prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Where is McPherson County located?

Read the following text and answer briefly.

Context 1: McPherson County (standard abbreviation: MP) is a county located in the U S  state of Kansas   As of the 2020 census, the county population was 30,223  The largest city and county seat is McPherson  The county is named for Civil War General James B  McPherson 

History

Early history

For many millennia, the Great Plains of North America was inhabited by nomadic Native Americans  From the 16th century to 18th century, the Kingdom of France claimed ownership of large parts of North America  In 1762, after the French and Indian War, France secretly ceded New France to Spain, per the Treaty of Fontainebleau  In 1802, Spain returned most of the land to France, but keeping title to about 7,500 square miles 

In 1803, most of the land for modern day Kansas was acquired by the United States from France as part of the 828,000 square mile Louisiana Purchase for 2 83 cents per acre  In 1848, after the Mexican–American War, the Treaty of Guadalupe Hidalgo with Spain brought into the United States all or part of land for ten future states, including southwest Kansas  In 1854, the Kansas Territory was organized, then in 1861 Kansas became the 34th U S  state 

19th century

From the 1820s to 1870s, the Santa Fe Trail passed through, what is now McPherson County  The trail entered the county, east of Canton, then south of Galva, then north of Inman, and west towards Lyons  In 1855, Charles O  Fuller established a ranch adjacent to the Running Turkey Creek Crossing about two miles south and one mile east of Galva  Fuller's Ranch provided accommodations for travelers on the Santa Fe Trail and was probably the first white settlement in McPherson County 

Peketon County was established in 1860, by the passage of a bill by S  N  Wood:  An act to establish Peketon County  Section 1  - That all that territory west of the sixth principal meridian and south of Township 16, in Kansas Territory, be and the same is hereby erected into a county, to be known by the name of Peketon County  On February 17, 1865, Peketon County was abolished, and McPherson County was made a part of Marion County, which extended from the west line of Chase County to the present western boundary of Kansas 

In 1868, Solomon Stephens and L  N  Holmberg were appointed Justices of the Peace—the first officers in what is now McPherson County  The next year (1869) occurred the first election for the township, now the county of McPherson  McPherson was regularly organized as a county in the spring of 1870, a mass meeting being held at Sweadal  Sweadal, the county seat thus selected, was located about one mile and a half southwest of the present site of Lindsborg  In September, however, the County Commissioners resolved to meet at the latter place, McPherson which had already been located some two years 

In April, 1873, a petition was filed for the county seat re-location  It was signed by 483 voters, and a special election was accordingly ordered for June 10  Upon that day, McPherson received 605 votes, New Gottland 325, King City 3 and Lindsborg 1; McPherson's majority over all, 276  In May the McPherson Town Company had offered, as an inducement for the location of the county seat at this point, the free use of rooms for ten years, and the donation of two squares of land on the town site  The offer was accepted the next month, the County Commissioners selecting blocks 56 and 65  Thus the county seat was established at McPherson and has remained since 

As early as 1875, city leaders of Marion held a meeting to consider a branch railroad from Florence  In 1878, Atchison, Topeka and Santa Fe Railway and parties from Marion County and McPherson County chartered the Marion and McPherson Railway Company   In 1879, a branch line was built from Florence to McPherson, in 1880 it was extended to Lyons, in 1881 it was extended to Ellinwood  The line was leased and operated by the Atchison, Topeka and Santa Fe Railway  The line from Florence to Marion, was abandoned in 1968  In 1992, the line from Marion to McPherson was sold to Central Kansas Railway  In 1993, after heavy flood damage, the line from Marion to McPherson was abandoned  The original branch line connected Florence, Marion, Canada, Hillsboro, Lehigh, Canton, Galva, McPherson, Conway, Windom, Little River, Mitchell, Lyons, Chase, then connected with the original AT&SF main line at Ellinwood 

In 1887, the Chicago, Kansas and Nebraska Railway extended its main line from Herington to Pratt   This main line connected Herington, Ramona, Tampa, Durham, Waldeck, Canton, Galva, McPherson, Groveland, Inman, Medora, Hutchinson, Whiteside, Partridge, Arlington,  Langdon, Turon, Preston, Natrona, Pratt   In 1888, this main line was extended to Liberal   Later, this line was extended to Tucumcari, New Mexico and Santa Rosa, New Mexico, where it made a connection with the Southern Pacific from El Paso, Texas   The Chicago, Kansas and Nebraska Railway was absorbed by the Chicago, Rock Island and Pacific Railway  This line is also called the "Golden State Route" 

20th century
The National Old Trails Road, also known as the Ocean-to-Ocean Highway, was established in 1912, and was routed through Windom, Conway, McPherson 

Geography

According to the U S  Census Bureau, the county has a total area of , of which  is land and  (0 3%) is water 

Adjacent counties
 Saline County (north)
 Dickinson County (northeast)
 Marion County (east)
 Harvey County (southeast)
 Reno County (southwest)
 Rice County (west)
 Ellsworth County (northwest)

Major highways
  Interstate 135
  U S  Route 56
  U S  Route 81
  K-4
  K-61
  K-153

Demographics

The McPherson Micropolitan Statistical Area includes all of McPherson County 

2000 census
As of the census of 2000, there were 29,554 people, 11,205 households, and 7,966 families residing in the county   The population density was 33 people per square mile (13/km2)   There were 11,830 housing units at an average density of 13 per square mile (5/km2)   The racial makeup of the county was 96 53% White, 0 81% Black or African American, 0 34% Native American, 0 32% Asian, 0 06% Pacific Islander, 0 79% from other races, and 1 16% from two or more races   1 94% of the population were Hispanic or Latino of any race  37 1% were of German, 12 9% Swedish, 12 1% American, 6 7% English and 6 3% Irish ancestry according to Census 2000 

There were 11,205 households, out of which 33 00% had children under the age of 18 living with them, 62 50% were married couples living together, 6 00% had a female householder with no husband present, and 28 90% were non-families  25 50% of all households were made up of individuals, and 11 80% had someone living alone who was 65 years of age or older   The average household size was 2 49 and the average family size was 2 99 

In the county, the population was spread out, with 25 40% under the age of 18, 10 30% from 18 to 24, 25 20% from 25 to 44, 21 80% from 45 to 64, and 17 30% who were 65 years of age or older   The median age was 38 years  For every 100 females there were 95 90 males   For every 100 females age 18 and over, there were 92 90 males 

The median income for a household in the county was $41,138, and the median income for a family was $48,243  Males had a median income of $33,530 versus $21,175 for females  The per capita income for the county was $18,921   About 4 20% of families and 6 60% of the population were below the poverty line, including 5 20% of those under age 18 and 8 10% of those age 65 or over 

Government

Presidential elections
McPherson county is often carried by Republican candidates  The last time a Democratic candidate has carried this county was in 1964 by Lyndon B  Johnson 

Laws
Following amendment to the Kansas Constitution in 1986, the county remained a prohibition, or "dry", county until 1996, when voters approved the sale of alcoholic liquor by the individual drink with a 30 percent food sales requirement 

Education

Colleges
 McPherson College in McPherson
 Bethany College in Lindsborg
 Central Christian College in McPherson

Unified school districts
 Smoky Valley USD 400
 McPherson USD 418
 Canton-Galva USD 419
 Moundridge USD 423
 Inman USD 448

School district office in neighboring county
 Goessel USD 411
 Little River-Windom USD 444

Museums
 Birger Sandzén Memorial Gallery in Lindsborg
 McCormick-Deering Days Museum in Inman
 McPherson Museum in McPherson
 Lindsborg Old Mill & Swedish Heritage Museum in Lindsborg
 Kansas Motorcycle Museum in Marquette

Communities

Cities

 Canton
 Galva
 Inman
 Lindsborg
 Marquette
 McPherson (county seat) 
 Moundridge
 Windom

Unincorporated communities
† means a Census-Designated Place (CDP) by the United States Census Bureau 
 Conway
 Elyria†
 Groveland
 Johnstown
 New Gottland
 Roxbury†

Ghost towns
 Alta Mills
 Battle Hill
 Christian
 Doles Park
 Elivon
 King City
 Sweadal

Townships
McPherson County is divided into twenty-five townships   The cities of Lindsborg and McPherson are considered governmentally independent and are excluded from the census figures for the townships   In the following table, the population center is the largest city (or cities) included in that township's population total, if it is of a significant size 

See also
 List of people from McPherson County, Kansas
 National Register of Historic Places listings in McPherson County, Kansas
 McPherson Valley Wetlands
 Maxwell Wildlife Refuge

References

Notes

Further reading

 Wheeler, Wayne Leland  "An Analysis of Social Change in a Swedish-Immigrant Community: The Case of Lindsborg, Kansas " (PhD dissertation, University of Missouri-Columbia; ProQuest Dissertations Publishing, 1959  5905657) 

County
 Through the Years: A Pictorial History of McPherson County; McPherson Sentinel' Heritage House Publishing Co; 1992 
 McPherson County First Courthouse Built About 1869 or 1870; Lindsborg News-Record; March 30, 1959 
 Pioneer Life and Lore of McPherson County, Kansas; Edna Nyquist; Democratic-Opinion Press; 1932 
 A History of the Church of the Brethren in Kansas (includes McPherson College history); Elmer LeRoy Craik; McPherson Daily; Republican Press; 397 pages; 1922 
 Portrait and Biographical Record of Dickinson, Saline, McPherson, and Marion Counties, Kansas; Chapman Bros; 614 pages; 1893 
 Standard Atlas of McPherson County, Kansas; Geo  A  Ogle & Co; 82 pages; 1921 
 Plat Book of McPherson County, Kansas; North West Publishing Co; 50 pages; 1903 
 Edwards' Atlas of McPherson County, Kansas; John P  Edwards; 51 pages; 1884 

Trails
 The Story of the Marking of the Santa Fe Trail by the Daughters of the American Revolution in Kansas and the State of Kansas; Almira Cordry; Crane Co; 164 pages; 1915  (Download 4MB PDF eBook)
 The National Old Trails Road To Southern California, Part 1 (LA to KC); Automobile Club Of Southern California; 64 pages; 1916  (Download 6 8MB PDF eBook)

Mennonite Settlements
 Impact of Mennonite settlement on the cultural landscape of Kansas; Brenda Martin; Kansas State University; 1985/1988  
 Mennonite settlement : the relationship between the physical and cultural environment; Susan Movle; University of Utah; 1975/1886 
 Status of Mennonite women in Kansas in their church and home relationships; Eva Harshbarger; Bluffton College; 1925/1945 

External links

County
 
 McPherson County - Directory of Public Officials
Historical
 , from Hatteberg's People'' on KAKE TV news
Maps
 McPherson County Maps: Current, Historic, KDOT
 Kansas Highway Maps: Current, Historic, KDOT
 Kansas Railroad Maps: Current, 1996, 1915, KDOT and Kansas Historical Society

 
Kansas counties
1867 establishments in Kansas

Context 2: \section{Introduction}
Underwater robot picking is to use the robot to automatically capture sea creatures like holothurian, echinus, scallop, or starfish in an open-sea farm where underwater object detection is the key technology for locating creatures  Until now, the datasets used in this community are released by the Underwater Robot Professional Contest (URPC$\protect\footnote{Underwater Robot Professional Contest: {\bf http://en cnurpc org} }$) beginning from 2017, in which URPC2017 and URPC2018 are most often used for research  Unfortunately, as the information listed in Table \ref{Info}, URPC series datasets do not provide the annotation file of the test set and cannot be downloaded after the contest  
Therefore, researchers \cite{2020arXiv200511552C,2019arXiv191103029L} first have to divide the training data into two subsets, including a new subset of training data and a new subset of testing data, and then train their proposed method and other \emph{SOTA} methods  On the one hand, training other methods results in a significant increase in workload  On the other hand, different researchers divide different datasets in different ways, 
\begin{table}[t]
\renewcommand\tabcolsep{3 5pt}
\caption{Information about all the collected datasets  * denotes the test set's annotations are not available  \emph{3} in Class means three types of creatures are labeled, \emph{i e ,} holothurian, echinus, and scallop  \emph{4} means four types of creatures are labeled (starfish added)  Retention represents the proportion of images that retain after similar images have been removed }
\centering 
\begin{tabular}{|l|c|c|c|c|c|}
\hline
Dataset&Train&Test&Class&Retention&Year \\ 
\hline 
URPC2017&17,655&985*&3&15\%&2017 \\
\hline
URPC2018&2,901&800*&4&99\%&2018 \\
\hline
URPC2019&4,757&1,029*&4&86\%&2019 \\
\hline
URPC2020$_{ZJ}$&5,543&2,000*&4&82\%&2020 \\
\hline
URPC2020$_{DL}$&6,575&2,400*&4&80\%&2020 \\
\hline
UDD&1,827&400&3&84\%&2020 \\
\hline 

\end{tabular}
\label{Info}
\end{table}
\begin{figure*}[htbp]
\begin{center}
\includegraphics[width=1\linewidth]{example pdf}
\end{center}
   \caption{Examples in DUO, which show a variety of scenarios in underwater environments }
\label{exam}
\end{figure*}
causing there is no unified benchmark to compare the performance of different algorithms 
In terms of the content of the dataset images, there are a large number of similar or duplicate images in the URPC datasets  URPC2017 only retains 15\% images after removing similar images compared to other datasets  Thus the detector trained on URPC2017 is easy to overfit and cannot reflect the real performance 
For other URPC datasets, the latter also includes images from the former, \emph{e g }, URPC2019 adds 2,000 new images compared to URPC2018; compared with URPC2019, URPC2020$_{ZJ}$ adds 800 new images  The URPC2020$_{DL}$ adds 1,000 new images compared to the URPC2020$_{ZJ}$  It is worth mentioning that the annotation of all datasets is incomplete; some datasets lack the starfish labels and it is easy to find error or missing labels  \cite{DBLP:conf/iclr/ZhangBHRV17} pointed out that although the CNN model has a strong fitting ability for any dataset, the existence of dirty data will significantly weaken its robustness 
Therefore, a reasonable dataset (containing a small number of similar images as well as an accurate annotation) and a corresponding recognized benchmark are urgently needed to promote community development 


To address these issues, we introduce a dataset called Detecting Underwater Objects (DUO) by collecting and re-annotating all the available underwater datasets  It contains 7,782 underwater images after deleting overly similar images and has a more accurate annotation with four types of classes (\emph{i e ,} holothurian, echinus, scallop, and starfish)  
Besides, based on the MMDetection$\protect\footnote{MMDetection is an open source object detection toolbox based on PyTorch  {\bf https://github com/open-mmlab/mmdetection}}$ \cite{chen2019mmdetection} framework, we also provide a \emph{SOTA} detector benchmark containing efficiency and accuracy indicators, providing a reference for both academic research and industrial applications  It is worth noting that JETSON AGX XAVIER$\protect\footnote{JETSON AGX XAVIER is an embedded development board produced by NVIDIA which could be deployed in an underwater robot  Please refer {\bf https://developer nvidia com/embedded/jetson-agx-xavier-developer-kit} for more information }$ was used to assess all the detectors in the efficiency test in order to simulate robot-embedded environment  DUO will be released in https://github com/chongweiliu soon 

In summary, the contributions of this paper can be listed as follows 

  $\bullet$ By collecting and re-annotating all relevant datasets, we introduce a dataset called DUO with more reasonable annotations as well as a variety of underwater scenes 

  $\bullet$ We provide a corresponding benchmark of \emph{SOTA} detectors on DUO including efficiency and accuracy indicators which could be a reference for both academic research and industrial applications  


\pagestyle{empty}
\section{Background}
In the year of 2017, underwater object detection for open-sea farming is first proposed in the target recognition track of Underwater Robot Picking Contest 2017$\protect\footnote{From 2020, the name has been changed into Underwater Robot Professional Contest which is also short for URPC }$ (URPC2017) which aims to promote the development of theory, technology, and industry of the underwater agile robot and fill the blank of the grabbing task of the underwater agile robot  The competition sets up a target recognition track, a fixed-point grasping track, and an autonomous grasping track  The target recognition track concentrates on finding the {\bf high accuracy and efficiency} algorithm which could be used in an underwater robot for automatically grasping 

The datasets we used to generate the DUO are listed below  The detailed information has been shown in Table \ref{Info} 

  {\bf URPC2017}: It contains 17,655 images for training and 985 images for testing and the resolution of all the images is 720$\times$405  All the images are taken from 6 videos at an interval of 10 frames  However, all the videos were filmed in an artificial simulated environment and pictures from the same video look almost identical  
  
   {\bf URPC2018}: It contains 2,901 images for training and 800 images for testing and the resolutions of the images are 586$\times$480, 704$\times$576, 720$\times$405, and 1,920$\times$1,080  The test set's annotations are not available  Besides, some images were also collected from an artificial underwater environment 
  
  {\bf URPC2019}: It contains 4,757 images for training and 1029 images for testing and the highest resolution of the images is 3,840$\times$2,160 captured by a GOPro camera  The test set's annotations are also not available and it contains images from the former contests 
  
  {\bf URPC2020$_{ZJ}$}: From 2020, the URPC will be held twice a year  It was held first in Zhanjiang, China, in April and then in Dalian, China, in August  URPC2020$_{ZJ}$ means the dataset released in the first URPC2020 and URPC2020$_{DL}$ means the dataset released in the second URPC2020  This dataset contains 5,543 images for training and 2,000 images for testing and the highest resolution of the images is 3,840$\times$2,160  The test set's annotations are also not available 
  
  {\bf URPC2020$_{DL}$}: This dataset contains 6,575 images for training and 2,400 images for testing and the highest resolution of the images is 3,840$\times$2,160  The test set's annotations are also not available 
  
  {\bf UDD \cite{2020arXiv200301446W}}: This dataset contains 1,827 images for training and 400 images for testing and the highest resolution of the images is 3,840$\times$2,160  All the images are captured by a diver and a robot in a real open-sea farm 

\begin{figure}[t]
\begin{center}
\includegraphics[width=1\linewidth]{pie pdf}
\end{center}
   \caption{The proportion distribution of the objects in DUO }
\label{pie}
\end{figure}



\begin{figure*}
  \centering
  \subfigure[]{\includegraphics[width=3 45in]{imagesize pdf}}
  \subfigure[]{\includegraphics[width=3 45in]{numInstance pdf}}
  \caption{(a) The distribution of instance sizes for DUO; (b) The number of categories per image }
  \label{sum}
\end{figure*}
\section{Proposed Dataset}

\subsection{Image Deduplicating}
As we explained in Section 1, there are a large number of similar or repeated images in the series of URPC datasets  Therefore, it is important to delete duplicate or overly similar images and keep a variety of underwater scenarios when we merge these datasets together  Here we employ the Perceptual Hash algorithm (PHash) to remove those images  PHash has the special property that the hash value is dependent on the image content, and it remains approximately the same if the content is not significantly modified  Thus we can easily distinguish different scenarios and delete duplicate images within one scenario  

After deduplicating, we obtain 7,782 images (6,671 images for training; 1,111 for testing)  The retention rate of the new dataset is 95\%, which means that there are only a few similar images in the new dataset  Figure \ref{exam} shows that our dataset also retains various underwater scenes 

\subsection{Image Re-annotation}
Due to the small size of objects and the blur underwater environment, there are always missing or wrong labels in the existing annotation files  In addition, some test sets' annotation files are not available and some datasets do not have the starfish annotation  In order to address these issues, we follow the next process which combines a CNN model and manual annotation to re-annotate these images  Specifically, we first train a detector (\emph{i e ,} GFL \cite{li2020generalized}) with the originally labeled images  After that, the trained detector predicts all the 7,782 images  We treat the prediction as the groundtruth and use it to train the GFL again  We get the final GFL prediction called {\bf the coarse annotation}  Next, we use manual correction to get the final annotation called {\bf the fine annotation}  Notably, we adopt the COCO \cite{Belongie2014} annotation form as the final format 
\subsection{Dataset Statistics}
{\bf The proportion of classes}: The total number of objects is 74,515  Holothurian, echinus, scallop, and starfish are 7,887, 50,156, 1,924, and 14,548, respectively  Figure \ref{pie} shows the proportion of each creatures where echinus accounts for 67 3\% of the total  The whole data distribution shows an obvious long-tail distribution because the different economic benefits of different seafoods determine the different breed quantities 

{\bf The distribution of instance sizes}: Figure \ref{sum}(a) shows an instance size distribution of DUO  \emph{Percent of image size} represents the ratio of object area to image area, and \emph{Percent of instance} represents the ratio of the corresponding number of objects to the total number of objects  Because of these small creatures and high-resolution images, the vast majority of objects occupy 0 3\% to 1 5\% of the image area 

{\bf The instance number per image}: Figure \ref{sum}(b) illustrates the number of categories per image for DUO  \emph{Number of instances} represents the number of objects one image has, and \emph{ Percentage of images} represents the ratio of the corresponding number of images to the total number of images  Most images contain between 5 and 15 instances, with an average of 9 57 instances per image 

{\bf Summary}:
In general, smaller objects are harder to detect  For PASCAL VOC \cite{Everingham2007The} or COCO \cite{Belongie2014}, roughly 50\% of all objects occupy no more than 10\% of the image itself, and others evenly occupy from 10\% to 100\%  
In the aspect of instances number per image, COCO contains 7 7 instances per image and VOC contains 3  In comparison, DUO has 9 57 instances per image and most instances less than 1 5\% of the image size 
Therefore, DUO contains almost exclusively massive small instances and has the long-tail distribution at the same time, which means it is promising to design a detector to deal with massive small objects and stay high efficiency at the same time for underwater robot picking 

\section{Benchmark}
Because the aim of underwater object detection for robot picking is to find {\bf the high accuracy and efficiency} algorithm, we consider both the accuracy and efficiency evaluations in the benchmark as shown in Table \ref{ben} 

\subsection{Evaluation Metrics}
Here we adopt the standard COCO metrics (mean average precision, \emph{i e ,} mAP) for the accuracy evaluation and also provide the mAP of each class due to the long-tail distribution 

{\bf AP} -- mAP at IoU=0 50:0 05:0 95 

{\bf AP$_{50}$} --  mAP at IoU=0 50 

{\bf AP$_{75}$} --  mAP at IoU=0 75  

{\bf AP$_{S}$} --   {\bf AP} for small objects of area smaller than 32$^{2}$ 

{\bf AP$_{M}$} --   {\bf AP} for objects of area between 32$^{2}$ and 96$^{2}$ 

{\bf AP$_{L}$} --   {\bf AP} for large objects of area bigger than 96$^{2}$ 

{\bf AP$_{Ho}$} --  {\bf AP} in holothurian 

{\bf AP$_{Ec}$} --  {\bf AP} in echinus 

{\bf AP$_{Sc}$} --  {\bf AP} in scallop 

{\bf AP$_{St}$} --  {\bf AP} in starfish 


For the efficiency evaluation, we provide three metrics:

{\bf Param } --  The parameters of a detector 

{\bf FLOPs} --  Floating-point operations per second 

{\bf FPS} --  Frames per second 

Notably, {\bf FLOPs} is calculated under the 512$\times$512 input image size and {\bf FPS} is tested on a JETSON AGX XAVIER under MODE$\_$30W$\_$ALL  

\subsection{Standard Training Configuration}
We follow a widely used open-source toolbox, \emph{i e ,} MMDetection (V2 5 0) to produce up our benchmark  During the training, the standard configurations are as follows:

  $\bullet$ We initialize the backbone models (\emph{e g ,} ResNet50) with pre-trained parameters on ImageNet \cite{Deng2009ImageNet} 

  $\bullet$ We resize each image into 512 $\times$ 512 pixels both in training and testing  Each image is flipped horizontally with 0 5 probability during training 

  $\bullet$ We normalize RGB channels by subtracting 123 675, 116 28, 103 53 and dividing by 58 395, 57 12, 57 375, respectively 

  $\bullet$ SGD method is adopted to optimize the model  The initial learning rate is set to be 0 005 in a single GTX 1080Ti with batchsize 4 and is decreased by 0 1 at the 8th and 11th epoch, respectively  WarmUp \cite{2019arXiv190307071L} is also employed in the first 500 iterations  Totally there are 12 training epochs 

  $\bullet$ Testing time augmentation (\emph{i e ,} flipping test or multi-scale testing) is not employed 



\subsection{Benchmark Analysis}
Table \ref{ben} shows the benchmark for the \emph{SOTA} methods  Multi- and one- stage detectors with three kinds of backbones (\emph{i e ,} ResNet18, 50, 101) give a comprehensive assessment on DUO  We also deploy all the methods to AGX to assess efficiency 

In general, the multi-stage (Cascade R-CNN) detectors have high accuracy and low efficiency, while the one-stage (RetinaNet) detectors have low accuracy and high efficiency  However, due to recent studies \cite{zhang2019bridging} on the allocation of more reasonable positive and negative samples in training, one-stage detectors (ATSS or GFL) can achieve both high accuracy and high efficiency 

\begin{table*}[htbp]
\renewcommand\tabcolsep{3 0pt}

\begin{center}
\caption{Benchmark of \emph{SOTA} detectors (single-model and single-scale results) on DUO  FPS is measured on the same machine with a JETSON AGX XAVIER under the same MMDetection framework, using a batch size of 1 whenever possible  R: ResNet } 
\label{ben}
\begin{tabular}{|l|l|c|c|c|ccc|ccc|cccc|}
\hline
Method&Backbone&Param &FLOPs&FPS&AP&AP$_{50}$&AP$_{75}$&AP$_{S}$&AP$_{M}$&AP$_{L}$&AP$_{Ho}$&AP$_{Ec}$&AP$_{Sc}$&AP$_{St}$ \\ 
\hline 
\emph{multi-stage:} &&&&&&&&&&&&&& \\

\multirow{3}{*}{Faster R-CNN \cite{Ren2015Faster}}
&R-18&28 14M&49 75G&5 7&50 1&72 6&57 8&42 9&51 9&48 7&49 1&60 1&31 6&59 7\\
&R-50&41 14M&63 26G&4 7&54 8&75 9&63 1&53 0&56 2&53 8&55 5&62 4&38 7&62 5\\
&R-101&60 13M&82 74G&3 7&53 8&75 4&61 6&39 0&55 2&52 8&54 3&62 0&38 5&60 4\\
\hline

\multirow{3}{*}{Cascade R-CNN \cite{Cai_2019}}
&R-18&55 93M&77 54G&3 4&52 7&73 4&60 3&\bf 49 0&54 7&50 9&51 4&62 3&34 9&62 3\\
&R-50&68 94M&91 06G&3 0&55 6&75 5&63 8&44 9&57 4&54 4&56 8&63 6&38 7&63 5\\
&R-101&87 93M&110 53G&2 6&56 0&76 1&63 6&51 2&57 5&54 7&56 2&63 9&41 3&62 6\\
\hline

\multirow{3}{*}{Grid R-CNN \cite{lu2019grid}}
&R-18&51 24M&163 15G&3 9&51 9&72 1&59 2&40 4&54 2&50 1&50 7&61 8&33 3&61 9\\
&R-50&64 24M&176 67G&3 4&55 9&75 8&64 3&40 9&57 5&54 8&56 7&62 9&39 5&64 4\\
&R-101&83 24M&196 14G&2 8&55 6&75 6&62 9&45 6&57 1&54 5&55 5&62 9&41 0&62 9\\
\hline

\multirow{3}{*}{RepPoints \cite{yang2019reppoints}}
&R-18&20 11M&\bf 35 60G&5 6&51 7&76 9&57 8&43 8&54 0&49 7&50 8&63 3&33 6&59 2\\
&R-50&36 60M&48 54G&4 8&56 0&80 2&63 1&40 8&58 5&53 7&56 7&65 7&39 3&62 3\\
&R-101&55 60M&68 02G&3 8&55 4&79 0&62 6&42 2&57 3&53 9&56 0&65 8&39 0&60 9\\
\hline 
\hline 
\emph{one-stage:} &&&&&&&&&&&&&& \\
\multirow{3}{*}{RetinaNet \cite{Lin2017Focal}}
&R-18&19 68M&39 68G&7 1&44 7&66 3&50 7&29 3&47 6&42 5&46 9&54 2&23 9&53 8\\
&R-50&36 17M&52 62G&5 9&49 3&70 3&55 4&36 5&51 9&47 6&54 4&56 6&27 8&58 3\\
&R-101&55 16M&72 10G&4 5&50 4&71 7&57 3&34 6&52 8&49 0&54 6&57 0&33 7&56 3\\
\hline 

\multirow{3}{*}{FreeAnchor \cite{2019arXiv190902466Z}}
&R-18&19 68M&39 68G&6 8&49 0&71 9&55 3&38 6&51 7&46 7&47 2&62 8&28 6&57 6\\
&R-50&36 17M&52 62G&5 8&54 4&76 6&62 5&38 1&55 7&53 4&55 3&65 2&35 3&61 8\\
&R-101&55 16M&72 10G&4 4&54 6&76 9&62 9&36 5&56 5&52 9&54 0&65 1&38 4&60 7\\
\hline 

\multirow{3}{*}{FoveaBox \cite{DBLP:journals/corr/abs-1904-03797}}
&R-18&21 20M&44 75G&6 7&51 6&74 9&57 4&40 0&53 6&49 8&51 0&61 9&34 6&59 1\\
&R-50&37 69M&57 69G&5 5&55 3&77 8&62 3&44 7&57 4&53 4&57 9&64 2&36 4&62 8\\
&R-101&56 68M&77 16G&4 2&54 7&77 3&62 3&37 7&57 1&52 4&55 3&63 6&38 9&60 8\\
\hline 

\multirow{3}{*}{PAA \cite{2020arXiv200708103K}}
&R-18&\bf 18 94M&38 84G&3 0&52 6&75 3&58 8&41 3&55 1&50 2&49 9&64 6&35 6&60 5\\
&R-50&31 89M&51 55G&2 9&56 8&79 0&63 8&38 9&58 9&54 9&56 5&66 9&39 9&64 0\\
&R-101&50 89M&71 03G&2 4&56 5&78 5&63 7&40 9&58 7&54 5&55 8&66 5&42 0&61 6\\
\hline 

\multirow{3}{*}{FSAF \cite{zhu2019feature}}
&R-18&19 53M&38 88G&\bf 7 4&49 6&74 3&55 1&43 4&51 8&47 5&45 5&63 5&30 3&58 9\\
&R-50&36 02M&51 82G&6 0&54 9&79 3&62 1&46 2&56 7&53 3&53 7&66 4&36 8&62 5\\
&R-101&55 01M&55 01G&4 5&54 6&78 7&61 9&46 0&57 1&52 2&53 0&66 3&38 2&61 1\\
\hline 

\multirow{3}{*}{FCOS \cite{DBLP:journals/corr/abs-1904-01355}}
&R-18&\bf 18 94M&38 84G&6 5&48 4&72 8&53 7&30 7&50 9&46 3&46 5&61 5&29 1&56 6\\
&R-50&31 84M&50 34G&5 4&53 0&77 1&59 9&39 7&55 6&50 5&52 3&64 5&35 2&60 0\\
&R-101&50 78M&69 81G&4 2&53 2&77 3&60 1&43 4&55 4&51 2&51 7&64 1&38 5&58 5\\
\hline 

\multirow{3}{*}{ATSS \cite{zhang2019bridging}}
&R-18&\bf 18 94M&38 84G&6 0&54 0&76 5&60 9&44 1&56 6&51 4&52 6&65 5&35 8&61 9\\
&R-50&31 89M&51 55G&5 2&58 2&\bf 80 1&66 5&43 9&60 6&55 9&\bf 58 6&67 6&41 8&64 6\\
&R-101&50 89M&71 03G&3 8&57 6&79 4&65 3&46 5&60 3&55 0&57 7&67 2&42 6&62 9\\
\hline 

\multirow{3}{*}{GFL \cite{li2020generalized}}
&R-18&19 09M&39 63G&6 3&54 4&75 5&61 9&35 0&57 1&51 8&51 8&66 9&36 5&62 5\\
&R-50&32 04M&52 35G&5 5&\bf 58 6&79 3&\bf 66 7&46 5&\bf 61 6&55 6&\bf 58 6&\bf 69 1&41 3&\bf 65 3\\
&R-101&51 03M&71 82G&4 1&58 3&79 3&65 5&45 1&60 5&\bf 56 3&57 0&\bf 69 1&\bf 43 0&64 0\\


\hline 
\end{tabular}
\end{center}
\end{table*}
Therefore, in terms of accuracy, the accuracy difference between the multi- and the one- stage methods in AP is not obvious, and the AP$_{S}$ of different methods is always the lowest among the three size AP  For class AP, AP$_{Sc}$ lags significantly behind the other three classes because it has the smallest number of instances  In terms of efficiency, large parameters and FLOPs result in low FPS on AGX, with a maximum FPS of 7 4, which is hardly deployable on underwater robot  Finally, we also found that ResNet101 was not significantly improved over ResNet50, which means that a very deep network may not be useful for detecting small creatures in underwater scenarios  

Consequently, the design of high accuracy and high efficiency detector is still the main direction in this field and there is still large space to improve the performance 
In order to achieve this goal, a shallow backbone with strong multi-scale feature fusion ability can be proposed to extract the discriminant features of small scale aquatic organisms; a specially designed training strategy may overcome the DUO's long-tail distribution, such as a more reasonable positive/negative label sampling mechanism or a class-balanced image allocation strategy within a training batch 

\section{Conclusion}
In this paper, we introduce a dataset (DUO) and a corresponding benchmark to fill in the gaps in the community  DUO contains a variety of underwater scenes and more reasonable annotations  Benchmark includes efficiency and accuracy indicators to conduct a comprehensive evaluation of the \emph{SOTA} decoders  The two contributions could serve as a reference for academic research and industrial applications, as well as promote community development 
Context 3: Paper Info

Title: Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?
Publish Date: 10 Mar 2023
Author List: Mamoru Komachi (from Tokyo Metropolitan University), Takashi Okumura (from Kitami Institute of Technology), Hiromasa Horiguchi (from National Hospital Organization), Yuji Matsumoto

Figure

Fig  1 Example of part of a discharge summary which is a dummy we created 
Fig  2  Overview of our proposed method A new feature embedding layer encoding hospital, physician, disease, and length of stay is added to the standard transformer architecture The figure shows an example of hospital embedding 
Statistics of our data for experiment 
of summarization models with different meta-information The best results are highlighted in bold Each score is the average of three models with different seeds The BS and BR indicate BERTScore and BLEURT, respectively 
Statistics on the number of cases handled by physicians C/P denotes Cases/Physician, which indicates how many cases an individual physician has Method of Grouping Physician IDs A most naive method of mapping physician IDs to features is without any grouping process The data contains 4,846 physicians, so |M | was set to 4,846 However it caused our model's training to be unstable This might be due to the many physician IDs appearing for the first time in the test time Table

abstract

During the patient's hospitalization, the physician must record daily observations of the patient and summarize them into a brief document called "discharge summary" when the patient is discharged  Automated generation of discharge summary can greatly relieve the physicians' burden, and has been addressed recently in the research community 
Most previous studies of discharge summary generation using the sequenceto-sequence architecture focus on only inpatient notes for input  However, electric health records (EHR) also have rich structured metadata (e g , hospital, physician, disease, length of stay, etc ) that might be useful  This paper investigates the effectiveness of medical meta-information for summarization tasks 
We obtain four types of meta-information from the EHR systems and encode each meta-information into a sequence-to-sequence model  Using Japanese EHRs, meta-information encoded models increased ROUGE-1 by up to 4 45 points and BERTScore by 3 77 points over the vanilla Longformer  Also, we found that the encoded meta-information improves the precisions of its related terms in the outputs 
Our results showed the benefit of the use of medical meta-information 

INTRODUCTION

Clinical notes are written daily by physicians from their consults and are used for their own decision-making or coordination of treatment  They contain a large amount of important data for machine learning, such as conditions, laboratory tests, diagnoses, procedures, and treatments  While invaluable to physicians and researchers, the paperwork is burdensome for physicians ,  
Discharge summaries, a subset of these, also play a crucial role in patient care, and are used to share information between hospitals and physicians (see an example in Figure )  It is created by the physician as a summary of notes during hospitalization at the time of the patient's discharge, which is known to be very time-consuming 
Researchers have begun to apply automatic summarization techniques to address this problem -   Previous studies used extractive or abstractive summarization methods, but most of them focused on only progress notes for inputs  Properly summarizing an admission of a patient is a quite complex task, and requires various meta-information such as the patient's age, gender, vital signs, laboratory values and background to specific diseases 
Therefore, discharge summary generation needs more medical meta-information, than similar but narrower tasks such as radiology report generation  However, what kind of meta-information is important for summarization has not been investigated, even though it is critical not only for future research on medical summarization but also for the policy of data collection infrastructure 
In this paper, we first reveal the effects of meta-information on neural abstractive summarization on admissions  Our model is based on an encoder-decoder transformer with an additional feature embedding layer in the encoder (Figure )  Hospital, physician, disease, and length of stay are used as meta-information, and each feature is embedded in the vector space 
For experiments, we collect progress notes, discharge summaries and coded information from the electronic health record system, which are managed by a largest multi-hospital organization in Japan  Our main contributions are as follows: • We found that a transformer encoding meta-information generates higher quality summaries than the vanilla one, and clarified the benefit of using meta-information for medical summarization tasks 
• We found that a model encoding disease information can produce proper disease and symptom words following the source  In addition, we found that the model using physician and hospital information can generate symbols that are commonly written in the summary  • We are the first to apply the abstractive summarization method to generate Japanese discharge summaries 
In the studies of summarization of medical documents, it is common to retrieve key information such as disease, examination result, or medication from EHRs -   Other researchs more similar to our study targeted to help physicians get the point of medical documents quickly by generating a few key sentences -  
Studies generating contextualized summaries can be categorized by the type of model inputs and architectures  Some studies produced a whole discharge summary using structured data for input - The sensitivity of the gram stain for bacterial meningitis is about 60%, and the sensitivity of the culture is not high either 
Also, the glucose in the cerebrospinal fluid would have been slightly lower  Although no definitive diagnosis could be made, bacterial meningitis was the most suspicious disease  The causative organism was assumed to be MRSA, and vancomycin and meropenem (meningitis dose) were used to cover a wide range of enteric bacteria 
a whole discharge summary from free-form inpatient records -   The free-form data is more challenging since it is noisier than structured data  In inputting of the free-form data, extractive summarization methods, which extract sentences from the source, are commonly used , -   On the other hands, an encoder-decoder model was used for abstractive summarization , , with a limited number of studies 
The various issues in the abstractive generation of discharge summary would be studied in the future  Studies using medical meta-information have long been conducted on a lot of tasks -   In abstractive summarization on discharge summary,  developed a model incorporating similarity of progress notes and information of the record author 
They presented an idea of integrating meta-information into the abstractive summarization model on medical documents, but did not reveal how meta-information would affect the quality of the summaries  Our method is based on the encoder-decoder transformer model  The transformer model is known for its high performance and has been widely used in recent studies, thus it is suitable for our purpose 
As shown in Figure , the standard input to a transformer's encoder is created by a token sequence T = [t 0 , t 1 ,    , t i ] and position sequence P = [p 0 , p 1 ,    , p i ], where i is the maximum input length  The token and position sequences are converted into token embeddings E T and positional embeddings E P by looking up the vocabulary tables 
The sum of E T and E P is input into the model  In this paper, we attempt to encode meta-information to feature embeddings  We follow the segment embeddings of BERT and the language embeddings of XLM , which provide additional information to the model  It is not a new idea but is suitable for our validation 
Our method is formulated as follows: Let M be feature type, M ∈ {Vanilla, Hospital, Physician, Disease, Length of stay}, since we set five types of features  Feature embeddings E M is created by looking up the feature table where m j is featue value (e g , pysician ID, disease code, etc ) and |M | is the maximum number of differences in a feature 
In our study, |M | is set to four different values depending on features  Specifically, they are as follows  a) Hospital: As shown in Table , the data includes five hospital records  They were obtained mechanically from the EHR system  b) Physician: Physicians are also managed by IDs in the EHR systems  We hashed the physician IDs into 485 groups containing 10 people each 
Specifically, as a naive strategy, we shuffled and listed the cases within each hospital, and hashed them into groups in the order of appearance of the physician IDs



ground_truth: McPherson County is located in the U.S. state of Kansas.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Does DUO contain more instances per image than COCO?

Read the following text and answer briefly.

Context 4: Xpp-pdf support utility
Xpp-pdf support utility
PATENT, TRADEMARK
& COPYRIGHT !
Reproduced with permission from BNA’s Patent,Trademark 11/20/09, 11/20/2009  Copyright ஽ 2009 by The Bu-reau of National Affairs, Inc  (800-372-1033) http://www bna com As the patent community anticipates a decision by the U S  Supreme Court on subject matter patentability, recent rulings by the Federal Circuit and the Board of Patent Appeals and Interferences suggest strategies for preparing method patent applications that will sur- vive the Federal Circuit’s ‘‘machine-or-transformation’’ test 
The Changing Landscape of Method Claims in the Wake of In re Bilski:What We Can Learn from Recent Decisions of Federal Courts and the Board ofPatent Appeals rulings on software-based and other business methodpatent applications 
On review before the high court is the en banc ruling ‘‘Pure’’ business methods are out  Algorithms by the U S  Court of Appeals for the Federal Circuit1 are out  Machines and data transformations that, in order to be eligible for patent protection, an in- ventive method must either be tied to a machine or re- While the patent community waits for the Supreme cite a transformation of an article 2 This ‘‘machine-or- Court’s decision in Bilski v  Kappos, No  08-964 (U S 
transformation’’ test replaced the Freeman-Walter- argued Nov  9, 2009) (79 PTCJ 33, 11/13/09), patent ap- Abele3 test and the ‘‘useful, concrete and tangible plicants seeking to write patentable claims are stuckwith trying to conform to the lower courts’ most recent 1 In re Bilski, 545 F 3d 943, 88 USPQ2d 1385 (Fed  Cir 
2008) (en banc) (77 PTCJ 4, 11/7/08) 
2 ‘‘The machine-or-transformation test is a two-branched Adriana Suringa Luedke and Bridget M  Hay- inquiry; an applicant may show that a process claim satisfies den are lawyers at Dorsey & Whitney, Min- § 101 either by showing that his claim is tied to a particular neapolis  Luedke can be reached at machine, or by showing that his claim transforms an article ’’ leudke adriana@dorsey com  Hayden can be reached at hayden bridget@dorsey com  3 In re Freeman, 573 F 2d 1237, 197 USPQ 464 (C C P A 
1978); In re Walter, 618 F 2d 758, 205 USPQ 397 (C C P A 
COPYRIGHT ஽ 2009 BY THE BUREAU OF NATIONAL AFFAIRS, INC 
result’’ inquiry advocated in State Street,4 each of gregating, and selling real estate property and claims which had been applied by the Federal Circuit and its reciting a method of performing tax-deferred real estate predecessor court in various cases, and both of which property exchanges were not statutory under Section 101  Since no machine was recited, the only issue be- In this article, we examine the 2008 decision of the fore the court was whether the claims met the ‘‘trans- Federal Circuit, federal district court decisions, and de- formation’’ prong of the Bilski test 13 The court held cisions of Patent and Trademark Office’s Board of that the claims ‘‘involve[d] only the transformation or Patent Appeals and Interferences  Based upon the out- manipulation of legal obligations and relationships’’ comes in these cases, we offer guidance as to what is that did not qualify under Bilski 14 patent-eligible under 35 U S C  § 101, strategies for pre- Concerning the recitation of the ‘‘creation of deed- senting methods in patent applications and claiming shares’’ in some of the claims, the court found that the these methods, and possible ‘‘fixes’’ for applications deedshares themselves were not physical objects, but drafted pre-Bilski that must now withstand scrutiny un- only represented intangible legal ownership interests in der the new machine-or-transformation test 
property 15 Therefore, the creation of deedshares wasnot sufficient to establish patent eligibility under Bil- A number of recent federal court and board decisions have applied the patent eligibility test set forth in Bilski, implemented step to an otherwise obvious method was not sufficient to avoid invalidity of the claim  In KingPharmeuticals Inc  v  Eon Labs Inc ,17 the district court held invalid claims to a method of increasing the oral Several cases have addressed (and rejected) claims bioavailability of metaxalone because the claims were obvious over the prior art asserted by the accused in- In In re Ferguson,6 the Federal Circuit reviewed the board’s rejection of claims directed to a method of mar- Two dependent claims added a step of informing the keting a product and a ‘‘paradigm’’ for marketing soft- patient of certain results, which the patentee argued ware as nonstatutory subject matter under Section was not obvious  The court rejected this argument, con- 101 7 The appellate court affirmed the board’s rejection, cluding that ‘‘[b]ecause the food effect is an inherent concluding that the method claims were neither tied to property of the prior art and, therefore, unpatentable, a particular machine or apparatus nor did they trans- then informing a patient of that inherent property is form a particular article into a different state or thing 8 The court defined a machine broadly as ‘‘a concrete The court also commented that the added step of in- thing, consisting of parts, or of certain devices or com- forming the patient did not meet the patent eligibility binations of devices,’’ which did not include the ‘‘shared standard set forth in Bilski because the step did not re- marketing force’’ to which the method claims were quire use of a machine or transform the metaxalone into a different state or thing 19 Notably, this conclusion The claims directed to a ‘‘paradigm’’ were non- runs counter to the Supreme Court’s instruction that statutory because the claims did not fall within any of claims are to be examined ‘‘as a whole’’ and not dis- the four statutory categories (machines, manufactures, sected into old and new elements and that are evaluated compositions of matter and processes)  Concerning the two closest possible categories, the court concluded Recent board decisions have been consistent with the that the claimed paradigm was not a process, because holdings of the federal courts  For example, in Ex parte no act or series of acts was required, and was not a Roberts,21 the board found ineligible under Section 101 manufacture, because it was not a tangible article re- a ‘‘method of creating a real estate investment instru- sulting from a process of manufacture 10 Concerning ment adapted for performing tax-deferred exchanges’’ the recitation of a ‘‘marketing company’’ in the para- because the claim did not satisfy either the machine or digm claims, the court concluded that the patent appli- cants did ‘‘no more than provide an abstract idea—a Similarly, in Ex parte Haworth,23 a method for ‘‘at- business model for an intangible marketing com- tempting to collect payments from customers having delinquent accounts concurrently with a partner that In Fort Properties Inc  v  American Master Lease owns the delinquent accounts’’ was found to be patent LLC,12 the California district court held that claims re- ineligible because the claim wording was ‘‘broad in that citing a series of transactions involving acquiring, ag- 1980); In re Abele, 684 F 2d 902, 214 USPQ 682 (C C P A 
4 State Street Bank & Trust Co  v  Signature Financial 16 See Ex parte Roberts , 2009-004444 at 4-5 (B P A I  June Group, 149 F 3d 1368, 1370, 47 USPQ2d 1596 (Fed  Cir  1998) 19, 2009) (holding a ‘‘method of creating a real estate invest- ment instrument adapted for performing tax-deferred ex- changes’’ patent ineligible as not passing the machine-or- 7 The court accepted the board’s definition of ‘‘paradigm’’ 17 593 F  Supp 2d 501 (E D N Y  2009) 
to mean ‘‘a pattern, example or model ’’ Id  at 1362 
20 See Diamond v  Diehr, 450 U S  175, 188 (1981) 
21 No  2009-004444 (B P A I  June 19, 2009) 
12 2009 WL 249205, *5 (C D  Cal  Jan  22, 2009) 
23 No  2009-000350 (B P A I  July 30, 2009) 
it refers generally to extending an offer, receiving an machine  Accordingly, the process claims       are not acceptance, and paying a commission’’ and did not in- voke, recite or limit the method of implementation us-ing any particular machine or apparatus 24 The court also evaluated similar claims that recited the use of a ‘‘comparator’’ to perform the recited pixel- B  Software Claims Not Expressly Tied to a ‘Particular by-pixel comparison and held that this recitation also did not mandate a machine 29 While the court acknowl-edged that software was offered as one ‘‘option,’’ the Other cases have addressed software methods where court concluded that the claimed function of the com- the claim language was either not expressly tied to com- parator could also be performed in one’s mind or on pa- puter hardware components or the ties to computer per such that a machine was not required  The court components were somewhat ambiguous  In several further noted that, even though the ‘‘comparator’’ was cases, courts have rejected the recitation of generic defined as a ‘‘device,’’ ‘‘the use of the term ‘device’ is computer components as sufficient to satisfy the ‘‘ma- not synonymous with machine ’’30 As a result, none of chine’’ prong of the Bilski test  A number of these deci- the claims at issue met the ‘‘machine’’ prong of the Bil- sions also addressed the ‘‘transformation’’ prong of the Concerning the ‘‘transformation’’ prong, the court re- In Research Corporation Technology Inc  v  Mi- lied in particular upon the Abele decision in expanding crosoft Corp ,25 the district court considered the patent the requirements of this test by requiring that the eligibility of method claims in six patents directed to claimed transformation process be both ‘‘(1) limited to methods of halftoning of gray scale images by using a transformation of specific data, and 2) limited to a vi- pixel-by-pixel comparison of the image against a blue sual depiction representing specific objects or sub- noise mask  Relying on the Federal Circuit’s Bilski stances ’’31 It then concluded that a number of the analysis as well as a decision of its predecessor court, patent claims did not meet the second prong of this ex- In re Abele,26 the judge concluded that a number of the panded test because the claims did not ‘‘require any vi- sual depiction or subsequent display’’ even though the transformation test set forth in Bilski 27 claimed method did transform specific image data 32 Concerning the ‘‘machine’’ prong, the district court The district court also found other claims patent- found that the pixel-by-pixel comparison recited in the eligible under Section 101 because these claims recited claims did not require the use of a machine, but could the use of the comparison data ‘‘to produce a halftoned ‘‘dictate[d] a transformation of specific data, and [were] be done on a sheet of paper using a pen  The com- further limited to a visual depiction which represents parison uses formulas and numbers to generate a bi- specific objects ’’33 Thus, the patent eligibility of the nary value to determine the placement of a dot at a claims turned on whether the claims recited the use of location  Formulas and numbers not tied to a particu- the transformed data to generate a display 
lar machine cannot be patented, under the machine In DealerTrack Inc  v  Huber,34 the district court prong, even with a field-of-use limitation because granted a summary judgment of invalidity under § 101 they represent fundamental principles, and to do so of patent claims directed to ‘‘a computer aided method’’ would preempt the entire field  The patent claims      
of managing a credit application reciting the following do not mandate the use of a machine to achieve their algorithmic and algebraic ends  Simply because adigital apparatus such as a computer, calculator, or [A] receiving credit application data from a remote the like could assist with this comparison does not render it patent eligible material  RCT’s argument [B] selectively forwarding the credit application data that a pixel by its nature is electronic and therefore to remote funding source terminal devices; necessitates a machine is a post solution argumentand the Court rejects it  The claim construction specifies that the comparison is of a value to a mask 29 The term ‘‘comparator’’ was construed by the court to be (or set of values) to determine whether the dot is a ‘‘device (or collection of operations, as in software) that com- turned on at a specific location  This process does pares an input number (called the operand) to a number pre- not require a particular machine  The Bilski test is stored in the comparator (called the threshold) and produces clear: the process claims must be tied to a particular as output a binary value (such as ‘‘0,’’ zero) if the input is alge-braically less than the threshold [the result of comparing anoperand against a fixed threshold and setting an operand less 24 Id  at 9-10  See also, e g , Ex parte Farnes, 2009-002770 than the threshold to one value and an operand greater than (B P A I  June 2, 2009) (rejecting a method claim for develop- or equal to the threshold to another value], and produces the ing a solution to a customer experience issue including steps opposite binary value (such as ‘‘1,’’ one) if the input is algebra- of: ‘‘identifying a target customer,’’ ‘‘defining a current cus- ically greater than or equal to the threshold ’’ Id  at *17 (em- tomer experience,’’ ‘‘summarizing values and benefits’’ to pro- vide to the customer, and ‘‘identifying metrics for measuring success’’); Ex parte Salinkas, 2009-002768 (B P A I  May 18, 31 Id  at *9  Notably, Bilski concluded that the Abele visual 2009) (finding patent ineligible a method of launching a depiction was ‘‘sufficient’’ to establish transformation (545 knowledge network involving ‘‘selecting an executive spon- F 3d at 963), while the Research Corporation court went fur- sor,’’ ‘‘forming a core team of experts,’’ and ‘‘providing pre- ther by making visual depiction ‘‘required’’ to establish trans- 25 2009 WL 2413623 (D  Ariz  July 28, 2009) (78 PTCJ 432, 26 684 F 2d 902, 214 USPQ 682 (C C P A  1982) 
34 2009 WL 2020761 (C D  Cal  July 7, 2009) (78 PTCJ 341, PATENT, TRADEMARK & COPYRIGHT JOURNAL [C] forwarding funding decision data from at least tation of ‘over the Internet’ suffices to tie a process one of the remote funding source terminal de- claim to a particular machine’’ and concluded that it vices to the remote application entry and display The internet continues to exist despite the addition [D] wherein the selectively forwarding the credit ap- or subtraction of any particular piece of hardware  It may be supposed that the internet itself, rather than [E] sending at least a portion of a credit application any underlying computer or set of computers, is the to more than one of said remote funding sources ‘‘machine’’ to which plaintiff refers  Yet the internet is an abstraction  If every computer user in the world [F] sending at least a portion of a credit application unplugged from the internet, the internet would to more than one of said remote funding sources cease to exist, although every molecule of every ma- sequentially until a finding [sic ] source returns a chine remained in place  One can touch a computer or a network cable, but one cannot touch ‘‘the inter- [G] sending       a credit application       after a prede- Additionally, the court found that the recitation of the [H] sending the credit application from a first remote internet in this case merely constituted ‘‘insignificant funding source to a second remote funding extra-solution activity’’ and therefore did not qualify as a ‘‘particular machine’’ under Bilski 41 ‘‘[T]ossing in In concluding that the claim did not satisfy the Bilski references to internet commerce’’ was not sufficient to machine-or-transformation test, the court held that the render ‘‘a mental process for collecting data and weigh- claimed central processor, remote application and dis- ing values’’ patent-eligible 42 Additionally, ‘‘limiting’’ play device, and remote funding source terminal device the claim to use over the Internet was not a meaningful could be ‘‘any device’’ and did not constitute a ‘‘’par- limitation, such that the claims ‘‘broadly preempt the ticular machine’ within the meaning of Bilski ’’35 The fundamental mental process of fraud detection using court relied upon several board decisions to support its associations between credit cards ’’43 premise that ‘‘claims reciting the use of general purpose processors or computers do not satisfy the test ’’36 claim,44 notwithstanding the Federal Circuit’s holding In Cybersource Corp  v  Retail Decisions Inc ,37 the in In re Beauregard,45 the district court concluded that district court held claims for ‘‘a method for verifying the ‘‘there is at present no legal doctrine creating a special validity of a credit card transaction over the Internet’’ ‘‘Beauregard claim’’ that would exempt the claim from and ‘‘a computer readable medium containing program the analysis of Bilski ’’ Moreover, ‘‘[s]imply appending instructions for detecting fraud in a credit card transac- ‘A computer readable media including program instruc- tion       over the Internet’’ invalid under § 101 based tions’ to an otherwise non-statutory process claim is in- upon the court’s interpretation of Bilski 
sufficient to make it statutory ’’46 Consequently, this Concerning the method claim, the court considered claim also failed the Bilski test 
both the ‘‘transformation’’ and ‘‘machine’’ prongs of the In at least one instance, the U S  International Trade Bilski test  In concluding that there was no transforma- Commission has interpreted the ‘‘machine’’ prong of tion, the court focused on the intangibility of the ma- Bilski less stringently than did the district courts in the nipulated data  According to the court, transformation cases discussed above  In In the Matter of Certain Video is limited to transformation of a physical article or sub- Game Machines and Related Three-Dimensional Point- stance  Accordingly, the method claim did not qualify ing Devices,47 the accused infringer filed a motion for because the data representing credit cards did not rep- summary judgment alleging that the asserted claims resent tangible articles but instead an intangible series impermissibly sought to patent a mathematical algo- of rights and obligations existing between the account rithm  According to the movant, the recitations of a ‘‘3D pointing device,’’ ‘‘handheld device,’’ or ‘‘free space Concerning whether the claimed method was tied to pointing device’’ were not sufficient to tie the claims to a particular machine, the court assessed whether ‘‘reci- a particular machine, but served ‘‘only to limit the field-of-use of the claimed mathematical algorithm and [did] not otherwise impart patentability on the claimed math- Id  at *3  The court relied upon the holdings in Ex parte Gutta, No  2008-3000 at 5-6 (B P A I  Jan  15, 2009) (stating In denying the motion for summary judgment, the ‘‘[t]he recitation in the preamble of ‘[a] computerized method ITC first noted that, ‘‘[w]hile the ultimate determination performed by a data processor’ adds nothing more than a gen- of whether the asserted claims are patentable under eral purpose computer that is associated with the steps of the § 101 is a question of law, the Federal Circuit has ac- process in an unspecified manner ’’); Ex parte Nawathe, No 
2007-3360, 2009 WL 327520, *4 (B P A I  Feb  9, 2009) (finding‘‘the computerized recitation purports to a general purpose processor [], as opposed to a particular computer specifically programmed for executing the steps of the claimed method ’’); and Ex parte Cornea-Hasegan, No  2008-4742 at 9-10 (B P A I 
Jan  13, 2009) (indicating the appellant does not dispute ‘‘the recitation of a processor does not limit the process steps to any 44 Claims having this format are called ‘‘Beauregard’’ specific machine or apparatus ’’)  The court also cited Cyber- claims and were found to not be barred by the traditional source Corp  v  Retail Decisions Inc , (discussed below), in sup- printed matter rule in In re Beauregard, 53 F 3d 1583, 1584, 35 port of its interpretation of the required ‘‘particular machine ’’ 37 620 F  Supp  2d 1068, 92 USPQ2d 1011 (N D  Cal  2009) 47 2009 WL 1070801 (U S I T C  2009) 
knowledged that ‘there may be cases in which the legal given a dataset of feature vectors associated with the question as to patentable subject matter may turn on subsidiary factual issues’ ’’ (citation omitted)  In con- for each binary partition under consideration, rank- struing the claims, the tribunal found that there was a ing features using two-category feature ranking; and genuine dispute as to whether the claimed ‘‘devices’’represented a ‘‘particular machine’’ under the Bilski while the predetermined number of features has not test and whether the claimed ‘‘two-dimensional rota- yet been selected: picking a binary partition p; tional transform’’ was merely a mathematical calcula- selecting a feature based on the ranking for binary tion or instead meant ‘‘changing the mathematical rep- resentation of a two-dimensional quantity from oneframe of reference to a differently-oriented frame of ref- adding the selected feature to an output list if not al- erence’’ as asserted by the patentee  Additionally, the ready present in the output list and removing the se- dispute over the meaning of the claimed ‘‘two- lected feature from further consideration for the bi- dimensional rotational transform’’ also raised a dis- puted issue as to whether this element recited a trans- Notably, while the independent claim failed the formation that would qualify under the ‘‘transforma- machine-or-transformation test, its dependent claim tion’’ prong of Bilski  Given these disputed issues, the was eligible because it recited, ‘‘further comprising us- ITC concluded that it was inappropriate to grant sum- ing the selected features in training a classifier for clas- mary judgment as to the patent eligibility of the claims 
sifying data into categories ’’ In view of the specifica- A similar conclusion was reached in Versata Soft- tion, the board indicated that the ‘‘classifier’’ was a par- ware Inc  v  Sun Microsystems Inc ,48 in which the dis- ticular machine ‘‘in that it performs a particular data trict court denied the defendant’s motion for summary classification function that is beyond mere general pur- judgment of invalidity under Section 101 based upon pose computing ’’53 The board also concluded that the the Bilski court’s refusal ‘‘to adopt a broad exclusion claim ‘‘transforms a particular article into a different over software or any other such category of subject state or thing, namely by transforming an untrained matter beyond the exclusion of claims drawn to funda- classifier into a trained classifier ’’54 In Ex parte Casati,55 the board reversed the examin- Less stringent ‘‘machine’’ prong analyses are also er’s Section 101 rejection of a method claim reciting: found at the board level  For example, in Ex parteSchrader,50 the board held patent-eligible under Bilski A method of analyzing data and making predictions, reading process execution data from logs for a busi- A method for obtaining feedback from consumers re- ceiving an advertisement from an ad provided by anad provider through an interactive channel, the collecting the process execution data and storing the process execution data in a memory defining a ware-house; creating a feedback panel including at least one feed-back response concerning said advertisement; and analyzing the process execution data; generatingprediction models in response to the analyzing; and providing said feedback panel to said consumers, using the prediction models to predict an occurrence said feedback panel being activated by a consumer to of an exception in the business process 
provide said feedback response concerning said ad-vertisement to said ad provider through said interac- In this case, giving consideration to the specification, which ‘‘unequivocally describes the data warehouse aspart of the overall system apparatus, and subsequent Here, the board found ‘‘interactive channel’’ to be descriptions describe the memory/warehouse device in part of an ‘‘overall patent eligible system of appara- terms of machine executable functions,’’ the board con- tuses’’ when viewed in the context of the specification, cluded that ‘‘one of ordinary skill in the art would un- which included ‘‘the Internet and World Wide Web, In- derstand that the claimed storing of process execution teractive Television, and self service devices, such as In- data in a memory defining a warehouse constitutes formation Kiosks and Automated Teller Machines ’’51 patent-eligible subject matter under § 101 because the In another recent decision, Ex parte Forman,52 the memory/warehouse element ties the claims to a particu- board found a ‘‘computer-implemented feature selec- tion method’’ including a ‘‘classifier’’ eligible under Other recent board decisions have reached the oppo- Section 101 because it satisfied both the machine and transformation prong  Here, the ‘‘classifier’’ was recitedin a dependent claim, in which its independent claim re-cited: 53 Id  at 13 
54 Id  See also Ex parte Busche, No  2008-004750 (B P A I 
A computer-implemented feature selection method May 28, 2009) (holding a process claim and a computer pro- for selecting a predetermined number of features for gram product claim, each reciting training a machine, ‘‘are di- a set of binary partitions over a set of categories rected to machines that have such structure as may be adaptedby training



ground_truth: Yes, DUO has 9.57 instances per image while COCO contains 7.7.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the future direction mentioned in the conclusion?

Read the following text and answer briefly.

Context 3:  So each group has the information about the relevance of the hospitals  The reason for employing a grouping strategy is described in Appendix A 
c) Disease: Two types of disease information exist in our EHRs: disease names and disease codes called ICD-10   We did not use any disease names in the inputs for our experiment  Instead, we encoded diseases with the first three letters of the ICD-10 code, because they represent well the higher level concept 
The initial three letters of the ICD-10 codes are arranged in the order of an alphabetic letter, a digit, and a digit, so there are a total of 2,600 ways to encode a disease  In our data, some ICD-10 codes were missing, although all disease names were systematically obtained from the EHR system  For such cases, we converted the disease names into ICD-10 codes using MeCab with the J-MeDic (MANBYO 201905) dictionary 
Also, diseases can be divided into primary and secondary diseases, but we only deal with the primary diseases  d) Length of stay: The length of stay can be obtained mechanically from the EHR system and the maximum value was set to 1,000 days  We set |M | for vanilla, hospital, physician, disease, and length of stay to 1, 5, 485, 2,600, and 1,000, respectively  
The vanilla embedding is prepared for the baseline in our experiment and to equalize the total number of parameters with the other models  The input to our model is the sum of E T , E P and E M   We also prepare an extra model with all features for our experiments  This takes all four feature embeddings (hospital, physician, disease, and length of stay) added to the encoder 

Datasets and Metrics

We evaluated our proposed method on a subset of data from National Hospital Organization (NHO), the largest multiinstitutional organization in Japan  The statistics of our data are shown in Table  , which includes 24,630 cases collected from five hospitals  Each case includes a discharge summary and progress notes for the days of stay 
The data are randomly split into 22,630, 1,000, and 1,000 for train, validation, and test, respectively  Summarization performances are reported in ROUGE-1, ROUGE-2, ROUGE-L and BERTScore in terms of F1  In addition, we also employed BLEURT , which models human judgment 

Architectures and Hyperparameters

Due to our hardware constraints we need a model that is computationally efficient, so we employed the Longformer instead of the conventional transformer  Longformer can    In our model, number of layers, window size, dilation, input sequence length, output sequence length, batch size, learning rate and number of warmup steps are 8, 256, 1, 1024, 256, 4, 3e-5 and 1K, respectively 
Other hyperparameters are the same as in the original Longformer, except for the maximum number of epochs is not fixed and the best epoch  It is selected for each training using the validation data based on ROUGE-1  Also, the original Longformer imports pretrained-BART parameters to initial values, but we do not use pre-trained Japanese BART in this study 
We used three GeForce RTX 2080 TI for our experiments  Our vocabulary for preparing input to Longformer is taken from UTH-BERT , which is pre-trained on the Japanese clinical records  Since the vocabulary of UTH-BERT is trained by WordPiece , we also tokenize our data with WordPiece  However, the vocabulary does not include white space and line breaks, which cannot be handled, so we add those two tokens to the vocabulary, resulting in a total size of 25,002 
The vocabulary has all tokens in full characters, so we normalized full-wdith characters by converting all alphanumeric and symbolic characters to half-width for byte fallback  , we found that all the models with encoded medical meta-information perform better in ROUGE-1, ROUGE-L and BLEURT than the vanilla Longformer 
However, in BERTScore, only hospital and disease models outperform the vanilla  Specifically, disease information is most effective, improving ROUGE-1, ROUGE-2, ROUGE-L, BERTScore and BLEURT by 4 45, 0 73, 3 12, 3 77 and 0 21 points over the vanilla model, respectively  This seems to be because disease information and the ICD-10 ontology efficiently cluster groups with similar representations 
In contrast, in ROUGE-2 and ROUGE-L, the model with physician embedding is inferior to the vanilla model  This seems to be a negative effect of grouping physicians without any consideration of their relevance  It would be better to cluster them by department, physician attributes, similarity of progress notes, etc  Regarding low ROUGE-2 scores in all models, a previous study using the English data set also reported a low ROUGE-2 score of about 5%, which may indicate an inherent difficulty in discharge summary generation 
In BERTScore, the models with the physician and the length of stay did not reach the performance of the vanilla model, suggesting that the system's outputs are semantically inferior  The model with all features performed the lowest of all models in BERTScore  The reason for the low score of the model with all features seems to be that its number of parameters in feature embedding was four times larger than that of the model with the individual feature, and the amount of training data was insufficient 
In BLEURT, all models with meta-information outperform vanilla, which suggests that they are more natural to humans  To analyze the influence of encoded meta-information on the outputs, we evaluate the precisions of the generated text  Specifically, we measure the probability that the generated words are included in the gold summary to investigate if the proper words are generated 
Some previous studies on faithfulness, which also analyze the output of summarization, have employed words or entities -   In this study, we focused on words, not entities, because we wanted to visualize expressions that are not only nouns  The words were segmented by MeCab with the J-MeDic  For each segmented word, the numeral and symbol labels were assigned as parts of speech by MeCab, the morphological analyzer, while the disease and symptom were assigned by the J-Medic dictionary 
The results, shown in Figure , indicate that the encoded disease information leads to generate more proper disease and symptom words  This indicates that the meta-information successfully learns disease-related expressions  The encoded hospital or physician information also improved the precision of symbols generation 
This suggests that different hospitals and physicians have different description habits (e g , bullet points such as "•", "*" and "-", punctuation such as "。" and " ", etc ), which can be grouped by meta-information  In this paper, we conducted a discharge summary generation experiment by adding four types of information to Longformer and verified the impact of the meta-information 
The results showed that all four types of information exceeded the performance of the vanilla Longformer model, with the highest performance achieved by encoding disease information  We found that meta-information is useful for abstractive summarization on discharge summaries  Our limitations are that we used Japanese EHR, the limited number of tested features and not performing human evaluations 
As for the efficacy of the meta-information, we believe that our results are applicable to non-Japanese, but it is left as Fig    The precisions of words in the generated summaries  The vertical axis shows the probability that the words exist in the gold summary  a future work  Other meta-information may be worth verifying such as the patient's gender, age, race, religion and used EHR system, etc 
It is hard to collect a large amount of medical information and process it into meta-information, so we may need to develop a robust and flexible research infrastructure to conduct a more large scale cross-sectional study in the future  In the discharge summary generation task, which demands a high level of expertise, the human evaluation requires a lot of physicians' efforts and it is a very high cost which is unrealistic 
This is a general issue in tasks dealing with medical documents, and this study also could not perform human evaluations  On this research, informed consent and patient privacy are ensured in the following manner  Notices about their policy and the EHR data usage are posted at the hospitals  The patients who disagree with the policies can request opt-out and are excluded from the archive 
In case of minors and their parents, followed the same manner  In the case of minors and their parents are same  To conduct a research on the archive, researchers must submit their research proposals to the institutional review board  After the proposal is approved, the data is anonymized to build a dataset for analysis 
The data is accessible only in a secured room at the NHO headquarters, and only statistics are brought out of the secured room, for protection of patients' privacy  In the present research, the analysis was conducted under the IRB approval (IRB Approval No : Wako3 2019-22) of the Institute of Physical and Chemical Research (RIKEN), Japan, which has a collaboration agreement with the National Hospital Organization 
This data is not publicly available due to privacy restrictions  shows the detailed number of cases handled by physicians  In all hospitals, there is a large difference between the median and the maximum of cases/physician  This indicates that a few physicians handle a large number of cases and many physicians handle fewer cases 
It is impossible to avoid physician IDs first seen at test time without some process that averages the number of cases a physician holds 



ground_truth: Verifying other meta-information such as patient's gender, age, race, etc.