dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental setup We experimented on the INRIA  [18]  and Caltech-USA (reasonnable set)  [25]  datasets with HoG  [18]  and Haar-LBP  [16]  features. HoGs are configurated with 12 × 6 cells, 2 × 2 blocks and 12 angle orientations, for a total of 2640 values. Block as well as full histogram normalisation are performed. The LBP descriptor follows the same structure, features a value per channel for each cell, and non-uniform patterns are pruned out  [47]. Candidates are selected using a multi-scale sliding window approach  [73]  with a stride of 4 pixels. Approximately 60% are loosely filtered according to "edgeness" and symmetry. No background subtraction or motion features are employed on the Caltech dataset. The training set size is up-bounded at 12K samples. Increasing this threshold doesn't improve performance.
We also clean the data based on Tomek links  [60]. We employ euclidian distance and restrict the deletion to negative data to spare the scarce minority class. Approximately 0. 1% to 0. 2% of the negative data are removed this way. No generative resampling is performed. The model height is set to 100 pixels for the INRIA dataset, 50 pixels for Caltech. We also implemented a cascade-of-rejectors baseline with the commonly used settings: Rejector optimisation is done according to the log-average miss rate metric. We tested this baseline with a typical initial validation set containing 4 times more negative samples than positive ones. To compare with the common multi-dataset training technique, we also used an augmented version with PETS2009  [32]  positive samples. This set gathers 50K positive instances from both Inria datasets and 110K negatives from the sole INRIA benchmark.
We set the overlap threshold to 0. 6 and d = 3 for all our experiments. Log-Average Miss Rate (LAMR)  [51]  is employed as metric for all runs. 69% 0. 5fps DPM  [31]  63. 26% < 1fps FeatSynth  [3]  60. 16% < 1fps MultiFeatures+CSS  [62]  60. 89% No FPDW  [19]  57. 4% 2-5fps Channel Features  [20]  56. 34% 0. 5fps Roerei  [6]  48. 35% 1 fps MOCO  [14]  45. 5% <1fps JointDeep  [49]  39. 32% <1fps InformedHaar  [72]  34. 6% < 0. 63fps katamari-v1  [7]  22. 49% < <1fps CrossTalk cascades  [23]  53. 88%  Detailed method results on the INRIA dataset, including the influence of its various components and settings are reported in Table  1. The expectation-maximisation (EM) training leads to an average 6% improvement over simple HoG-based classification, or 2. 3% over the common cascade-of-rejectors.
This demonstrates the importance of proper data selection. The main boost is obtained by our modified version of the cascade-of-rejectors, with a minimum 24% performance increment. Finally, the combined algorithms lead to a further 3% improvement. To sum up, FairTrain gives a simple HoG based detector an impressive 27% boost, therefore competing with state-of-the-art real-time references. It significantly outperforms the usual blend of datasets strategy (see Table  1  -run 3), scoring only 42. 44%.
Run The main factor responsible for the cascade-of-rejectors significant results is the balanced generative dataset. max-recall optimisation also has a slight, but steady influence on performance, with an average 0. 51% better score. Similarly, we found random forest to outperform Adaboost as final classifier by approximately 0. 4% on all EM runs. Finally, in accordance with the literature  [17] [73][8], we observed that optimal results are obtained for 5 iterations of the rejection cascade, the addition of extra iterations being impactless, as shown in Table  1  -run 12. We have tested this algorithm for various validation set sizes (see Table  1  -runs 8 to 11) to replicate the section 4 theoretical experiment (see Figure  2 ). Results show much more stable performance, therefore validating our primary assumption as well as the approach stemming from it.
Conclusion This paper explored data selection mechanisms for near real-time pedestrian detection. Via Fair-Train, we introduced an expectation-maximisation data weighting scheme for enhanced training set generation. We also improved the cascade-of-rejectors by enforcing balanced datasets at every step of the classification and separately optimizing for recall and precision. The method showed competitive results compared to the state-of-the art and major performance boost when compared to original features. By proving that blind data addition is not the best way to enhance a training set, this work brings up an important question: Then, what makes a good training set ? Or even a good training datum ? Future experimentation will be an attempt of response. Also, the addition of more recent features and further work on classifier combination is envisioned.
The diameters of the inductive posts depend on the electrical requirements and also the characteristics of the waveguide. One critical point here was the diameters of posts 1 and 7, at the extremities of the filter, which were too tiny to be drilled. To overcome this issue, one solution is to substitute each of these posts with a pair of posts, as shown in Figure  6. All these technological requirements inevitably modify the electrical response of the filter, and require the structure to be re-adjusted in order to retrieve the original electrical response given by an ideal rectangular SIW filter. The work of re-adjustment of the electrical response is necessarily made with the commercial electromagnetic software ANSYS HFSS. Without any experience, this can be time consuming and it is not easy to obtain exactly the same response.
The work of re-adjustment of the electrical response is necessarily made with the commercial electromagnetic software ANSYS HFSS. Without any experience, this can be time consuming and it is not easy to obtain exactly the same response. Some differences, such as modification of the bandwidth, can be difficult to eliminate. In this case, the problem needs to be foreseen when designing the prototype. This work was done for the modified SIW filters in our study (dimensions given in Table  2 ). The responses of the two filters with relative bandwidths of 2. 5% and 4. 5%, before and after modifications, are shown in Figure  7a ,b, respectively. As mentioned above, we can obviously observe a slight increase of the bandwidth, but it is important to note that the insertion losses in both cases remain nearly unchanged, meaning that the quality factor was not significantly impacted by the technological modifications.
Building Modelling Approach Three main approaches have been used for building energy management systems: white box models, black box models and gray box models. White box models  [13, 14]  are a physical modelling approach relying on thermodynamic and/or mathematical equations and engineering methods for energy modelling, analysis and control. White box-based modelling approach examples are the building energy analysis simulation software such as: EnergyPlus [15], Transient System Simulation Tool (TRNSYS)  [16] , eQuest  [17] , etc. These software tools basically are used during building planning and designing phases, prior to the building construction. They calculate overall energy consumption  [18] , HVAC design  [19] , operation scheduling, lighting information [20], etc. , based on the detailed building physical properties [21], occupancy schedule, geographical conditions, and type of building and climatic parameters.
White Box Models White box models are thermal dynamics modelling, which are based on fundamental laws of physics, thermodynamics, and heat transfer. Generally this approach can be roughly classified into two types: physics-based models using simulation tools such as: TRNSYS, EnergyPlus, etc. , and physics governing laws-based thermal dynamics state-space equation or lumped capacitance model  [41]. Due to the diversified and heterogeneous behaviour of building parameters, complexity of hardware network (sensors, and actuators), and occupants interaction with buildings, it is difficult to carry out full scale experiments. Hence, software tools are affordable and ease-to-use platforms that can be used for building dynamics evaluation and analysis. Numerous software tools have been developed over the last few decades for the analysis of energy consumption, HVAC design, operation scheduling, lighting information, renewable energies, etc.
, Regular up-gradation is performed on these simulation tools to improve the performance efficiency and decreasing computational cost. The list of US Department of Energy (DOE) organisation approved building simulation tools is available in  [42]. EnergyPlus is a building energy performance analysis simulation software and console-based program that reads inputs and writes outputs in text files, developed based on DOE-2  [43]  and the Building Loads Analysis and System Thermodynamics (BLAST) by National Renewable Energy Laboratory(NREL) and U. S. DOE Building Technologies Office (BTO)  [15]. Zhao et al. [44]  used EnergyPlus to procure raw data for commercial building to perform overall energy performance and dynamic pricing by using Cyber Physical Systems (CPS)-enabled BEMS  [45]. Furthermore, a fuzzy logic controller (FLC) is initially designed based on the data probation from EnergyPlus and a GA method applied to FLC for evolution of improved member functions population for improved comfort control energy optimization of a food service center.
The results were compared with stand-alone EnergyPlus output and investigated that the GA-FLC-based method resulted notable decrease in energy consumption during both cooling and heating  [46]. This shows that EnergyPlus lacks interactive control optimization techniques and would be coupled with other dynamic computational software (Matlab, Modelica  [47] ) via co-simulation software building controls virtual test bed (BCVTB). Kim et al. [19]  used EnergyPlus for training ANNs and coupled GA for optimization of integrated daylighting and HVAC systems. This was performed to ensure large database available for training ANNs in less time, otherwise 3 months would have been needed to procure data from sensors. A multi-objective optimization strategy is validated using non-dominated sorting genetic algorithm (NSGA), implemented in the GenOpt  [48]  optimization engine through the Java genetic algorithms package, to instruct the EnergyPlus simulation tool  [12].
Meanwhile, Huh et al. [49]  developed a system to generate predicted real-time weather data for 24 h duration using EnergyPlus coupled with GenOpt through BCVTB  [50]. This approach improves the building energy control optimization based on real time prediction control. TRNSYS (Transient systems simulation program) is a dynamic computational software for building energy performance analysis  [16]. It is incorporated with Matlab for database generation to train 2 ANNs used for predictive and adaptive approach applied to hotel building. The developed model efficiently improved thermal comfort and building energy optimization during summer season resulting in 18-38% energy savings over the simulation period  [28]. TRNSYS was used for determining building cooling load needed from the chiller plant, Later, these data were fed to Matlab for performing optimization analysis  [51].
BCVTB is used for coupling various simulation tools for co-simulation and co-simulation to hardware. Refs. [13, 46, 52]  used BCVTB to ease the simulation process by coupling EnergyPlus, Matlab in their model for building energy performance analysis. Meanwhile, BCVTB was coupled with EnergyPlus to generate real-time weather data file for energy consumption prediction in one day  [49]. Figure  1  shows the process of generating a weather data file based on the forecasted weather using BCVTB as a coupling platform between EnergyPlus and other tools. The weather elements from weather forecast by national weather station, calculated weather parameters, measured weather elements, and default parameters are coupled together in BCVTB platform for generating weather forecast. Although simulation tools are the first choice for building energy performance analysis because of accuracy and effectiveness, they require proper data of building, weather parameters, and physical properties.
• Standard data available in public benchmark datasets such as ASHRAE's data  [23]. Black box models are known for prediction techniques and gained huge consideration over last decade. Commonly, these models are used in prediction of building energy consumption  [23, 25, 55] , indoor temperature  [54, 56] , heating/cooling load demand  [24] , HVAC parameters, occupancy [57,58], and energy generation from RES. Some major algorithms of these models are ANNs  [59] , SVM  [60] , GAs  [61] , decision trees  [62] , and other statistical machine learning methods. Indoor comforts are influenced by various parameters, especially weather conditions (e. g. , wind speed, air temperature, and humidity) that are volatile in nature. In such conditions, indoor thermal comfort can be rapidly affected and could cause excessive energy consumption to maintain temperature range.
Laboratory rainfall Hg(II) photoreduction experiments. To study aqueous phase Hg(II) photoreduction, ten rainfall events were sampled in suburban Toulouse and at the high altitude (2877 m) Pic du Midi Observatory (PDM, France) in the summer of 2017. Rainfall samples were irradiated in a quartz reactor with natural sunlight or with a solar simulator (see Methods). We observe (Supplementary Figure  1a  and 1b, Supplementary Data 1 and Supplementary Table  1 ) no statistically significant differences between rainfall Hg(II) reduction rates under natural (0. 063 ± 0. 013 h -1 ) and simulated sunlight (0. 037 ± 0. 016 h -1 ), and for filtered (0. 058 ± 0. 011 h -1 ) and unfiltered (0. 039 ± 0. 020 h -1 ) suburban rainwater (t-test, all p > 0. 05). The mean photochemical reduction rate of suburban rainfall was 0.
4  (see also Supplementary Table  2  and Supplementary Figure  2  for zonal-averaged atmospheric lifetimes). The species with the longest lifetime is HgCl 2 (48 years), and the species with the shortest lifetime is HgBrOBr (<1 s). These lifetimes were calculated assuming a complete UV-VIS photodissociation under atmospheric conditions. In the case of the parent HgBr 2 compound, it is well known that irradiation with ~200 nm UV light yields the monohalide (HgBr) with nearly 100% efficiency  25, [28] [29] [30]. Moreover, detailed quantumchemical computations of the Cl-and Br-dihalides predict further efficient photodissociation at wavelengths 21 longer than 200 nm. There are no comparable experimental or calculated photolysis data for the other HgBr-X compounds studied here. Nevertheless, a similar very efficient photodissociation step is to be expected for these mercury halides considering the even lower dissociation energies of the HgBr-X bond, as compared with that of the parent HgBr-Br dihalide (Supplementary Table  3 )  9, 27.
Methods Computation of UV-Vis absorption spectra and cross sections. A theoretical methodology previously calibrated  36  was used for calculating the electronic absorption spectra and cross-sections of HgCl 2 , HgBr 2 , HgBrI, HgBrOBr, HgBrOI, HgBrOCl, HgBrNO 2 , HgBrONO, HgBrOH, HgBrOOH, and HgO (The absorption cross sections are reported in Supplementary Data 2). Specifically, vertical transitions energies from the ground to electronically excited states and the corresponding oscillator strengths were computed with the highly accurate multireference complete-active-space self-consistent field/multistate completeactive-space second-order perturbation theory (CASSCF/MS-CASPT2) method  37  , and the atomic-natural-orbital relativistic correlation-consistent valence triple-ζ plus polarization (the ANO-RCC-VTZP) basis set  38  , taking into account scalar relativistic and spin-orbit coupling (SOC) effects (see below and Supplementary Table  5  for further details).
Scalar relativistic effects were included by means of the third-order Douglas-Kroll and Hess (DKH3) Hamiltonian, and the spin-orbit coupling (SOC) was computed using the restricted active space state interaction (RASSI) method, as implemented in the MOLCAS 8 program  39. The atmospheric modelling methods used in the present work require as input data the absorption cross sections of any compound that may undergo photolysis in the UV-VIS range under atmospheric conditions. To estimate the vibrational resolution and determine the band shapes, absorption spectra were generated for all the compounds studied here by sampling the nuclear coordinates of the groundstate equilibrium structure and frequencies according to a Wigner distribution, as described in refs  40, 41  and subsequently computing the vertical transition energies and oscillator strengths at each structure.
The Wigner distribution of geometries was obtained with the Newton-X 1. 4 program  42, 43  , and an in-house program was used to compute the cross sections from the energies and oscillator strengths generated by the MOLCAS program. The ground-state structures and frequencies needed to generate the Wigner distribution were obtained by using the PBE0 functional  44  with the Def2QZVP basis set  [45] [46] [47]  as implemented in the Gaussian 09 package  48. The minor differences observed in the simulated spectra due to groundstate geometries generated using either CASPT2, CCSD, or DFT methods are presented in Supplementary Figure  11. For the di-and triatomic systems, appropriate symmetry point groups available in the MOLCAS program and which enabled for every possible displacement of atoms, were used in the calculations-C 2v in HgO and C s in HgCl 2 , HgBr 2 , and HgBrI.
Some orbitals from the valence space of the ONO and NO 2 groups had to be omitted due to the computational limitations. Tests have shown that for the relevant wavelength region in the UV-VIS, one σ-type bonding NO at the O-N-O and NO 2 groups of atoms remains doubly occupied and it was moved to the inactive space, whereas the Hg 6p z orbital and higher σ * -type virtual orbitals do not contribute significantly and therefore were kept in the secondary space. The measurement dataset is the same as in ref. 35  In the case of HgBrOOH, the optimal active space was similar to those of HgBrOBr and HgBrOI. It consisted of 16 electrons distributed in 12 NOs formed by Hg 6 s and AOs of p-type: O 2p x/y/z , Br 4p x/y/ and Hg 6p x/y. As for in previous cases, the Hg 6p z orbital was kept inactive since it was not relevant.
The model calculates the transport and chemistry of tracer species Hg(0) and Hg (II). The parametrization of gas-particle partitioning of Hg(II) is from ref. 56  , and the mercury redox chemistry (described in detail in Supplementary Table  6 ) includes Br-and Cl-initiated oxidation. Radical concentrations for Hg redox chemistry are from ref. 57  with a diurnal cycle based on solar zenith angle imposed on top of monthly averages. Photolysis of HgBr-X species is calculated using the GEOS-Chem implementation  58  of the Fast-JX code  59. Description of the GLEMOS model. For evaluation of the new Hg chemical mechanisms under the atmospheric conditions we apply the 3D multi-scale chemical transport model GLEMOS (Global EMEP Multi-media Modelling System). The model simulates atmospheric transport, chemical transformations and deposition of Hg species  33, 34, 60.
In this study the model grid has a horizontal resolution 3°× 3°and covers troposphere and lower stratosphere up to 10 hPa (ca. 30 km) with 20 irregular terrain-following sigma layers. The atmospheric transport of the tracers is driven by meteorological fields generated by the Weather Research and Forecast modelling system (WRF)  61  fed by the operational analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) (ECMWF, 2018)  62. In the current version the model transports Hg(0) and four Hg(II) species (HgBr 2 , HgBrOH, HgBrOOH, HgBrNO 2 ) as separate species. Gas-particle partitioning of Hg(II) is parameterized following ref. 56. A two-step mechanism of Hg (0) oxidation by Br in gas phase is included (ref. 63 ): Hg 0 ð Þ þ X þ M ! Hg I X þ M ð1Þ Hg I X þ M ! Hg 0 ð Þ þ X þ M ð2Þ Hg I X þ Y ! Hg 0 ð Þ þ XY ð3Þ Hg I X þ Y þ M ! Hg II ð ÞXY þ M; ð4Þ The full reaction scheme is listed in Supplementary Table  6.
Acknowledgements This work was supported by the  Consejo Superior de Investigaciones Científicas (CSIC) Spain. The  National Center for Atmospheric Research (NCAR)  is funded by the  National Science Foundation  (NSF). Computing resources were provided by the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory (CISL) , sponsored by the  NSF , provided computing resources. The  CESM  project (which includes CAM-Chem) is supported by the  NSF  and the  office of Science (BER)  of the  US Department of Energy. This study has received funding from the  European Research Council Executive Agency  under the European Union´s  Horizon 2020 Research and innovation programme (Projects ' ERC-2016-COG 726349 CLIMAHAL'  to A. S. L. and 'ERC-2010-STG 258537' to J. E.
S. ), and from  H2020 ERA-PLANET 689443. S. P. S. acknowledges the  Basque Government  for funding through a  PhD fellowship  ( PRE 2017 1 0403 ). D. R. S. is thankful to the  Spanish Ministerio de Economía y Competitividad (MINECO)  (Projects  CTQ2017-87054-C2-2-P , RYC-2015-19234 grant, and  Unit of Excellence María de Maeztu   MDM-2015-0538 ). J. M. O-E. is grateful to program " Salvador de Madariaga " Project No. PRX17/00488  ( Ministerio de Educación, Cultura y Deporte ). We thank the  UMS 831 Pic du Midi Observatory team  for help with rainwater sampling and  Franck Gilbert  for use of the solar simulator.
To test this hypothesis, we developed novel statistical models and likelihood-ratio tests for using (continuous) eRNA expression data in causal inference, based on existing methods for discrete eQTL data, and implemented these in the Findr software  [18]. We applied this new method to Cap Analysis of Gene Expression (CAGE) data generated by the FANTOM5 Consortium. Unlike RNA sequencing, CAGE allows sequencing of only the five prime ends of mRNAs, providing genome-wide transcription start site quantification at much lower sequencing depth (and therefore, lower sequencing cost per sample) than RNA-seq. The FANTOM Consortium has generated a unique resource of enhancer and promoter expression across hundreds of human and mouse cell types  [6, 17]  and validated predictions using ChIP-seq and perturbation data.
The log-likelihood ratios (LLRs) are computed for all possible targets of each gene and then converted into p-values and posterior probabilities  [19]  of the alternative hypothesis being true; see  [18]  for details. We applied three treatments to enhancer expression data. First, we regarded enhancers as binary (on/off) variables, and after binarizing the data (see Methods), we used the existing Findr to predict TF targets directly. This approach will be referred to as Findr-B ("binary"). Second, we adapted all five tests in Findr to use continuous instead of discrete causal anchor data, and we used this method on (untransformed) eRNA data. This approach will be referred to as Findr-C ("continuous"). Third, to accommodate the co-existence of binary and continuous enhancers for different TFs within the same dataset, we developed an automatic adaptive method to treat each enhancer independently as binary or continuous, depending on the relative strength of the primary enhancer-TF linkage with either method.