dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Ms. Masae Suzuki ,  Ms. Keiko Kayahara , and several students at  Osaka University  for helping with the laboratory culture of O. dioica. We also appreciate the staff of the  Comparative Genomics Laboratory  at the  National Institute of Genetics (NIG)  and the  Scientific Computing section of Research Support Division at OIST  for supporting genome sequencing,  Ms. Kiyomi Imamura  and  Ms. Terumi Horiuchi  for their support in the collection of RNA-seq data,  Dr. Shunsuke Yaguchi  for his advice regarding making a genome browser,  Dr. Garth R. Ilshley  for critical reading of the manuscript, and  Editage  (www. editage. com)for English language editing. Computations were partially performed on the NIG supercomputer at the  ROIS National Institute of Genetics.
Propellers are a vital component to achieve successful and reliable operation of drones. However, the drone developer faces many challenges while selecting a propeller and a common approach is to perform static thrust measurement. However, the selection of a propeller using a static thrust measurement system is time-consuming. To overcome a need for the static thrust system a virtual model has been developed for measuring both the static and dynamic thrust of a single and coaxial propeller. The virtual model is reliable enough to minimize the need for full-scale tests. The virtual model has been built using two open-source software Qblade and OpenFoam. Qblade is employed to obtain the lift and drag coefficients of the propeller's airfoil section. OpenFoam is utilized to perform the flow simulations of propellers and for obtaining the thrust and torque data of the propeller.
Hence, they suggested an improvement over the hover model which is commonly known for the high-speed condition. Sartori and Yu  [9]  proposed a systematic method to identify parameters that can provide accurate thrust and drag prediction using Blade Element Theory (BET). As a theoretical basis, they used the Blade Element Theory (BET) as formulated by Leishman  [10]  coupled with the flapping model of Hoffman  [5]. The unknown parameters were evaluated using data from static thrust setup and flight data. They concluded that the thrust estimation based on BET has difficulties in representing the trend of the experimental data for low rotor angular speeds. In particular, when the rotors were spinning under 6200 rpm the model considerably underestimates the thrust force. They proposed to use (BEMT) for the estimation of the rotor inflow since it provides a more accurate representation of flow variable along the blade span.
Thrust force (2) and torque (1) are measured by respective load cells in the nacelles positioned at the top of each column. Nacelles were designed the way to allow the sensors for optimal measurement condition without off-axis forces or moments of force that could falsify the results. By measuring propeller torque, the rig is also able to measure the efficiency of the propeller(s) and the motor/ESC combo(s) separately. All sensors and actuators are interfaced with a custom data acquisition system (DAQ). In the setup control signal was fraction of throttle, 0. 0-1. 0 = 0%-100%. The data collection frequency in the current setup is 200 Hz. In the current setup following parameters can be measured individually for each propeller (1) thrust (axial load), (2) torque, (3) RPM (it is motor electrical RPM (ERPM) measured at the motor phase wire), (4) motor temperature, (5) current to ESC, (6) bus voltage (the bus voltage is the same for each ESC since they are on the same bus),  (6)  Power consumed [W], and (7) Power conversion (g/W).
• Thrust measurements are calibrated for offset, gain, and linearity in one direction with a 3rd-order polynomial using test weights; and • Torque measurements are calibrated in both directions for offset, gain, and linearity using a torque arm and test weights. The step static testing was conducted by a sequential increase of throttle from zero in a pre-defined number of steps to provide consistent data for each step. For temperature measurements to be valid, the duration of the static hover thrust test had been conducted from cold and limited to a few minutes to overcome the motor's thermal time constant. The duration of each step was set to be 10 s with an increment of 10% of the throttle resulting in 11 steps in total. Data were considered stable when the noise level became stable and was determined by filtration of the signal.
CFD-BEMT: A Numerical Approach for Modelling the Propeller Qblade is open-source software that computes lift and drag coefficients of a given airfoil. Qblade is based on XFOIL (https://web. mit. edu/drela/Public/web/xfoil/). XFOIL is an interactive program for the design and analysis of subsonic isolated airfoils. It can be used in the viscous (or inviscid) analysis of an existing airfoil, airfoil design and redesign by interactive modification of surface speed distributions, and airfoil redesign by interactive modification of geometric parameters  [17]. OpenFoam is an open-source computational fluid dynamic (CFD) (https://openfoam. org/) software used in solving flow related problems and in the present studies OpenFOAM has been applied for a flow analysis of the propellers. In CFD, the propeller can be modelled in two ways (1) direct method and (2) indirect method.
Model Validation The CFD-BEMT approach developed in the previous section has been validated with two APC propeller  [23]. To test the validity of the CFD-BEMT approach, the experimental data of a real propeller: APC 10 × 7 and APC 11 × 7 Slow Flyer available in the UIUC propeller database site have been selected. The propeller information including geometry details of the APC can be found online on the website of APC  [23]. The APC Slow Flyer is a two-bladed propeller, with a fixed pitch and a diameter. The propeller might be consisting of thin airfoil profiles with a specific combination of a low Reynolds number Eppler E63 and a Clark-Y airfoil near the tip, inserted to form a sharp leading edge blade design  [23]. One of the challenges in the CFD-BEMT method is the accurate representation of the airfoil section of the blade.
Single Propeller Setup The geometry of several airfoils sections along the propeller length is shown in Figure  9. The propeller blade was scanned along the chord of the blade and airfoil distribution is shown in the figure. The aerodynamics coefficients of these airfoil sections were computed and the average value of lift and drag coefficient of the propeller is presented in Figure  10. However, the estimated aerodynamics coefficients might not be that accurate because the airfoil's geometric shape of the propeller is not accurate enough. This could be due to the post-processing and scanning of the propeller geometry. Furthermore, the conversion from 3D scanned data to a digital format is not always accurate. Additionally, the blade geometry was also scanned to obtain the blade angle and chord distribution along the length of the blade.
Such privacy preserving query system can be supported either by a trusted party managed ledger, or by a real time query computation mechanism over a distributed dataset. In general, techniques based on secure multi-party computation enable different parties to perform distributed computation on secret inputs: following this paradigm, it is possible to compute any public function and share the output among the parties, while preserving the privacy of the inputs: after the execution of the protocol each party does not learn anything more than the computed values. In this paper we propose a privacy preserving query system that protects users' data, still allowing the detection of misbehavior from one of the participants. Synergies between ACCs and their customer FFs and synergies among ACCs motivate the adoption of the above described solutions: the cost of such audit system could be shared among the participants.
The multiple reservation detection model We model the multiple reservation detection as a query returning a boolean value, which represents the presence of a given descriptor in a database. This can be modeled as querying the database resulting from the union of the databases owned by each participant. Note that those databases hold sensitive data that cannot be shared in public, since the disclosure could affect the business of the involved parties (competitors could take advantage of the information offering lower prices and increasing their market shares). The solution we propose is based on proxy re-encryption in a cloud-based scenario. In the next subsection we give an introduction to this cryptographic technique.
Acknowledgements The authors acknowledge the support of the  Information and Communication Technology Fund (ICT Fund ) at  EBTIC/Khalifa University of Science and Technology ,  Abu Dhabi ,  UAE  (Project number  88434000029 ). The work was partially founded also by the  EU   Horizon 2020 research and innovation programme, within the projects Toreador  (grant agreement No. 688797 ),  Evotion  (grant agreement No. 727521 ) and  Threat-Arrest (Project-ID  No. 786890 ).
The Reachy robot is a commercial robot torso developed by Pollen robotics 3 in 2017. The robot has 7-DOF arms and can lift up to 500 g  (Mick et al. , 2019). The software of Reachy is Python-based. The strengths of the robot seem to be in the field of manipulation, as the capabilities of the arm are sophisticated for a robot of this size category. The Pepper by Softbank (formerly Aldebaran)  (Pandey and Gelin, 2018)  is mainly designed for Human-Robot Interaction. Its human-like torso is fitted onto a wheeled platform. Pepper has 20 DoF and human-like arms with five-fingered hands; however, its arms and fingers are mainly designed as a means for making gestures. Soft-skin platforms offer a more realistic human-like appearance. The CB2 (Child with Biomimetic Body) from Osaka University  (Minato et al.
, 2007)  is a 130-cm tall platform for cognitive developmental robotics and features soft skin and flexible pneumatic actuators; it has a total of 63 degrees of freedom. Its face has actuators for eyeballs, eyelids, eyebrows, cheeks, and mouth to display emotions. In addition to cameras and microphones, skin tactile sensors in the skin can mediate haptic interaction. It is designed with a view to social interaction with a human caregiver. Affetto  (Ishihara and Asada, 2015)  has a similar design. It is an upper-body platform that has the proportions of an 80-cm tall child and has 22 degrees of freedom. 2 https://github. com/poppy-project/pypot 3 https://www. pollen-robotics. com It is designed to appear human-like, including in terms of its visual and tactile impression. Among the adult-sized robots, the high-performance biped Talos from PAL robotics  (Stasse et al.
API and Virtual Model The NICO API supports direct control via Python and the robot operating system ROS. Python allows easy integration into the most common frameworks for GPU processing and deep learning like Tensorflow  (Abadi et al. , 2016). This gives scientists an easy way to embody neurocognitive models into the robot. NICO's full functionality can also be accessed via ROS  (Quigley et al. , 2009) , the de facto standard in the robotics community, to allow easy sharing of software modules. The low-level motor control of the API is based on PyPot  (Lapeyre et al. , 2014) , which was extended to support NICO's hands. The use of Python makes it possible to utilize existing libraries for control and preprocessing of sensory information, such as OpenCV for camera and PyAudio for microphone recordings.
Embodied Sensing Evaluation Embodied Visual Perception. Compared to the typical applications of computer vision approaches, there are no differences in a robotic vision system. Therefore, the performance of state-of-the-art approaches for object detection, e. g. , RetinaNet  (Lin et al. , 2017)  can be utilized without limitations on NICO. However, a robot offers the ability to combine vision with active object manipulation: the robot can move and turn an object to learn a more elaborate visual representation. Additionally, object manipulation can also be used to train and evaluate models for object tracking under deformation and occlusion. Heinrich et al. (2019)  recorded the NICO-object interaction dataset featuring sixty object-hand interactions, including different push, pull, grasp, and lift actions on a broad range of toy objects that show diverse behaviors, such as rolling, bouncing, or deformation.
The exploration procedures were inspired by typical child-like behavior that could be realized on a humanoid. The dataset was used to evaluate the HIOB framework, an adaptive convolutional object tracker based on an incremental update mechanism. Josifovski et al. (2018)  applied a convolutional neural network for object detection and pose estimation trained with 3Dmodels of NICO's hand. Though the pose of the hand can be computed via forward kinematics, such models can contribute to developmental approaches in which the kinematics of the robot are learned. Furthermore, the work demonstrates the transfer of models trained on the simulated model to the real world. Embodied Audio Perception. Like computer vision, audio processing on a robotic platform does not differ greatly from any non-robotic audio task.
However, the robot's ego-noise during operation and its ability to actively manipulate objects to elicit audio information have to be considered. Humanoids can perform common audio exploratory procedures like shaking an opaque container to gain insight into its content. Eppe et al. (2018)  and  Strahl et al. (2018)  recorded an audio dataset with 1,080 samples of active audio exploration of 30 capsules filled with different materials, which NICO could shake with its hand, to train a recurrent neural network classifier. High classification accuracy of 91% was achieved due to the low ego-noise of the robot in the head area. Embodied Haptic Perception. In contrast to audio and vision approaches, haptic perception is inherently based on active exploration to gain information about handled objects and materials.
Compressing or squeezing an object can give information about the compliance of the material, while forces along the movement direction during lateral motions reveal static and slip friction as well as texture information. NICO's haptic sensory setup enables the use of human-like haptic exploratory procedures: in two studies, the use of lateral motion across surfaces to gain texture information and the use of squeezing objects to gain information about their compliance and shape was evaluated. Kerzel et al. (2017a)  collected a 3200-sample dataset of lateral motions over a set of 32 samples of common household materials ranging from metal to different fabrics or cardboard. High classification accuracy of 99% could be achieved with a neural model. The study could also positively evaluate the robustness of the sensors; in over 5,000 trials, no wear and tear to the sensor occurred.
Kerzel et al. (2019b)  collected a dataset of human-inspired active haptic exploration of 16 different toys by enclosing and squeezing the objects in the robot's hand. These objects range from foam dice to different plush and plastic figures. The dataset contains 100 active exploration trials for each of the 16 objects; in each trial, seven haptic sensory channels were recorded for 52 time steps. A neural model that integrates the different haptic sensory channels over time achieved a 66. 6% classification accuracy. Both studies showed that a key to recognizing haptic properties is the integration of motoric, proprioceptive, and tactile information. To this end, the NICO API is designed to synchronize motor commands and different sensory streams.
Datasets A series of mono and crossmodal datasets from the abovedescribed active sensing studies that have been recorded with NICO have been published for further use by the scientific community. Kerzel et al. (2019b)  provide a haptic dataset of tactile and proprioceptive information during active haptic exploration of objects. Heinrich et al. (2019)  provide a vision dataset of 60 object-hand interactions. Heinrich et al. (2018 Heinrich et al. ( , 2020) )  recorded the EMIL dataset on embodied multi-modal interaction for language learning. The dataset focuses on low-level crossmodal perception during the environmental interactions from a body-rational perspective. The robot explored a set of toy objects through different actions like shoving, pulling, lifting, and scooting the object across the table.
In summary, these studies utilize NICO's ability for crossmodal sensing, motion, and social interaction to extend existing neurocognitive models, e. g. , for emotion recognition, which were previously trained on prerecorded datasets (e. g. ,  Barros et al. , 2018) , to live interactions. This allows the evaluation of the model's ability to adapt to individual users and, more importantly, it allows the effect of different HRI strategies on subjective user rating under realistic conditions to be studied. In the following section, we will further extend this research by evaluating how these more engaging interactions can benefit the learning of neurocognitive visuomotor models.
Early, simplistic attempts to implement genomic medicine using a 'one cancer gene, one drug' approach, have been replaced by a more detailed understanding of the molecular bases of therapies. Cancer-related genes harbor thousands of variants that require an unprecedented level of granularity in assessing their effects. The problem has thus less to do with the actual production of molecular data -the required logistics, their reliability and comparability across instruments -than with their interpretation and consequent translation into clinical practices  (Jordan 2015). As one oncologist argued, "the fundamental problem is we're generating more information than we can readily interpret as individuals"  (Titus 2014b). While precision medicine has its critics (e. g. , Prasad 2016; see Subbiah and Kurzrock 2017 for a rebuttal), all major cancer centers and agencies have jumped on the genomic bandwagon.
Knowledgebases and Databases Instancing "the production of dozens to thousands of potential tumor-driving events that must be interpreted by a skilled analyst and synthesized in a report",  Good et al. (2014)  explained that: Each event must be researched in the context of current literature, drug-gene interaction databases, relevant clinical trials and known clinical actionability from knowledgebases. In our opinion, this attempt to infer clinical actionability represents the most severe bottleneck of the process. The  Good et al. (2014)  paper predates the NIH distinction between databases and knowledgebases by 4 years, which suggests that the distinction has been in use for some time. While the term 'database' needs no further elaboration, having entered common parlance several decades ago, the notion of knowledgebase requires explanation.
Although both 'bases' act as repositories for 'data' 2  derived from published papers, conference abstracts, datasets established by large-scale collaborations, and results of tumor profiling analyses of patients enrolled in clinical trials or undergoing routine treatment, it is not clear that we are talking about the 'same' kind of information. It is similarly unsure that both bases treat data in the same way. Are we, in other words, confronted with similar data journeys, and does this metaphor actually apply to knowledgebases? Both kinds of repositories use equivalent software tools and packages, arguably making one a mere subtype of the other. But as scores of technology studies have shown (e. g. Bijker and Law 1992) , it would be simplistic to reduce devices to their technical components.
Moreover, the very fact that actors differentiate between them suggests important differences. While acknowledging that many genomic resources incorporate elements from both databases and knowledgebases,  Pitel (2017)  reiterates the usefulness of the distinction: Although data and knowledge are dependent on each other, it is important to understand that data portals contain observations, like those typically seen in the results section of an article. … Knowledgebases, on the other hand, contain critically processed data, contextualized for significance and meaning, much like what you might find in a conclusion section of an article, and are often more appropriate for immediate use in clinical laboratory practice. At this point, we could be accused of uncritically adopting the actors' categories.
We focus on "topics or themes which preoccupy particular groups, and which resonate with social sciences issues" (M. Lynch, personal communication). As an actor-derived categorization, the database/knowledgebase distinction can be used both by analysts as a language of description, and by concerned groups as a language for action  (Lynch 1993; Hatchuel 1996). Figure  1  depicts the funnel running from the initial sequencing to the bottleneck of interpretation by/for the clinician. It appears that much of what precedes the bottleneck (stage 5) can be categorized as the domain of databases, whereas the bottleneck and its knowledgebases interrupt the data journey. Knowledgebases come into play when oncologists receive a sequencing report listing mutations of possible clinical import. Instead of manually scouring the entire published literature for information about those mutations, they turn to one of several interpretation knowledgebases that offer a synthetic summary and description of a given variant's clinical significance.
Practitioners contrast databases with knowledgebases in two different (albeit complementary) ways. The first claims that knowledgebases contain interpretationladen and action-oriented data, as contrasted with raw data. 3 The introduction of the database/knowledgebase distinction thus reifies the content of databases as theoryneutral data unaffected by interpretation. The distinction also elevates the status of the interpretations embedded in knowledgebases as (temporarily) reliable knowledge. The second demarcation refers to the practices and goals that establish those two infrastructures, which we can for now summarize as follows: whereas databases aim at the production of resources that will be available for use by different communities of practice, oncology's knowledgebases are typically the result of initia-tives derived from practical clinical concerns.
As compared to much larger databases, knowledgebases address specific audiences. They are characterized by a high degree of 'situatedness'  (Suchman 1987) , i. e. they act as resources for clinical decisionmaking that are grounded in a collective understanding of possible therapeutic pathways once the local contingencies of clinical work are considered. For instance, the fact that several knowledgebases consist of an outward-facing website that only reports information with literature support, and an internal component that can exclusively be accessed by members of that institution, is justified as follows: In the absence of a community that understands the nuances of the potentially actionable, it's a little harder to relay that [kind of genomic] information. The treating physicians at [our institution] get a report that says: "We think this is potentially actionable because of the following reasons", and they can understand how grey that call is.