dataesr/scientific-paragraphs-categorization huggingface dataset:
Decision-maker Decision from contextual data without human intervention: trajectory replanning, exploration of unknown environments, management of data completeness, vehicle breakdowns or objectives priorities,. especially for underwater robots and generally all indoor robots. However, native communication with high-level architecture would allow the problem to be circumvented by using SLAM-type algorithms. Similarly, obstacle avoidance can be easily implemented for air and land environments, but not in the underwater environment where distance measurement is complex. Finally, decision-making for trajectory re-planning is not included in Ardupilot and must also be managed independently on a high-level architecture.
Introduction Computational models of hemodynamics are powerful tools for studying the cardiovascular system in health and disease. In particular, three-dimensional models of blood flow in the vasculature-with or without fluid-structure interaction (FSI)-have applications in non-invasive diagnostics, medical device design, surgical planning, and disease research. Due to the scarcity of direct data on flow and pressure, achieving pathophysiologically accurate results often requires specification of boundary conditions via reduced order models such as lumped parameter networks (LPN). Furthermore, in the case of FSI models, the parameters defining the structural stiffness also affect the solution greatly. It follows then that a primary challenge in constructing patient-specific models is the determination of parameters (LPN or structural stiffness) which make the simulation results agree with clinical data.
Due to the high computational demand of such models, an efficient and automatic parameter estimation strategy is highly desirable. Such parameter estimation, when based on observations of a system, is called data assimilation. Broadly, this involves combining different sources of data with a mathematical model of a physical system, in order to estimate that system's true state. This permits the discovery of "hidden" quantities (i. e. model parameters) that may not be directly observable. In our case, the underlying mathematical model is a 3D FSI formulation of the Navier-Stokes equations, describing hemodynamics and vessel wall mechanics in an vascular geometry, and including coupled LPN boundary condition models of the downstream vasculature. Previous studies have described algorithms for automatically identifying outflow boundary conditions and arterial wall material properties.
The key feature of ROUKF is that the uncertainty is confined to the parameters we wish to estimate, as opposed to the entire state space, making filtering of 3D PDE problems computationally tractable. A core advantage is that-unlike in many previous approaches-the high-fidelity forward model is used, without reliance on simplified surrogates. This ensures that the effect of complex, pathophysiological geometries can be retained, and is critical when spatial localisation of the observation data is important. We also introduce a ROUKF method augmented by constrained least squares (ROUKF-CLS), which enables filtering when the LPNs are more complex than the basic three-element Windkessel. All the methods described in this paper are implemented as part of the CRIMSON (Cardiovascular Integrated Modelling and Simulation) software package  [17] , which comprises both a GUI for vessel segmentation from medical images, finite element mesh generation and boundary condition design and specification; and a stabilized, massivelyparallel incompressible Navier-Stokes flowsolver.
Of particular relevance is CRIMSON's unique system for designing arbitrary LPN boundary condition circuits-the so-called Netlist boundary condition system-which integrates the ROUKF-CLS method introduced herein. The core ROUKF functionality is provided via integration with the Verdandi data assimilation library  [18]. The outline of this paper is as follows: we first describe the methodology for data acquisition, followed by the formulation for the 3D FSI computational model and the ROUKF estimation algorithm. We then describe the augmented ROUKF-CLS method for filtering LPNs more complex than the three-element Windkessel. Finally, the functionality of these methods is demonstrated in a number of cases. The key results are a determination of Windkessel parameters in a subject-specific model of the whole aorta and main branches, and a demonstration of parameter recovery in a synthetic coronary LPN model using ROUKF-CLS.
Subject-specific data 3D anatomical MRI was obtained from a 28-year-old male volunteer on a 1. 5 T MRsystem (Achieva, Philips Healthcare, the Netherlands) using cardiac triggering and respiratory navigation. From this, the aorta and surrounding major vessels were segmented and meshed, in preparation for simulation. Through-plane velocity data was acquired with high temporal and spatial resolution at three levels of the aorta (ascending, distal descending, and infra-renal) using 2D phase-contrast MRI (PC-MRI); the supra-aortic vessels; the left renal artery, and the left iliac artery. Figure  1.
Model observations and real-world data The measurement term Z k at time step k is a vector of clinically-recorded flow and pressure waveforms. The innovation term Z k -H(X k ) in Eq. 12 is thus the discrepancy between the model-predicted and real-world values of blood pressure and flow. The observation operator H extracts data from the model. To mimic the patient data we wish to assimilate, we define it as: H(X k ) = [Q 1 , Q 2 ,. , Q n obs-Q , P 1 , P 2 ,. , P n obs-P ] T k , where volumetric flow rate and spatially-averaged pressure are respectively defined by: Q i = i v h ( x, t) • n i da, P i = 1 area( i ) i p h ( x, t)da, ( 21 ) where i is the intersection of a plane with the 3D domain. Here, n i is a choice of unit normal to i. Compare the PC-MRI planes shown in Fig. 1 ; typically, these planes mimic the location of the PC-MRI measurement planes used to record the patient data.
Idealized carotid artery with synthetic data Estimation of Windkessel parameters from pressure and flow A straight, deformable vessel with a prescribed inflow at one end, and a Windkessel model at the other was studied, as shown in Fig. 2. The inflow velocity was chosen to represent a typical carotid flow waveform, mapped to a parabolic velocity profile. The stiffness E of the vessel wall was 0. 7 MPa and the thickness h was chosen to be 0. 3 mm. Forward problem Synthetic flow and pressure waveforms were generated using a simulation with known Windkessel parameters, run with a time-step of 1 ms, until cycleto-cycle periodicity was achieved. During the final cardiac cycle, volumetric flow and cross-sectional averaged pressure waveforms were obtained at the vessel midplane (see Fig. 2 ) with a time-spacing of 20 ms (50 Hz sampling rate).
06 × 10 -5 cm 5 /dyne = 0. 106 mm 3 /Pa. From the final cardiac cycle, wall deformation observations composed of a series of twenty-four wall surfaces was extracted. Then, pressure data was obtained at the observation plane at 100 Hz, and subsequently adding Gaussian white noise at 30 dB SNR. Estimation problem We employed n w nodal distance observations for each of the twentyfour wall surfaces, and one pressure observation. Initial guesses for the parameters were set to E = 0. 65 MPa (uniformly down the length of the vessel) and C = 0. 175 mm 3 /Pa. Initial estimation error covariance values were s i = 0. 5. The observation covariance matrix W k was block-diagonal, with a diagonal entry w P = (12. 3 mmHg) 2 for the pressure variance, and a n w × n w block (w -1 M w ) -1 for the wall node covariances, where w = (0.
25 ms. Forward problem Synthetic data was generated using known parameter values for the Windkessel LPN in all nine outlets. The simulation was run to full periodicity, then crosssectional averaged pressure and volumetric flow waveforms in the aortic branches were recorded during the final cardiac cycle, at 160 Hz; see Fig. 8  for the locations at which these observations were made. Estimation problem Motivated by the difficulty of obtaining pressure data in the clinic, we examined three different estimation cases, each with differing availability of pressure data. Case A considered no pressure data, Case B used a single pressure observation at the left carotid artery, and Case C assumed pressure observations in every outlet vessel. In all three cases, flow data in every outlet vessel were available; this choice is justified due to I -Innominate Artery Q Q P,Q II -L.
Subject-specific aorta with PC-MRI and applanation tonometry data We studied the same aortic geometry, now using subject-specific PC-MRI flow and applanation tonometry pressure data. The flow data were recorded in the ascending aorta, the Case A has no pressure data, Case B has pressure data only in the left carotid artery, and Case C has pressure data at all nine outlets of the model (c. f. Fig. 8 ). See Fig. 9  for the parameter evolution in each case. Errors less than 5% in absolute value are highlighted in bold; those less than 2. 5% are shown in italic Flow [cc/s] Fig. 11  Left: the flow observation sites (roman numerals) in the simulated aorta, which coincide with the flow acquisition planes in the PCMRI scans. Waveforms at oulets IV and VI were not measured directly with MRI, but were assumed to be identical to those measured at sites V and VII, respectively.
Parameters at outlets 4 (celiac trunk) and 5 (superior mesenteric artery) were not estimated due to the lack of flow data. For simulation, three-element Windkessel models are attached at each outlet, indicated by the asterisks. Right: flow waveforms derived from PCMRI data were used in the estimation problem three branches of the aortic arch, the left renal and the left iliac arteries, and two other locations within the aorta. Right renal and right iliac flow data were synthesized as copies of those for the left renal and left iliac. Observation locations in the model were chosen to agree with the PC-MRI acquisition planes, as shown in Fig. 11. Pressure data consisted of the cycle-averaged pressure waveform acquired in the left carotid artery. Details on the data acquisition are given in "Data acquisition" section.
The impact of constrained least squares Figure  18  shows the 2 error (Eq. 18) between the solution to the CLS system and the overdetermined target for the LPN coronary model results of Fig. 17. This measures the extent to which CLS is adjusting the system to maintain LPN consistency as the parameters Fig. 18  Log plot of the 2 norm of the error between the constrained least squares solution and the desired right-hand side for the coronary LPN case, as given by L over LPN xb (cf. Eq. 18). The data is presented over time, demonstrating how the magnitude of the perturbations required to enforce LPN consistency reduces over time, as the parameters converge. The actual parameter values over time for this simulation are shown in Fig. 17  are adjusted. As the parameters converge, the CLS system comes close to being solved exactly and therefore little adjustment is taking place on the system.
13. 2. We demonstrated that the ROUKF can also be used to simultaneously estimate regional wall material properties and Windkessel parameters, by using a distance operator to incorporate wall motion data, in conjunction with a pressure observation. The key results were given in Fig. 6. 3. For cases where the boundary condition models are more complex than threeelement Windkessel LPNs, we introduced a modified filtering procedure, the ROUKF-CLS, which augments ROUKF to enable estimation of parameters in arbitrary LPN boundary condition circuits. We demonstrated this in the case of a five-element coronary LPN boundary condition. The key result here was shown in Fig. 15. ROUKF-CLS greatly expands the class of computational hemodynamics problems to which the ROUKF can be applied. An implementation of ROUKF-CLS was developed and tested as part of the Netlist arbitrary boundary condition design system in the computational hemodynamics software package CRIMSON.
Pressure data acquisition Pressure data was obtained at the carotid arteries using applanation tonometry. The sampling rate was 128 Hz and the pressure measurements were calibrated using systolic and diastolic pressures obtained from a pressure cuff (Omron Healthcare). The pressure and PC-MRI flow data were not acquired simultaneously; however, the phase lag between pressure and flow was derived by time-shifting the flow and pressure waveforms relative to each other so that a linear relationship between pressure and flow during early systole is achieved  [47].
The image coding plays a very important role in 3D information extraction in stereo vision. The motion of the object is determined using image coding. This translation movement generates frame to frame displacement of the moving objects  [21]. Therefore, a displacement estimation is necessary for the UAV for identifying and avoiding the obstacle. The most popular way of estimating the translation motion is the block matching algorithm. Here the motion of the pixels (block) is represented using a displacement vector. This displacement INCAS BULLETIN, Volume 12, Issue 3/ 2020 vector is determined by the matching technique which generally follows three stages, namely motion detector to detect the moving blocks, displacement estimator to estimate the displacement vector and data compressor to encode the differences after motion compressor  [22].
a) Algorithm on LabView An algorithm to compute the depth image from the block matching algorithm is developed in LabView. NI Vision module along with ROS tool kit developed by Tufts University  [34]  were used to develop the algorithm. The video streams were received by HTTP stream in the form of MJPEG compressed by ROS. Virtual web cams were simulated on laptop with VCAM software. VCAM software acts as a wireless interface between the LabView and the HTTP stream. All the inputs were fed in with respect to each camera in HTTP address. The VCAM software simulates the camera just like USB cameras, which can be accessed by NI Max. Therefore, NI Vision module can be accessed wirelessly. The next step is to develop block matching algorithm in NI Vision module. The algorithm was divided into two categories: a.
to calibrate the camera and, b. to compute the disparity and deduce depth from block matching algorithm. The calibration is necessary to interface the camera with LabView. Parameters like distance between the cameras, their orientation and angle of view are added and adjusted in calibration process. The calibration is done by the visual interface (VI) developed by Mark Szaboo  [35]. The calibration data file is obtained after the completion of the calibration. The entire algorithm is divided into four steps: a. In Express VI, cameras were run in 640x480 frames at 20 frames per second. The 32bit color images are converted to 8-bit gray scale images to get depth image from stereo module. A binocular vision session is run in parallel. This binocular session reads the calibrated data. The output is two video streams and binocular session is calibrated.
SYSTEM DESIGN Two platforms were used to implement the algorithm: LabView on laptop and Robot Operating System (ROS)  [36]  on Raspberry Pi 3  [37]. ROS allows to link the camera to the hardware platform as well as the NI Vision module of LabView on the laptop. LabView student version has been used for the implementation of the project as it provides higher frame rates compared to OpenCV and Matlab Simulink tool (10 fps compared to 2 fps)  [38] , also allows interfacing of sensors and has NI Vision Module for image processing. Therefore two cameras of Microsoft studio lifecam with a resolution of 640×480 pixels at 20fps were used  [39]. They are connected to Raspberry pi 3 using USB cables. An image of Ubuntu mate 3 and ROS Kinetic was installed on Raspberry Pi 3. Ubuntu Mate 3 is a light version of Ubuntu for ARM processor as installed Raspberry Pi 3.
ROS Kinetic version is chosen instead of the latest version because of its large community of developers on the Internet. ROS is a grouping of libraries, which permits to read data from the sensors and write the data on the computer /actuators/mechatronic system  [40]. Cameras are connected to Raspberry Pi 3 and the system is mounted on the UAV, as shown in fig. 4. Communication between raspberry pi 3 and laptop is done via Wi-Fi. The following chart in fig. 5 , summaries the system architecture. ROS is a grouping of libraries, which permits to read data from the sensors and write the data on the computer/ actuators/ mechatronic system  [40]. Cameras are connected to Raspberry Pi 3 and the system is mounted on the UAV, as shown in fig. 4. Communication between raspberry pi 3 and laptop is done via Wi-Fi.
The following chart in fig. 5 , summaries the system architecture. It can deploy a network (in ROS) with the notion of node, topic, master, and message. Nodes can write messages into publisher topic and read messages into subscriber topic. A node can be a sensor (cameras in this case), a laptop or a C/ C++, Python or Java program. In the present case, wireless cameras with LabView has been used. Recover data from cameras and transmit computed data to a flight control board with just a Raspberry Pi 3. ROS operating scheme for the entire system has been shown in fig. 6. Following ROS, packages were been used for cameras wired by USB on the Raspberry Pi 3: • uvc_camera: This package allows cameras grabbing on raspberry pi. • mjpeg_server: This package allows camera streams by HTTP.
SIMULATION AND HARDWARE IMPLEMENTATION An X-configuration quadcopter is used to implement the project. For the ground control station a QGroundControl (QGC) simulation software integrated with gazebo has been used  [41]. The QGC software helps to make a backup of the parameters of the simulation and to implant them in the real system to approximate the conditions of the simulation. The simulation was done to test the functioning of the algorithm. Both Hardware in the loop (HITL) and software in the loop (SITL) can be performed in the proposed simulation. SITL is used to check the efficiency of the algorithm in the software and HITL is used to check the efficiency of the algorithm in the hardware. To perform the SITL a similar quadcopter model was selected and "Commander Takeoff" command was written for the drone to takeoff.
This paper presents our work on the Dialog System Technology Challenges 7 (DSTC7). We took part in Track 1 on sentence selection which evaluates response retrieving in dialog systems on more realistic test scenarios compared to the state-of-the-art evaluations. Our proposed dialog system matches the context with the best response by computing their semantic similarity on word and sequence levels. Evaluation results on the datasets provided show the effectiveness of our system by achieving higher performance compared to the provided baseline system. Our system enjoys the advantages of its simple and end-to-end architecture making its training and adaptation to other domains easier.
Then, we multiply these two vectors in order to obtain their sequence level similarity. In parallel, we compute a world level similarity matrix as the product between the word embeddings of the context and the candidate response. We encode this matrix into a vector. Finally, we concatenate both word and sequence similarity vectors and we produce the final score that we use to rank the candidate responses given the context of the conversation. The experiments carried out on two datasets provided by the challenge organizers show that our model achieves 73. 2% on Recall@10 and 55. 1% on MRR outperforming the baseline system by 37% on the first dataset  (Boussaha et al. , 2019a). Also, we evaluate our system on benchmark datasets such as the Ubuntu Dialogue Corpus  (Lowe et al. , 2015)  and the Douban Conversation Corpus  (Wu et al.
, 2017)  that are widely used in evaluating retrieval based dialogue systems and show that our simple and efficient system can outperform several complex systems while being conceptually simpler  (Boussaha et al. , 2019b). The remainder of this paper is organized as follows: we review the most recent works on retrieval-based dialogue systems in Section 2. In Section 3, we describe the challenge and the tasks to which we are participating. In Section 4, we describe our proposed system, the experimental setup and the system parameters. The results are discussed in Section 5. We compare our system to other state-of-the-art systems on two widely used datasets in Section 8. Finally, in Section 9, we conclude with perspectives for future work.
The main aim of the first track of DSTC7 is to address these limitations and to push goal-oriented dialog systems towards more realistic problems that every practical automated agent has to deal with. In this track, two dialogue datasets were provided: the Ubuntu Dialogue Corpus and the Advising Corpus. Five subtasks were proposed where each subtask concerns one or both datasets. In Table  1 , we summarize the subtasks of the sentence selection task and the datasets on which they are applied and they are described as follows: Subtask 1 Given a context of a conversation and a set of 100 candidate responses, the task consists of selecting the correct response. On 100 candidate responses, only one is correct. This subtask is available on both datasets. The aim of this subtask is to evaluate the ability of the participating systems to retrieve all the correct responses (the correct response and its paraphrases) by ranking them on top of the candidate responses.
Datasets DSTC7 provided two new goal-oriented dialog datasets in order to build and evaluate retrieval-based dialog systems. Each dataset is split into training, validation and testing sets. # + responses 1 1 1 1 1 1 1 1-5 1-5 1-5 1-5 0-1 0-1 0-1 0-1 0-1 0-1 0-1 Min # turns per C 3 3 3 1 1 1 1 1 1 1 1 3 3 3 1 1 1 1 Max #.
, 2001)  with RBF kernel as described in Figure  3. For every candidate response and a context, our response retrieval system (described in Section 4) provides a ranking score. Once we have the ranking scores of all the candidate responses, we feed them to the SVM clas- sifier. We train this classifier to predict the presence of a correct response among the candidate responses using the labeled training data. Subtask 5 requires participants to include the external knowledge into their system, while maintaining the end-to-end property of their architectures. Man pages and course descriptions were provided as external data for the Ubuntu Dialogue Corpus and the Advising Corpus respectively. We extracted plain text from these external data and we trained word embeddings on them using fastText.
System Parameters The only pre-processing performed on the dataset is tokenization using Keras Tokenizer. The system parameters were updated using Stochastic Gradient Descent with Adam algorithm  (Kingma & Ba, 2015). The initial learning rate was set to 0. 001 and Adam's parameters β 1 and β 2 were set to 0. 9 and 0. 999 respectively. As a regularization strategy we used early-stopping and to train the model we used mini batch of size 256. The size of word embeddings 4 and the size of the hidden layer of the encoder LSTM were set to 300. Whereas the size of the hidden layer of the second LSTM that encodes the WLSM matrix was set to 200. All the hyper-parameters 405 were obtained with a grid search on the validation set. Our system was implemented with Keras  (Chollet et al. , 2015)  and with Theano  (Theano Development Team, 2016)  in backend that we trained on a single Titan X GPU.