dataesr/scientific-paragraphs-categorization huggingface dataset:
Modeling studies 2. 4. 1 | Molecular docking Molecular docking studies were then performed in an endeavor to clarify the kinase inhibition activity of compounds IIIa and VIIIb, the two most active indoline derivatives, through illustrating their positing and fitting into the EGFR active binding site, compared to the standard drug erlotinib. Firstly, to validate the docking protocol, redocking of erlotinib into EGFR (PDB code: 4HJO) using AutoDock Vina was carried out. The redocking of erlotinib gave a value of root-mean-square deviation (RMSD) of 0. 72 Å, predicting a correct cocrystallized binding pose.
compounds were synthesized and evaluated against the NCI F I G U R E 5 Molecular interactions of EGFR tyrosine kinase and erlotinib at the domain active site (PDB code: 4HJO). Key bonds are shown by dashed lines connecting the atoms. Ligand is colored by element type (C, grey; N, blue; O, red; S, yellow; F, green). EGFR, epidermal growth factor receptor. F I G U R E 6 Molecular interactions of EGFR tyrosine kinase with compound IIIa (a and b) and compound VIIIb (c and d) at the domain active site. The compounds are shown as stick representation and the enzyme is represented as cartoon mode. EGFR, epidermal growth factor receptor. 60-cancer cell line panel. The SAR studies indicated that a fluoroindoline moiety is more favorable than a bromoindoline group. Moreover, the presence of an ortho-fluorophenyl group substituting the hydrazinylidene moiety increased the antiproliferative activity of the compounds.
2. 4 | 4 Modeling studies 2. 4. 1 | Molecular docking Molecular docking studies were then performed in an endeavor to clarify the kinase inhibition activity of compounds IIIa and VIIIb, the two most active indoline derivatives, through illustrating their positing and fitting into the EGFR active binding site, compared to the standard drug erlotinib. Firstly, to validate the docking protocol, redocking of erlotinib into EGFR (PDB code: 4HJO) using AutoDock Vina was carried out. The redocking of erlotinib gave a value of root-mean-square deviation (RMSD) of 0. 72 Å, predicting a correct cocrystallized binding pose.
(A) (B) (C) Figure 1. Comparing shapes is a central problem in computational geometry and computer graphics. This problem is exacerbated when those shapes cannot be matched partially. We propose an algorithm that combines multiple techniques from statistical physics from physics-based shape signature to alignment based on optimal transport to solve this problem. When registering the head of a cat (orange) (A) to the full corresponding cat using shape signatures and either balanced optimal transport (B), or unbalanced optimal transport (C), our algorithm enables either full or partial matching, respectively. We are well aware that with the increase of computing power and the number of shape datasets available, deep learning techniques dominate the domain of shape comparisons (see, for example,  [49] [50] [51] [52] ).
Applications of deep learning, however, are contingent on the access to large datasets of shapes that are relevant to the shapes under study. It is not our intent to compete with such approaches. Instead, we focus on a physics-based approach that provides an alternate framework for solving partial shape matching. Ultimately, our formalism should prove useful for developing better loss functions for machine learning. The paper is organized as follows. In the next section, we introduce the different elements of our framework, namely, signatures of vertices on surfaces and unbalanced optimal transport. In the results section, we compare the two types of signatures considered, as well as the two types of OT solutions proposed for registering those signatures, on nonrigid full and partial 3D matching examples, as well as on anatomical datasets.
( 28 ) The normal n at v is then computed as the cross product of the two dominant eigenvectors of C. The whole mesh M is then rendered onto the plane P(v) that is normal to n and passing through v, using a method adapted from Patch Software Render by Dirk-Jan Kroon  [60]. The corresponding image size is set to 21 × 21 pixels, with each of its dimensions covering 2 × S(v) on P(v), centered at v. SIFT descriptors are then computed for the point at the center of the image (which corresponds to v) using the method implemented in VLFeat  [61]. Two sets of descriptors are computed, for the image and for image rotated by 180 • , and averaged out. This leads to a vector of 128 descriptors that forms the LD-descriptor of v. Given LD-SIFT signatures for all vertices in a mesh, the distance between two such vertices is computed as the L2-norm of the differences between their signatures.
Experimental Results In this section, we present experimental results highlighting the relative advantages of using either the LD-SIFT or WKS vertex signatures combined with balanced or partial OT to find the correspondences between vertices, for full and partial 3D shape comparisons. We perform experiments focused on shape similarity and correspondence. We tested our different procedures first on the synthetic TOSCA dataset  [65, 66] , available at http://tosca. cs. technion. ac. il/book/resources_data. html, accessed on 1 March 2019. We used eight classes of objects from this dataset (see Figure  2 : cat, centaur, horse, dog, seahorse, two male figures (Michael and David), and one female figure (Victoria)). Each class consists of the same shapes under different poses and/ or different triangulations.
The differences between the poses echo nonrigid transformations within objects (see  [65, 66]  for details). In addition, the poses within a class are represented with different topologies (i. e. , different triangulations), and may even have different genera. The corresponding dataset, TOSCA8, includes six instances in each class, i. e. , a total of 48 classes. Each pose is represented with a triangulated surface mesh with approximately 3400 vertices and 6600 faces, with the exception of the seahorse meshes that include approximately 2100 vertices and 4200 faces. In the first set of experiments, we compared four different approaches for shape similarity, with two different signatures for representing the vertices of the shape, LD-SIFT and WKS, combined with two different methods to compute the correspondences between those vertices, namely, balanced (with fixed masses) optimal transport, which we will refer to as simply OT, and unbalanced (with variable masses) optimal transport, which we will name Partial OT.
Anatomical Data Our second test considers three anatomical datasets, representing three different parts of the skeletons of primates. The first dataset includes 61 shapes corresponding to the proximal first metatarsals (MT1) of prosimian primates, the second dataset includes 45 shapes of the radii of apes and humans, and the third dataset includes 116 molars of prosimian primates and nonprimate close relatives (see Figure  4  for examples in each dataset)  [14]. For each dataset, we performed an all-against-all comparison experiment. We used either the LD-SIFT or the WKS signatures in conjunction with unbalanced OT to find the correspondences between vertices. To evaluate the performance of our approaches, we compared the outcomes to those determined by Boyer et al. [14]. We ran two classification analyses on each dataset with a "leave one out" procedure: each surface (treated as unknown) is assigned to the taxonomic group of its nearest neighbor among the remainder of the surface in the dataset (treated as known).
Tables 2-4 list success rates (in percentage) for our approaches compared to those of Boyer el al. for the three datasets. By construction, the conformal Wasserstein neighborhood distance, cWn, is the closest distance to the ones we have introduced in this study, with two significant differences. First, cWn uses a different dissimilarity measure between vertices of the two surfaces under consideration, computed as follows. The procedure starts by projecting the surfaces onto the planar disk using conformal maps. The distance between two vertices, v and v , on the two different surfaces is then computed by comparing their conformal factors (namely, parameters associated with the conformal flattening of the surface) in their neighborhood on the plane, modulo a Möbius transform (see Ref. [14]  for details).
All of those distances form a cost matrix between the two surfaces. Second, cWn is obtained by solving the balanced OT problem based on this cost matrix, in opposition to the unbalanced OT formalism we introduced. We found that the distance SU with the WKS signature introduced in this study outperforms cWn on all three datasets, at all phylogenic classification levels. Interestingly, we found that the WKS signatures outperform the LD-SIFT signatures for the full shape comparison on those anatomical datasets. We assign the difference between LD-SIFT and WKS to the fact that, by design, the WKS signatures capture the actual geometry of the mesh representing a shape by analyzing its Laplace-Beltrami operator, while the LD-SIFT signatures are more focused on local point distributions and, as such, miss the overall geometry.
We use the algorithm from  [67]  to compute this geodesic distance. We compared the LD-SIFT and WKS vertex signatures combined with either balanced or partial OT in their abilities to define those correspondences. In each experiment, a pair of shapes i and j is represented with their complete sets of vertices, S i and S j. For each vertex x on i, its correspondence y on j is set to the index of the maximum value on the row corresponding to x in the OT transport plan. Figure  6  shows the corresponding cumulative geodesic errors for the four algorithms for the four test sets in SHREC19. As observed with the TOSCA8 dataset as well as with the anatomical dataset, the combination of WKS signatures with balanced OT leads to the best results for all four SHREC19 test sets. The Workshop on 3D Object Retrieval, which took place in Genova, Italy, in May 2019, included a competition on comparing 3D shape registration.
The SHREC19 dataset was initially utilized as a benchmark for that competition  [53]. We contrast the outcomes of the five best techniques that competed with the outcomes based on the OT framework. We use the areas under the curve (AUCs) for the cumulative distribution functions of the geodesic normalized errors to evaluate the differences between the different methods: the larger the AUC value, the better the method. Results are presented in Table  5  separately for each test set in SHREC19, as well as when the test sets are combined. From Table  5 , we see that the correspondences computed with the WKS signatures are better than those computed with the LD-SIFT signature, and that balanced OT performs better than partial OT for full shape correspondence. Furthermore, the four registration-based methods, RTPS, NRP, WRAP, and KM, perform better than the approach that merely computes correspondences.
Partial Shape Similarity For nonrigid partial shape similarity, we considered four classes, the cats and horses from TOSCA8, as well as their isolated heads. The heads are taken directly from the meshes of the corresponding full shapes. Those heads contain approximately 700 vertices and 1400 faces. The corresponding dataset, HEAD, includes six instances for both the cats and horses, and all corresponding heads, for a total of 24 shapes. We ran an all-against-all comparison of all the shapes in that dataset, using the four different approaches for shape similarity described above, namely, based on two types of signatures, LD-SIFT and WKS, and two frameworks for computing the correspondences of the vertices, balanced OT and partial OT. We computed the matrix of distances at convergence in β, D(∞), for all four approaches.
Main Control The main control module contains five sub-modules: Get heatmap and Build a graph, Drones available, Update graph/info, Leader election and Drones returned. These submodules are detailed next. Get heatmap and build graph. The heatmap is essential information to start the search for a solution to the DSRP. The procedure on how to build the heatmap and transform it into a complete graph is illustrated in Figure  3. This procedure consists of five steps. The first step consists of obtaining the map of the post-disaster search area through some data source, for instance, the open data source OpenStreetMap (OSM). Then, the second step consists of defining all possible search locations for the drones, and setting the radius of action according to the range detection of the drones. The goals are to identify victims and cover the entire search area using this information.
Development and Settings The MC module makes use of a main thread, while the DA uses more than one thread, working in parallel. This distinguishes our development from that of  [18] , who employ only one thread. Figure  5  shows the interface of CoppeliaSim as well as an example of a loaded solution for the DSRP, containing four drones and nine vertices (one base and eight search areas). Each drone is positioned at a certain height in order to facilitate viewing, even if they are in a V-shaped formation. This has been conducted to simplify the simulation. A code color is used to identify the leaders, the drones following the leader, drone recharging, for example, the yellow and blue vertices that represent, respectively, the base stations, and the potential nodes with victims. The size of each yellow circle is proportional to the expected number of victims in the corresponding area.
Two of these tests were used in this study, conducted as two experiments performed one after the other in a single session: Experiment I, voice cue sensitivity (similar to  [8, [10] [11] [12] ) and Experiment II, voice gender categorisation (similar to  [7, 12] ). The PICKA tests can be run both in English, Dutch and Turkish, the former of which was used in this and the larger comparative study. Until this study, the tests (developed in Matlab  [46] ) had been implemented on a laptop, some of which have interfaces with cartoons and animations, which we used in this study for a fairer comparison to the robot  [12]. In each experiment, tests were performed both via the laptop (identical to that reported by  [12] ) and the new robot interface, Sam.
Since the current version of the PICKA test battery was developed and designed in Matlab, this was not compatible with Sam if it were to function as an independent interface. Therefore, the PICKA tests were rewritten into Python, which allowed all tests and stimuli to be stored and run directly on Sam. However, it should be noted that the processor of Sam (1. 6 GHz) is slower than the laptop (2. 5 GHz); thus, from the beginning of the experiments, it was known that the real-time local generation of stimuli would possibly result in longer durations of the tests.
Experimental setup The laptop used was an HP Notebook (Intel Core i5 7 th gen) running Ubuntu 16. 04. The PICKA test battery was run using MATLAB 2019b. Stimuli for all tests were played through the internal speakers and sound card of the laptop. Responses were logged using a connected mouse to the laptop. The game-like interface with which children were previously tested  [12]  was used. Although all other details of the implementation were also identical to the aforementioned study, the only exception was the use of English stimuli in the present study, differing from the use of Dutch by  [12]. For the robot setup, the stimuli for all tests were played through the internal stereo loudspeakers located in Sam's head and using the onboard soundcard. In both tests the tactile sensors on Sam's hands and head were used to log responses.
This conversion of the voice cues to semitone units allows both F0 and VTL values to be expressed in comparable units, instead of relying on the original Hertz or millimetre for F0 and VTL, respectively. To obtain the F0 contour and spectral envelope of each syllable when using the laptop, the analysis module STRAIGHT  [53]  was used. Extraction of these same parameters in Sam's Python implementation of the PICKA battery was performed using the analysis module PyWORLD [54] in place of STRAIGHT. Application of the modified voice cue parameters was made with methods identical to  [8]. The F0 of the reference voice was set to 242 Hz. This reference F0 was the same value used by  [12] , despite using a different female speaker since the language was different (average F0 across all English syllable stimuli used was 248 Hz).
An estimate of this evidence is presented as the Bayes' factor, which provides the relative likelihood of the data with respect to the null hypothesis (BF 01 = H0/H1) or any other hypothesis (BF 10 = H1/H0). it should also be noted that these two notations of the Bayes' factor are indeed reciprocals of one another (i. e. , BF 01 = 1/BF 10 ). Bayesian analyses were performed using the statistical software JASP  [58] , which categorises the evidence based on the Bayes' factor as shown in Fig 2. Further discussion on classical vs Bayesian inference is presented in the General Discussion (also see  [56]  for a detailed introduction to the Bayesian method). Organisation of the data was performed using the data analysis software R  [59] , and JASP was used for all statistical analyses. Bayesian analyses for both vocal cues used the default priors suggested by JASP: a uniform model prior with r scale fixed effects = 0.
In comparison, also being in the Netherlands and conducting the tests in English, participants included in the present study were only required to have a good understanding of English and did not have to be native speakers. Although the speakers on the laptop may have been of poorer quality compared to what is typically used in auditory research, the consistency in results obtained on Sam with those obtained in previous studies does show that the JND test is robust not only across languages, but also regarding implementations and procedures. It was seen that the duration of the test on Sam was longer than on the laptop; however, the duration of the test did not differ significantly between Sam and from durations reported by  [12]  [F0: t(39. 48) = -1. 206, p = 0. 235, Cohen's d = -0. 336, BF 10 = 0.
The average processing time for a single stimulus was around two seconds on Sam, and one second on the laptop. This is a limitation of Sam's hardware, and it is expected that newer models of the NAO could solve this processing discrepancy. Despite the longer testing duration on Sam, both including and excluding the breaks, Sam was still able to collect comparable JND thresholds to the laptop interface. One could argue that a more powerful humanoid robot, such as Pepper (another of Aldebaran's robots), could be used in place of Sam to compensate for the processing delays. While this is technically true as both robots use (nearly) identical software platforms, are designed to portray a friendly agent, and have been used in various HRI scenarios such as engagement and social robot application (for a review please see  [37] ), Sam offers other advantages over Pepper for our specific application, most notably, its small size and lower cost.
Stimuli To investigate this voice cue influence, pre-generated stimuli from English consonant-vowelconsonant spoken words taken from the same corpora as in Experiment I (CAPT and CCT) were used. Each stimulus presented during the test was randomly chosen from a limited list of words: "bike", "pool", "watch" and "hat"; which were altered in F0 and VTL. Similar to methods described by  [12] , stimuli were root-mean equalised, after which stimuli were modified using the STRAIGHT Matlab module on the laptop or PyWORLD on Sam. F0 and VTL were manipulated independently from each other, and in cases where both cues were altered in a single stimulus, F0 alterations were first applied followed by VTL alterations. When no alterations were made (F0 = 0. 0 st and VTL = 0. 0 st), the original stimulus was still resynthesized to account for any potential synthesis artefacts.
Although this feedback only takes one or two seconds, as mentioned with the hand identification during the gender categorisation test, the summation of these delays after each stimulus can result in a longer than intended test. An additional, albeit more complex, technical modification that could be made to the implementation of both tests could be the incorporation of automatic speech recognition (ASR). Rather than logging responses by touching Sam's sensors, verbal responses could be given. Although the NAO does have speech recognition capabilities, it is not expected that it would be sufficient for such testing purposes, as the speech recognition module performs poorly and often requires higher than natural speech volumes in order to recognise any speech. However, should this problem be overcome through the use of third-party software, such as the speech recognition system Kaldi  [82] , this could be a viable alternative to the tactile response logging method.
Results Setting of numerical simulations. The classical numerical simulations reported in this section have been carried out with an in-house code, using the Openfermion-PySCF module  17  for integral computations and OpenFermion  18  for the second quantization and Jordan-Wigner mappings. All calculations are performed within the minimal STO-3G basis set  19  without considering frozen orbitals unless otherwise specified. Note that the number of qubits that a simulation requires is equal to the number of spin-orbitals of a system, which therefore limits the quality of the single-particle basis and the size of the system that can be simulated. All optimization routines use the Broyden-Fletcher-Goldfarb-Shanno algorithm implemented on the SciPy Python module  20. We use a pool of non-spin-complemented restricted single-and double-qubit excitations evolutions.
Instead, we propose the use of an intermediate, classically computed, multi-configuration wave-function as the overlap-guided target, an approach that has the consequent advantage of not costing additional quantum resources. While many different choices of classically computed wave-functions are possible, in this study, we choose to employ the so-called CI perturbatively selected iteratively (CIPSI) algorithm implemented in QP2  23  (see the Methods section for a brief recap of the CIPSI method). CIPSI-Overlap-ADAPT numerical results. We performed CIPSI calculations through the open-source quantum chemistry environment Quantum Package  23  for the different molecular systems. As mentioned previously, the CIPSI wave-function is used as a target for the overlap-guided adaptive algorithm and is therefore not required to be very accurate.
Acknowledgements This work has been funded in part by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant No. 810367 ), project  EMC2  (J. -P. P. and Y. M. ). Support from the  PEPR EPiQ  ( ANR-22-PETQ-0007 ) and HQI programs is acknowledged. C. F. acknowledges a Ph. D. fellowship from  Qubit Pharmaceuticals.
Global Navigation Satellite System (GNSS) multipath has always been extensively researched as it is one of the hardest error sources to predict and model. External sensors are often used to remove or detect it, which transforms the process into a cumbersome data set-up. Thus, we decided to only use GNSS correlator outputs to detect a large-amplitude multipath, on Galileo E1-B and GPS L1 C/A, using a convolutional neural network (CNN). This network was trained using 101 correlator outputs being used as a theoretical classifier. To take advantage of the strengths of convolutional neural networks for image detection, images representing the correlator output values as a function of delay and time were generated. The presented model has an F score of 94. 7% on Galileo E1-B and 91. 6% on GPS L1 C/A.