset and localization stability methods have a single land type. The coreset method preferentially samples images in the ocean background, and the localization stability method preferentially samples images in the dark scenes. Although the sample distribution in the images collected by the two methods is relatively sparse, which can effectively alleviate the problem of redundant annotation. However, the two methods pay too little attention to the tail category, resulting in a low-performance improvement of the final model. These results demonstrate that our proposed method can achieve cost-effective querying in remote sensing scenarios.

\begin{table*}[h]
\centering
\caption{Ablation studies on strategies of Image-guided Uncertainty Sampling module (IGUS), Distribution-guided Class Balancing module (DGCB) and partial training loss $\mathcal{L}_{partial}$.}
\scalebox{1.2}{
\begin{tabular}{ccccccccc}
\hline
\noalign{\smallskip}
{Method}&IGUS&DGCB&$\mathcal{L}_{partial}$& Cycle-0 & Cycle-1 & Cycle-2 & Cycle-3 & Cycle-4\\
\noalign{\smallskip}
\hline
\noalign{\smallskip}
{Random}&&&&22.8&24.6&25.1&25.3&26.2 \\
\hline
\noalign{\smallskip}
(a)&\checkmark&&&22.8&33.0&36.2&38.1&39.9\\
(b)&&\checkmark&&22.8&32.9&37.0&37.3&40.5 \\
(c)&\checkmark&\checkmark&&22.8&32.4&36.3&38.3&39.9 \\
(d)&\checkmark&\checkmark&\checkmark&22.8&\textbf{32.9}&\textbf{38.3}&\textbf{40.9}&\textbf{41.2}\\
\noalign{\smallskip}
\hline
\end{tabular}}
\label{table:ablation}
\end{table*}
\subsection{Ablation Study}
To further investigate the effectiveness of each component of MUS-CDB, we conduct ablation experiments on the DOTA-v2.0 task. As shown in Table \ref{table:ablation}, satisfactory and consistent gains from the Random baseline to our whole method demonstrate the validity of each module. From Table \ref{table:ablation} we can see that both IGUS and DGCB module has achieved significant performance improvements compared to the Random baseline. This shows that the module of IGUS and DGCB are helpful for sampling highly informative predicted bounding boxes. In addition, we can see that (d) is obviously better than (c), which suggests that the newly designed partial training loss effectively reduces the interference of background noise, making the informativeness of the object predicted by the model more reliable.

\section{Conclusion}\label{Conclusion}
In this paper, we propose an object-based active learning method MUS-CDB to alleviate the huge burden of data annotation of aerial object detection. On the one hand, we devise an Image-guided uncertainty sampling selection criterion in active querying to identify the most informative instances. On the other hand, we consider the long-tailed problem of the remote sensing image dataset, and impose class preference during active sampling to promote the diversity of selected objects. An effective training method for partially labeled data is also proposed to fully utilize the queried knowledge. Extensive experiments on the DOTA-v1.0 and DOTA-v2.0 benchmarks demonstrate the superiority of the proposed MUS-CDB method. In the future, we will further study the effectiveness of taking into account the angle information in the active selection for aerial object detection.

\section{References Section}
\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


Cruises! Perfect, all-in-one vacations with AACI, all inclusive and without changing "hotels" and packing and unpacking. We visit many countries and ports during a well-planned travel experience replete with our Jewish-flavored land tours. In addition, AACI cruisers enjoy fascinating lectures by our scholars-in-residence, along with the finest amenities and services that sea travel can offer.
as upon which the African continent's epic story is written is itself astonishing and is reason enough to visit. Wildlife and unparalleled landscapes are brought to life and give special personality to the African wild. Taking part in a safari and seeing the wildlife world up close and in motion will leave an everlasting impression.
AACI's Jewish Heritage Tours take you back to a time when Jewish life teemed with study halls, urban commerce, and timeless tradition, a tapestry of culture and passion that is a window into our past, is slowly re-emerging into modern, proud communities. These must-do tours are led by superbly informed guides who expertly convey the rich cultural past with glimpses into the future.
The homeland and heart of the Jewish people. Come learn, explore, and feel Israel and all her wonders as we travel the length and breadth of our multi-faceted homeland. AACI trips and study vacations bring out the fascinating, lesser-known aspects of our country that are steeped in our Jewish roots and history.
As an ecozone, Oceania includes all of Micronesia, Fiji, and all of Polynesia except New Zealand. New Zealand, along with New Guinea and nearby islands, part of Philippines islands, Australia, the Solomon Islands, Vanuatu, and New Caledonia, constitute the separate Australasian ecozone.
AACI's goals are to support the successful klitah (absorption) of new olim while providing social and professional programs that enhance the lives of Israel's English-speaking community. The AACI Travel Experience generates much-needed income for our programs. We hope you feel pride in knowing that a small portion of your travel fees are used for these purposes.


text:
<?xml version="1.0" encoding="utf-8"?>
<RelativeLayout
    xmlns:android="http://schemas.android.com/apk/res/android"
    android:layout_width="match_parent"
    android:layout_height="match_parent"
    xmlns:app="http://schemas.android.com/apk/res-auto">

    <android.support.v7.widget.Toolbar
        android:id="@+id/settings_toolbar"
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        app:theme="@style/ThemeOverlay.AppCompat.Dark.ActionBar"
        app:popupTheme="@style/ThemeOverlay.AppCompat.Light"
        android:background="@color/light_blue_700"
        android:title="@string/cornmenu_item_settings"
        app:title="@string/cornmenu_item_settings" />

    <View
        android:layout_width="match_parent"
        android:layout_height="5dp"
        android:background="@drawable/toolbar_dropshadow"
        android:layout_below="@id/settings_toolbar" />

    <FrameLayout
        android:layout_width="match_parent"
        android:layout_height="wrap_content"
        android:id="@+id/settings_pref_content_frame"
        android:layout_below="@id/settings_toolbar" />

</RelativeLayout>

redpajama_set_name: RedPajamaGithub


Dapplo.Windows.Icons has supporting code for icons.
Blazorise is a component library built on top of Blazor and CSS frameworks like Bootstrap, Bulma and Material.
CookieBar is a lightweight library for showing a brief message at the top or bottom of the screen.
An umbraco property editor for Font Awsome Icons which is User Upgradable to keep up todate with the latest Font Awesome Icons.
Iconator is an Umbraco datatype that allows the picking of CSS icon classes.
This is a simple dropdown box loaded with font awesome icons. The labels are unicode , while the values are the class name.
Provides a WPF value converter that converts an ISO 3166-1 alpha-2 country code into a WPF Image of that country's flag.
Library for handling and retrieving system icons.


Tip Jar | Tip Jar | News : Happy New Year!
Happy new year everyone! May all your plans for the new year work out well! While we're working on some new material, we'd like to share with you a compilation of Tip Jar songs we played in De Amer on December 20th. Thanks to Roel Dokter for sending us a DVD from that show.


Need a helping hand out on the road?
Don't entrust your vehicle to an anonymous towing service – call the people who care about you and your car.
At Kew Accident Repair Centre, your safety and the security of your vehicle is our highest priority. We provide a quick response time and can get your insurance claim moving even before your car arrives at our Kew premises.
Our experienced operators will quickly and efficiently recover your vehicle without causing further damage to it or any surrounding property.
In most cases accident towing is included in your car insurance policy, so you won't be left with any out-of-pocket expenses. Call 0385 778 377 Now!


The exterior of your home makes the first impression on potential buyers. Studies show that high curb appeal helps sell homes more quickly. Many potential buyers will not even get out of their car to view a house if the exterior does not appear inviting. With so many people shopping for homes online, it is crucial that you not only have great curb appeal, but also have excellent photographs. In order to get people to view the interior of a home, you must have an alluring exterior. Here are 8 easy ways to improve your home's curb appeal.
This is the focal point of your home. The front door should be spotless and clean. Even polishing the doorknob is helpful. Consider adding color to the door to make it really "pop." A classic wreath or elegant declaration can be added to make it more captivating.
It is vital that the front of your home have a symmetrical appearance. Make sure that all lighting, landscaping, and decorations are in perfect balance. This is an easy way to boost curb appeal. Simply moving a few things around to create symmetry has a huge impact.
Your mailbox should match and complement the exterior of your home. Many people overlook the impact of an attractive mailbox. Dressing up the mailbox is an inexpensive way to improve the overall exterior appearance of a home.
If your home does not have a walkway, it is recommended that you add one. It does not need to be an expensive project. A basic walkway will suffice. Nothing is more inviting than a nice path on which to walk. People do not like trudging through the grass to get to the front door. It is practical and attractive to create a clear walkway.
You should camouflage certain blemishes that cannot be removed or repaired. Things like air conditioning units can easily be covered with lattice or shrubbery. If there is something unattractive on the outside of your home that cannot be removed, simply find a way to hide it. This is not intended to be misleading, it is just to improve the outside appearance.
Shutters make windows appear larger and are visually appealing. They break up large walls, making the home more interesting to view. Choose shutters that fit the tone of the house. You want them to stand out, but not be so loud that they overshadow the other elements of the home.
Look closely at the condition of the concrete around your home. Any cracks or blemishes in patios, driveways, walkways, and porches should be repaired. If the concrete is in good condition, consider adding a stain to give it a modern appearance. A simple coat of concrete stain can breathe new life into an old patio or porch.
Store things like water hoses and trash bins out of view. Routine yard maintenance ensures that your landscaping remains clean and clutter-free. Walk the premises frequently, picking up any trash or debris that may have blown into your yard. You never know when a potential buyer will drive by to have a quick look.
There are a number of ways to boost your home's curb appeal. You should do everything possible to make the exterior of your home as inviting and attractive as possible. You want potential buyers to be drawn inside. Your home's curb appeal can make the difference between a home selling very quickly or a home sitting on the market for years. It truly is that important. Do not underestimate the importance of an alluring exterior.
If you are considering a fix and flip of your own, Contact us for more information and assistance. We are glad to help and share our experiences with you.


text:
African American Artists at Paulson Bott Press: An Interview with Pam Paulson and Rhea Fontaine
September 20, 2016 By Rhea Fontaine
Gary Simmons at Paulson Bott Press.
In its 20-year history, Paulson Bott Press has worked with an unusually high number of African American artists, including Edgar Arceneaux, Radcliffe Bailey, McArthur Binion, Thornton Dial, the Gee's Bend quilters, Lonnie Holley, David Huffman, Kerry James Marshall, Martin Puryear, Lava Thomas, and Gary Simmons. Kenneth Caldwell sat down with Pam Paulson and Rhea Fontaine to talk about their experiences with some of these artists.
Phrygian by Martin Puryear. 35″ x 28″ color softground etching with spitbite aquatint, aquatint and drypoint, 2012.
A number of the artists you've invited to make prints with you have been African American. Was there an intentional strategy?
Pam Paulson: We saw that there was a real vacuum to be filled. There was no one really representing artists of color in a big way.
Rhea Fontaine: And not just in fine art print publishing, but in the larger art world.
PP: Prints are democratic. They make an artist's work affordable, giving more people the opportunity to get into the art market. We've always wanted to have wide-ranging conversations with a broad audience. At the same time, whenever we invite artists to work with us, it's because we respond to their art.
Tiger Wants to be Alone by Thornton Dial. 22″ x 30″ Watercolor and graphite on Lanaquarelle paper, 1990.
RF: There is a real moment right now for artists of the African Diaspora in institutions because audiences want to see these voices better represented. I was at the Whitney Museum's inaugural exhibition, America Is Hard to See, and it was so good to see paintings by Al Loving, Alma Thomas, Noah Purifoy, and many others who, at points, were overlooked by history.
Passing By by Mary Lee Bendolph. 39″ x 43″ Color softground etching with aquatint, spitbite aquatint and chine colle, 2007.
Talk about how you ended up working with the Gee's Bend quilters.
PP: I went to New York at the end of 2002 and took my family to the Whitney Museum when the Gee's Bend quilt show was there. We saw the beauty in the patterns and the improvisational genius. Their work was the visual equivalent of jazz.
Then my dad, who was in his mid-80s at the time, was at my house one night, and I was showing him the catalog for the show. I told him, "I was so blown away by this, Dad. You would have loved it." And he said, "Why don't you make prints for them?"
But we needed a way in. We couldn't just walk into Gee's Bend and ask these ladies to come make prints. We hit a lot of dead ends. Finally I called up Radcliffe Bailey, who had told me he had been to Gee's Bend. I asked him, "Would you drive down there with me? I want to see if I can get some of the quilters to make prints with me." The next night, he was at a dinner in Atlanta with a group of art people, and Matt Arnett was there—and Matt's family represents the Gee's Bend quilters. Radcliffe knew Matt because they grew up in the same town. He sat next to Matt and said, "My friend Pam, who I make prints with, wants to make prints with the Gee's Bend quilters."
Radcliffe Bailey applying spitbite in Paulson Bott Press.
Matt called me the next day. Then we were on the phone for the better part of a week. He really wanted to make sure that we were sincere and in no way going to take advantage of the quilters. He was also really interested in taking their work out of the purview of craft and putting it under the purview of art. The show at the Whitney had started to do that, of course, but working in another media besides quilting was going to take it one step further.
At one point, I said to Matt, "If they come and we don't get anything, I don't care. I'm just in it for the experience of trying." And he was satisfied with that, so the quilters came to our studio.
Untitle Woman by Kerry James Marshall. 24.5″ x 19″ hardground etching, 2010.
RF: It was our first time working with textile-based artists. Pam and Renee devised this idea of working with soft ground. We had no idea that the translation would even meet our expectations, but it far exceeded them. I came in the day after our printers started working with the quilters, and there were already so many works on the walls. It's like we dove into a whole new world.
PP: It was so rewarding. It was like being at church. There was a lot of song and praise and gratitude going on while they worked. It was one of the most fun projects we've ever done.
Then after we did the prints, we took them to the print fair in New York the next year, and Jo Carole Lauder from the Foundation for Art and Preservation in Embassies saw them. She asked us to do prints with the quilters for their program. So four of the quilters did an edition of 50, and they are placed in US embassies worldwide.
The Things of Life (To See or Not to See) by Lonnie Holley. 19.5″ x 39″ color aquatint, 2013.
After Gee's Bend, David Huffman and Lonnie Holley came, didn't they?
RF: Yes. David is a good friend and someone whose work I have admired for many years. He is the first artist we published who was working with the stereotypical iconography of race in America. One of his early characters, named Trauma Eve, made a major impact on me. His fearless exploration of themes related to the legacy of slavery, postindustrialism, Afrofuturism, and the counterculture continue to fascinate.
Lonnie Holley at Paulson Bott Press.
How did Lonnie Holley come to work with you?
PP: The Arnetts have a large collection of artworks by self-taught artists from the South. After the Gee's Bend experience, we went down the Arnetts' Atlanta warehouse, which is gigantic, and talked about all the different artists they collected. Lonnie Holley was in their collection—his sculptures using found objects were beautiful. Lonnie also introduced the Arnetts to Thornton Dial, a groundbreaking African-American artist known for his large-scale assemblages using found objects.
McArthur Binion at Paulson Bott Press.
RF: People like the Arnetts, who support the Gee's Bend quilters, and Lowery Stokes Sims, whose pioneering work at the Metropolitan Museum and the Studio Museum in Harlem introduced so many minority artists to larger audiences—these are the people who are taking risks that others aren't willing to take, saying things that other people aren't willing to say, seeing things that other people are not seeing. We've always wanted to connect ourselves with that type of visionary. Because of them, a number of extremely talented artists have become known to us and the world at large.
← An Interview with Tauba Auerbach
MINE: New Etchings by McArthur Binion and Alicia McCarthy →
Alicia McCarthy: Untitled (2)
In the Studio at Paulson Fontaine Press
Interview with David Huffman
Paulson Fontaine Press Featured in the East Bay Express
MINE: New Etchings by McArthur Binion and Alicia McCarthy
Kenneth Caldwell (4)
15 Years: Part 2
Christopher Brown at John Berggruen Gallery
Tauba Auerbach Embossment Paintings
Pam Paulson (6)
On the Road to Thornton Dial
IFPDA Fair-Re-cap
Shy Town Summer
Slow Art
Paulson Bott Press (12)
Liam Everett
Book Signing with Alice Waters and Drawings by Patricia Curtan
Paulson Bott Press Featured in Berkeleyside
Interview with Hung Liu on art-rated.com
Our artist: Gary Simmons featured on the cover of ARTVOICES magazine
Celebrating 15 Years: Part 1
Paulson Bott Press featured in Art In America
Paulson Bott Press featured on Post & Grant by Martha Angus
Squeak Carnwath
San Francisco Arts Quarterly Issue: 6 August 2011
Renee Bott (9)
Bold Statement
Hamsterbite, Softpound and Pianos. Making Prints with Caio Fonseca
Shaping a Master Printer
Book/Shop
Collector Profile: Ross Evangelista
Collector's Profile: Martin Maguss and Mari Iki
Gary Simmons: An Account of Time
Getting to know Ross
Rhea Fontaine (18)
An Interview with Tauba Auerbach
The Mission School at Paulson Bott Press
"Celebrating 20 Years" at the de Young Museum
Paulson Bott Press: Celebrating Twenty Years
Sharing the Passion. An Art Collection at Stanford.
Horror Story: Eight Artists Engage with Mass Culture through Traumatic Imagery
Obamas' Collection
"Have Been Swimming"
Keep on Trek-King!
New Year, new Blog!

redpajama_set_name: RedPajamaCommonCrawl


text:
Jan Theiler (né en 1967) est un artiste allemand d'inspiration dadaïste. Il a pour pseudonymes : Pasteur Leumund, Michael Fieling, MC Abitur.

Biographie
Durant les années 1990, pendant qu'il étudie l'art, il rejoint des collectifs de squatteurs de Berlin-Est et est commissaire d'exposition, artiste de performance et musicien de la scène artistique underground. Il organise des concerts et des performances de grande ampleur comme dans le Tacheles. Il crée la Fondation Kroesus, occupe le Cabaret Voltaire, se fait porte-parole des néo-dadaïstes et organise avec Mark Divo la "Semaine du festival international Dada" à Zürich. Il se fait aussi compositeur, parolier, prédicateur laïque, rappeur, président de parti et "interactiviste".

Notes et références

Liens externes

Crédit d'auteurs

Chorégraphe allemand
Performeur
Artiste contemporain allemand
Artiste conceptuel allemand
Anarchiste allemand
Naissance en 1967


text:
Q: Add Excel upload control to grid toolbar Is there a way to get the grid control that allows one to upload data from an Excel sheet?  I haven't found a command that will put this control on the toolbar.

A: You need to decorate data view with PXImportAttribute.
Example:
[PXViewName(Messages.SOLine)]
[PXImport(typeof(SOOrder))]
[PXCopyPasteHiddenFields(typeof(SOLine.completed))]
public PXSelect<SOLine, Where<SOLine.orderType, Equal<Current<SOOrder.orderType>>,
             And<SOLine.orderNbr, Equal<Current<SOOrder.orderNbr>>>>,OrderBy<Asc<SOLine.orderType, Asc<SOLine.orderNbr, Asc<SOLine.lineNbr>>>>> Transactions;

PXImport attribute enables the user to load data from the file to the grid. The attribute is placed on the data view the grid uses to retrieve the data.
We have used PXImportAttribute(Type) constructor where input parameter is the first (Primary) DAC that is referenced by the primary view of the graph where the current view is declared.
Set Grid's AllowUpload property to True.

AllowUpload property controls the display of the Load Records from file toolbar button.

redpajama_set_name: RedPajamaStackExchange