dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we note that the comparison among the assignation algorithms mainly underlines that the two methods optimizing the assignation according to the specific dataset get almost the same results. The other two scenes allow to gain more insight about the comparison of the sharpening algorithms and of the assignation algorithms. The EO-1 data have been extracted by the same images, while the multi-platform data have very different characteristics. In fact, while the WV3 image of the Agnano dataset has been acquired within a few days from the EO-1 data, the WV3 image of the Capodichino was collected more than 10 years earlier. Accordingly, the layout of the object present in the Capodichino scene is very different among the two passages, also because the area contains rapidly changing objects.
A comparison of the two images can be achieved by having a look at Figure  2(a)  and (b). The latter scene refers to Naples Airport and contains a plane on the runway that is not detectable in the corresponding WV3 MS image shown in Figure  3(d). Furthermore, different manmade objects are present in the illuminated area at the two acquisition times. The results related to the Agnano dataset (see Table  2 ) confirm the conclusions drawn from the analysis of the Harlem dataset. They correspond to the most typical situation in which the images to fuse are ordered to a data provider, minimizing as much as possible the difference between the passage times of the two satellites. Accordingly, in both cases, the illuminated areas contain very similar features that make the multi-temporal data particularly valuable.
Close-ups of the products achieved by applying the pansharpening algorithms to the RR Harlem dataset (red, green, and blue bands). The resolution enhancement factor is R = 3 (first two rows), 6 (3rd and 4th rows), 12 (5th and 6th rows). (WorldView-3 satellite images courtesy of the DigitalGlobe Foundation). The figure labels indicate the fusion algorithm (see Section 3. 3) and the high resolution data utilized.
Moreover, the spectral quality of the final products is significantly compromised if the WV3 MS images are used. The spatial quality differences among the various algorithms can be further investigated by resorting to a quality index that allows a band-by-band analysis of the quality of the algorithms output. To this aim, we report in Figures  4  and 5  the behavior of the Q-index as a function of the HS band. The two images reveal both similarities and discrepancies in the algorithms' performance. In particular, we can note that for the HS channels with support contained in the frequency range    covered by the ALI PAN, the use of single-platform data is always preferable, except for the case of the Hyper algorithm applied to the Agnano dataset with R =6. Clearly, this consideration is all the more true in the experiment related to the Capodichino dataset.
Instead, different trends are experienced for the near-infrared (NIR) bands. All the algorithms (except the GSA algorithm with R = 3) obtain better performance by using multi-platform data working on the Agnano dataset. On the contrary, using the Capodichino dataset, the GSA algorithms always obtain superior results by using the ALI PAN image, while the other two methods obtain a slightly better performance in the NIR region that is not able to balance the scarce quality in the visible range, thus resulting in an inferior overall performance of the multiplatform approach. Finally, it is very clear from both Figures 4 and 5 that the CC-AA and the SAM-AA algorithms are able to obtain significant improvements with respect to CEN-CC, especially in the NIR frequencies.
Conclusions The aim of this work was to illustrate the recent advances in the field of hyperspectral image sharpening through single-platform and multi-platform data. The study was conducted on real data acquired by the Earth Observing-1 and the WorldView-3 satellites in order to highlight the practical issues to be addressed when fusing images acquired by different platforms. We focused on well-known algorithms based on classical approaches borrowed from the pansharpening literature and on techniques developed on purpose. We evaluated the possibility of completing the fusion process, both in the absence and presence of temporal misalignments between the scenes illuminated by the sensors mounted on the two satellites. The study highlighted the suitability of the employment of multi-platform data especially in the presence of high-resolution enhancement factors.
Harlem dataset: the images have been collected in New York, USA, in the neighborhoods of the Harlem River. The size of the Hyperion and PAN ALI data, acquired on July 21, 2016, is 144 Â 144 pixels and 432 Â 432 pixels, respectively, while the native dimension of the WV3 MS image, acquired on June 9, 2016, is 4320 Â 4320 pixels.
Harlem dataset: the images have been collected in New York, USA, in the neighborhoods of the Harlem River. The size of the Hyperion and PAN ALI data, acquired on July 21, 2016, is 144 Â 144 pixels and 432 Â 432 pixels, respectively, while the native dimension of the WV3 MS image, acquired on June 9, 2016, is 4320 Â 4320 pixels. • Agnano dataset: the images refer to the area of the Agnano Racecourse, next to the city of Naples, Italy. The size of the Hyperion data is 144 Â 72 pixels, and thus the corresponding ALI and WV3 images are composed by 432 Â 216 pixels and 4320 Â 1660 pixels, respectively. The acquisition dates of the EO-1 and WV3 sensors are May 20, 2015, and June 8, 2015, respectively. • Capodichino dataset: the images refer to the east surrounding Naples, Italy, around Capodichino Airport.
5362 3. 0548 0. 8462 3. 2128 1. 9735 0. 7323 3. 8668 1. 1926 0. 5824 MS-SAM 2. 5133 3. 0672 0. 8469 3. 1841 1. 9742 0. 7343 3. 8640 1. 1927 0. 5827 CBD PAN 2. 4126 2. 5037 0. 8832 3. 3688 1. 8193 0. 7261 4. 5275 1. 2246 0. 4657 MS-CEN 2. 6355 3. 1271 0. 8385 3. 6260 2. 1248 0. 7234 4. 7470 1. 3681 0. 6321 MS-CC 2. 5513 3. 0624 0. 8432 3. 4488 2. 0790 0. 7322 4. 2992 1. 3201 0. 6488 MS-SAM 2. 5421 3. 0807 0. 8436 3. 4306 2. 0895 0. 7336 4. 2828 1. 3220 0. 6487 Hyper PAN 2. of the quality indexes, related to the reduced resolution assessment procedure, using the Agnano dataset, for resolution enhancement ratio R ¼ 3, 6, 12.
Materials and measurements All measurements were acquired using a Wasatch Photonics 800nm SD-OCT system, with 2048 spectrometer elements. The system's imaging arm included a length of optical fibre, sufficient to introduce a dispersion discrepancy against the reference mirror. We recorded 1024 A-scans over a 2 mm lateral distance using its standard protocol, and extracted the raw spectrometer data for processing. In each case the focal point was adjusted, by eye, to lie within the sample, and at the zero delay position. Preprocessing from the raw data consisted of background subtraction, obtained by averaging across Ascans, and non-linear calibration from detector element to wavenumber, according to parameters from the manufacturer. The samples used were as follows: 1. Beaded gel: TiO 2 micro-beads suspended in 2% agarose gel, at a concentration of 1 mg/ml.
Conclusions We have demonstrated full-range ISAM through dispersion encoding, and presented a MBIR algorithm for its implementation. While an naive DEFR+ISAM is an alternative way to achieve this, it does not fully exploit the ISAM signal model or multidimensional sparsity, resulting in structural degradation and observed artifacts. In contrast, MBIR produces images of enhanced structural clarity and significantly lower errors. Within this, we have adopted an efficient NUFFT implementation of ISAM, which is numerically stable through many iterations. Ongoing and future work includes reducing the computational time of MBIR, actively modelling autocorrelation artifacts, extending the method to work efficiently with 3D datasets, and to explore compelling biomedical applications.
Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases, and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPackets descriptions supported by GA4GH ( http://phenopackets.
org/ ), calculate a prioritized gene list based on a probabilistic model, and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed, and acts as a real-time phenotype driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license ( https://github. com/WGLab/Phen2Gene ), we also developed a web server and web service ( https://phen2gene. wglab. org/ ) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients' de-identified HPO term data from CHOP.
The Human Phenotype Ontology (HPO) database  6  associates human diseases with phenotypic abnormalities. These terms possess ever-increasing interoperability with other ontologies  [7] [8] [9] [10] [11]  and allow for computational deep phenotyping, making it the prevailing standard terminology for human phenotypes. We have developed a few computational tools  [12] [13] [14]  that use phenotype data for gene prediction and prioritization. Although these methods are useful, they cannot provide real-time decision support in clinical settings; for example, users may need to add or remove one phenotype term for a patient and immediately observe how the candidate gene list changes. Recent studies have shown the utility of incorporating phenotype data such as HPO terms in identifying causal genes from NGS data, which increases time efficiency and diagnostic yields  15.
Phen2Gene takes HPO terms for a patient and generates a patient-specific ranked list of candidate genes using our precomputed database, the HPO2Gene Knowledgebase (H2GKB), in a mere second. Unlike existing binary HPO-gene annotations in the HPO annotation database, the H2GKB is a new database that links each HPO term to its own ranked list of candidate genes, each with a confidence score. These scores represent a substantial accuracy improvement over the previous version of Phenolyzer. We also provide open source code under the MIT license, together with a web server for downloading and accessing the H2GKB, and a RESTful API web service for automated queries of phenotype terms with JSON output.
HPO2Gene Knowledgebase Construction In order to construct the H2GKB, we first extract every term from the Human Phenotype Ontology Database, underneath the root term 'Phenotypic abnormality' (HP:0000118) ( Figure  2a  ). For each HPO term, we run an enhanced version of Phenolyzer (ver. 0. 4. 0), dubbed Enhanced Phenolyzer, which incorporates HPO-gene annotations from the Jackson Laboratory 6 , and gene-disease annotations from OMIM 20 , ClinVar 21 , Orphanet  22  , and GeneReviews 23. It then adds information from gene-gene databases HPRD 24 , NCBI's Biosystems Database  25  , HGNC Gene Family  26  , and HTRI  27  , and prioritizes and outputs the associated genes. This generates a ranked list of candidate causal genes for each HPO term, which are then consolidated into the H2GKB ( Figure  2b  ).
Enhanced Phenolyzer The original version of Phenolyzer (ver. 0. 2. 2) processed free-text terms supplied by users. We updated the databases inside Phenolyzer, incorporated new HPO-gene annotations from the Jackson Laboratory database, fixed some bugs, and released it as Enhanced Phenolyzer (ver. 0. 4. 0). Enhanced Phenolyzer contains a new function to turn HPO terms into a list of genes, by generating a prioritized gene list for each HPO term. Unlike the original Phenolyzer, Enhanced Phenolyzer first generates two seed gene sets. Seed Gene Set 1 is built on HPO-gene annotation files downloaded from the Jackson Laboratory for Genomic Medicine available at https://hpo. jax. org/app/download/annotation , while Seed Gene Set 2 construction follows the method outlined in the original Phenolyzer paper, which translates phenotype terms to disease names, and incorporates the five precompiled gene-disease databases to search for seed genes.
Candidate Genes Prioritization For seed genes in Set 1, we gave an equal score to each gene and HPO term pair, , S 1 Gene , ( j HP i) = 1 since JAX annotation only lists genes for each individual HPO term, but without quantitative scores representing the strength of associations. In Set 2, we followed the calculation method in the original Phenolyzer for each seed gene using gene-disease databases associated with each individual HPO term, and noted as. S 2 Gene , ( j HP i) We sum up the two scores, = , S total Gene , ( j HP i) S 0. 1 * S 1 Gene , ( j HP i) + 2 Gene , ( j HP i) and normalized it to a range between 0 and 1 as the final seed gene score,. In the following steps, we used the original Phenolyzer's method for expanding the list of candidate genes and reprioritizing the seed gene list using gene-gene databases.
Acknowledgements We thank the affected individuals and their family members who participated in published genetic studies to improve the diagnostic rates in clinical exome testing. We thank the original authors of the studies to provide detailed phenotype information in published manuscripts, to help us benchmark performance of different software tools. We thank the developers of the  Human Phenotype Ontology  for continuous development of this ontology over the past few years, which greatly facilitated and standardized clinical diagnosis of affected individuals with suspected genetic disorders. This study is supported by  NIH/NLM/NHGRI  grant  R01LM012895  (C. W. and K. W. ).
Data Availability The current version of Phen2Gene is 1. 1. 0. The source code and scripts for figures are available at https://github. com/WGLab/Phen2Gene. Additionally, we built a Phen2Gene Web Server available at http://phen2gene. wglab. org , to facilitate users who prefer to use web interface for gene prioritization. The current version of H2GKB is also downloadable at https://github. com/WGLab/Phen2Gene/releases/download/V1. 1. 0/H2GKB. tar. gz. All the benchmark datasets are available in the Supplementary Materials of this manuscript.
The 3D Reconstrucion Structure from Motion (SFM) as applied in geoscience and survey is more than a single technique is a workflow employing multiple algorithms developed from traditional photogrammetry, survey techniques, and three-dimensional (3D) computer vision. The full workflow is known as Structure from Motion Multi-View Stereo (SFM-MVS) to account for the Multi-View stereo algorithms used in the final stages. Many commercial SFM-MVS software packages do not detail the specific procedure applied to solve the problem. The aim of this paragraph is to understand the basic concept for 3D reconstruction starting from uncalibrated imagery, for a deep understanding of mathematical formulas the interested reader can find relevant information on. The basic process to reconstruct the 3D scene geometry from a set of images where the extrinsic and intrinsic calibration parameters are unknown, could be divided into three main steps: (1) Image analysis for matches and estimation of unknown camera parameters, (2) application of Structure from Motion (SFM) algorithm and (3) Multi-View Stereo for 3D dense cloud generation  [18, 19].
Built Database From the constructed three-dimensional model, geometric information (base, beams, cross) relating to our viaduct was automatically extrapolated, through an appropriate point cloud segmentation methodology (Table  2 ). An example of segmentation of the span is provided in Figure  8. Simultaneously, we decided to insert in the FE model the experimental elastic modules of the actual materials (steel and concrete). Table  1  shows the comparison between experimental frequencies and numerical frequencies obtained by using a commercial program. In particular, the second column of Table  1  reports the experimental frequencies, while column 3 reports numerical frequencies after some iteration of the model updating procedure. It is easy to verify the effectiveness of the FE model. The identification of the modal parameters of the viaduct was developed using the frequency domain decomposition.
All these parameters were used to recreate boundary conditions in the various scenarios required for neural network training, neural network that is at the base of the predictive system  [11]. For the real monitoring phase, the direct measure of the movements is of particular importance. It has been made through the two GPS (rover) receivers and one base station near the viaduct. The GPS observations are real-time kinematic (RTK) with differential GPS (DGPS) system. The receivers are LEICA-GMX902 antenna (24-channel L1/L2 code and phase, 20 Hz data rate, Smart Track technology for high precession, accuracy of 1 mm + 0. 5 ppm (horz. ), 2 mm + 1 ppm (ver. )) and the GPS data were preprocessed using the software LeicaSpider to improve and adjust the collected GPS data. The GPS receivers are linked via the internet to the Spider server.
The raw data is streaming out from the receiver to the Spider server. The server generates RINEX-RTK corrections and they are streamed out via TCP/IP to the field. The GPS measurements were free of any obstructions of the horizon view and more than six satellites were tracked continuously. The coordinate components for each observation epoch were derived. The output of the two rovers was the time series of global coordinates of the installed receivers. Therefore, the coordinate's data were converted to a local bridge coordinate system (BCS) for the analysis and evaluation procedures. In this coordinate system, x-axis is aligned with the traffic direction and z-axis gives the vertical direction of the viaduct. To analyze the viaduct performance, the GPS observations were first filtered to denoise the time series of GPS receiver's outputs after converting the coordinates into the local viaduct coordinate system  [9, 25, 26].
Moreover, to estimate the semistatic movement of components of the pillars, the low-pass moving average (MA) filter (0. 025 Hz) was applied to remove the dynamic and noise components from the data. The dynamic response can be extracted from the short period components by applying a bandpass filter (Figure  9 ). GPS data were preprocessed using the software LeicaSpider to improve and adjust the collected GPS data. The GPS receivers are linked via the internet to the Spider server. The raw data is streaming out from the receiver to the Spider server. The server generates RINEX-RTK corrections and they are streamed out via TCP/IP to the field. The GPS measurements were free of any obstructions of the horizon view and more than six satellites were tracked continuously. The coordinate components for each observation epoch were derived.
Comparison of Captured Data to Model Data The sensor-captured data were compared (specifically, we report data acquired and calculated on the beam of the span 4) with data obtained from the model to verify the goodness of the model itself. The result attests good adaptability of the model to the real state with an average error of about 10% (see Table  3 ). Figure  10  shown the sensors' positions. The sensor-captured data were compared (specifically, we report data acquired and calculated on the beam of the span 4) with data obtained from the model to verify the goodness of the model itself. The result attests good adaptability of the model to the real state with an average error of about 10% (see Table  3 ). Figure  10  shown the sensors' positions.
In the first feed-forward phase, input x (Load sources: Wind, seismic action, traffic. Structural responses: Displacements, deformations, accelerations, and inclinations. Environmental effects: Temperature, precipitation, humidity) are inserted and propagated through the multilayer network, to calculate the correct y output value for each output unit (displacement and risk level). The input variables related to environmental effects clearly, are variables that can be acquired from existing databases, as well as from daily weather forecasts. Traffic load conditions to date have been calculated through a statistics of traffic or average daily study, but work is being done to estimate this variable using a numerical counter based on an automatic or vehicle recognition system based on the Yolo neural network.
The outgoing signal of the soft computing system was processed and sent, through communication channels, to a hardware system, which is responsible for recording the data to compare it with the previous ones and informing the operator of possible future developments. The monitoring was realized by the design and subsequent implementation of an integrated software/hardware system, able to control the movements and deformations of the structure in realtime and to expect them to be at least one month. The integrated system was designed in EasyEDA ® environment. The board was properly designed and integrated with components needed for data processing. Data taken in real-time and data processed using soft-computing techniques were transmitted with 4G LTE internet key to the monitoring platform.
The platform was implemented in the WordPress ® environment. At the same time, in order to prevent the disadvantages inherent in programming in WordPress ® , SSL security protocols were integrated, which provided a fundamental level of online security, essential when transmitting sensitive information. The final prototype produced (Figure  12 ) consists of a capture and processing board that receives incoming displacement signals, such signals picked up by appropriate sensors are managed by a system intelligent System On Module (SOM) or an independent electronic device capable of managing a Linux operating system and able to carry out numerical processes, even complex. The captured signals are then sent to a database that stores them and makes them available for management from an online platform.
The final prototype produced (Figure  12 ) consists of a capture and processing board that receives incoming displacement signals, such signals picked up by appropriate sensors are managed by a system intelligent System On Module (SOM) or an independent electronic device capable of managing a Linux operating system and able to carry out numerical processes, even complex. The captured signals are then sent to a database that stores them and makes them available for management from an online platform. then assigning them to the neural network (Figure  11 ). The outgoing signal of the soft computing system was processed and sent, through communication channels, to a hardware system, which is responsible for recording the data to compare it with the previous ones and informing the operator of possible future developments.