dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and discussion To illustrate MutSpec functionalities and show an example of analysis workflow, we have analysed a public dataset reporting mutation data on 106 cases of oral squamous cell carcinomas (OSCC) from India  [22]. The aetiology of OSCC is linked to several risk factors, including tobacco smoking, tobacco chewing, alcohol drinking, HPV infection and UV radiations. These risk factors vary between different geographical regions, with tobacco chewing being prevalent in the Indian population while the association with tobacco smoking and alcohol drinking play major roles in Western countries. Tobacco and UV are strong mutagens that create specific types of DNA damage; one can thus expect to identify various mutation signatures reflecting exposure to these mutagens in the selected dataset.
OSCC data retrieval and annotation Somatic mutation data were retrieved from the ICGC data portal (ORCA-IN dataset, downloaded on June 2015). The dataset was available as a tab-delimited text file containing a single list of mutations for the 106 samples. This list was uploaded in a Galaxy history. ICGC formatted files are supported by MutSpec-Annot so no further formatting was needed. The first step was to annotate the file with MutSpec-Annot. Variants in OSCC-IN dataset were mapped to the genome build hg19, we thus selected "hg19" as reference genome. Another option to specify is the length of the sequence context to retrieve; here we chose 1 as we are only interested in the trinucleotide sequence context (one base on each side of the variant base). One output file, OSCC-IN_annotated was thus created and appeared in the history.
We then proceeded to the next step, which is to filter out potential polymorphisms with MutSpec-Filter. We filtered against all databases but dbSNP. Although the data analysed have been curated and thus should have been already filtered, 673 (5 %) variants were removed. Then, we used MutSpec-split to parse the file into individual sample files using the sample ID column. MutSpec-split automatically creates a collection of files, so we obtained a dataset collection of 106 files.
Mutation spectra in OSCC The dataset collection created by MutSpec-Split was then used as input for MutSpec-Stat to generate statistics on mutation spectra for each sample and to compute the mutation matrix to be used for extracting mutation signatures. We ran the tool with the "pool sample" option in order to obtain statistics for the pooled samples. The reference genome should be specified again at this step. Finally, we also selected the option that calculates statistics for estimating the number of signatures present in the dataset. A summary of the results are shown in Fig. 2  for the sample pool (see detailed results in Additional file 1). The overall mutation pattern shows that the majority of variants are non-synonymous SBS (Fig. 2a ), and that the most frequent SBS types are C:G > A:T followed by C:G > T:A (Fig.
2b ). The trinucleotide sequence context distribution of these mutations show specific patterns, with C > A occurring preferentially within 5'-GCN-3' motifs and C > T within CpG sites (Fig. 2c ). The third most frequent SBS are C > G. Both C > G and C > T occur preferentially within 5'-TCN-3' motifs, suggesting the presence of APOBEC-induced mutations  [23]. Based on the cophenic and rss statistics calculated for estimating the NMF factorization value, 4 signatures may be present in this dataset as it is the first value for which the cophenetic coefficient starts decreasing and where the rss curve presents an inflection point (Fig. 2d ).
Extraction and identification of mutation signatures in oral cancers To analyse mutation signatures present in the dataset, we ran MutSpec-NMF using the output report of MutSpec-Stat and factorisation value was set to 4. We then compared the obtained signatures with published signatures using the tool MutSpec-Compare. Fig. 3a  shows the 4 signatures obtained. Signature A matched best with signature 1 (Age), signature B with signatures 29 (tobacco chewing) and 24 (aflatoxin), signature C with signature 7 (UV) and signature D with signature 13 (APOBEC) (Fig. 3b ). MutSpec-NMF also produces a graph showing the total number of SBS per sample and the proportion contributing to the 4 signatures (Fig. 3c ). On this graph, one sample is standing out with the largest number of SBS and a close to 100 % contribution to the UV signature (sig 7).
There is no limit in the number of samples or mutation per sample that can be analysed. However, the capacity to open Excel files with large number of datasheets (over 500) will depend on user's computer settings and performance (ie, a file with 530 samples takes 18 s to open up on a computer with 2 GB of RAM). This limitation can be overcome by adapting the design of the analysis workflow to limit the number of samples included in one Excel file. All graphical outputs generated by the tools can be downloaded as high resolution images suitable for publication (300 dpi). Mut-Spec toolbox is well documented with short descriptions of input and output formats and options for each tool. While the methods for defining and extracting mutation signatures implemented in MutSpec correspond to current standards well accepted in the field, we will provide package updates and upgrades reflecting progresses in the field, such as new format or definition for mutation signatures.
Reasonable deployment cost. We propose that NRR could be deployed in as a software plug-in regional computers of SCATS, a system already in use in 27 countries and over 37,000 intersections. Additionally, Vehicle-to-Infrastructure (V2I) communication module needs to be added in NRR. This update solution is feasible and practical due to high similarity between the protocol of V2I (IEEE 802. 11p) and existing Wi-Fi  [26]. Efficient MAS architecture. In our novel Multi-agent System (MAS) design, for each intersection, traffic lights and outgoing roads represent an intelligent agent. Compared to other vehicle based MAS solutions  [9] -  [12]  which heavily rely on Vehicle-to-Vehicle (V2V) communication, and regionbased MAS solutions  [13]  which need an impractical time to converge iteratively, our MAS architecture is not only much easier to deploy on the existing infrastructure, but can also coordinate agents by making use of the natural traffic propagation without incurring excessive computation and storage cost.
2) Testing Map and Traffic: The evaluation of NRR is carried out in both realistic and synthetic scenarios. A sub-set of TAPASCologne 0. 17. 0  [16]  is chosen as a realistic evaluation scenario for NRR. TAPASCologne is an open source project providing a large-scale dataset with the highest realism for urban vehicular simulation based on SUMO. It uses a realistic map of Cologne extracted from OpenStreetMap  [18]  and generates traffic demand from 6:00am to 8:00am using Travel and Activity PAtterns Simulation (TAPAS) methodology  [27]  and Gawron's traffic assignment algorithm  [20]. A subset only of this map is used in our evaluation because the original size of TAPASCologne is too large (1129. 71 km 2 ) to investigate the impact of a single closed road. The chosen sub map is a 3. 69 km 2 large area located on the west of the river in the Cologne city center.
Contribution Our contribution is two-fold. First, we propose ControllAR, a novel approach that facilitates the appropriation of rich visual feedback on control surfaces by remixing graphical user interfaces. We describe its implementation and provide insights on key elements of the software pipeline. Second, we use ControllAR to investigate users practices during a study on digital musical instruments. Based on the results, we provide insights and guidelines to inform the design of applications and hardware that would help appropriation of visual feedback.
CONTROLLAR Contrary to existing controllers for which the choice of content and spatial organization of visual feedback is limited, ControllAR allows users to select and place freely graphical elements on any controller. It relies on the fact that the GUIs of multimedia software applications, designed for mouse and keyboard interaction, provide rich and high resolution feedback on the manipulated content. With ControllAR, cuts can be made in these GUIs and placed in zones directly on the controller with either a projector or an optical combiner. This design process is detailed in Figure  3. However, our approach requires a series of steps to ensure that the GUI content can be transformed and displayed as visual feedback on the controllers.
Capturing application GUIs ControllAR runs on the Mac OS X, GNU/Linux and Microsoft Windows operating systems (OS). Lists of opened application windows and arrays of pixels for each of them can be accessed through the respective system graphics layers and software libraries: CoreGraphics, Xlib, GDI. These three OS feature compositing window managers. In order to allow for visual effects on the windows (such as transparency, blur, live preview. ), the window managers render all windows to offscreen buffers. It is therefore possible to access the pixels of windows even when they are not visible and combine cuts from multiple GUIs. This proves to be useful for software applications that rely on multiple windows (e. g. plugins, video preview) which are not always all visible at once. As shown in Figure  4 , in ControllAR a dedicated CutWindow manages the pixels array of each currently accessed OS window.
Adaptation to changes in GUIs Within each application window the arrangement and selection of visual content may change during the interaction. For example, in music production / performance software such as Renoise, a section of the GUI is often dedicated to effects and parameters of the selected audio track. Whenever the selected track changes, all the graphical elements are replaced by the ones associated with the new track. Therefore, the cut displayed by a zone might be hidden after the change. A key element of our appropriation pipeline is the detection and management of these changes in GUIs. To that extent, Con-trollAR allows for the creation of multiple scenes for each zone. Each scene can be associated to a state of the GUI and to a set of properties of the zone, such as the cut position and size.
GUIs provide a great variety of graphical elements, which can all be used as visual feedback on the control surfaces. As depicted in Figure  5. E, cuts can be : static (e. g. text labels) or dynamic (e. g. VU meter); input only (e. g. buttons), output only, or input and output (e. g. video clips in visual performance software); internal to the application or external (e. g. from a live camera filming a stage). On the contrary to most research on GUI remixing, in ControllAR this content needs to be transformed to match the sensors on which users want to add visual feedback. We draw inspiration from the Transmogrification project  [2]  and propose a set of transformations that correspond to the shapes of sensors commonly found on hardware controllers. As depicted in Figure  5 , a filled square cut of a VU meter that fits a rubber pad (5.
During the exploration, each participant created between one and twelve zones (mean=5. 4, sd=3. 7). The results suggest that the visual feedback both currently used by participants and explored during the study with Control-lAR originates from three specific sections of the software application: the mappings layer, application parameters and application processes/content. Figure  7  illustrates these three sections with examples taken from the study and indicates their origin in the application. Our results suggest that for each of these categories, feedback can be improved by the use of ControllAR. Although this study was conducted for DMIs, we believe these categories may also apply to other applications. Mappings feedback reveals the mappings section of the application, that is the set of connections between sensors and application parameters.
CONCLUSION We presented ControllAR, a novel system for the appropriation of visual feedback on control surfaces for multimedia software applications. We also provided an analysis of the current and potential appropriation possibilities leading to guidelines for design. A first perspective for this work is the study of the long-term impact of ControllAR on how musicians design and play their instrument. Another perspective is the extension to handheld / gestural controllers. In fact, many users now combine traditional sensors with gestural sensing (i. e. distance, 3D position, poses, gestures) which provide even less visual feedback on their impact on application parameters. Tracking these controllers or body parts and projecting correctly transformed cuts on them could lead to better integration in multimedia production systems.
8m MOA-II telescope at the Mt. John Observatory in New Zealand  (Sumi et al. 2003) , and designated as MOA-2015-BLG-292. Observations were in the "MOA-Red" filter (a wide R/I filter), with a cadence of 1-5 times per night. The MOA data were reduced using their routine DIA procedure  (Bond et al. 2001). The Spitzer team selected and announced OGLE-2015-BLG-1319 as a Spitzer target on June 25 UT 2:00 (HJD'=7198. 58) and aggressively alerted it as being an extreme high-magnification event in the following days. Based on these alerts, sustained follow-up observations were carried out. First, the RoboNet team observed the event using 5 telescopes from the Las Cumbres Observatory Global Telescope (LCOGT) in Chile, South Africa, and Australia. These observations were designed to increase the planet sensitivity by obtaining continuous coverage of the entire peak region.
35m telescope at the Possum Observatory in New Zealand and by the µFUN 0. 3m Perth Exoplanet Survey Telescope (PEST) in Australia, both with a "clear" filter. These observations densely cover the first bump. Finally, the event was also observed by the µFUN 1. 3m SMARTS telescope at CTIO, with ANDICAM, giving simultaneous I band and H band measurements (a few additional V band observations were taken). This multi-filter imaging was important for the source characterization, complementing the OGLE observations. All µFUN data were reduced using DoPhot  (Schechter et al. 1993). In summary, the results reported here depend overwhelmingly on follow-up data, both to cover the anomaly and for color information of the source, which makes it possible to both detect and characterize the lens companion.
9 (see definition in  Yee et al. 2015). Nevertheless, the team selected the event subjectively due to its predicted high magnification, immediately announced it as a Spitzer target, and urged the follow-up teams to monitor the event in order to have high planet sensitivity. OGLE-2015-BLG-1319 was observed by Spitzer on the final two weeks of the 2015 campaign (July 3-19), with a cadence of 1-2 times per day, and with each epoch composed of six 30-second dithered exposures. The observations covered the peak of the event as seen from Spitzer, due to the microlens parallax, when the ground-based light curve was already almost back to baseline. The event was indeed faint in Spitzer images, reaching L eff = 15. 9, fainter than the assumed sensitivity limit. However, the data were reduced using the new algorithm for Spitzer photometry in crowded fields  (Calchi Novati et al.
The smooth curved interval between the two bumps seen in OGLE-2015-BLG-1319 thus clearly favors the binary solution. A standard binary-lens microlensing model requires seven parameters to calculate the magnification as a function of time, A(t). In addition to the point-lens parameters, (t 0 , u 0 , t E ), and the scaled finite source size, ρ = θ * /θ E (where θ * is the angular source size), the companion introduces three parameters. These are the mass ratio between the companion and the primary, q, their scaled, instantaneous projected separation in units of the angular Einstein radius, s, and an angle, α, measured counter-clockwise from the source trajectory to the companion in the lens plane. For a given model geometry, two flux parameters are assigned for each dataset, i, accounting for the source flux, which is being magnified, f s,i , and any additional blend flux, f b,i : f i (t) = f s,i A(t) + f b,i.
It is important to determine the source (V -I) color for two reasons. First, this quantity enters into the measurement of the angular source size θ *. Second, it is needed to estimate the I -L Spitzer source color, via a V IL color-color diagram derived from nearby field stars. A standard way to determine the instrumental (V -I) s color is from regression of V versus I flux as the source magnification changes. We apply this technique to OGLE data and find (V -I) s,ogle = 1. 87±0. 07, from which we derive (V -I) s,0 = 1. 00±0. 07 by correcting to the clump offset found above. Note that we must also account for the fact that the OGLE instrumental (V -I) is a factor of 1. 09 larger than standard. This measurement suffers from two separate potential problems. First, the error is relatively large.
Triple-location observations, such as conducted for OGLE-2015-BLG-1319, are planned with Spitzer and Kepler, monitoring events in the K2 C9 fields during the last 10 days of the Kepler campaign, when both satellites can observe the bulge (see discussion in Calchi Novati & Scarpetta 2015). For the first time, Swif t was used to observe a microlensing event and was able to detect the event while it was magnified. The unique target-of-opportunity override capability of the Swif t spacecraft is, by design, ideal for the observation of transient variables of all kinds and in particular for microlensing. However, the microlens parallax could not be measured with Swif t data for this event. From our preliminary study of Swif t's ability to detect the microlens parallax signal we find that it is sensitive to nearby BDs and free-floating planets in high magnification events (A > 1000).
The  OGLE  project has received funding from the  National Science Centre, Poland , grant  MAESTRO 2014/14/A/ST9/00121  to  AU. OGLE Team  thanks  Profs. M. Kubiak  and  G. Pietrzyński , former members of the OGLE team, for their contribution to the collection of the OGLE photometric data over the past years. TS acknowledges the financial support from the  JSPS ,  JSPS23103002 , JSPS24253004  and  JSPS26247023. The  MOA  project is supported by the grant  JSPS25103508  and  23340064. Work by AG and SC was supported by  JPL  grant  1500811. Work by AG and WZ was supported by  NSF  grant  AST-15168. Work by JCY was performed under contract with the  California Institute of Technology (Caltech) / Jet Propulsion Laboratory (JPL)  funded by  NASA  through the  Sagan Fellowship Program  executed by the  NASA Exoplanet Science Institute.
This work is based in part on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. The Spitzer Team thanks  Christopher S. Kochanek  for graciously trading us his allocated observing time on the CTIO 1. 3m during the Spitzer campaign. We thank the Swif t operations team for approving and implementing the Target of Opportunity request. This publication was made possible by NPRP grant #  X-019-1-006  from the  Qatar National Research Fund  (a member of  Qatar Foundation ). Work by SM has been supported by the  Strategic Priority Research Program  "The  Emergence of Cosmological Structures" of the Chinese Academy of Sciences  Grant No. XDB09000000 , and by the  National Natural Science Foundation of China (NSFC)  under grant numbers  11333003  and  11390372.
Test beam measurements at the test beam facilities of DESY have been conducted to characterise the performance of the EUDET-type beam telescopes originally developed within the EUDET project. The beam telescopes are equipped with six sensor planes using MIMOSA 26 monolithic active pixel devices. A programmable Trigger Logic Unit provides trigger logic and time stamp information on particle passage. Both data acquisition framework and offline reconstruction software packages are available. User devices are easily integrable into the data acquisition framework via predefined interfaces. The biased residual distribution is studied as a function of the beam energy, plane spacing and sensor threshold. Its standard deviation at the two centre pixel planes using all six planes for tracking in a 6 GeV electron/positron-beam is measured to be (2.
This is achieved by well defined interfaces on both the hardware and the software level. Fast LHC-type tracking devices are integrable in the same manner as slower rolling-shutter readout devices. The EUDET-type beam telescope each consist of six pixel detector planes equipped with fine-pitch MIMOSA 26 sensors  [6] , the mechanics for precise positioning of the device under test (DUT) and the telescope planes in the beam, a Trigger Logic Unit (TLU) providing trigger capabilities and a data acquisition system. The chosen design meets most user requirements in terms of easy integration capabilities, spatial resolution, and trigger rates. The telescope planes are designed and built to keep the material budget as low as possible in order to achieve an excellent track resolution even at the rather low particle energies of up to 6 GeV at the DESY test beam facilities.
The original EUDET beam telescope, which was modified to become the AIDA telescope, is operated at SPS beamline H6 (CERN). Responding to the increasing demand of the sensor R&D community, several replicas, collectively called EUDET-type beam telescopes, have been built since then: ACONITE for the ATLAS group, which is also operated at the beamline H6, ANEMONE at ELSA (University of Bonn), the copy for the Carlton University called CALADIUM, and two copies, DATURA and DURANTA, which are operated at DESY. Within the AIDA2020 project, another copy is going to be built -operation is foreseen at the PS beamline (CERN). All replicas are based on the MIMOSA 26 sensors and are equipped with the same data acquisition system and software framework. The EUDET telescope has been used since 2007 in various stages of development by hundreds of users and played an important role in sensor studies employed by a wide community.
From January 2013 until March 2014 alone, about 300 users utilised an EUDET-type beam telescope at DESY for a total of 80 user weeks. The results reported here are based on data taken with DATURA at test beam area 21 at DESY-II and are comparable to other beam telescope copies with a similar thickness of the epitaxial layer  [7]. This paper is organised as follows: The DESY beamlines are introduced in section 2, followed by the description of the beam telescope and the data acquisition framework in sections 3 and 4, respectively. Section 5 details the offline analysis and reconstruction software. Results of the EUDET-type beam telescope performance and track resolution predictions for different telescope configurations and beam momenta are presented in section 6. Additionally, predictions of the standard deviation of the angular scattering distribution are compared to measurements with an electron beam.
Components of the EUDET-type beam telescopes The EUDET-type beam telescopes are tabletop tracking detectors featuring six pixelated silicon sensors, four scintillators with photo multiplier tubes (PMTs) for trigger purposes, a Trigger Logic Unit (TLU) providing trigger logic and time stamp information on particle passage, and a data acquisition system for the readout. Figure  1  shows the beam telescope with its rectangular aluminium sensor jigs, wherein the pixel sensors are embedded, and its auxiliary boards providing connections for power, sensor configuration, clock signals and data transmission. The planes are organised in two telescope arms holding three sensors each. A DUT can be inserted between the arms or at either end of the beam telescope. In the Cartesian, right-handed coordinate system chosen, the y-direction points vertically down and the z-direction along the beam direction.