dataesr/scientific-paragraphs-categorization huggingface dataset:
IMPLEMENTATION AND EVALUATION The trajectories used to implement the proposed framework are taken from the Geolife benchmark dataset  (Zheng, Li, Chen, Xie, and Ma, 2008; Zheng, Xie, and Ma, 2010; Zheng, Zhang, Xie, and Ma, 2009). The Geolife dataset was collected by 182 users over 5 years from 2007 to 2011. A large part of this data is recorded in the city of Beijing in China. The GPS points of this collection contain information such as longitude and latitude, time, date of collection, user index, and transportation mode. The data are collected using different GPS devices and mobile phones and have different sampling rates. Several modes of transportation are available such as 'walk', 'bus', 'train', 'taxi', and 'car'. Daily trajectories were selected representing 4326 points and four transportation modes (bus, train, walk, taxi) for implementation purposes, but the proposed algorithm can be extended to the entire data set and all transportation modes.
CONCLUSION AND FUTURE WORK This paper introduces a framework for spatial trajectory compression, which is a flexible extension of the wellknown DP algorithm applied to line compression. The peculiarity of this FDP algorithm is that, while also considering the spatial dimension, additional semantic constraints are considered to reflect different transportation modes that qualify the considered trajectories. Accordingly, different thresholds are derived to reflect respective displacement speeds associated with these transportation modes when applying point displacements and filtering constraints. The framework has been applied to the Geolife data set that has the advantage of qualifying trajectories according to transportation modes. The results show that the compression algorithm not only has a substantial compression rate of 0.
However, the light was only received by the side that faced the light source. Strictly speaking, it was not the gas exchange measurement but the light orientation that varied, causing inverted light gradients through the leaf. For the 2019 experiment, measurements were conducted only at the tillering stage. Light and CO 2 response curves were measured at the same position at both adaxial and abaxial sides of the leaves. The curves for net photosynthetic rate (A) response to incident irradiance (I inc ) were obtained with I inc in a decreasing series of  2000, 1500, 1000, 500, 280, 150, 100, 80 , and 50 μmol m -2 s -1 (6-8 min per step), while maintaining ambient CO 2 level (C a ) at 400 μmol mol -1. The CO 2 response curves were measured at I inc of 1000 μmol m -2 s -1 , with the C a steps of  400, 250, 150, 80, 50, 400, 400, 400, 650, 1000 , and 1500 μmol mol -1 (3-5 min per step; note that using the three repeated 400 μmol mol -1 steps was merely to re-adapt leaves, and the data from these three points were excluded in the analysis).
Both curves were measured at ambient O 2 (21%) level. To estimate day respiration (R d ) and establish a calibration factor (s) that converts chlorophyll fluorescence-based electron transport efficiency of photosystem II (PSII) into linear electron transport rate (see  Yin et al. , 2009) , we also conducted half of the light response curve (with I inc being 280, 150, 100, 80, and 50 μmol m -2 s -1 ) under non-photorespiratory conditions (2% O 2 combined with C a at 1000 μmol mol -1 ). These low light levels were applied to ensure that data for calibration were within the range where A is limited by electron transport. The low O 2 level was realized by using a cylinder containing a gas mixture of 2% O 2 and 98% N 2. For the 2022 experiment, photosynthesis was measured on both adaxial and abaxial leaf surfaces at three stages: tillering stage (TS), flowering stage (FS), and grain-filling (~15 days after flowering (DAF)).
e. J=sI inc Φ 2 ). Then photosynthetic parameters δ, V cmax , T p , and α S can be estimated simultaneously by fitting combined Equations 1, 3b, and 6 to all CO 2 exchange data from both light-and CO 2 -response curves. For data from 2022, as only CO 2 response curves were measured yet starting with C a from 400 μmol mol -1 onwards that only covered A j -and A p -limited parts, we thus combined J=sI inc Φ 2 and A j and A p parts of Equation 6 to estimate parameters δ, T p , and α S simultaneously. Once photosynthetic parameters were estimated, the transition point from A j -to A p -limited rates can be solved. We also estimated the transition point by solving the second-order polynomial regression equations that were fitted to A j and A p ranges, respectively, of A-C i curves. The estimated threshold C i was highly consistent (see Results); thus, we used the polynomial-based values for showing the transition.
Statistical analyses and curve fitting Simple linear regressions were conducted using Microsoft Excel. Nonlinear regressions were performed using the Gauss method in PROC NLIN of SAS (SAS Institute Inc. , Cary, NC, USA). An analysis of variance (ANOVA) of multiple experimental factors (i. e. genotype, adaxial versus abaxial, pruning, N level, stage), and their interaction effects on each parameter was performed in the 2022 experiment. A multiple comparison of means was then performed using the LSD (least significant difference) test.
Video surveillance was deemed as a robust and effective method to measure the exact number of honey bees at the entrance of the colony. The experimental beehive was located nearby the town of La Rochelle (France) (46°8'N, 1°8'W), 200 km from where the Asian hornet was first spotted in 2004  (Haxaire et al. , 2006; Villemant et al. , 2011). The video surveillance was carried out from October 16th to October 25th 2015. In this oceanic climate, the ambient temperature ranged between 6°C and 17°C, wind speed ranged between 11 km. h -1 and 39 km. h -1 , and the relative humidity ranged between 65 and 82%. Video tracking was performed from 9 am to 6 pm over 10 consecutive days, providing a total of 90 hours of recorded activity, corresponding to more than a terabyte of compressed data (i. e. , RGB-D videos above 50 fps).
| Automated selection of interest scenes Each video segment, for which both hornet(s) and honey bee(s) were detected, using the process just described, was automatically extracted using specifically developed software. This software included a step-by-step procedure composed of the following processes: (i) stereovision acquisition, (ii) target detection, in each image independently, on RGB-D, (iii) temporal aggregation for multitarget tracking in 3D  (Chiron et al. , 2013) , (iv) signature extraction from the individual trajectories, (v) hierarchical segmentation of the trajectory data into temporal entity, and (vi) behavioral modeling by multilevel clustering  (Chiron et al. , 2014). The video segments were then visually reviewed by an observer in order to detect potential successful predation of a honey bee by a hornet (Figure  S3 ).
| Flight performance To compare flight performances between honey bees entering or leaving and hornets, we analyzed their distribution of maximum speed, curvature, and hovering percentage. We checked for normality using an Anderson-Darling normality test (adapted for large datasets >5000 pts, using the nortest R-Package) and for variance homogeneity with a Levene test (using the car R-Package). We then used Kruskal-Wallis rank sum tests to assess differences in flight parameters between the three types of flight (hornets, honey bees entering, honey bees leaving the hive, followed by a pairwise Wilcoxon test as a post hoc test with a Bonferroni P value adjustment method (using the stats R-Package).
resulting in a dataset made of the trajectories of every insect flying in front of the beehive (Figure S2, Video S1). The subject of each trajectory (either Asian hornet or honey bee) was determined using both the flight dynamics such as max speed (i. e. , using a clustering approach inspired by Chiron et al. , 2014) as well as appearance features such as the body size. More details on size/depth ratio F I G U R E 1 Examining 3D flight performance in honey bees and Asian hornets. (a) Conceptualization of the experimental setup with a stereovision camera fixed over a hive flight board, registering all activity in the volume of air in front of the hive on a computer, which analyses the trajectories of Asian hornets (blue) and honey bees (green). (b) Daily time series of the total number of honey bees (green) and hornets (blue) (trajectories).
Funding information Biotechnology and  Biological Sciences Research Council , Grant/Award Number: grant number  BB/S015523/1 ;  European Community Program , Grant/Award Number:  European Community Program  ( 797/2004 ) for French beekeeping ( RISQAPI  project);  H2020   Environment, Grant/Award Number:  under grant agreement no  862665 ICT-AGRI-FOOD ; grant number  ANR-21-ICAF-0001.
The intracranial signals on contact pairs have been compared to simultaneously recorded scalp signals at a regional and whole-brain level, using multivariate pattern analysis. The results show that the task conditions are best captured by intracranial sensors, despite the limited spatial coverage of SEEG electrodes, compared to the whole-brain non-invasive recordings. Applying beamformer source reconstruction or independent component analysis does not result in an improvement of the multivariate task decoding performance using surface sensor data. By analyzing a joint scalp and SEEG dataset, we investigated whether the two types of signals carry complementary information that might improve the machine-learning classifier performance. This joint analysis revealed that the results are driven by the modality exhibiting best individual performance, namely SEEG.
, 2012). These are known to involve lateral and deep structures such as the hippocampus  (Rutishauser et al. , 2006; Merkow et al. , 2015). Recognition memory has been extensively studied with EEG, using recordings made either on the scalp  (Ratcliff et al. , 2016)  or in the brain  (Merkow et al. , 2015) , but not simultaneously. Here, we assess to what extent the postulated processes are visible on scalp EEG by validating the source localization results with the simultaneous scalp intracranial recordings. The data are collected from patients undergoing stereoelectroencephalographic (SEEG) pre-surgical evaluation for drug-resistant epilepsy; they performed a standard task requiring them to encode and later recognize pictures of objects  (Besson et al. , 2012; Despouy et al. , 2020). In animal studies, memory processes have been widely studied using electrophysiological recordings.
In addition, as part of this research protocol, scalp electrodes were attached, allowing for simultaneous surface and intracranial long-term recordings. This study included only patients with unmodified anatomy, no previous major resection, and no major cognitive deficit. The study has been performed under Bucharest University ethical committee approval CEC 23/20. 04. 2019. All patients, or their legal guardian/next of kin, signed a written informed consent, in accordance with the Declaration of Helsinki, for the simultaneous recordings and data sharing procedures.
Experimental paradigm We used the same experimental visual memory paradigm as in  López-Madrona et al. (2022). In summary, we used 168 images from the database of  Duñabeitia et al. (2018)  that were organized in blocks of 12 or 24 images, presented on a computer screen. There were two block types: encoding ("ENC"), where a set of 12 images were presented to the patient, followed by a recognition block where the same 12 familiar images ("OLD") were randomly interleaved with other 12 novel images ("NEW"). The patient was required to indicate by pressing two buttons on the keyboard, using two fingers of the right hand, whether the images were familiar or not, within 1500 ms. A distracting video of 1 min was presented in between encoding and recognition blocks. The sequence of 36 image presentations was repeated seven times using different images from the 168-image set and pseudo-random distribution of the OLD and NEW items, with the constraint that there were never more than three "old" or "new" items in a row.
Raw data were acquired at a sample rate of 4096 Hz. The hardware was synchronized using digital triggers to both systems and a 50 Hz sine reference signal, recorded simultaneously using DC inputs of the two systems. Patients 9-12 were recorded with a single Quantum 256-channel amplifier that did not require external synchronization hardware. The data were combined and saved in a single file in AnyWave ADES format  (Colombet et al. , 2015) , containing both types of signals. The analysis workflow is shown in Figure  1. The synchronization between stimuli presentation and (S)EEG recordings has been performed using a photodiode part of the Chronos response box (Psychology Software Tools, Pittsburgh, PA) attached to a corner of the screen where trial start synchronization flashes were presented.
Source localization To test the inverse solution of scalp EEG for finding brain areas that are involved in task decoding, we have calculated source signals at the location of the intracranial electrodes. To achieve that, we have performed a beamformer analysis on the standard FreeSurfer's fsaverage template, brain electrical model, and 10-20 electrode positions available in MNE-Python  (Gramfort et al. , 2014). The beamformer spatial filters calculated using linearly constrained minimum variance (LCMV) (  Van Veen et al. , 1997)  were used to calculate source time courses on a 5-mm grid covering the brain. The source time course on the grid point nearest to the midpoint between a pair of SEEG contacts that were part of a bipolar-recorded signal was considered to approximate the source signal at each intracranial site.
Multivariate pattern analysis For multivariate pattern analysis, we have generally followed the workflow described in  Grootswagers et al. (2017). The processing has been performed using the MNE-Python toolbox  (Gramfort et al. , 2013 (Gramfort et al. , , 2014) )  and custom Python and MATLAB (MathWorks, Natick, MA) scripts. A logistic regression linear classifier was trained to discriminate between responses for the OLD and NEW conditions using the L-BFGS-B -(Large-scale Bound-constrained Optimization) solver. The model was fitted to the standardized data, and its performance was scored using the receiver operating characteristic (ROC) area under the curve (AUC). The scores were evaluated using 20-fold cross-validation, and time intervals where they were statistically different from chance were evaluated using a one-sample permutation cluster test applied to the set of scores calculated for each fold  (Maris and Oostenveld, 2007).
The processing pipeline was applied to SEEG bipolar signals, to the EEG signals, to the entire set of independent components of scalp EEG, or to the scalp source signals at the SEEG sensor location obtained using a beamformer. Specific to our study, the simultaneous collection of the scalp and SEEG data allowed the pooling of the signals for the two modalities to investigate whether combined data provide a classifier performance significantly different from analyzing individual sets. We have calculated the contribution of signals at each intracranial sensor location (recorded or reconstructed) to the recognition process by calculating the activation patterns associated with fitting the data with a linear model  (Haufe et al. , 2014)  using the MNE-Python toolbox, which in turn resorts extensively to scikit-learn Python toolbox  (Pedregosa et al.
Results A total of 136 intracranial electrodes having 1,885 contacts were implanted in 12 patients. Additional 436 surface electrodes were attached to the scalp. After data curation and application of inclusion criteria, signals recorded at 965 intracranial sites and 391 scalp locations were further included in the analysis. The subjects correctly identified stimulus novelty in 89. 53% of the trials. The MVPA analysis was applied to 1,729 correct recognition trials (OLD: 822, NEW: 907) having a mean ± SD response time of 719. 1 ± 162. 4 ms (OLD) and 765. 0 ± 191. 2 ms (NEW).
Conclusion Analysis of invasive EEG provides the highest amount of information related to stimulus novelty, compared with scalp recordings, despite the limited spatial sampling of the brain with depth electrodes. This may be related to the limited scalp visibility of the activity related to memory processes in deep brain structures, particularly if containing higher frequency components. The synergy between the two modalities-enabled by pooling data recorded simultaneously-is limited, with the SEEG sensors providing the best decoding performance driving the combined, overall, performance.
Hirshfeld surface analysis The Hirshfeld surfaces were calculated using Crystal Explorer  24, 25. This program allows the normalized contact distance d norm to be mapped onto the generated Hirshfeld surface. It is customary to map d norm using a red-white-blue scheme, where red denotes close intermolecular contacts (negative d norm ), blue denotes longer contacts (positive d norm ) and white denotes intermolecular contacts equal to the van der Waals radii of atoms in contact (d norm = 0). It is possible to obtain two-dimensional plots (fingerprint plots) from the surfaces mapped with d norm values. Derived from the Hirshfeld surface, these 2D-fingerprint plots provide a visual summary of the frequency of each combination of d e (radius of external atom) and d i (radius of internal atom) across the surface of a molecule, so they not only indicate which intermolecular interactions are present, but also the relative area of the surface corresponding to each kind of interaction.
The light irradiation of 1 and 2 at 365 or 405 nm leads to the isomerization of the Ru-NO (GS) group of the ruthenium cation trans-[RuNOPy 4 Br] 2+ to the metastable isomer Ru-ON (MS1). In 2 the side-bond isomer Ru-η 2 -(NO) (MS2) can be photoinduced as well. Based on the IR-spectroscopy data the total population of linkage isomers in both compounds does not exceed 3%. Importantly, the backward reaction MS1/MS2→GS can be induced by red-infrared light. The thermal stability of both metastable states was investigated by IR-spectroscopy in isothermal mode. It was found that MS1 decays efficiently at T>200 K and MS2 is less stable decaying at T>140 K. For the compound 2 the activation energies (E a ) and frequency factors (lgk 0 ) of the MS1→GS and MS2→GS reactions were determined amounting to 38.
Acknowledgements The work has been supported by grant of  Russian Science Foundation  ( 22-43-09001 , https://rscf. ru/project /22-43-09001/) and  ANR  (grant no. ANR-21-CE30-0045-01 ) in the part of synthesis of ruthenium compounds and spectroscopic experiments. The research was supported by the  Ministry of Science and Higher Education of the Russian Federation , N 121031700315-2 in the part of physical chemical characterization of ruthenium complexes. Artem Mikhailov  is grateful for financial support from the Metchnikov bourse program 2021. Authors thank  Piryazev D. A. for the measurement of X-ray diffraction of 2 single crystal on XRD Facility of  NIIC SB RAS.
Any quasi-smooth derived scheme is Zariski-locally presented as the (derived) zero locus of a section of a vector bundle on some smooth scheme. The Lefschetz hyperplane theorem then gives a way of understanding the cohomology of such a zero locus from the data The author acknowledges funding from the grant of the Agence Nationale de la Recherche "Categorification in Algebraic Geometry" ANR-17-CE40-0014. of that of the ambient scheme and of the vector bundle. The quantum Lefschetz principle, similarly, gives the quantum cohomology, that is the Gromov-Witten theory, of the zero locus from that of the ambient scheme and the Euler class of the vector bundle. Let X be a smooth projective variety and let E be a vector bundle on X, and consider the abelian cone stack R 0 p * ev * E on M g,n (X, β), where ev : C g,n (X, β) → M g,n (X, β) is the canonical evaluation map (corresponding by the isomorphism C g,n (X, β) ≃ M g,n+1 (X, β) to evaluation at the (n+1)th marking) and p : C g,n → M g,n is the projection.
1] via derived geometry. The derived moduli stack of stable maps RM g,n (X, β) was constructed in  [CFK02]  and  [STV15]. Finally,  [MR18]  showed that the virtual structure sheaf really is given by the structure sheaf of the derived thickening, or rather its image by the isomorphism expressing that G-theory does not detect thickenings. Hence, in order to understand theorem A from a completely geometric point of view, the role of the virtual classes should indeed be played by derived moduli stacks. We may now state the main result of this note, which addresses the question of similarly understanding the virtual statement of the quantum Lefschetz principle as a derived geometric phenomenon, and of deducing an expression for the "virtual structure sheaf" of γ M g,n (Z, γ), understanding along the way the appearance of the Euler class of the bundle.
Analyses were conducted using a NanoSight NS500 system (Malvern Panalytical, UK) in light scattering mode using the EUNCL PCC-023 validation protocol  [43]. The system is equipped with a 405-nm laser and operated using NanoSight 3. 2 software. NPL and NP samples were incrementally diluted, in line with the Malvern Panalytical optimization protocol and as presented in the EUNCL PCC-023 SOPs, to achieve an optimal concentration of 20-100 particles per field acquisition (camera field) at a final working volume of 1 mL. A total of six videos of 60 s were recorded for each sample, the detection threshold during analysis was selected to ensure that only distinct nano-objects were analyzed, and the value was kept constant during the recording.
The resulting size distribution is absorbance based and can be transformed to mass-and number-based distributions with the help of further additional input data on optical properties at the applied wavelength  [51, 52]. Shape also affects sedimentation speed; therefore, the instrument manufacturer suggests correcting the apparent density of the particles following a semi-empirical equation to gain proper equivalent sphere diameter data  [53]. Experiments were performed using a UH 24000 disk centrifuge (CPS Instruments Europe, Netherlands) equipped with a sedimentation-type disk and a 405-nm light source. A 0-8% sucrose gradient in water (9 steps, each 1. 6 mL) was applied at 22,000 rpm rotational speed. Polyvinyl chloride NPLs (d = 237 nm) were used as a calibrant before each injection. For non-spherical particles (i.
Electron microscopy (EM) Electron microscopy (SEM and TEM) is a group of imaging techniques based on the interaction of an electron beam with the sample. Size, shape, and surface characteristics can be obtained using these methods  [4]. However, representativeness of the acquired data might not be sufficient, especially for heterogeneous samples, due to the low number of particles that can be measured. Furthermore, sample preparation by solvent evaporation may lead to agglomeration which can be avoided using more advanced techniques like environmental SEM or cryo electron microscopy  [10]. SEM measurements were performed using a Sigma 300 VP Field Emission SEM (FE-SEM) from Carl Zeiss AG, Germany, equipped with secondary electron and in-lens detectors. A 30-µm aperture was used for all measurements.