dataesr/scientific-paragraphs-categorization huggingface dataset:
The rotation matrix evaluated using the accelerometer is calculated as R A = R A (φ)R A (γ ) (8) where R A (φ) =   1 0 0 0 cos(φ) -sin(φ) 0 sin(φ) cos(φ)   , and R A (γ ) =   cos(γ ) -sin(γ ) 0 sin(γ ) cos(γ ) 0 0 0 1   Furthermore, accelerometer signals do not contain information about the rotation around the vertical axis (yaw angle) and hence do not provide orientation information  [36]. Hence, we need to fuse both sensor readings for compensating the head movements in egocentric videos. c: FUSING GYROSCOPE AND ACCELEROMETER SENSOR DATA  [37]  Accelerometer and gyroscope orientations are achieved by using rotational matrices. Since accelerometer and gyroscope sensors offer advantages by providing the motion and angular velocities respectively, a single estimate through the fusion of both sensors is estimated as follows: R = R A R G ( 9 ) The 3D gaze information (gaze and eye positions) a(t) at time instant t is multiplied with R to compensate for head movements.
D. REPRESENTATIVE SCANPATH IDENTIFICATION We identified the gaze samples as either fixation or saccades and evaluated their scanpaths. At this stage, we identify a pattern in scanpaths considering all the participants gaze data for all museum exhibits separately. We call this generalized viewing pattern (GVP) of an exhibit as the representative scanpath. We follow a procedure similar to the one detailed in  [11]. Though there are a few other works for this operation, we consider this since others have not discussed representative scanpaths using gaze duration analysis. This procedure consists of three parts: 1) preprocessing of eye gaze data, 2) scanpath aggregation, and 3) gaze duration analysis. In order to identify the viewing pattern, we aim to exploit the fixation order, duration and position.
V. STATE TRAJECTORY ESTIMATOR BASED ON ANCESTOR SAMPLING (STEAS) In this section, we demonstrate the application of EG-SNIK dataset on a statistical learning model for characterizing the inherent gaze dynamics of human beings. We have reported this model in  [12]  as STEAS. First, from EG-SNIK dataset, we plot the velocity distribution of a random viewer observing an exhibit in Fig. 10. It is observed that the fixations (in red) and saccades (in green) are identified with a threshold of 90 degree per sec (as explained earlier). We use a Gaussian Mixture Model (GMM ) on this distribution and use the corresponding model parameters for approximating the gaze transitions empirically. A scanpath is shown in Fig. 11  corresponding to the data shown in Fig. 10. A. VELOCITY DISTRIBUTION ANALYSIS USING N-WAY ANOVA Fig.
10  shows the velocity distribution for the gaze data of a random viewer watching a museum exhibit. We would like to observe whether the velocity distributions of saccades and fixations follow a typical pattern for any viewer's gaze information watching any exhibit. Hence, we randomly choose gaze data of 20 viewers and analyse their gaze velocity variations using N -way analysis of variance (ANOVA). It also ensures the velocity variations of fixations and saccades are the same for all the 20 viewers. A p-value greater than 0. 05 indicates no significant difference between variations in three state velocities between velocity vectors of 4 groups. Table  2  shows the variations in their parameters. Table  3  shows the ANOVA results for the data in Table  2. We see that p-values are 0. 75 and 0.
B. GAZE DATA CLASSIFICATION AND VIDEO RETRIEVAL 1) CLASSIFICATION Table  4  shows the performance comparison of STEAS model with other related models on EG-SNIK dataset. STEAS model is found to perform better than the other models. This happens since the state and observation equations of our statistical model are designed after studying the dynamics of our data and its noise variability. Thereby, the tuned parameters are optimized for this dataset resulting in its optimal performance during classification. However, it is not the case with the other models in Table  4. SubsMatch 2. 0  [52]  considers smaller subsequences from the gaze data of a viewer and understands how frequently they occur based on AOIs. These subsequences are used for training an SVM model providing certain ranks to the features based on their discriminative power.
However, since our data has diverse exhibits with just two or three interesting objects in them, participant may not have very frequent visits to them, thus, SubsMatch 2. 0 results in inferior performance. MinHash  [53]  also works in a similar style as that of SubsMatch 2. 0. But, MinHash approximates the Jaccard Index considering only a few subsequences, and therefore cannot achieve the classification performance of SubsMatch. HMM based model performs comparatively closer to our approach since ours is also a statistical model. However, HMM based model involves a large group of parameters whose roles and weights are hard to interpret  [25]. It is also one of the major drawbacks of the HMM based model approach. Thus, it results in slightly inferior performance on our EG-SNIK dataset. Hence, a promising value of classification accuracy highlights that the STEAS model extracts inherent features from the gaze data irrespective of the viewer.
VII. CONCLUSION In this work, we propose a novel egocentric vision dataset acquired at a technological museum in IIT Kharagpur (named as EG-SNIK dataset). We also propose an end-to-end pipeline of processing the data on its video and gaze data streams. A group of 25 students aged between 18 and 35 years with normal vision wear Tobii Pro Glasses 2 eye tracker and observe musuem exhibits. We then build a representative scanpath for each of the museum exhibits aggregating 25 viewers' filtered gaze data. We employ STEAS model to extract inherent gaze features from raw gaze data and optimize it while learning on the training set of EG-SNIK. The optimized model is evaluated on the EG-SNIK test set for gaze data classification and retrieval. We witness a superior performance of STEAS model over other techniques with 77%, 0.
727, 0. 705 of accuracy, precision and recall, respectively for classification and a MRR of 0. 722 for retrieval. The proposed dataset is readily useful for many computer vision problems like object detection, semantic segmentation, etc. Our work has these advantages over others: 1) data collected using the state-of-the-art Tobii pro glasses 2, 2) addressed all possible issues and made the dataset ready-to-use, and 3) shown two vision related use-cases, i. e. , classification and retrieval.
This work was supported by the  Institute Challenge Grants, IIT Kharagpur , through the project titled as '' Visual Attention Assisted Image and Video Compression '' under Grant  IIT/SRIC/CS/VIV_ICG_2017_SGCIR/2018-19/085. This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the  Institute Ethical Committee, IIT Kharagpur.
Integrating water and solute transport models within RSA could therefore provide a means to account for all the pre-cited aspects and, ultimately, investigate the multiple effects of WD on root water and solute transport parameters. Boursiac et al. recently  proposed an inverse modeling approach, based on pressure chamber measurements in excised roots, to simultaneously assess axial conductance and radial conductivity of complex branched root structures  (Boursiac et al. , 2022a). This approach, called cut-and-flow, consists in measuring exuding sap flow rate, at a constant working pressure, in a root system that is successively cut from its distal part. A Hydraulic Tree Model named HydroRoot (DOI: 10. 5281/zenodo. 6584200) was inverted to fit the cut-and-flow data, thereby allowing a determination of the axial conductance profile and radial conductivity on the same root system.
We used the optimize. minimize function of the SciPy Python library  (Virtanen et al. , 2020)  to perform these minimizations. Local optimization routines were used because global optimizations, such as dual annealing, were often unable to get a correct fit. Due to the heterogeneity of the plants and condition panel, an identical numerical workflow was sometimes not possible. Thus, we used different optimization routines, running several times the optimization process. The main workflow was: (i) to start from the first guesses with the adjustment of K and k on the cut-and-flow data without any solute transport using the workflow of  (Boursiac et al. , 2022a). (ii) to keep the K obtained in (i), adjusting k, J s * and P s on the J v (P) data. Here, the solver of optimize. minimize that was mainly used was Sequential Least Squares Programming (SLSQP).
Jv(P) and cut-and-flow experiments in roots under control and WD conditions Focusing on functional responses of maize primary roots to WD, the pressure chamber technique was used to measure the flow-to-pressure relationship [J v (P)] and obtain cut-and-flow data in detopped roots. WD was induced by addition of PEG-8000 (150 g/L), lowering the water potential of the bathing solution to -0. 336 MPa. Three different sets of growth and experimental conditions were used. Control (CTR) plants were grown in a standard hydroponic solution and de-topped roots were studied in the same solution. PEG plants were exposed to WD for 5 d with water transport measurements done in the same PEG bathing solution. PEG-CTR roots were derived from plants grown in a PEG solution and transferred into a control solution for one hour prior to pressure chamber measurements in a control solution.
The four other curves, derived from PEG and PEG-CTR roots, show a less convex shape, with higher slopes from the first cut. Thus, the cutand-flow curves catch the impact on root functional properties of plant growth under WD. The same conclusions can be drawn from J v (P) and cut-and-flow data obtained in a total of 15 additional CTR, PEG or PEG-CTR roots (Supplemental Figures  2, 3  and 4 ).
91 (Supplementary Fig. S5 ). Figure  7  shows the axial conductance (K) profiles obtained by Locally Weighted Scatterplot Smoothing (lowess) of CTR (n=8), PEG (n=8) and PEG-CTR (n=5) data sets. The K profiles of roots grown in the presence of PEG, whatever the J v (P) measuring solution (PEG or CTR), are very close to each other. Since measurements in PEG-CTR roots were performed one hour after their transfer into a CTR solution, we assume that their xylem anatomy (and therefore their K profile) was similar to that of PEG roots. Thus, these results indicate that the bathing solution used during the cut-and-flow experiment, and in particular the presence or absence of PEG, does not interfere with the determination of K. As a consequence, our approach reveals, with respect to CTR roots, a dramatic increase of K in root tips grown under WD.
Determination of radial hydraulic conductivity and solute transport parameters Figure  8  shows the distribution of the three radial transport parameters giving the best fits. With respect to CTR roots, PEG roots showed a tenfold decrease in mean radial hydraulic conductivity (k). This result is in accordance with the decrease in hydraulic conductivity commonly observed in roots under WD  (Steudle, 2000b) , such as in Arabidopsis grown in a 150 g/L PEG solution  (Rosales et al. , 2019). PEG-CTR roots showed k values similar to those of CTR roots pointing to a quick reversal of the WD-induced inhibition of k. The solute transport parameters (J s * , P s ) were more difficult to interpret with respect to known effects of WD. This difficulty was reflected by the fact that the CTR and PEG-CTR data were very scattered.
The Jv(P) curve shape The J v (P) data reported in the present work showed two key properties: negative sap flow rates specific to PEG roots, and a non-linearity that was particularly accentuated in PEG roots. A nonlinearity of sap flow according to applied pressure has already been described in maize  (Gorska et al. , 2008; Miller, 1985b)  or other plant species  (Fiscus, 1986; Jackson et al. , 1996; Markhart and Smit, 1990; Rüdinger et al. , 1994). For instance, Miller  (Miller, 1985b)  reported J v (P) curves that were linear for positive relative pressures but showed a non-linear shape at negative relative pressures. Gorska et al. (Gorska et al. , 2008)  observed that the J v (P) relationship can be considered as linear only for pressure above 0. 1 MPa. These reports are in accordance with our measurements in roots bathing in a CTR solution, whatever the plant growth conditions (CTR or PEG).
7 ± 29. 6, 2. 3 ± 0. 7 and 12. 5 ± 3. 0 mol. m -4 , for the CTR, PEG and PEG-CTR roots, respectively. As discussed above, the coupling of solute and water transport in the present root model prevented from significant errors in hydraulic parameter determination. In addition, the model had to represent the possible inflow of PEG in the root vasculature during cut-and-flow experiments. Effects of PEG on both radial osmotic potential differences and sap viscosity had to be modeled throughout RSA. To assess these additional modeling constraints, we closely inspected the physiological consistency of data obtained in plants exposed to WD. In brief, both PEG-CTR and PEG plants were submitted to WD during their growth. Yet, the roots of the former plants were exposed to a CTR hydroponic solution for one hour prior to water transport experiments.
Conclusion In the present work, we have extended the toolbox developed by  (Boursiac et al. , 2022a) , combining experiment and modeling to assess the water and solute transport parameters of maize root systems under WD. The experimental data consisted of two successive measurement sets done with a pressure chamber: J v (P) determination, followed by a cut-and-flow experiment. A critical step was to formalize solute transport to account for the specific shape of the J v (P) curve of plants under WD. Furthermore, maize roots grown under WD were investigated under varying measurements conditions, thereby assessing the robustness of our inverse modeling approach. Thus, this novel procedure allows a concomitant determination of water and solute transport parameters and of their alteration upon exposure to WD.
However, other formats may be relevant as well such as audio  [50] , annotations  [13, 15]  or design files  [72]. Going beyond these universal types of capture material, many activities can be recorded in the form of structured data specific to the fabrication context. For example, Troxler  [74]  introduced FabML, a specification format based on XML to capture and exchange fabrication activities. FabML aims to provide a structured language to capture an activity together with relevant contextual information such as a personal identification, the name of a project, the processes and methods involved, the used machines (e. g. , 3D printer), tools (e. g. , screw driver), components, materials and the type of source files as well as corresponding spatio-temporal data such as location or timestamp  [57] , tags (which can add semantics to captured content)  [64] , information about the environment  [37] , or serial output  [38].
Some fabrication workshops provide makers with community devices to capture content, often in the form of dedicated stations specifically for capturing image and video. They generally include one or multiple cameras pointing towards an area reserved for capturing artifacts during a project, some form of lighting system to ensure good visibility, and in some cases additional mechanisms to facilitate the annotation of a project. Examples of such stations include: Dodoc  [28] , which features different modules including a lamp and a fixed camera pointing down at a documentation area to record video, still images, or build animations; Protobooth, which uses either a turntable  [38]  or several fixed webcams pointing toward a platform to capture different point of views of a prototype  [21] ; Fabnavi  [78] , a camera/projector assembly mounted on top of a table; DIY mounted toolkits to hold a smartphone or tablet and enhance the salience of the capture device  [36] ; and Spin, a camera combined with a turntable to generate animated GIFs of a 360 • overview of a fabricated object  [76].
n=66 first main activity. Consequently, out of the 49 people who indicated doing electronics (as shown in Figure  3 ), 19 where queried on their current habits regarding electronics (as shown in Figure  4 ) and only 12 were queried how Capush could be used to capture an electronics activity. How to capture. A clear majority of respondents appreciated the concept of Capush to capture content (see Figure  8 ) and rated it as better than what they are currently using both concerning capturing itself and retrieving content from a shared repository. In particular, they appreciated not to have to use their own device and risk to break them: "I don't have space on me to carry a phone all the time; I don't want to damage my own camera; it might be a good reminder to take process pictures" -p62. However, other respondents also wished for openness, to be able to continue to use their personal devices and software tools together with Capush.
g. , programming or designing) that are not covered by Capush, such as code files, CAD files or sketches; and, (2) the parameters of machines and tools (e. g. , Gcode), which could be saved and associated with the captured content. These two types are not currently included in the list of values for the What dimension of our framework. How. As we mention in the discussion subsection 7. 1, designing systems that can integrate dedicated capture devices with existing ones would enable makers to also use equipment that they own and master. It is also possible to include contraptions which provide some of the functionality of a Capush unit but with personal devices, for example, a clamp with a flexible arm which could either hold a mobile unit or makers' own devices. The combination of a clamp and a flexible arm would enable more precise manual control over camera viewpoints but with makers' own devices.
Identification of MITEs in H. zea Both H. zea and H. armigera genomes were sequenced by Pearce et al. (2017) using the same technologies, from pupae of laboratory colonies derived from material collected 20 years ago  [15]. The genomes were assembled by the same team using AllPaths version. JAN-2012 method. The quality of the two assemblies was tested using the QUAST tool  [23]  (Figures  S1  and S2 ). The H. zea genome available in GenBank-NCBI (BioProject PRJNA378438) is 341,147 Mb. This genome is assembled in 27,984 contigs and 2975 scaffolds corresponding to 20,602 kb and 201,477 kb N50 in length, respectively  [15]. The H. zea assembled genomic sequences were downloaded and submitted to the MITE tracker tool with default parameters  [6]. To find MITE candidates, MITE Tracker first searches for valid inverted repeat sequences of a given length (between 50 and 800 bp).
e. , 3 by default). Finally, identified MITEs in H. zea and MITE sequences of H. armigera from previous study  [21]  were searched against GenBank, Repbase and i-MITE databases to find homologous MITEs from other insect species  [9, 25, 26]. The evolutionary dynamics of MITEs was analyzed using the "TE" package implemented in R and based on statistical models  [27]. This package estimates the age distribution based on mismatch values reflecting the deletion rate and random mutations in the sequences.
MITE Sequences Comparison in the H. armigera and H. zea Genomes All annotated MITEs in H. armigera and H. zea were compared using BLAST searches with thresholds of 80% alignment length and 80% identity. The obtained MITE sequences were classified into families and superfamilies, using the 80:80:80 similarity rule and according their TSD sequences  [28]. The identified MITE sequences in the two genomes were compared to each other and clustered based on a sequence identity higher than 80% and a minimum alignment coverage of 80% to their longest sequence. The obtained lineages were visualized by Cytoscape 3. 7. 2 software  [29]. Additionally, the clustered MITE sequences were aligned based on a MUSCLE sequence alignment and a phylogenetic tree was generated using the maximum-likelihood method with 500 replications of bootstrap in MEGAX software  [30].
CACTA, PiggyBac, hAT, Transib and Mavercik were the less represented superfamilies in the two analysed genomes. (*) Values from our previous study  [21]. Blast analysis of all MITEs identified in the two lepidopteran genomes against the Repbase and iMITE databases showed that H. armigera carries only one MITE which has a high similarity (83%) with a PIF/harbinger MITE from Bombyx mori. For H. zea, also only one MITE having an identity of 85% with a PIF/harbinger MITE from Spodoptera frugiperda was identified. The MITE dynamic was evaluated, and the age distribution of these MITEs showed an expansion of MITE sequences at ∼90 million years ago (Mya) in H. armigera and a peak at 30 Mya in H. zea (Figure  S3 ).
Three MITE sequences were previously identified in the genome of H. zea and were inserted in the cytochrome P450 gene CYP9A14 (GenBank accession DQ788840. 1) [22]. Blast searches with the CYP9A14 gene sequence identified the genomic scaffold KZ117493. 1  (35, 331  bp in length) in the public H. zea genome assembly (80% identity and 65% query cover). The gene position in this scaffold was between 13,600 bp and 16,000 bp, and only one MITE sequence, located from 17,076 bp to 17,206 bp, was identified upstream the gene. This MITE was 130 bp and belonged to the TC1/mariner superfamily (Table  3 ).
Eight novel families of MITEs were discovered in the African malaria mosquito, Anopheles gambiae  [5]. In Bombyx mori, 17 MITE families with a total of 5785 members were identified  [8]. However, MITE sequences available in databases are very restricted. The database of transposable elements, Repbase, contains only seven insect MITE sequences from Aedes aegypti  [25]  and the iMITE database include 6012 MITE sequences from 98 insect genomes  [9]. Blast analysis of MITEs against Repbase and iMITE databases have shown a high similarity for only two MITEs of H. armigera and H. zea with two known MITEs in Bombyx mori and Spodoptera frugiperda, suggesting that the two lepidopteran genomes contain a high number of new or unknown MITE sequences. This may also be due to the lack of significant sequence conservation between MITEs of divergent species.
Theoretical data for the CO-CO collisional system also exist. 15 The calculations were performed by combining the time-independent closecoupling method in the low collisional energy regime with the multi-configuration time-dependent Hartree (MCDTH) approach at higher collisional energies. However, the available rate coefficients only cover transitions from the rotational level j 1 = j 2 = 0 to levels with j 0 1 ; j 0 2 o 4, j 1 and j 2 being the rotational states of the two colliders. Our goal in the present work is to improve existing data by performing time-independent quantum calculations for rotational energy levels of CO up to, possibly, j 1 = j 2 = 10 that can be used in modeling of cometary atmospheres. In this paper, we present cross sections and rate coefficients for transitions between the first 37 rotational states of the CO-CO system (e.
Scattering calculations All calculations presented in this work were performed using the MOLSCAT (version 14)  23  scattering code. 2. 2. 1 Scattering calculations of two identical molecules. Before proceeding with the details of the scattering calculations, we report an issue with the MOLSCAT code found during this work. Because the scattering system consists in two identical molecules and in order to consider the exchange symmetry of the system, the IDENT option should in principle be applied. Such an option considers the fact that the basis functions corresponding to ( j 1 , j 2 ) and ( j 2 , j 1 ) are indistinguishable and that only one should be kept, allowing a reduction of the number of channels in the basis by a factor B2. Such a reduction allows saving a significant amount of computational time and memory.