dataesr/scientific-paragraphs-categorization huggingface dataset:
" http://t. co/ErJCPObh (thanks @juliaxgulia) while this one was not (not informative, not contextualizable from the Wikipedia): Mom: "All you do is sit on that computer all day!" Me: "Lies! I sit on the chair. " From 2012, from the same set of accounts, about 1,000 tweets each year were then collected automatically. These tweets were added to the evaluation set, in order to avoid that fully manual, or not enough systems could achieve the task. All tweets were to be treated by participants, but a short list only was used for evaluation. Indeed, human evaluation is time consuming and can only hardly be done at large scale with trustable results. Participants did not know which topics were selected for evaluation. These tweets were provided in a text-only format without metadata and in a JSON format with all associated metadata.
Reference System A state of the art XML-element retrieval/summarization system called qa. termwatch (qaTW)  [69]  combining a Question-Answering engine based on multiword term extraction  [70]  powered by Indri with a summarization system based on terminology graphs  [18]  has been made available for participants through an online interface and a perl API both available at http://tc. talne. eu. The system is available online through a web interface 7  that allows to query: • an index powered by Indri 8  that covers all words (no stop list, no stemming) and all XML tags, • a PartOfSpeech tagging powered by TreeTagger  [73]   9  , • the state of the art summarization algorithm used in  [18]. Three kinds of queries can be used: standard bag of words, sets of multiword terms or more complex structured queries using Indri language  [55].
It is therefore difficult to apply it when the size of the produced summaries may vary depending on the amount of relevant information found in the document collection. On the other hand, KL is not defined for zero probability, thus it is necessary to introduce smoothing techniques initially introduced in language models. ROUGE relies on a small set of reference summaries and thus can be very sensible to the absence / presence of words in the reference summaries. On the contrary, in KL there is no reference and the whole document is taken into account, any word counting somehow. Even though ROUGE measures may be too sensitive to the presence / absence of word sequences that occur both in references and produced summary, KL has the opposite disadvantage not to allow to distinguish between a very low frequency and a total absence.
Informativeness Results Table  2  presents the results obtained by the best runs for the best 5 systems in 2011 using informativeness as presented in Section 3. 2. or top 5 best automatic participant systems. Runs are ranked by decreasing informativeness dissimilarity. The scores of the reference system described in 2. 4 is denoted by qaTW. The three first system results are not significantly different one from the other; but they are different from the following ones. In the same way UPF and Avignon systems are not statistically different from the reference system qaTW. The best system IRIT used a Vector Model (VM) IR system different from the Language Model (LM) based system proposed by organizers as reference system. Moreover, it uses special weighting for POS, named entities, document structural elements and a smoothing from local context when calculating sentence/tweet cosine similarity.
Maximal informativeness scores obtained by participants from 19 different groups are between 10% and 14%. There is also room for improving readability. The goal of the task and the evaluation metrics have been kept unchanged between 2011 and 2013 allowing multiple comparative analysis. After 2011, the tweets we selected as topics reflected more the diversity of tweets. The various collections we used are available at http://tc. talne. eu and http://qa. termwatch. es/data as well as the runs from the participants and the papers they published at INEX. The evaluation tool is also available at the same address. After having analyzed the four editions of the tweet contextualization task, we can summarize the lessons we have learned as follows: • Considering the collections we built: while the simulated tweets we used the first year were very convenient for evaluation purposes (we built an automatic reference that makes sense) and thus for the organizers, the content and format of the tweets were too different from real tweets (no URL, no hashtag) which has confused somehow the participants.
Introduction The ever increasing number of people who need continuous medical attention coupled with the rising costs of healthcare has triggered the concept of remote patient monitoring. This can be achieved using wireless sensor nodes attached to the human body. These nodes ensure periodic measurements of vital signs  [1]  such as heart rate, blood pressure, temperature, etc. Data acquired is first sent to a local gateway (cellphone, tablet, PDA …) through short distance wireless protocols. It is then uploaded to a medical database, analyzed and viewed by healthcare professionals (Figure  1 )  [2, 3]. Remote patient monitoring is frequently used with the elderly and the chronically ill. It allows these patients and their physicians to closely monitor their medical condition and, if needed, intervene  [4].
This technology is mainly limited by the amount of available energy. Thus estimating the power consumption of a node prior to its physical implementation will allow the designer to properly adapt its architecture (choosing energy sources, selecting components, etc. ). Some ready to use network simulators already have energy profiling functions. Nevertheless none of these can guide a designer in the decision process at electrical architectural level. The developed tool is power oriented and has two advantages. The main is an easy configurable component based environment. It allows fast creation of new component description based on the datasheets thus producing a flexible growing database. The second lies in many built-in solutions coupled with optimization processes that provide the user with guidance to properly adapt his design within the constraints of the application: choosing the more power efficient configuration, the required energy sources, etc.
A Rapid Prototyping Matlab Based Design Tool of Wireless Sensor Nodes for Healthcare Applications Plenty of networked embedded systems simulators have been developed in the last decade. This is mostly due to the uncertainty faced by designers regarding the system's energy consumption. A first example is the general network simulators such as NS-2, NS-3  [5]  and OPNET  [6]. These simulators lack in the infrastructure for easy and accurate hardware modelling despite having an important amount of protocol implementations. A second example is specific ad hoc networks simulators. Those were created to overcome general network simulators deficiencies regarding system models for simulating sensor nodes. They included some specific models for radio propagation, sensors and hardware systems. SENSE (SEnsor Network Simulator and Emulator) is one of these simulators.
Concept Our goal is to simulate a wireless sensor node and estimate its power consumption taking into account a physical implementation. The first step was to select an efficient modeling approach. We used a Model Based Design (MBD) approach as it brings cost and time saving in the development. Moreover the function models can be easily reused for different setups. MATLAB/SIMULINK offers an adapted environment and many suitable features to implement our system as an executable specification. Our purpose is to determine the power consumption of a wireless sensor node similar to the one shown in Figure  2. In order to do that, we need to identify the activity of each component. It is obtained from the time spent in each state (active, sleep, idle …) as well as state transitions  [10]. This conducted us to establish the cycle of operation presented in Figure  3   [11]  and explained here under: • The processor wakes up periodically in accordance with a predefined measurements rate following an assessment of the patient's medical condition • The processor activates the sensor to start converting the physiological signal into an electrical one • Simultaneously, an analog to digital conversion is taking place in the ADC • Once the measurement/conversion process is done, both the sensor and the ADC go to sleep mode • The microprocessor processes the ADC output and data is sent to the RF transceiver in order to be transmitted to the gateway • Meanwhile the processor becomes idle: the CPU is disabled during transmission, and the processor waits for an interruption from the wireless module • The RF transceiver informs the processor of the outcome of the transmission and is put to sleep • At the end, the whole node goes back to sleep mode Based on this cycle, we have modeled the instantaneous current consumption of each component as well as the whole unit using Matlab.
Built-in functions In order to provide an efficient reconfigurable tool, we decided to adopt a model based approach. It consists in two simple built-in functions coded in Matlab. The first one computes current consumption of a given node, while the other provides the user with guidance during the design process. Estimating the current consumption of the node: Our goal is to determine for a given node, current consumption throughout the whole monitoring period which extends to weeks, maybe months. This consumption is sometimes affected by the outcome of the connection to the gateway. In fact, the RF module's operation may differ whenever we are connected or not. This is the case for some RF communication standards such as ZigBee  [12] , Bluetooth  [13] , and Bluetooth Low Energy  [14]. Since these protocols are the most used in sensor network, calculating the current consumption of a node throughout the whole monitoring period has to take into account any disconnection imposed by the daily routine of the patient.
Conclusion This paper presented a Matlab based design tool for wireless sensor nodes used in remote patient monitoring. It is power oriented and enables early design explorations. It has many built-in solutions to guide the user during an energy aware design process. It can also be easily used for different setups. The deviation between simulation and experimental measurements recorded over the mean value is around 5%. In order to guarantee a patient's wellbeing, it is vital not to lose any information. Storing data over a memory chip will ensure that and make the system more reliable. Thus it is mandatory to model memory access operations, guide the user to the data backup strategy that best suits his application and study its effect over the consumption of the node. In addition, most medical conditions require monitoring simultaneously more than one vital sign.
Introduction The main goal of the MADERE platform is the determination of the specific activity level of solid radioactive dosimeters. These dosimeters are activated by the irradiations in the CEA reactor mockups (EOLE, MINERVE, MASURCA), in Material Testing Reactors (OSIRIS, future JHR) and in PWR vessels. Irradiations in the PWR are part of the surveillance program related to the vessel embrittlement under irradiation. Reactor mock-ups and MTR reactors host dosimeters for the experimental programs that provide information for nuclear data and validation of calculation schemes. MADERE is accredited by the French Accreditation Committee (COFRAC) for Gamma measurements of standard level specific activity and relative measurements by X-ray spectrometry. In the last years dosimetry has been applied widely to the reactor mock-ups that have a low flux values.
X-rays Spectrometry: Measurement Methods and Optimization This section begins by the description of a collimator that can be used in comparative and in absolute measurement. Afterwards, both methods are explained. It is interesting to note that MADERE is dimensioned for measurements on solid metal samples even if the radiochemistry measurement is commonly used by other laboratories. The reason for measuring directly the solid sample is that, in the context of reactor dosimetry, the laboratory is not allowed to destroy the dosimeter and the solid sample must be available after the X-ray measurement. Concerning the absolute method, it was developed in order to be able to measure Rhodium samples since the information given by this isotope provides data for the neutron flux in the energy range near 1 MeV.
The software called COLEGRAM  [5] , developed by LNHB, was first used in 2007 in order to fit the experimental measured curves to the analytic functions (Fig. 3 ) and deduce peaks emerging from the spectrum. The same COLEGRAM procedure is used for the current X-ray measurements. Correction coefficients have to be taken into account in order to create the total absorption efficiency curve. The main coefficients are detailed in the next part of this article. Uncertainties are also calculated.
The obvious solution to this problem is to measure a very thin sample, but the low activity level should be considered. Since the lack of activity for the measurement is partially solved by increasing the mass of the sample, equilibrium should be reached between a weak selfabsorption and enough specific activity. C (auto-abs) depends on the attenuation coefficient for the sample element. Rh 103m attenuation coefficients were calculated by both experiments and MCNP simulations for increasing samples widths  [4]. The first available data on self-absorption coefficients were taken from NIST XCOM and NIST X-Ray database  [6]. Experimental validation on SOLEX (Source Of Low Energy X-rays) facility was performed for Nb 93m , Rh 103m and mylar. The results proved that the values for Nb 93m and Rh 103m from XCOM shouldn't be used for the interpolation.
X-Ray database is more coherent with experimental values obtained in SOLEX. This study also validates the procedure of selfabsorption calculation in MADERE. Uncertainties are calculated for all efficiency values during the calibration and for the final sample activity. The total uncertainty is the sum of the partial ones considering that they are independent. The general law of uncertainties calculated for y = f(x) is: U 2 tot (y) = n k=1 *f *x k 2 U 2 (x k ) • (4) The total uncertainty for the efficiency value is a sum of an absolute uncertainty of the peak surface, absolute uncertainty due to the fitting by COLEGRAM or GENIE2000 (in case of X-ray or measurements), absolute uncertainty on the photons flux of the source, absolute uncertainty due to every correction factor. This uncertainty sum-up is calculated during the calibration of efficiency curve and also during the activity measurements of the sample in order to calculate the outgoing flux of photons.
That is why the use of vision techniques is the most suitable solution even though it presents a more demanding constraint in terms of quantity of information to be processed. Different visual perception devices are proposed in literature, offering a large view of the environment. However, a trade-off between images resolution, cost, efficiency and real time processing has to be taken into consideration. The use of cameras and computer vision techniques represents a common choice for autonomous scene reconstruction. Nevertheless, the computational complexity and large amount of data make real-time processing of stereo vision challenging because of the inherent instruction cycle delay within conventional computers  [1]. In this case, custom architecture implementation on FPGA (Field Programmable Gate Arrays) is one of the most recommended solutions.
XSG Design Flow Diagram For rapid prototyping, a development environment in which the hardware and software modules can be designed, tested, and validated is required. For this purpose, the Xilinx System The workflow gives the processing chain of the following steps: off-line calibration applied on a set of stereo images of pattern test placed on both cameras common field of view, image segmentation that highlights the laser projections, outliers elimination based on region-constraints, laser spot extraction along the laser beam direction, and the underwater 3D shape reconstruction algorithm based on distance measurements between the optical center and each laser projection.
XSG Design Flow Diagram For rapid prototyping, a development environment in which the hardware and software modules can be designed, tested, and validated is required. For this purpose, the Xilinx System Generator tool for DSP (Digital Signal Processor) development was used. It permits a high level implementation of DSP algorithms on FPGA circuit. It consists on a graphical interface under Matlab/Simulink environment used for modeling and simulating dynamic systems. A library of logic cores, optimized for FPGA implementation, is provided to be configured and used according to the user's specifications. When implementing System Generator blocks, the Xilinx Integrated Software Environment (ISE) is used to create "netlist" files, which serve to convert the logic design into a physical file and generate the bitstream that will be downloaded on the target device through a standard JTAG (Joint Test Action Group) connection.
Simulation under Matlab Xilinx System Generator allows us testing the FPGA application with the same conditions with the physical device and with the same data width of variables and operators. The simulation result is "bit-to-bit" and "cycle" accurate, which means that each single bit is accurately processed and each operation will take the exact same number of cycles to process. Thus, there will be almost no surprises when the application is finally running in the FPGA. Figure  7  presents the processing flow of System Generator tool from the hardware architecture design to the generation of the reconfiguration file used for the RTL (Register Transfer Level) design generation and the co_simulation process. The architecture designed using XSG blocks will be simulated under Simulink to compare calculation results obtained by the Matlab code algorithm and those obtained by the designed hardware module.
Hardware Co_Simulation Results Comparison In hardware co-simulation concept, the main treatment (processing module) is really implemented in the FPGA circuit and data acquisition and visualization are performed using Matlab. The system reads the image from MATLAB workspace; pass it to Xilinx Blockset where the different operations are performed and the result is sent over back to MATLAB workspace. The data used to evaluate the final system accuracy corresponds to the hardware co-simulation results which are really obtained from the FPGA circuit and which represent the final data that will be generated by the system. To implement the design on FPGA, ISE tool and core generator automatically generate a programming bit file using the System Generator blockset. A system block is created to point into the configuration file (bit) for the co_simulation.
The generated co_simulation block (Figure  17 ) replaces the XSG model (Figure  8 ) and will be loaded on the FPGA device through the JTAG cable by closing the loop after connecting the board (Xilinx ML605). different operations are performed and the result is sent over back to MATLAB workspace. The data used to evaluate the final system accuracy corresponds to the hardware co-simulation results which are really obtained from the FPGA circuit and which represent the final data that will be generated by the system. To implement the design on FPGA, ISE tool and core generator automatically generate a programming bit file using the System Generator blockset. A system block is created to point into the configuration file (bit) for the co_simulation. The generated co_simulation block (Figure  17 ) replaces the XSG model (Figure  8 ) and will be loaded on the FPGA device through the JTAG cable by closing the loop after connecting the board (Xilinx ML605).
All along the robot movement, a stereo image is acquired and analyzed in order to generate 3D shape of the confined structure using the generated 3D world points. In order to verify if the behavior of the XSG model corresponds to the theoretical Matlab code, results of both models are compared noting that it could be relative errors according to the number of bits used. Figure  18  illustrates an example of square shape sparse reconstruction with both Matlab code (blue markers) and the proposed FPGA implemented system (red markers). An average error of about 0. 46 cm is obtained between the software algorithm and the XSG architecture for the object of Figure  18. All along the robot movement, a stereo image is acquired and analyzed in order to generate 3D shape of the confined structure using the generated 3D world points.
In order to verify if the behavior of the XSG model corresponds to the theoretical Matlab code, results of both models are compared noting that it could be relative errors according to the number of bits used. Figure  18  illustrates an example of square shape sparse reconstruction with both Matlab code (blue markers) and the proposed FPGA implemented system (red markers). An average error of about 0. 46 cm is obtained between the software algorithm and the XSG architecture for the object of Figure  18.
The maximum pixel clock frequency could reach 66. 67 MHz, which corresponds to a pixel period of 15 ns. The maximum FPS (frames per second) of the camera is 30 FPS at a maximum resolution of 1024 × 768. This means that between two frames, it exists a period of T = 33 ms. The system should respond during this period in order to satisfy the application constraints. The proposed XSG architecture for the pre-processing and laser spots extraction presents a total latency of 1024 × 768 clock cycles (the resolution of the image). The 3D reconstruction module, which is designed to be looped twelve times to generate the 3D coordinates of the 12 laser pointers presents a total latency of 1200 clock cycles. For a maximum clock frequency our system can attain the following performances: (1) Maximum throughput of 275 Mega-pixel per second (2) Pre-processing and laser spots extraction execution time = 2.
Conclusions The use of rapid prototyping tools such as MATLAB-Simulink and Xilinx System Generator becomes increasingly important because of time-to-market constraints. This paper presents a methodology for implementing real-time DSP applications on a reconfigurable logic platform using Xilinx System Generator (XSG) for Matlab. In this paper, we have proposed an FPGA-based stereo vision system for underwater 3D shape reconstruction. First, an efficient Matlab-code algorithm is proposed for laser spot extraction from stereo underwater images and 3D shape reconstruction. Algorithm evaluation shows that a mean error scale of distance measurements accuracy is of 2. 5%, between real and estimated measurements, which is quite satisfactory regarding bad resolution of underwater images and light refraction effects.