dataesr/scientific-paragraphs-categorization huggingface dataset:
The evaluation of this method is illustrated by first using the index and consumption data of calorie sensors operated by the SGE and, secondly, by using datasets from the scientific literature. We compare our algorithm to five methods belonging to different anomaly detection techniques. Based on the precision, recall, f-measure, evaluation criteria, we show that our algorithm is the most efficient at detecting different types of anomalies by minimizing false detections. There are several extensions of this research, among them: i) Use learning methods to automate the algorithm, model the patterns automatically and improve its performance in terms of calculation ii) Apply our algorithm on data streams, which are generated continuously, to trace alarms as early as possible and detect anomalies even before storing them in the databases.
The difference between data and MC for the calibration samples reconstructed within the expanded FV is shown in Figure  18. The total systematic error follows that of  [6]  and is the sum in quadrature of the absolute energy scale, which is defined as the largest data-MC difference across all samples, and momenta with the average time variation of these samples throughout SK-IV. The uncertainty from the up/down asymmetry of the detector is measured with Michel electrons from cosmic ray muons by comparison of their momenta for data and MC as a function of zenith angle. The difference between data and MC at the most deviated direction is taken as the uncertainty from the detector asymmetry. As summarized in Table  6 , the uncertainty of energy scale on the conventional (expanded) FV is 2. 17% (2.
Orange lines denote the sensitivity when events are reconstructed with APFit in the conventional FV. Figure  28  shows the expected sensitivity to mass hierarchy as the function of livetime for both reconstruction algorithms and their fiducial volumes. The expected improvement in sensitivity with the new algorithm becomes more apparent as data is accumulated, even assuming the conventional FV. While the new reconstruction has only been applied to the 3118. 5 day SK-IV fully contained data set, future efforts will expand this study to include other Super-K samples and run periods, which constitute an additional 2,800 days of livetime.
Acknowledgments We gratefully acknowledge the cooperation of the  Kamioka Mining and Smelting Company. The Super-Kamiokande experiment has been built and operated from funding by the  Japanese Ministry of Education, Culture, Sports, Science and Technology , the  U. S. Department of Energy , and the  U. S. National Science Foundation. Some of us have been supported by funds from the  National Research Foundation of Korea   NRF-2009-0083526  (KNRC) funded by the  Ministry of Science, ICT, and Future Planning , the  European Union   H2020 RISE-GA641540-SKPLUS , the  Japan Society for the Promotion of Science , the  National Natural Science Foundation of China  under Grants No. 11235006 , the  National Science and Engineering Research Council (NSERC) of Canada , the  Scinet and Westgrid consortia of Compute Canada , and the  National Science Centre of Poland  ( 2015/17/N/ST2/04064 ,  2015/18/E/ST2/00758 ).
Testing the Power Generation of the Device For testing the stretchable power generator device, a simple longitudinal gliding system was used to elongate/stretch the prototype with a stepper motor, M-23 NEMA 23 6. 0A with a step of 1. 8° (Schneider Electric Motion, Andover, MA, USA). The forward and backward movements of the stepper motor allowed the gliding system to produce a smooth linear translation. These linear movements of the gliding system produced stretching with controlled strain percentage and returned to the initial non-stretched position of the prototype accurately. This stepper motor was chosen because it was capable of high microstep resolution. The position and the speed of the motor were controlled by Uno R3 Arduino card and the software Arduino (Arduino ® , Evrea, Italy).
Using a worm screw that was rotated by the stepper motor, the linear position of the lower jaw of the gliding system was monitored. A LabVIEW program (National Instruments, Austin, TX, USA) was conceived to control the movements of the stepper motor. First, a digital oscilloscope (TBS1032B Tektronix, Tektronix, Beaverton, OR, USA) was used to measure output voltage from the power generator. The power generator was situated on the gliding system of the stepper motor and connected to an oscilloscope using the bottom and top collection electrodes. The output voltage measured with the oscilloscope was 2 V. The frequency of elongating/stretching the prototype of the stepper motor was 1 Hz and the elongation strain was 8%. After the oscilloscope measurements proved that the power generator is producing a voltage, the output power was measured using a Keithly 2400 source meter (Keithley, Solon, OH, USA).
Using the source meter controlled by the LabVIEW program, the stretchable device output voltage and current and calculated output power under different strain conditions were recorded. The device was connected straight to the source meter using the two wires connected to the upper and bottom electrodes. The Arduino software allowed the control of the gliding system movements and the LabVIEW program recorded the measurements of voltage and current from the Keithly source meter. In order to increase the accuracy of the measurement, the algorithm used was a loop with all devices. For each loop, LabVIEW gave an order to the motor to run a defined number of steps which corresponded to a certain elongation. For each step, the elongation was 0. 0225 mm. To improve the accuracy of the measurement, a displacement sensor was fixed on the gliding system between the two jaws (moving and fixed).
Testing the Power Generation of the Device For testing the stretchable power generator device, a simple longitudinal gliding system was used to elongate/stretch the prototype with a stepper motor, M-23 NEMA 23 6. 0A with a step of 1. 8 • (Schneider Electric Motion, Andover, MA, USA). The forward and backward movements of the stepper motor allowed the gliding system to produce a smooth linear translation. These linear movements of the gliding system produced stretching with controlled strain percentage and returned to the initial non-stretched position of the prototype accurately. This stepper motor was chosen because it was capable of high microstep resolution. The position and the speed of the motor were controlled by Uno R3 Arduino card and the software Arduino (Arduino ® , Evrea, Italy).
Introduction Scene understanding plays a key background role in most vision-based mobile robots. For example, autonomous navigation in indoor/outdoor scenes asks for a rapid and comprehensive understanding of surroundings for obstacle avoidance and path planning  [1] [2] [3]. Vehicle movement in limited temporal and spatial contexts always requires knowledge of what there are around ego-vehicle and where it is located. Robotic maps, such as Occupancy grid map and OctoMap, traditionally provide geometric presentation of the environment. However, they lack the correlation in data between map points and semantic knowledge; thus, they could not be directly utilized for scenes understanding. Scene parsing/semantic segmentation is an important and promising step to address this issue. It has been an active topic for a long time, and it benefits from the state-of-the-art Deep Convolutional Neural Networks (DCNNs), which contributes to better performance of 2D pixel labeling than traditional methods.
e. , local points with same semantic label should be approached in 3D space. Two datasets, Cityscapes  [11]  and KITTI  [12] , are used to evaluate our approach. This work is an extension of our previous work  [13]. We not only modify the 2D semantic segmentation module, but also revise the offline regularization module with new potential constraint. More experiments and theoretical details are involved in this work. The main contributions involve the improvement of 2D semantic segmentation model, the associative hierarchical Conditional Random Field (CRF) with High Order Potential towards the point cloud, the extended experiments and the quantitative evaluation of the performance including accuracy and runtime. This paper is presented as follows. In the Section 2, a review of the related work is given.
Related Work Our work is motivated by  [10]  which contributes an indoor 3D semantic SLAM from the RGB-D input. It aims towards a dense 3D map based on ElasticFusion SLAM  [14]  with semantic labeling. Pixel-wise semantic information is acquired from a Deconvolutional semantic segmentation network  [15]  using the scaled RGB information and the depth as the input. Depth information is also used to update surfel's depth and normal information to construct 3D dense map during loop closure. In addition, a previous work, SLAM++  [16] , creates a map with semantically defined objects, but it is limited to predefined database and hand-crafted template models. In this paper, we make use of an incremental Bayesian fusion strategy with state-of-the-art visual SLAM and semantic segmentation. Visual SLAM usually contains sparse, semi-dense, and dense types depending on the methods of image alignment.
The early DeepLab models have a reasonable accuracy but require much computation overhead. Recently proposed efficient convolution neural network, such as MobileNets  [25, 26]  boosts real-time performance of semantic segmentation without losing the accuracy too much. The state-of-the-art DeepLab-v3+  [7]  contains a simple effective decoder module to refine the segmentation results especially along object boundaries. Furthermore, combining the encoder part of MobileNet-v2 in its structure, DeepLab-v3+ could achieve a better trade-off between precision and runtime. In the topic of scene understanding and mapping, recent researchs employ 3D priors of objects increasingly. Salas-Moreno et al. [16]  project 3D mesh of objects to the RGB-D frame in a graphical SLAM framework. Valentin et al. [27]  propose a triangulated meshed representation of the scene from multiple depth measurements and exploit the CRF to capture the consistency of 3D object mesh.
2D Scene Parsing We explore the DeepLab-v3+ deep neural network proposed by Chen et al. [7]. Two important components in the DeepLab series are the atrous convolution and atrous spatial pyramid pooling (ASPP), which enlarge the field of view of filters and explicitly combine the feature maps at multiple scales. The improvement in the DeepLab-v3+ involves the encoder-decoder structure and the augmentation of ASPP module with image-level feature. The former is able to capture sharper object boundaries by regaining the spatial information, while the latter encodes multi-scale contextual information to capture long range information. These contributions make DeepLab successfully handle both large and small objects and achieve a better trade-off between precision and run-time. For the semantic segmentation of road scenes, we exploit the Cityscapes dataset and the KITTI dataset and adopt the predefined 19-class label space L = {l 1 , l 2 ,.
Experiments and Results We demonstrate the performance of our approach on the KITTI dataset  [12] , which contains a variety of urban scene sequences involving lots of moving objects in various lighting conditions. It consists of various datasets, such as the semantic dataset, the odometry dataset, and the detection dataset. Thus, it is very challenging for the 3D reconstruction. The KITTI dataset contains a 2D semantic segmentation data of 200 labeled training images and 200 test images (http://www. cvlibs. net/ datasets/kitti/eval_semseg. php?benchmark=semantics2015). Its data format and metrics conform with the Cityscapes dataset  [11]. The Cityscapes dataset involves 19 classes within high quality pixel-level annotations of 5000 images with a resolution of 2048 × 1024, including 2975 training images, 500 validation images, and 1525 testing images.
In our experiment, we train the model on the Cityscapes and then tune it on the KITTI taking the volume size of dataset into account. For the training of 2D semantic segmentation model, various encoder models in the DeepLab-v3+ are evaluate including ResNet  [41] , Xception  [42] , and MobileNet-v2  [26]. We find that the "poly" stochastic gradient descent is better than the "step" one on these datasets. The TensorFlow library is employed to do the training and inference on the workstation with 4 Nvidia Titan X GPU cards. The hyper-parameters used in training are set corresponding to the datasets and models as shown in Table  2. We benchmark the performance of our semantic mapping system on the KITTI odometry dataset (http://www. cvlibs. net/datasets/kitti/eval_odometry. php). There are 22 sequences with the consecutive RGB frames, in which there are 11 sequences with the ground-truth poses for evaluation.
Qualitative Results First, we present some qualitative results of the KITTI semantic dataset in Figure  5. Then, we use the trained model to make prediction on the KITTI odometry dataset, and the results are exemplified as shown in Figure  6. Take the sequence odometry_03 as an example of our semantic mapping approach. The sequence consists of 801 RGB frames on a urban road of about 560m. Figure  7  shows the semantic reconstruction with a close-up view including large-scale annotations such as road, building, and even small-scale objects like traffic signs. Note we discard some keyframes at the beginning, due to random initialization of LSD-SLAM. A close-up view is exemplified to illustrate the offline CRF processing as shown in Figure  8. Another possible qualitative comparison on the KITTI odometry_05 as used in Kundu et al.
Quantitative Results For the quantitative performance of our approach, we focus on the 2D semantic segmentation and the runtime of the entire system, since the 3D reconstruction mainly depends on the SLAM module. Semantic Segmentation: Table  3  shows the quantitative results of 2D semantic segmentation based on different DeepLab-v3+ models on the KITTI datasets. We evaluate these models by the mean intersection/union (mIOU) score, the model size, and the computational runtime. The mIOU score is defined as mIOU = 1 |L | |L| ∑ i=1 TP i /(TP i + FP i + FN i ) (21) in terms of the True/False Positives/Negatives for a given class i. We do not resize the image to evaluate the models here. Whereas, for the 3D semantic mapping process, we need to half resize the input images in order to make a trade-off between accuracy and computational speed.
In multi-modal electron tomography, tilt series of several signals such as X-ray spectra, electron energyloss spectra, annular dark-field, or bright-field data are acquired at the same time in a transmission electron microscope and subsequently reconstructed in three dimensions. However, the acquired data are often incomplete and suffer from noise, and generally each signal is reconstructed independently of all other signals, not taking advantage of correlation between different datasets. This severely limits both the resolution and validity of the reconstructed images. In this paper, we show how image quality in multimodal electron tomography can be greatly improved by employing variational modeling and multichannel regularization techniques. To achieve this aim, we employ a coupled Total Generalized Variation (TGV) regularization that exploits correlation between different channels.
See DOI: 10. 1039/C8NR09058K when transformed to a suitable basis. If such a transformation is possible, minimization of the ℓ 1 -norm in this domain enforces sparsity and can lead to an accurate solution. The most important type of sparsity in electron tomography is sparsity of the image (or volume) gradient. 19, 23, 24 Known as Total Variation (TV)  25  minimization, ℓ 1 -minimization of the image gradient leads to an image (or volume) with locally constant regions, separated by sharp interfaces. ] [28] [29]  It has been shown that TV minimization can be especially favorable for analytical electron tomography experiments, where usually only limited and noisy data are available. 14, 30 wo major limitations can be identified with current TV minimization approaches and their application in analytical tomography.
Here we present an approach for resolving these issues for multi-modal tomography. We apply coupled Total Generalized Variation (TGV)  [32] [33] [34]  regularization, which allows for both sharp interfaces and gradual intensity changes in the reconstruction and exploits inter-channel correlation. This yields a joint multi-channel reconstruction of HAADF and EDXS data. We demonstrate our approach on phantom data and on an Al-5 wt% Si alloy with 50 ppm Na and 6100 ppm Yb,  14  where the precipitation of Yb and Si takes place at the nanoscale, but it is generally applicable to analytical electron tomography of all types of nanostructured materials. The basic concept of multimodal STEM tomography is depicted in Fig. 1. Several tilt series are acquired at the same time in the microscope. In the joint reconstruction all tilt series are used together to obtain a joint multi-modal reconstruction.
We note that in two recent, independent studies,  35, 36  a coupled Total Variation approach was proposed to link data and create smooth reconstructions. While these studies overcome the second limitation of existing approaches as mentioned above, the usage of TV regularization still limits their applicability to piecewise constant densities with sharp interfaces. However, a major benefit of three-dimensional local elemental distributions lies in analyzing local concentration variations. While the compositions of well separated phases are often accessible already by 2D investigations after appropriate sample preparation, local variations are always masked due to projection. A recent example is the unveiling of elemental concentration variations within spherical precipitates in an AlMgScZr-alloy.
17 In such situations it would not be possible to use TV regularization. Beyond overcoming this drawback, a second novelty of our approach is that we developedand make publicly available  37  a Python 38 /OpenCL 39 -based implementation for all components of the reconstruction algorithm, which is crucial in particular for 3D reconstructions as it significantly reduces the computation time. A graphical user interface connected to the code allows for simple and efficient execution. In particular, we developed a custom, GPUbased implementation of the forward operator and its adjoint, ensuring, in contrast to other existing implementations, numerical adjointness of the two, which is in particular crucial for the convergence of iterative minimization algorithms. ] [42] [43] [44] [45]  In particular, in the context of joint magnetic resonance (MR) imaging and positron emission tomography (PET), multi- channel TGV regularization was shown to allow for improved recovery of quantitative measurements compared to singlechannel approaches.
Results The main principles of multi-modal TGV reconstruction The proposed tomographic reconstruction framework is based on a convex variational method employing volumetric coupled second order Total Generalized Variation (TGV) regularization, and the Kullback-Leibler (KL) divergence as a discrepancy measure. Given some measured multi-channel ‡ data f = ( f 1 , …, f C ), a reconstruction u = (u 1 , …, u C ) is obtained as a solution to the convex minimization problem min u!0 X C c¼1 μ c D KL T c u c ; f c ð Þ " # þ TGV α 2 u ð Þ: ð1Þ Here, D KL ũ; f À Á :¼ Ð ũ À f TGV α 2 u ð Þ ¼ min w α 1 k ∇u À wj frob k 1 þ α 0 k Ewj frob k 1 ; ð2Þ where ∇u denotes the Jacobian of the multi-channel image u, w is a matrix field with the same dimensions as ∇u and Ew denotes a symmetrized Jacobian of w. The norms |•| frob are pointwise Frobenius matrix and tensor norms and k•k 1 denotes the one-norm, i.
e. , the sum of moduli over all image voxels. For solving the minimization problem (1), we employ a duality-based algorithm for which we developed a Python 38 / OpenCL 39 implementation. Further details of the algorithm and the reconstruction approach are provided in the section Materials and methods. We can identify two main features of the proposed coupled TGV regularization functional: first, the functional itself optimally balances between a penalization of the first and second order derivatives. Choosing the auxiliary variable w to be locally zero or equal to ∇u, it is possible to penalize either the one-norm of ∇u or the one-norm of E∇u, where E∇u corresponds to the Hessian of u. For this reason the proposed method can be expected to recover both sharp interfaces and gradual intensity changes.
Reconstruction of phantom data To illustrate the capability of the joint TGV algorithm, we first test it in an artificial example. For this purpose we created a multi-channel phantom object. The phantom is inspired by the sample investigated in our experiments and is assumed to consist of three elements (Yb, Si and Al), with both abrupt and gradual concentration changes in the sample. For this phantom, we calculate the sinograms of EDX signals for Yb, Si and Al and of the HAADF signal assuming typical experimental parameters for an analytical tomography experiment and add Poisson noise to the calculated sinograms (see the Materials and methods section). To investigate how a full 3D implementation of the reconstruction algorithm compares to a 2D implementation, we replicate the same slice 60 times rotating each slice slightly compared to the previous to obtain a 3D volume with changes along all three dimensions.
3  we provide the reconstruction results, where three orthogonal slices for each method for both the HAADF and the EDXS signals are compared to the original phantom (Fig. 3a ). The SIRT reconstruction (Fig. 3b ) of the low-noise HAADF data is quite acceptable, showing only some typical artifacts stemming from the limited number of projections. Due to the low signal of the EDXS data however, these reconstructions are dominated by the Poisson noise of the sinograms and hardly anything can be discerned. ‡ It should be noted that as one channel we denote one type of dataset to be reconstructed, such as the HAADF signal or a single EDXS elemental map, meaning if we consider sinograms for HAADF, EDX aluminum, EDX silicon and EDX ytterbium, then C = 4. This should not be confused with spectral channels in EDXS or EELS.
In the TGV 2 reconstruction, both sharp and gradual concentration changes can be recovered and also in the TV reconstruction, staircasing artifacts are reduced. By linking all slices in the third spatial dimension, the 3D implementation of the algorithm is even more robust to noise for both the uncoupled (Fig. 3g  & h ) and the joint reconstruction (Fig. 3i  & j ). The 3D joint reconstruction methods make full use of data correlation, along all three spatial dimensions as well as between the different channels and therefore give the best reconstruction for both TV and TGV  2. With TGV 2 , constant areas and hard transitions are equally well reconstructed as with TV, but, by promoting piecewise linear functions in addition to sharp interfaces, TGV 2 is superior to TV when it comes to gradual intensity changes.
Discussion In both simulations and experiments, we could observe that TGV 2 reconstruction is well suited for reconstructing gradual transitions inside a sample, in addition to sharp interfaces. Thereby it extends TV reconstruction, making sparse reconstruction applicable to a wider range of samples. In the investigated sample such gradual transitions are present between Ybrich and Si-rich precipitates. Linking different signals in the reconstruction significantly improves the quality of analytical STEM reconstructions by transferring morphological information present in the HAADF STEM data to the analytical data and in between the analytical datasets. Since this transfer is a key feature of our approach, we want to quickly illustrate its inner workings. For simplicity, assume that we have only two channels and consequently the reconstruction approach reduces to min u 1 ;u 2 ð Þ ¼ u!0 μ 1 D KL Tu 1 ; f 1 À Á þ μ 2 D KL Tu 2 ; f 2 À Á þ TGV α 2 u ð Þ; i.