dataesr/scientific-paragraphs-categorization huggingface dataset:
• Representing the full environmental gradient is key to a good model calibration. • Several studies have pointed to a minimum of 50-100 occurrences needed to characterize a species environmental niche; however, there is great variability depending on species properties, whether or not absences are considered, and how is the sample biased or not. • In studies considering multiple factors at a time, sample size usually comes up as the most influential factor determining model performance, especially at the lower end of the spectrum (< 50 occurrences). • Bias in presence-only datasets can be partially corrected by using pseudo-absence bias-correction strategies. • Notice that VS studies using a threshold simulation approach will inadvertently confound several of these factors with species and sample prevalence.
Instead, the VS approach is useful to test which methods work under realistic conditions: at what sample size can we hope to start seeing those differences and detecting them with our conventional statistical tools? What types of sampling biases are more likely to affect our results? Given a set of problematic datasets, can we correct the results? Those are questions that we cannot answer without a statistical test. On the opposite extreme,  Thibaud et al. (2014)  proposed a very specific statistical framework to follow under a VS framework involving the use of linear mixed-effects models. However, given the flexibility of the VS approach and the variety of topics that can be covered, it is unlikely that a single statistical framework will fit all purposes. We therefore recommend being careful about the conceptual pertinence of the question being asked, especially avoiding circularity between simulation and testing, and otherwise using classic statistical analyses to test the results, adapting them to the situation at hand in the same way we would do under a realcase scenario.
Our final recommendation is to provide scripts for every published simulation study. This will ensure that the study is reproducible, as well as enhance understanding of the simulation process and its implications. It will also guarantee that the results of any given study can be improved by subsequent ones. An interesting example of how the availability of scripts can help understand a problem is given by  Guillera-Arroita et al. (2014b). In this study the authors were replying to  Thibaud et al. (2014) , who had found that MAXENT often outperformed the other models tested, including GLM, under situations where GLM should have outperformed MAXENT. By re-working on  Thibaud et al. (2014) 's scripts,  Guillera-Arroita et al. (2014b)  were able to show that this result was an artifact of the fact that the VS created in  Thibaud et al.
These details are often difficult to present in a methods section that has space constraints and that should be readable by a more generalist audience, but they are all made explicit in a reproducible script. Therefore, the availability of well-documented scripts (and in particular scripts written using tools that enhance reproducibility, such as Rmarkdown) makes it really practical and easy to share, replicate and develop new tests to understand different facets of SDM approaches. In summary, we propose five general recommendations for future VS studies (Fig. 3 ): 1) implement multiple checking points at different simulations stages to make sure that the simulated species and sampling schemes have the desired properties; 2) clearly define the goals of the VS study to define accordingly at which stage iterations are the most useful and where more or less complexity should be used in the simulation process to achieve those goals; 3) include true presence-absence and/or true probability of occurrence in the evaluation process to take full advantage of the VS framework; 4) apply adapted statistical tests in the VS analyses; and 5) provide scripts for all simulations wherever possible.
This shows a disconnect between niche and metapopulation theories on the one hand, which usually assume bell-shaped environment-occurrence relationships and probabilistic occurrence patterns  (Hanski 1994 , Austin et al. 2006 , Austin 2007 , Soberón 2010) , and the VS simulation strategy on the other. This begs the question of whether or not there is any theoretical basis to think that threshold responses to environmental gradients should be prevalent in any SDM context. VS studies dealing with scaling issues provide for a good example of how this simulation choice (threshold versus probabilistic simulation approach) can feed into scaling theories, resulting in opposite expectations and becoming ingrained in the current scientific paradigm. If we look at threshold VS studies, they usually conclude that using finegrain datasets always results in increased classification rates and better model performance as compared to datasets that have been up-scaled to coarser resolutions  (Fernandez et al.
To demonstrate these points, we designed a very simple simulation exercise (scripts available in Supplementary material Appendix 3). We generated a VS with a logistic response to mean annual temperature in Belgium (real gradient). This species is more likely to be found in colder areas of Belgium; real environmental data was downloaded from Worldclim  (Hijmans et al. 2005 ) at 0. 5° resolution. To look at the effects of landscape structure, we also simulated two other landscapes: one with the same range and mean values for mean annual temperature but with a perfect east-to-west trend (perfect gradient), and another one where we added random noise around the perfect gradient (noisy gradient). Then we up-scaled distributions and temperature values in 2 × 2, 4 × 4 and 8 × 8 grid cells by marking the larger grid cells as present if any of its constituent smaller grid cells had a presence, and by taking average values of temperature at coarser spatial resolutions.
This confirms that expectations regarding model performance are opposite when using a threshold versus a probabilistic approach in this context. When we apply the same methods to a noisy gradient (Fig. 4b ), the threshold approach produces a pattern of degradation and then improvement of AUC at larger scales. Overall, however, when using a threshold simulation approach on this noisy gradient, it is always better to use the finer resolution dataset, as concluded by  Mertes and Jetz (2018). This contrasts with the probabilistic simulation approach results, where changes in AUC are more erratic across scales, and variance increases greatly as we aggregate over larger scales. Finally, the real temperature gradient (Fig. 4c ) is a mixed bag of results where AUC values for the threshold simulation approach decrease with the up-scaling, and AUC values for the probabilistic simulation approach increase and then decrease at coarser resolutions revealing an interaction between occupancy and landscape structure in the up-scaling process.
Software and virtual species simulation tools We hope that the recent publication of software dedicated to simulate VS will help standardize the implementation of VS studies, as well as make the underlying assumptions more explicit. Most VS studies initially used custom code to carry out their simulation work, and some of them published the scripts as a supplementary material  (Meynard and Kaplan 2012 , Guillera-Arroita et al. 2014b , Thibaud et al. 2014 ). However, there have been at least three software packages published for the generation of VS: sdmvspecies  (Duan et al. 2015) , virtualspecies  (Leroy et al. 2016)  and Niche Analyst or NicheA  (Qiao et al. 2016 ). The first package was published as an R library, but it contains a limited number of functions, which can be easily recovered from other packages, it is not associated with a comprehensive manual, and it has not been updated since its publication.
Therefore, we focus the rest of this section on virtualspecies and NicheA. In terms of usage, both packages are fairly distinct. virtualspecies is an R package that was specifically designed to generate VS with features that mimic real-world properties and datasets. Since it is an R package, it integrates easily with the rest of the functionality that R offers and follows the same programming language, making scripting and reproducibility easy. NicheA, on the other hand, is a stand-alone Java software. It was designed to explore the interplay between environmental and geographic spaces in the context of the biological-abiotic-mobility framework proposed by  Soberón and Peterson (2005) , with an emphasis on multidimensional graphical displays and a threshold hypervolume niche concept. NicheA also includes tools to analyze ecological niches outside of the geographical space  (Qiao et al.
2016 ), which we will not discuss further here. The graphical user interface of NicheA can be easier for non-programmers to understand, but may be limiting in terms of parametrization and options available for manipulation. The command-line interface of virtualspecies is more complex for users who are not used to the R language, but its open-source code offers more transparency, flexibility and possibilities in terms of batch execution stage, virtualspecies also permits spatial biases in sampling effort or detection probability to mimic real-world occurrence datasets.
Deep neural networks have been frequently used for semantic scene understanding in recent years. Effective and robust segmentation in outdoor scene is prerequisite for safe autonomous navigation of autonomous vehicles. In this paper, our aim is to find the best exploitation of different imaging modalities for road scene segmentation, as opposed to using a single RGB modality. We explore deep learning-based early and later fusion pattern for semantic segmentation, and propose a new multi-level feature fusion network. Given a pair of aligned multimodal images, the network can achieve faster convergence and incorporate more contextual information. In particular, we introduce the first-of-its-kind dataset, which contains aligned raw RGB images and polarimetric images, followed by manually labeled ground truth.
Most existing datasets and methods for outdoor scene semantic segmentation are mainly based on RGB camera. They are only well acceptable in general conditions excluding complex environment and small amount of samples. To develop additional practical solutions, one of the main challenges is data fusion from multi-modalities. Therefore, considering the RGB modality as a kind of imperfect sensor, we attempt to fuse the complementary feature information of the same scene from other modalities. Actually, several modalities are ubiquitous in robotic systems, such as RGB-D, LIDAR, near infrared sensor, etc. Figure  1  shows the multimodal images of our POLABOT dataset. In this work, we use a polarimetric camera, as a complementary modality, to provide a richer description of a scene. Polarization of light radiation has more general physical characteristic than intensity and color  (Wolff, 1997).
We can figure out that windows of a building, the asphalt road, and the puddle of water have reflected polarizations  (Walraven, 1977). Plenty of research have demonstrated that the use of polarization camera can significantly enhance the capabilities of scene understanding, especially for reflective areas  (Harchanko and Chenault, 2005). Over the past few years, a variety of deep learningbased end-to-end approaches have been proposed. One factor that increased the popularity of deep learning is the availability of massive data. In the case without large amount of samples, we attempt to acquire more features of the same scene using several modalities. To some degree, an effective encoding of complementary information enables learning without the need for massive data, therefore the use of smallscale dataset can also lead to good performances.
Recent works have shown promising results in extracting and fusing features from complementary modalities at pixel-level. The idea is to separately or jointly train the model using data from different sensors and integrate them into a composite feature at early or late stage. In this paper, we firstly review the existing fusion methods and datasets in section 2. Next, in section 3, we explore the two typical early and late fusion architectures, and propose our multi-stage Complex Modality network (CMnet), which has an encoder-decoder pattern and takes advantage of the state-of-art segmentation network. We evaluate the performances of the different fusion schemes using two different datasets in section 4. In particular, we introduce a new dataset, which to the best of our knowledge is the first multimodal dataset containing polarimetric images.
RELATED WORK In this section, we go through some of semantic segmentation methods, more details can be found in the review of  (Garcia-Garcia et al. , 2017). Then we summarize existing deep learning-based fusion schemes and various outdoor scene multimodal datasets. Deep Neural Network. Before deep learning achieved its current tremendous success, traditional computer vision methods were widely used, these methods are base on classifiers which operates on fixedsize feature inputs and a sliding-window. From the beginning with FCN  (Long et al. , 2015) , the end-toend fully convolutional network has become one of the most popular models for image segmentation. Re-cent years have witnessed a series of new encoderdecoder architectures along this line, including Seg-Net  (Badrinarayanan et al. , 2017) , and U-Net  (Ronneberger et al.
Besides, a late fusion based model, Long Short-Term Memorized Context Fusion, also called LSTM-CF, was proposed by  (Li et al. , 2016). This network extracts multimodal features from depth and photometric data sources separately, then concatenates the feature map at three different scales. Another simple late fusion network  (Eitel et al. , 2015)  was proposed for robust RGB-D object recognition. Furthermore, a convoluted mixture of deep experts technique  (Valada et al. , 2016a)  was used in the late fusion architecture. These early and late fusion architectures were studied and applied to various scenarios and fields, for instance, forested environments navigation  (Valada et al. , 2016b) , urban driving assistance  (Jaritz et al. , 2018). Datasets. Along with the development of computer vision techniques, a series of high-quality outdoor scene datasets have appeared, such as Cam-Vid  (Brostow et al.
, 2008b; Brostow et al. , 2008a) , Cityscapes  (Cordts et al. , 2016) , etc. They are widely used in outdoor semantic scene understanding. In addition, some research institutes publish their scenariobased multimodal dataset. For instance, KAIST dataset  (Hwang et al. , 2015)  is a multi-spectral pedestrian dataset of real traffic scenes, which was collected by a co-aligned RGB/Thermal camera, RGB stereo, 3D LiDAR and inertial sensors. Especially for semantic segmentation, there is KITTI dataset  (Geiger et al. , 2013)  which contains high-resolution RGB data, grayscale stereo cameras data, and 3D point cloud; Freiburg Multi-spectral Forest dataset  (Valada et al. , 2016b)  is also a multi-spectral dataset for fo- rested environment semantic segmentation, it contains RGB, Depth, NIR, Near-Infrared, Red, Green (NRG), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) images.
EXPERIMENTAL RESULTS In this section, we evaluate the different fusion models, and report a series of results on two datasets. One is the publicly available Freiburg multispectral forest dataset  (Valada et al. , 2016b) , and the second one is a new multimodal dataset containing polarimetric and RGB data, called POLABOT dataset. In this work, all the networks are implemented based on Pytorch framework with a Nvidia Titan Xp graphics processing unit (GPU) acceleration. The input data was randomly shuffled after each epoch. We initialize the learning rate as 0. 0001 and use the contraction segments of pre-trained VGG-16 model and ResNet-101 as encoders. Then we fine-tuned the weights of the decoders until convergence.
POLABOT Dataset As shown in Figure  4 , we collected multimodal images using a mobile robot platform equipped with four cameras: the RGB camera (IDS Ucam), a polarimetric camera (PolarCam), a depth camera (Kinect 2. 0), and a near-infrared camera. Our raw dataset contains over 700 multi-modalities images. All the images were acquired, synchronized and calibrated using the Robot Operating System (ROS) framework. Our benchmark also contains 175 images with pixel level ground truth annotations which were generated manually. These images have been dispatched into 8 classes: unlabeled, sky, water, windows, road, car, buildings and others. Benefiting from the use of a polarimetric camera, our mobile robot platform is more capable of discerning on windows, water and other reflective areas. That allows us to do much more exploratory research on polarimetric images in semantic scene understanding domain.
In this paper, we use aligned RGB and polarimetric images as inputs to train the fusion models. For integrating the acquired images, we apply an automatic homographic method to image alignment  (Moisan et al. , 2012). This method allows to transform the RGB images with respect to the polarimetric images, and crop to the intersecting regions of interest. Moreover, as deep learning models need large data sets of diverse examples, a certain amount of data should be guaranteed. For this reason, we employ geometric data augmentations to increase the effective number of training samples, including rotation and flipping. Data augmentation and multimodal data fusion help to train deep neural networks on small scale datasets.
Freiburg Multispectral Forest Dataset We train the segmentation architectures on the public Freiburg Forest dataset first. This dataset was collected by a modified RGB dashcam with NIR-cut filter in outdoor forested environment. It consists of over 15,000 raw images, and 325 images with pixel level ground truth annotations for 6 classes, which are the sky, trail, grass, vegetation, obstacle and others. In  We crop the RGB and EVI images as size 3×256×256, and use them as inputs correspondingly. We report several metrics to assess segmentation models: pixel accuracy (PA), mean accuracy (MA), mean intersection over union (MIoU), frequency weighted IoU (FWIoU). They are frequently used in semantic segmentation domain. The results shown in Table  1  show that segmentation using RGB images yields better results than EVI images on the whole.
This shows that RGB images provide better high-level features while training. For fusion architectures, late fusion methods outperform channel fusion method as we analyzed in the previous section. Our network yields around 1% ∼ 2% comprehensive improvements comparing with other methods. The results in Table  2  demonstrate the evaluations by class. We report the main four classes as Road, Grass, Veg/Tree and Sky. For uni-modality network, we can find that EVI shows good performance on Road and Grass classes, and RGB modality has a significant advantage on Sky class, which is susceptible to lighting changes. Moreover, the fusion architec- A note about the results is that Freiburg Forest dataset was collected from a series of frames, the scene of these frames are homogenized, the structure of each class in these images doesn't fluctuate a lot.
POLABOT Dataset In the following part, we report several experimental results on our POLABOT dataset. The metrics shown in Table  3  correspond to pixel accuracy (PA), mean accuracy (MA), F1 score (F1) and mean intersection over Union (MIoU). We process the RGB and polarimetric images with size 3×448×448. While training the networks, we experimentally found that stochastic gradient descent (batch size=1) doesn't work well. It is reasonable that online learning adds too much instability to the learning process as the weights widely vary with each batch, especially for small scale dataset with multi-classes. As a complement of previous analysis of training on small scale dataset, the data augmentation technology applied to POLABOT dataset gives the additional guarantee for weights learning. As a result, we can find that polarimetric images in our dataset provide high quality feature information, it is a beneficial premise for further data fusion.
CONCLUSIONS In this paper, we explored the typical early fusion and late fusion architectures that extract features from multi-modalities, and extensively evaluated theirs merits and deficiencies. We also proposed an extensible multi-level fusion scheme for semantic segmentation, which adopts advanced deep neural network techniques. It provides design choices for future research directions. We presented comprehensive quantitative evaluations of multimodal fusion on two datasets. The results show the benefits of fusing multimodal features to achieve state-of-the-art segmentation performance on small scale datasets. In addition, we introduced a first-of-a-kind outdoor scene segmentation dataset for road scene navigation, which contains high-quality aligned polarimetric images. We empirically demonstrate that the use of polarization camera enhance the capabilities of scene understanding.
The integration of multithreading into the Virtual Monte Carlo (VMC) design [3], lying at the core of both old and new ALICE simulation frameworks, and its impact on the Geant4 VMC  [4]  were presented at CHEP 2014  [5]. Geant4 VMC MT and the scaling behaviour of the computing time with the number of cores have been then tested using a simplified but realistic multithreaded application, based on a Geant4 VMC standard example. Particles from parameterised Pb-Pb generation were transported through the full ALICE geometry including a realistic field map; hits were generated and stored for the TPC  [6]. In this paper we will present the subsequent work that has followed for including the multithreading mode in the new ALICE simulation framework, O2 [7]. 2 The new ALICE simulation software in O2 The new ALICE software, O2, besides general libraries and tools, is based on two other frameworks: ALFA  [8]  and FairRoot  [9].
ALFA is the new message-queuing-based framework which is the result of a common effort of the ALICE and FAIR experiments. It was designed as a flexible, elastic system, which balances reliability and ease of development with performance using multi-processing and multithreading. It adopts a message-based approach that should support the use of the software on different hardware platforms, including heterogeneous systems, realised by the FairMQ module. The FairRoot framework is a simulation, reconstruction and analysis framework that is based on the ROOT [10] system. It includes core services for detector simulation and offline analysis. While its development started for one FAIR experiment at GSI, it has been gradually adopted by the other FAIR and GSI experiments and beyond, including ALICE O2.
The FairRoot simulation framework is based on VMC. VMC defines an abstract layer between a detector simulation user code (MC application) and the Monte Carlo transport code (MC). In this way the user code is independent of any specific MC and can be used with different transport codes within the same simulation application. It was developed by the ALICE Offline Project and, after the complete removal of all dependencies from the experiment specific framework, it was included in ROOT. From the three transport codes supported with VMC: GEANT 3. 21  [11] , Geant4  [2]  and FLUKA  [12] , only Geant4 can run in the multithreading mode, which is why only Geant4 simulation will be discussed in this paper. In the context of O2, the VMC application interfaces are implemented in FairRoot and some of them are further specialised in O2.