dataesr/scientific-paragraphs-categorization huggingface dataset:
Noise Analysis To evaluate the influence of the noise amplitude on PEGSNet's performance, we calculate the accuracy using different groups of samples of the test set, divided according to the noise level. We compute the standard deviation of a five-minute window of data before the P-wave arrival for each sample of the test set, and subsequently the median of the standard deviation over the whole seismic network. For both the 2010 and 2021 seismic networks, we then divide the test sets into four subsets defined by the three quartiles of the distribution: Q1, Q2, and Q3. The values of the three quartiles are displayed in Figures  11a  and 12a  for the 2010 and 2021 networks, respectively. In both cases, the accuracy of the estimations decreases when the level of noise increases. This is summarized in Table  1 , where the accuracy values have been calculated for the events with magnitude equal to or higher than 8.
Test on Noiseless Data To explore the theoretical limits of PEGSNet and find an upper limit in the performance, we train and test the model considering an idealized scenario of noiseless data. We use the synthetic PEGS database described previously, but do not add noise. The density plot of the residuals in Figure  14  shows again the evident difference in the timeliness of the estimations between the two networks: the 2021 network provides earlier estimates than the 2010 network. The estimations based on the 2010 network start at around 20 s and have errors of ±0. 2 after the first 50 s (Figure  14a ). The estimations based on the 2021 network start within 10 s after origin time and have errors of ±0. 2 but with higher dispersion up to around 60 s. According to the corresponding accuracy maps (Figure  13 ), the first estimates start earlier with the 2021 network but the accuracy is also poorer than with the 2010 network, as discussed in Sections 4 and 5.
Conclusion We trained PEGSNet, a Convolutional Neural Network that estimates the evolution of an earthquake's source time function based on Prompt Elasto-Gravity Signals, with data recorded by the seismic networks available in Chile in 2010 and 2021. The former contains fewer stations and noisier data. Our study confirms that PEGS can be used for rapid estimation of the magnitude of large earthquakes, with performance competitive with GNSS-based algorithms for the 2010 M w 8. 8 Maule earthquake, but without requiring a priori assumptions on slip distribution. This supports the potential implementation of the method as a complement to tsunami warning systems. Regarding the results for different setups, the model shows delayed estimations of moment release, but higher accuracy values when using the 2010 configuration.
Data Availability Statement The facilities of IRIS Data Services were used for access to waveform and metadata from IRISDMC (network code: C, C1, G, GT, IU, XH, and YS) and GEOFON (network code GE) datacenters. PEGSNet, the CNN used in this work was developed by  Licciardi et al. (2022). Figures were made with Matplotlib version 3. 3. 4.
, π(x m ) ∈ B, the map h π is a homomorphism from D ψ (the canonical database) to A, where h π (x) := π(x) , if x is a universal variable; and, σ x ( π| Yx ) , otherwise. (Here, Y x denotes the set of universal variables preceding x and π| Yx the restriction of π to Y x. ) Clearly, A |= ϕ iff the existential player has a winning strategy in the (A, ϕ)-game against the so-called full (rectangular) adversary A × A ×. × A (which we will denote hereafter by A m ). We say that an adversary B of length m dominates an adversary B of length m when B ⊆ B. Note that B ⊆ B and A |= ϕ B implies A |= ϕ B. We will also consider sets of adversaries of the same length, denoted by uppercase Greek letters as in Ω m ; and, sequences thereof, which we denote with bold uppercase Greek letters as in Ω = Ω m m∈N.
However, the limited power range (mainly below 250 W) in which the comparison was  performed limits the accuracy we can expect for higher powers. The relative mean absolute error (rMAE) was reduced from 2. 71% to 1. 27%. The sun tracker has a micro solar positioner model POZSOL36A1DR, which can be connected directly to a PC (PC-Tracker) via USB. Thus, the movement of the sun tracker can be controlled on the PC-Tracker through LabVIEW software. A LabVIEW program was developed to control the tracker from the measurements of a radiometric cube (see Sect. 3). This new control strategy will be explained in the third part of this article.
The electronic devices used to measure the voltages of the four sensors are a multiplexer with seven analog inputs and one output connected to a KEITHLEY 2000 multimeter. The multiplexer is connected to a NI USB-6008 DAQ to control its switching from a PC (PC-Cube) under LabVIEW software to measure the voltages of the four sensors with a time of 0. 3 s for each switch. A KEITHLEY 2000 multimeter is connected to the same PC (PC-Cube) to save and send simultaneously the four irradiance measurements in real-time to the PC-Tracker, which controls the tracker, through wireless UDP data transfer protocol. Figure  6  shows the principle of the measurements and communication between the radiometric cube system and the solar tracking system. After receiving the irradiances measured on the four faces of the radiometric cube, the PC-Tracker pre-processes the data according to the control strategy explained in the third part of this work to choose the optimal trajectory for the tracking system.
Algorithm for the optimal direction Equation (  15 ) gives the A and B angles that direct the solar tracker towards the optimal direction in function of the four irradiances on the radiometric cube. A LabVIEW program was implemented on the PC-Tracker to drive the solar tracker towards this optimal direction using equation  (15). In order to determine I min in the LabVIEW program, I E and I W are compared. Indeed, according to the hypothesis of uniform luminance of the celestial vault and Lambertian ground, I W = I min during the morning before the sun passed its maximum height in azimuth 0°( towards the South) and I E = I min in the afternoon if the meteorological condition is not totally overcast. Figure  10  shows the flowchart of the algorithm for controlling the T-Cube tracker from the radiometric cube.
This algorithm is implemented on the PC-Tracker. The first step is to receive the four irradiances measured on the radiometric cube during 5 min. The time step of the irradiance acquisitions is 5 s. Thus, the data received during 5 min corresponds to 60 measurements of the four irradiances. Then the last 2 min of measurements of I E , I Z , I W and I S (36 measurements each) are averaged I S ; I E ; I W ; I Z À Á. These averages are then used to determine I min and calculate the set points of A and B angles using equation  (15). The average of the measurements of the last 2 min was used to exploit the information of the latest variation of the solar irradiance on the PV module. Considering the values measured during the 5 min would be more relevant by applying a weighted average with a high coefficient on the last measurements.
Acknowledgments. The authors would like to express a sincere gratitude to the LabEx LaSIPS (ANR-10-LABX-0032-LaSIPS) managed by the French National Research Agency under the "Investissements d'avenir" program (ANR-11-IDEX-0003) for their financial support. The authors would also like to thank the 3rd Programme d'Investissements d'Avenir [ANR-18-EUR-0006-02] and the Foundation of Ecole polytechnique (Chaire "Défis Technologiques pour une Énergie Responsable" financed by TotalEnergies) for their support in this work.
Wide-spectrum vs. situated software Wide-spectrum software provides fundamental computing functionality to a large number of users. In order to serve a large user base, it addresses a wide range of application scenarios, each of which requiring only a part of the software's functionality. Word processors are a well-known example: a package like LibreOffice can be used to write a simple letter, but also a complex book. LibreOffice has huge menus filled with various functions, of which most users only know the handful that matters to them. General-purpose large language models are another example of wide-spectrum software. Situated software (a term introduced by Shirky  [Shirky 2004 ]) is software written for a specific use case or a specific user group. It addresses a specific need very well, but is not transferable to other application scenarios.
Spreadsheets are usually situated, as are games, and many shell scripts. A useful numerical proxy for estimating a software package's location on this scale is the ratio of the number of users to the number of developers, although there are exceptions. Games, for example, are situated software with few developers but many users. In scientific computing, the wide-spectrum end of the scale is well illustrated by mathematical libraries such as BLAS or visualization libraries such as matplotlib, which provide a large collection of functions from which application developers pick what they need. At the situated end, we have the code snippets and scripts that generate the plots shown in a paper, as well as computational notebooks and computational workflows. In between these extremes, we have in particular domain libraries and tools, which play a very important role in computational science, i.
Mature vs. experimental software Mature software is developed and maintained with the goal of providing a reliable tool. Signs of maturity in software are its age, a clear definition of its purpose, respect of standards, respect of software engineering practices, detailed documentation, and a low frequency of compatibility-breaking changes. The Linux kernel and the text editor Emacs are examples of very mature software. Experimental software is developed and maintained to test new ideas, be they technical (software architecture etc. ) or related to the application domain. Experimental software evolves at a much faster pace than mature software, and documentation is rarely up to date or complete. Users therefore have to stay in touch with the developer community, both to be informed about changes and to have an interlocutor in case of unexpected behavior.
Infrastructure software, i. e. packages that much other software depends on, should by definition be mature, and much of it is. This applies both to general-purpose infrastructure, such as the Linux kernel or the GNU Compiler Collection, and to scientific infrastructure, such as BLAS or HDF5. A grey zone is occupied by prototypes for future infrastructure software, such as the Julia programming language, which typically doesn't advertise its experimental nature and is easily taken to be mature by inexperienced users. There is also software that clearly positions itself as infrastructure but lacks the required maturity. Such software is often a cause of computational irreproducibility. The libraries of the scientific Python ecosystem are an example, suffering from frequent changes that break backward compatibility.
You may not want to let tech corporations spy on you via your smartphone, but the wide adoption of these devices means that you are excluded from more and more areas of life if you decide not to use one. Some research communities have fallen into this trap as well, by adopting proprietary tools such as MATLAB as a foundation for their computational tools and models. In between convivial and proprietary software, we have Free, Libre, and Open Source software (FLOSS). Historically, the Free Software movement was born in a universe of convivial technology. The few computer users in academia in the 1980s typically also had programming skills, and most of the software they produced and used was placed in the public domain. The arrival of proprietary software in their lives, exemplified by the frequently cited proprietary printer driver at  MIT [2002] , pushed them towards formalizing the concept of Free Software in terms of copyright and licensing, as they saw legal constraints as the main obstacle to preserving conviviality.
With the enormous complexification of software over the following decades, a license was no longer sufficient to keep software convivial in practice. The right to adapt software to your needs is of limited value if the effort to do so is prohibitive. Software complexity has led to a creeping loss of user agency, to the point that even building and installing Open Source software from its source code is often no longer accessible to non-experts, making them dependent not only on the development communities, but also on packaging experts. An experience report on building the popular machine learning library PyTorch from source code nicely illustrates this point  [Courtès 2021 ]. Conviviality has become a marginal subject in the FLOSS movement, with the Free Software subcommunity pretending that it remains ensured by copyleft licenses and much of the Open Source subcommunity not considering it important.
It survives mainly in communities that have their roots in technology of the 1980s, such as programming systems inheriting from Smalltalk (e. g. Squeak and Pharo), or the programmable text editor GNU Emacs. In scientific computing, there is a lot of diversity on this scale. Fully proprietary software is common, but also variants that do allow users to look at the source code, but don't allow them to compile it, or don't allow the publication of reviews. In computational chemistry, the widely used Gaussian software is an example for such legal constraints  [Hocquet and Wieber 2017]. FLOSS has been rapidly gaining in popularity, and receives strong support from the Open Science movement. Somewhat surprisingly, the move beyond FLOSS to convivial software is hardly ever envisaged, in spite of it being aligned with the traditional values of scientific research: before the arrival of computers, theories and models have always been convivial technologies.
Size of the minimal execution environment Each piece of software requires an execution environment, consisting of a computer and other pieces of software. The importance of this execution environment is not sufficiently appreciated by most researchers today, who tend to consider it a technical detail. However, it is the execution environment that defines what a piece of software actually does. The meaning of a Python script is defined by the Python interpreter. The Python interpreter is itself a piece of software written in the C language, and therefore the meaning of its source code is defined by the C compiler and by the processor which ultimately executes the binary code produced by the C compiler. As an illustration for the importance of the execution environment, it is an easy exercise to write a Python script that produces different results when run with version 2 or version 3 of the Python interpreter, exploiting the different semantics of integer division between the two versions.
Software environments have only recently been recognized as highly relevant for automated reasoning in science and beyond. They play a key role in computational reproducibility, but also for privacy and security, which are the prime motivations for the Reproducible Builds movement. The issues of managing software environments are now well understood, and two software management systems (Nix and Guix) implement a comprehensive solution. However, they have not yet found their way into mainstream computational science. In addition to ease of use issues that can be overcome with time, a major obstacle is that such management systems must control the complete software stack, which excludes the use of popular proprietary platforms such as Windows or macOS. Assuming that the proper management of scientific software envronments will be achieved not only in theory, but also in practice, it is the size of this environment that remains a major characteristic for reviewability.
An interesting line of research in software engineering is exploring possibilities to make complete software systems explainable  [Nierstrasz and Girba 2022]. Although motivated by situated business applications, the basic ideas should be transferable to scientific computing. The approach is based on three principles. The first one is the same as for computational notebooks: the integration of code with explanatory narratives that also contain example code and computed results. Unlike traditional notebooks, Glamorous Toolkit [feenk. com 2023], the development environment built to explore these ideas, allows multiple narratives to reference a shared codebase of arbitrary structure and complexity. The second principle is the generous use of examples, which serve both as an illustration for the correct use of the code and as test cases.
Simulation Environment The main parameters of the simulations are the bandwidth B ch = 200 kHz, the chirp duration τ ch = 0. 05 ms, the total number of symbols M ∈ {4, 8, 16}, and a data stream of 10 5 bits. The simulation was done on MATLAB and under additive white Gaussian noise (AWGN) channels with a stationary transmitter/receiver over the specified bandwidths. The performance metrics considered are the spectral efficiency and the bit error rate.
These reinforcements were randomly distributed during the sessions. During each test session, the caiman heard 12 The response of the animals to the acoustic stimuli was measured blindly from the videos using Kinovea software, v0. 8. 24 Beta 49 , during the 45 seconds following the emission of the first call of the tested sequence. For each trial, the response was scored as "0" (no approach) or "1" (reach the food reward system within 35 centimeters of the speaker). We also measured the latency between the first call in the sequence and the onset of the behavioral response (first movement), if any.
ACKNOWLEDGEMENTS The authors are grateful to " Crocoparc" zoo in Agadir, Morocco  ( Luc Fougeirol ,  Ariane Marinetti ,  Leila Sdigui , Philippe and  Christine Alleon , and the staff), to  Olivier Marquis  for providing us with the caimans and to  David Reby  for his advice. This research has been funded by the  Institut universitaire de France (NM) , the  Labex CeLyA (Lyon Center of Acoustics   ANR-10-LABX-60 , PhD funding to  Julie Thévenet ), the  CNRS  and the  University of Saint-Etienne.
Participants and Apparatus Twenty-one participants voluntary took part in our experiment, without compensation, and with normal or corrected-to-normal vision (5 females, 16 males; age: aver. 31. 8, SD: 6. 9; VR experience from 1 to 5: aver. 3. 2, SD: 1. 1; computer games experience from 1 to 5: aver. 3. 7, SD: 1. 2). Our study complied with the Declaration of Helsinki and was approved by our local ethics committee (COERLE). Participants were asked to wear a backpack computer and the HTC Vive Pro Eye VR head-mounted display (HMD). They carried two HTC Vive controller in their hands, and two Vive trackers were positioned on their shoulders. Participants' behavioural data were recorded through all these devices. The eye-tracking system of the VR HMD used here has a maximum eye-tracking sampling rate of 120 Hz, recording eye motions in a 110°field of view with an advertised precision between 0.
Data collection We collected two types of data, both quantitative: i) continuous participant's nonverbal behaviours during the VR experience, and ii) participant's responses to the questionnaires. We also got qualitative data by collecting participants' comments after the study. For i), gaze behaviour was collected using the HMD embedded eye-tracking system, and global and local locomotion behaviours through the VR tracking system. At each frame, participant's gaze data were logged, indicating the presence of a hit on virtual agents' heads, as well as the participant's head, hand and shoulder positions. For ii), data was collected through post-experiment questionnaires: demographics (age, gender, experience with VR and games), eye, hand, feet, and ear lateral preferences (using the Lateral Preference Inventory with scores ranging from -16 to 16 i.
ACKNOWLEDGMENTS We would like to thank the participants of our experiment. This work received funding from the  European Union  (No  856879 ,  H2020 ICT-25   RIA PRESENT  project), the  Brittany Region  (No  19023 ,  SAD-2019 -REACTIVE  project), the  French government  ( Investments for the Future program PIA ,  French National Research Agency ,  ANR-21-ESRE-0030 ,  CONTINUUM  project), and the  French National Centre for Scientific Research CNRS  ( INS2I ,  ETC-RAS-RV  project).
The data collection process can be extended to gather information from other sensors, such as a microphone for audio and infrared for distances. The semantic knowledge is represented in our knowledge base. We validated the studied concepts on a humanoid robotic platform through real use cases. The dual-process above-cited looks in two opposed directions: the aim is to grasp the benefits of the two groups. The first one is more abstract, and we name it top-down (TD), it contributes with semantics, and it is closer to human comprehension. The Semantic Web technologies, ontologies, linked open data, production rules systems fall into this category. Further details about the TD approach are provided in  [2]. The second one, discussed in this paper, is named bottom-up (BU). It is closer to machineunderstanding, and it is related to perception of the environment, e.
Bottom-up approach implementation As discussed in Sect. 4. 1, bottom-up approaches are based on unsupervised learning techniques largely used in machine learning and artificial intelligence research field. In particular, the approaches tested for this research make use of classical SOMs and the more advanced GHSOMs. The implementation used for both algorithms is based on two libraries, written in Python language. The first one is already packaged with the Python installation, while the second one is a third-party 1  implementation, which has been adapted to our needs. We discuss several experiments we have performed to evaluate the BU approach. Our analysis is focused in particular on the knowledge acquisition of a machine and its evolving ability; hence, we discuss both qualitative and quantitative measures given the operating context.
Data preparation At first, we manually selected 34 concepts (listed in Table  1 ) related to different semantic domains from our knowledge base. In particular, we defined 5 semantic classes. The first four are well defined, hence strongly coherent from a semantic point of view, i. e. , they share a strong semantic correlation. The last one instead includes two semantically uncorrelated concepts. The number of images per synset in the knowledge base is limited due to size constraints. Therefore, we collected further images from the service google images by using an automated Python script. We also manually checked inconsistencies in the retrieved images. By using this strategy, we   2  and 3. Figure  4  shows examples of images collected for some selected synsets. Each image was processed to extract global features (JCD, CEDD, etc.
Bottom-up approach evaluation Similarly to what we have done for the classical SOMs in our previous research  [1] , experiments have been carried out also for the approach based on GHSOMs. The same sets of data and deep features have been used. Moreover, a genetic algorithm has been developed for the  For what concerns the grouping of data in semantic clusters a slight difference has been introduced. Since the cluster of dishes is made of just three classes, it has been included in the cluster, named Others, which is used as a noise element. Hence, the meaning for colors is now as follows: red color is for animals, blue color for vehicles, yellow color for food, cyan color for others. Numbers on the right represents the labels for classes in the dataset. More in detail, Fig. 5  shows the parent map, that is the map located at the first level of the hierarchy.