dataesr/scientific-paragraphs-categorization huggingface dataset:
The method was validated following a leave-one-out approach. To generate the sCT a , the first step was to register all the MRIs in the database to the test subject's MRI. A robust affine registration  [18]  was used followed by a non-rigid cubic B-Spline registration using normalized mutual information as similarity measure, as implemented in NiftyReg 2. The robust affine step guarantees that each atlas MR image is well aligned with the test subject despite the large differences in the FOV size and location that were observed between the subjects for both anatomical sites. The transformations were then applied to map the atlas CTs to the test subject MRI. The sCT a was finally obtained by fusing the mapped atlases according to their local similarity to the test subject using a spatial-varying weighted averaging  [18].
An iterative process was then used to improve the synthesis  [18]. First, the initial sCT a obtained as described above was combined with the test subject MR image(s). Then, all the CT-MR image sets in the atlas database were aligned to the sCT a -MR image set using a multi-channel non-rigid registration. The refined sCT a was finally obtained by fusing the registered atlas according to a similarity measure computed between the sCT a -MR and mapped CT-MR sets. Combining multiple modalities (MRI and CT) at both the registration and image similarity stages is expected to provide more realistic mappings and improve the local selection of atlases, especially in low contrast areas. As the final step, the sCT a was aligned and resampled to the original planning CT space using the inverse non-rigid transformation mapping the test subject's CT and MR images, as the registration algorithm chosen is symmetric.
Building a reliable atlas database is a pre-requisite to guarantee the good performance of this atlas-based approach. CT and MR images need to be acquired for a number of patients on the same day, under treatment position and using the same fixation devices. Ideally, all data should be collected using the same MR sequences and scanner, as MR intensities are highly dependent on the equipment. However, establishing scanner-specific atlases would be challenging or even unpractical considering the clinical reality. By testing our approach using data from different scanners, as for the H&N patients, we demonstrated the robustness of our method to these differences. Nevertheless, using as atlases images of patients acquired with the same sequence and on the same scanner as the test patient would improve the results.
Acknowledgments This work was undertaken in The  Royal Marsden NHS Foundation Trust  which received a proportion of its funding from the  NHS Executive ; we acknowledge NHS funding to the  NIHR Biomedical Research Centre. The views expressed in this publication are those of the authors and not necessarily those of the  NHS Executive. This work was supported by the  Institute of Cancer Research (ICR) London , and  Cancer Research UK [CRUK ] grant number  C33589/ A19727  to the  ICR Division of Radiotherapy & Imaging. We also acknowledge funding from  CRUK   Programme Grant   A13407/ C7224. Also, funding was received from the  CMIC-EPSRC  platform grant ( EP/M020533/1 ), the  CRUK ART-NET  grant ( A21993 ), the  EPSRC  ( EP/H046410/1 ,  EP/K005278 ) and the  NIHR BRC UCLH/UCL High Impact Initiative-BW.
2009 , Wood et al. 2011). For example, some seals may leave US waters to move into Canada, whereas others may move from Canada into US waters  (Mohn and Bowen 1996 , Bowen et al. 2007 , Breed et al. 2009 , Wood et al. 2011). Therefore, to estimate seasonal regional abundances, detailed information about movement, behavior, and local abundances is needed. For example, seasonal estimates of abundance of gray seals in US waters might be combined using counts of animals hauled out, which are then corrected for the proportion of animals at sea at the time the count is made (e. g. ,  Lonergan et al. 2011). Here, we use opportunistic Earth observation imagery data to survey gray seal abundance in the coastal habitats of southeastern Massachusetts. Multiple surveys across multiple years were conducted using single-day synoptic imagery archived and publicly available in Google Earth, tallying the abundance of seals visible on coastal beaches.
For all images, seal haul outs and aggregations were identified visually and individuals counted by loading screen image captures into Logger Pro (Vernier Software and Technology, www. vernier. com/products/software/lp; see figure  2  for example image of haul out). Using software capabilities to mark and tally individuals within the image, survey counts were conducted for each image to produce a total number of seals at each haul out. The initial counts of dense haul outs, in which seals were tightly packed, were counted by three separate individuals to assess any observer bias arising from densely packed targets on images. The haul out counts were conducted individually by trained observers after multiple surveys of the same image revealed an acceptably low degree of observer bias (n = 5 image comparison, mean count in an image = 435.
e. , observable) during a low-tide survey  (Lonergan et al. 2011). Haul out data were collected from eight GSM or GPRS telemetry tags (Sea Mammal Research Unit, St. Andrews, Scotland) deployed on gray seals (n = 4 males, including 1 juvenile; n = 4 females, including 1 juvenile) caught in June 2013 at a local haul out near Chatham, Massachusetts, and attached with quicksetting two-part epoxy (note that one tag was deployed opportunistically on a juvenile in September 2012). In addition to monitoring movement, behavior, and local water conditions for the tag duration (range: 2-9 months), telemetry devices recorded haul out events as periods when the tag was continuously dry for more than 10 minutes until the tag was resubmerged for 40 seconds. A total of 8155. 4 hours of relevant haul out behavior were examined, excluding any records occurring at sea or away from land.
Digital survey results Over 4 years, Google Earth imagery of adequate resolution was available for surveys on three dates, all at least 1 year apart. The data from these surveys are presented in table 1, including the date of image capture, the approximate time of day and tidal cycle, the total number of seals counted, and the estimated number of gray seals corrected for interspecific mixing. The survey counts show a range of pinniped abundances (table 1), with two surveys (2012 and 2015 counts of 15,331 and 20,554 total seals, respectively) falling within the range of two maximum counts reported officially by the NOAA and National Marine Fisheries Service (NMFS): a March 2011 single-day maximum count of 15,756 gray seals  (NOAA and NMFS 2016)  and the most recent April 2016 molting survey of nearly 25,000 gray seals  (NOAA and NMFS 2016).
In our case, we employed a purposefully conservative modeling approach to insulate estimates from potential errors until such comparative analyses with targeted imagery could be made. Future applications, however, can capitalize on commercial databases that feature greater sampling frequency and more temporal targeting of imagery to ensure surveys match sensible time frames, preferred survey seasons, and environmental conditions that affect detectability  (Simpkins et al. 2003 , McMahon et al. 2014). For additional costs, opportunities to schedule commercial imagery acquisition further strengthens this method to develop robust and precise population estimates  (McMahon et al. 2014 ). Decisions about prioritizing species for commercial image acquisition should be guided by the species of interest, its management status, and requirements of precision and accuracy (e.
Acknowledgments We would like to thank generous support from  International Fund for Animal Welfare, the Bureau of Ocean Energy , and the  Oak Foundation  for funding support for the telemetry devices. In addition, we thank the  National Marine Fisheries Service  and the  Bureau of Ocean Management  for support to conduct live captures and health assessments of gray seals on Cape Cod. Digital aerial surveys were completed with the generous help of  Abigail Machernis ,  Sean Stanton ,  Lydia Utley ,  Logan Pallin ,  Susan Heaslip , and  Heather Heenehan. The manuscript was also much improved with helpful reviews from  Michael Moore  and  Elizabeth Josephson. Finally, we would like to acknowledge the insightful review and helpful comments from  Dr. Michael Lonergan  and an anonymous reviewer that greatly improved this article.
Stock prices The first dataset presents the daily closing prices of the stocks of the potash mining and exploration companies and their market capitalization in local currencies. We retrieved the closing prices from the stock market indices of the exchanges where the companies were listed. Using the closing data, we calculate logarithmic returns for each company and the exchange indices, such as rt = log(pt/pt-1)x100, where pt and pt-1 represent the closing price at time t and t-1, respectively. If a company was listed on two or more exchanges, we use only the information on the stock prices and main stock index from the country where the company is headquartered and/or incorporated. We obtained all the data from Bloomberg. The companies are divided into two categories: producers and greenfield firms.
Disasters A potash mining disaster may simply be defined as an accident that occurs in the process of mining potash above or beneath the surface of the earth. There are various causes for potash mining accidents, including collapsing of mine stopes, flooding, followed often by earthquakes, leaks of poisonous gases, or consequences from incorrectly used or malfunctioning mining equipment. There is no publicly available list of potash mining disasters that discloses the type of each disaster and the name of the company affected. Hence, to identify the disasters, we created a database by using the software Factiva, Bloomberg, and the Google news search engine. The Factiva software covers all major newspapers and publications in the world, including dailies such as the Wall Street Journal or the Financial Times.
INTRODUCTION Once Android has became the most popular mobile platform, it has also became the mobile operating system most heavily targeted by malware. The main goal of these malware is to make money thanks to the user and unbeknownst to the user. In the same time, app store providers try to prevent malicious apps from entering official market or to remove them once entered. For that purpose, these providers can resort to automated security analysis or manual reviews of security experts. The automated approaches are mainly based on static program analysis that tries to statically detect if applications are responsible of sending messages to premium services, or exfiltrating personal private data. In 2012, Zhou et al. claim that current marketplaces are functional and relatively healthy since they have discovered less than 0.
We believe that any malware will succeed to defeat classical program analysis by relying on obfuscation, dynamic code loading or ciphering. We claim here that trying to clean the market by resorting only to classical static program analysis is a lost cause. In this paper, we propose a different approach that aims to explore first how a benign Android application and its created processes should interact with other elements of the operating system. The originality of this approach is that we do not care about the nature of the executed code but we focus on how applications disseminate information in the operating sys-tem during one execution. More precisely, we study which operations a non-malicious application is able to perform: we translate these operations in terms of information flows between files, sockets and processes of the operating system.
We explain each information flow and this way, we build a generic graph of all allowed information flows in the operating system due to the execution of an Android application. We put this generic graph to the proof: we executed more than 3206 applications attested as benign by anti-virus software used by Virustotal 2. We verify that these benign executions are conformed to the expected normal pattern. Finally, we claim that an attack of a sophisticated malware induces behaviors that deviate from the legitimate one, from the operating system point of view. To evaluate the effectiveness of our approach, we led experiments on nine sophisticated malware such as rootkits, bootkits, and ransomware extracted from the Kharon dataset  (Kiss et al. , 2016). We show that these experiments easily spot malware since they exhibit graphs out of the expected pattern.
In the rest of this article, after reviewing the state of the art in Section 2, we develop this idea and we introduce in Section 3 how an Android application interacts with the system environment. We represent all existing information flows between files, sockets and processes in the Android system generated by the executed application under review. We also show, using two examples, the difference between a benign application and a malware. Then, Section 4 introduces formally the characterization model of a normal application execution. Finally, Section 5 verifies the accuracy of the approach on a dataset of 3206 goodware and Section 6 evaluates the efficiency of the approach on a collection of malware.
All the proposals use very similar features. Many papers use tainting techniques  (Enck et al. , 2010) , similarly to this paper, but they mainly use it to monitor access to private data. Without a real user in charge of running applications manually, it is a challenge to execute correctly a malware and observe it. Recent approaches have proposed solutions to target the suspicious code and then to stimulate the executed malware. In  (Zheng et al. , 2012) , a static analysis of the bytecode helps to trigger automatically the applications' UI. In  (Abraham et al. , 2015) , authors proposed to modify the application control flow to reach the suspicious code. In  (Wong and Lie, 2016) , authors compute the set of inputs that trigger the suspicious code using static analysis. All these techniques help to automatize the execution of Android malware.
apk. The installation process first consists in decompressing this archive. Usually, it contains the compiled code of the application (classes. dex) and a file AndroidManifest. xml that describes the application (activities, permissions,. ). Lastly, an. apk archive also contains all resources (i. e. images,. xml config files, databases) needed by the application. To build our detection methodology, we need to describe how an Android application contaminates the operating system during its execution. In particular, we do not care about the nature of the executed code but we are more interested to discover which files have been accessed, which processes were created, which IP addresses the application has contacted, and recursively how these contaminated objects have themselves influenced the operating system.
For this purpose, we propose to capture all the information flows inside the operating system caused by the execution of an application. We represent these information flows on a particular graph called System Flow Graph (or SFG for short)  (Andriatsimandefitra and Viet Triem Tong, 2014). Examples of SFG issued from malware observations and used latter in this paper can be found in the Kharon dataset  (Kiss et al. , 2016)  available at kharon. gforge. inria. fr/ dataset. Formally, a SFG is a pair (V , E) where V is a set of vertices and E is a set of edges. A vertex v in V denotes objects that contain information in the operating system as files, sockets or processes. An edge between two vertices denotes an information flow from a source to a destination. In practice, we construct these SFGs using An-droBlare, an information flow monitor at the operating system level combined with GroddDroid  (Abraham et al.
, 2015) , a framework to automatically execute applications and force suspicious code execution when needed. AndroBlare attaches a first mark to a particular content of an information container. Afterward, each time a marked object accesses a non marked one, the mark is propagated to the non marked object. The mark transits in the system through extended attributes of files and on processes. Each observed flow is logged and the whole log is transformed into a SFG. GroddDroid  (Abraham et al. , 2015)  automatizes the execution of applications by stimulating the graphical interface and monitors the execution. In the remainder of this paper, the presented graphs are computed from observing a single execu-tion of an Android application where only one piece of information is initially marked: the APK file that has been downloaded on the device.
In our figure, we learn that the application uses database files (. db), sounds files (. sf2) and a compiled C library (. so). The graph also shows a particular process system server, colored in yellow, which interacts with many other processes. System server is a particular process used to deliver Android services to other applications. This process firstly verifies if the application has the right permission to access the requested service and secondly accesses the service. In a benign application's graph, such as the virtual piano's one, interactions with the objects of the Android operating system can be explained: an application is allowed to create files to make persistent data and can request services through system server. On the contrary, malware graphs present interactions that cannot be explained easily and correspond to abnormal behaviors.
Lastly, we write X → 0. Y to denote that information flows from an object of type X towards an object of type Y may appear one or several times. We also introduce a terminology for objects. As we install a malware using an. apk archive (its code and resources), this archive is the unique source of information that we want to monitor, we introduce the notation Apk for this file. Then, the particular object that corresponds to the process executing the code of the application is denoted App. Objects of the Android operating system are denoted by their system name (as system server, android. browser, media server,. ). Lastly, file (resp. process, socket) is used to denote a variable object of type file (resp. process, socket). Using this terminology, we are now able to formalize an application behavior in terms of information flows.
To do this, we turn back to the Android permissions list. We translate each permission in terms of information flows involved in the operating system. For instance, to access the device camera, an application must declare the CAMERA permission in its manifest. To use it, the developer can use the android. hardware. camera2 package that provides an interface to individual cameras connected to the Android device. The execution of this package's methods implies a verification of the permissions granted to the application and the effective camera access. This access is indeed relayed by the particular process system server. Such an access will induced information flows App → system server → android. camera2 for the access and android. camera2 → system server → App due to the data sent by the camera.
4) Internet or network connection: Android applications are allowed to request access to remote servers if they have the right permission. They can also delegate this access to the web browser. This induce flows of the form App → 0. socket, App → 0. android. browser → 0. socket. 5) Dynamic code loading: an Android application can use a class loader that loads classes from. jar and. apk files. Tod o so, the process dexopt optimizes the archive and deploys the dex output in writable directories. This dynamic code loading is represented by the following flows: App → 0. file → 0. dexopt and file → 0. dexopt → 0. App. These flows are allowed, accountable but not mandatory. For example, on the left side of Figure  1 , we can observe that the application has created a java archive ads-216290356. jar then translated in ads-216290356.
CAN MALWARE MIMIC BENIGN APPS? Before concluding, we study the efficiency of our approach to detect sophisticated malware. As stated before we do not care about simple malware that earn money by calling or sending text messages to premium numbers. Moreover, malware that simply send text messages or call premium numbers exhibit a normal behavior for this approach: they ask for a service for which they have requested the permission. Thus, we focus on more sophisticated malware i. e. those that need to exploit a vulnerability on the phone, make themselves persistent, ransom the user or obey to a remote server. We claim that these malware have a behavior that differs from the previous normal behavior. To evaluate this idea we led experiments on a dataset  (Kiss et al. , 2016)  containing a ransomware (Simplelocker), an aggressive adware (MobiDash), two spyware (Cajino and SaveMe), a rootkit (Mazar), a data eraser (WipeLocker), a bootkit (PoisonCake) and lastly two malware that install undesired applications (Badnews and DroidKungFu).
Data sources The literature on the history, social organization and urbanization of Bobo-Dioulasso was reviewed. Aerial photographs from 1952 to 1987 (Institut Géographique National, in English French National Geographic Institute), and aerial photographs of 1994 (Institut Géographique du Burkina, in English: Burkina Faso Geographical Institute) were used to retrace the city growth in time and space. These photographs were georeferenced and then mosaics were generated for each year. The satellite images used for this study corresponded to: (1) two SPOT 5 images of 2004 and 2007 at high definition acquired in multi-spectral (M) at 10 m and panchromatic (P) mode from the ISIS programme of CNES (the French space agency). For 2004, the spatial resolution for the P mode image was 5 m and for 2007 it was 2.
Methodological approach The methodological approach was developed to meet different objectives: (a) to establish the urban growth and the displacement of the urbanization front by visual interpretation of photographs for the following years  : 1952, 1958, 1964, 1980, 1987, 1994  and of scenes for the following years: 2004, 2007 and 2012; (b) to divide the city in homogeneous spatial units based on the size and the organization of the built environment, the configuration of the road network, and the presence of vegetation by visual interpretation of the scene of 2012; (c) to extract the city's fabric, the vegetation and the road network from the satellite image of 2012 to produce a spatial database that should facilitate the analysis of health disparities (Table  1.
In the case of absence or doubts on the age of the people to be surveyed, the surveyor took an appointment. After three unsuccessful appointments, the household was abandoned and replaced by another household in the random sampling list for that sub-space. All collected and analysed geographical data 2  (aerial photographs, satellite images, geographical coordinates of urban infrastructures) were integrated in a geodatabase with a map projection WGS84 UTM 30 N (Fig. 5 ). In order to observe significant differences between AHU at the 5% threshold with a precision of 2. 6% based on a prevalence of 5% (which was the smallest expected prevalence for diabetes in adults), a sample size of 250 adults and 250 children (and then 250 households) was calculated. The same number of adults and children were chosen in each AHU to allow the comparability between AHU by multivariate analysis.