dataesr/scientific-paragraphs-categorization huggingface dataset:
D. COMPARISONS WITH RELATED WORK We have compared the best classification performances obtained in this paper with those obtained in  [23]  where the authors conducted classification experiments on the same database and for the same classification tasks, using the following classifiers: 1) k-NN with k = 1. 2) SVMs with a radial basis kernel. 3) Artificial Neural Networks. 4) RF with 300 bagged classification trees. Comparison results presented in Table  5  reveal that the approach proposed in this paper always outperforms  [23]  with positive accuracy gains reaching +13. 45% and +10. 36% respectively for F-8 and AF-17.
In this paper r = 100. 2) γ is the user-defined maximum number of iterations of the algorithm. In this paper γ = 100. 3) T is the number of vectors in the sequence w. In this paper T = 151. 4) m is the user-defined number of slices used to split the interval [0, 100]. In this paper m = 50. This time cost can be further reduced by gradually reducing the values of parameters like r or γ without negatively impacting the classification results. If the stationary distribution is discovered after r 0 iterations with (r 0 < r), this stationary distribution will not change during (rr 0 ) remaining iterations. Similarly, if the Baum-Welch algorithm reaches its local optimum after γ 0 iterations with (γ 0 < γ ), this local optimum will not change during the (γγ 0 ) remaining iterations. The experimental value T = 151 cannot be modified because it was fixed during the design of the experimental database.
2) EXPERIMENTAL TIME COST In order to measure the speed-up of the two most timeconsuming stages of the processing chain (i. e. the stages of HMM training and the meta-data extraction), we executed and benchmarked the program on two different architectures: a desktop and the Nvidia JETSON TX2  [99]  comparable in terms of hardware to an embedded platform. The main characteristics of the experimental desktop are the following: • CPU: AMD Ryzen 5 5600X (6 Cores) with dedicated core frequency when used at 4649. 877 MHz The System-On-a-Chip (SOC) used on the Nvidia JET-SON TX2 has in common 4 cores used on the SOC of the Exynos 7 Octa (5433)  [100]  clocked at 1. 9 GHz which is used on the Galaxy Note 4 and the Galaxy Tab S2. The experiment presented in this section has been performed using the 11.
771 MCs obtained when the k-means algorithm is executed with k = 20 (our smallest experimental value of k). The Nvidia Jetson TX2 has 8 GB L128 bit DDR4 of main memory. It runs on L4T (Linux for Tegra, i. e. Linux Kernel 4. 9) and has a Parker SOC consisting of: 1) A Pascal GPU at 256 Cuda cores (not used in this experiment). 2) One HMP (6 cores) including 2 Denver cores (custom core designed by Nvidia to run the ARMv8 ISA) and 4 Arm Cortex A57 cores (to run the ARMv8 ISA). All the cores are compatible with ISA ARMv8 which is the 64 bits architecture of Arm. The Nvidia Jetson TX2 has several power supply modes that influence the frequency of the different cores. The modes used during our tests were the 'Max-N' mode which allows to reach 2 GHz on each core and the 'Max-P Core-All' mode allowing each core to be used at 1.
4 GHz (i. e. a tradeoff between performance and energy consumption). During the current experiment, the program was executed on cores excluded from the Linux scheduler allowing to dedicate the core entirely to the program and thus, not to distort the performance measurements. The following units were selected to measure the performance of our program using the Performance Monitoring Unit and Syscall: 1) Milliseconds (ms) 2) Instructions per cycle (IPC) 3) Instructions per second (IPS) 4) Floating point operations per second (FLOPS) Tables  6  and 7  present the performances of our program on the experimental devices. According to these tables, the best performance is obtained by the desktop with a mean time cost of less than 2 seconds. Nevertheless, the performance on Nvidia Jetson TX2 is also very interesting with a mean time cost of around 12 seconds when the Core Denver 2 GHz is used.
A wattmeter was additionally used for measuring the energy consumption in order to deduce the Average Power have demonstrated the efficiency of the proposed approach with the best accuracies between 92% and 98. 85% for all the classification tasks. This performance is more than 10% better than state of the art for two classification tasks. The main contribution of the current work is the HMM-based sequential learning of the sample (raw data vector) w associated with a human activity. Meta-data extracted from the resulting HMM λ w are then used for deriving the corresponding feature vector -→ w. Consequently, the number of samples in the experimental database does not impact the components of -→ w since each sample in the database is handled individually, irrespective of the other samples in the database.
For this reason, the proposed approach will still exhibit good classification results even for large scale databases. Only the overall computation time for all the samples in the database will increase in these conditions. Parallel computation of all the feature vectors in the data base can also be implemented to reduce this overall computation time. The current work has a dual impact on further researches in HAR. Firstly, it has been theoretically and experimentally demonstrated that learning a human activity as a sequential process enhances the quality of the resulting feature vectors and consequently, induces better classification results. A lot of the research in HAR only considers discrete activities as opposed to activities in a continuum. The proposed method enables extracting salient features from a stream of data using HMM.
5) Efficiently performs HAR and outperforms prior work for the selected database. 6) The algorithm has been demonstrated viable on embedded platform processors from 2014 mobile phones and tablets, namely, ARM and Denver cores clocked at 1. 4GHz and 2GHz. This shows that current mobile phones and tablets would be much closer to the performances of a PC. This paper addresses the problem of HAR based on acceleration data from Smartphones. Existing approaches for this purpose either rely on conventional pattern recognition techniques or on deep learning techniques. Conventional pattern recognition techniques generate shallow handcrafted feature vectors which heavily rely on the human experience/ expertise. Deep learning techniques are preferable, but they require lots of computing resources while generating less interpretable feature vectors.
The current paper attempts to overcome these limitations by proposing an efficient HMM-based technique that generates interpretable feature vectors while requiring a reasonable time cost with a demonstrated feasibility of implementation on embedded processors, namely, Denver and ARM cores. Four different classification tasks have been tested on the UniMiB SHAR dataset containing 17 human activities including 9 types of ADLs and 8 types of falls. Classification results Efficiency(Time,Power) = (AET ) × (APC) (8) F. MAIN ASSETS The technique proposed in this paper: 1).
Abstract In breast surgical practice, various scans and medical examinations are performed before surgery. This includes identifying landmarks defining the operating procedure. In most cases, the position of the patient during the scan is vastly different from the one encountered during the operation. We address the challenge of mapping preoperative information to the operating field, with the following constraints: registration has to be done in less than 10 seconds to be compatible with a clinical workflow; the cost of the device must be small and we assume data scarcity, i. e. that our database has twenty scans of patients at most. We build anatomical complexity through a skinning model comprised of scalable bones (to account for pose and morphological variations) and deformable organs (blendshapes, to account for anatomical variations).
Statistical models also tackle this problem by using 3D morphable models. These statistical shape models are obtained by using dimensionalityreduction methods like Principal Component Analysis (PCA) to a set of training shapes. [29]  and  [17]  combined 3D morphable models with additional landmarks to impose breast shapes constraints for improving the fitting of the model. These robust methods are faster than non-rigid registration but rely on a large database (3. 15s per scan on average with a database of 310 scans in  [29] ) Therefore, in all previous models, all patients had a quasi-similar posture. Indeed, non-linear transformations such as large articulated movements can be hard to model. In our patient database, we noticed a non-negligible pose variation that makes all previous methods difficult to apply.
The main concept of SMPL is the use of corrective human body-shape and pose-dependent shape variation, learned from thousands of 3D body scans, to improve the accuracy of the skinned model. Originally designed for graphics purposes, the learned-model has been extended to the medical field. For example,  [12]  fit its SMIL to RGB-D sequences of freely moving infants for early detection of neurodevelopmental disorders. SMPL fulfills our timing and accuracy criteria. The main drawback of the model is the learning database, partially made of women wearing bras, which makes impossible to capture anatomical details that are only visible on naked-breasts. At the time of writing this paper and to our knowledge, no open-source database of 3D scans of exposed chest is available. In this paper, we propose a flexible model that can fit a set of patients in different surgical positions, without any previous training phase.
Data acquisition To obtain a 3D surface mesh of the patient, we used a depth camera 1. This scanning device suffers from a high noise-sensitivity and the possible creation of spurious gaps within the mesh. However, it satisfies our criteria in terms of rapid acquisition time, user-friendly interface, and reasonable price. The acquisition was made on 7 women, in preoperative and intra-operative positions. Their age varies between 30 and 70 years and their breast size from 95A to 110C with one notable case of asymmetry. All the patients were diagnosed with breast cancer and chose lumpectomy as the most suitable treatment, under medical recommendation. After obtained consent from the 7 patients for the study, scans were performed by the surgeon without any previous training. The result is a surface mesh of the patient obtained in less than 1 minute, scan and reconstruction included.
Skinning Skinning is the next step to animate the articulated model. It consists of deforming the surface mesh (i. e the skin of the model) depending on the pose of the rig  [14]  [13]  [20]. This can be achieved through blend weights that adjust the influence of each skeletal bone on the skin. In figure  2b , we displayed the colormap of the blend weights associated with the lower bone. The red regions are rigidly attached to the bone and follow the bone's motion, conversely, blue areas are not affected by the movements of the bone. The blend weight matrix can be manually given by the user or can be automatically calculated by 3D modeling software such as Blender  2. In a simple case on the figure  1a , we designed 2 bones linked by a spherical joint and applied a simple 45 degrees rotation around the z-axis on the upper bone.
Blendshapes Originally from computer graphics,  [26]  used blendshapes for the first time in facial animation. Years later, blendshapes are implemented in all animation software for providing realistic and quick facial expressions to animated characters  [15] [21]. Now widely used for efficient geometrical deformation  [23] , blendshapes are deformed versions of a template mesh. For our model, an artist created A = 55 blendshapes affecting global features such as shoulders or belly size as well as more local ones such as nipples and aureole shapes. Let v denotes the vertex positions of the template mesh, B the blendshape function computing the deformed vertex positions and b k the k th shape displacement matrix, with v, B, b k ∈ IR N ×3. Blendshape displacements provide a set of basis vectors that define a linear space which is used to generate a vector space, onto which the patient's shape is projected.
To reduce the computational time, we also used a coarse mesh but refinement of the mesh can lead to better registration. Validation on a larger population for creating a statistical database would be a useful step forward. This could be done by adding a Bayesian regularisation term  [28]  to the energy minimization term. The registration provides a patient-specific mesh, ready for biomechanical simulations, and can be the base for pre to intra-operative mapping of tumors using the finite element method. ².
Acknowledgements This study was supported by  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  811099  and the  Marie Sklodowska-Curie  grant agreement No. 764644. The medical images used in the present study were obtained in  Hopital Arnaud de Villeneuve, Département de Gynécologie Obstétrique  in collaboration with  Dr Gauthier Rathat.
The simulation environment can be made accessible to others by publishing the code open-source, providing good documentation and maintaining version control. This ensures the minimum requirements for any of the studies to be reproducible. However, access to the synthetic travel demand and the tools to reproduce it are also essential parts leading to the repeatability of agent-based modeling studies. To foster reproducibility of agent-based studies, we propose a general pipeline called eqasim that provides a clear path from raw data to a final agent-based mobility simulation. The paper is structured as follows. Section 2 gives a brief overview of agent-based simulation and models. Section 3 provides more information on the eqasim approach. Section 4 showcases one implementation of the pipeline with a model for Zurich, Switzerland.
The utility function for public transport makes a difference between routes that include a train with x inVehicleTime,train quantifying the travel time in the main stage and x inVehicleTime,feeder quantifying the rest. Only if no train is included in the route, x inVehicleTime,other has a value while the other two are set to zero. The pt quality parameters and variables are based on a methodology of the Federal Office for Spatial Development quantifying the accessibility by public transport of any location in Switzerland, based on vicinity to public transport infrastructure and frequencies of the accessible transport lines  [1]. The model includes two interaction terms ξ which establish non-linear dependencies of the utility of travel time and cost on distance ξ T D = x euclideanDistance θ referenceDistance λ TD and ξ CD = x euclideanDistance θ referenceDistance λ CD (1) 4 https://gitlab.
This is usual necessary as the interpretation of travel time in the survey data and in the simulation differ. Generally, the alternative-specific constant captures all components of the trip utility which are not described explicitly by the other terms. Hence, it can include the discomfort of parking search, paying for parking, and, in the specific case of this simulation, access and egress to the vehicle. Although recent versions of MATSim support simulating access and egress stages to and from the vehicle, this was not used here, and even then, the model does not include the locations of parking spots or garages, or any additional model of choosing between on-street parking, using a large garage or even parking facilities provided by the company for work trips. Such considerations will be important for the future development of the framework.
This study assesses whether currently available acute oral toxicity (AOT) in silico models, provided by the widely employed Leadscope software, are fit-for-purpose for categorization and labelling of chemicals. As part of this study, a large data set of proprietary and marketed compounds from multiple companies (pharmaceutical, plant protection products, and other chemical industries) was assembled to assess the models' performance. The absolute percentage of correct or more conservative predictions, based on a comparison of experimental and predicted GHS categories, was approximately 95%, after excluding a small percentage of inconclusive (indeterminate or out of domain) predictions. Since the frequency distribution across the experimental categories is skewed towards low toxicity chemicals, a balanced assessment was also performed.
An AOT in silico model offers an animal-free way to elucidate a compound's acute hazards to fulfill testing requirements, classification/labelling, or transportation purposes. Fundamental to the success of a global AOT in silico model is a sufficiently representative, large and high-quality database and algorithms which have the capability to make reliable predictions for a broad range of chemical structures. (In the case of a statistical QSAR, the model itself would be derived from the database using an algorithm, but the manner in which any (Q)SAR makes predictions of chemical hazard may be considered an algorithm, with data not seen during the model development procedure required for external validation of the final model. ) A reliable AOT in silico model could complement an existing laboratory study to further reduce animals or refine existing procedures.
(Q)SAR models There are two commonly used (Q)SAR methodologies referred to as expert rule-based and statistical-based  (Myatt et al. , 2017). Leadscope (an Instem company) has recently developed and made available a first generation of (Q)SAR models covering both methodologies to predict GHS categories for rat acute oral toxicity (Leadscope 2020). Both methodologies use a database of over 15,000 chemicals with rat AOT results from a number of sources including the Registry of Toxic Effects in Chemical Substances, ECHA, EU's Joint Research Council's Acu-toxBase, National Library Medicines (NLM) Hazardous Substances Data Bank, OECD (eChemPortal), PAI (NICEATM) and TEST (NLM Chem-IDplus)  (RTECS 2011; Kleinstreuer et al. , 2018). A series of individual models have been developed from this combined dataset and used to predict GHS categories (1-5 and NC).
The most conservative value (GHS category 3) was used as the final consensus model from the two methodologies. The models allow for inspection of the underlying model information, such as feature weightings, to support an expert review. In addition, it is possible to review analogs in the database to provide additional supportive evidence, as shown in Fig. 2. Collaborators were given access to the acute toxicity (Q)SAR models from Leadscope (Leadscope acute rat oral QSAR (v1) and alerts (v1) [System: Leadscope Model Applier v2. 4]) to use in this exercise. Each collaborator collected historical information on chemicals where a rat AOT had been performed, with a Klimisch score of 1 or 2  (Klimisch et al. , 1997)  where possible, along with information on the study protocol, study parameters and results (for the chemicals from the plant protection product sector, 24% of compounds were retrieved from the Pesticide Properties Database  (Lewis et al.
Curating and combining the results Each collaborator shared their in vivo results and predictions, as shown in Fig. 3. Initially, the individual results were analyzed to remove entries that could not be used in this exercise, based on the following rules: • When an in vivo LD 50 range was provided that spans multiple GHS categories (except for >2000 mg/kg since the 5000 mg/kg dose is often only used when it can be justified) • In cases where it was possible to identify whether a chemical was present in the underlying model's database from the software output In some cases, the individual collaborators provided both LD 50 and GHS category results, in others only LD 50 values or ranges were provided. The following rules were adopted to consistently process the data: • When only LD 50 values were provided, a GHS category corresponding to the LD 50 value or range was computed • When both an LD 50 and GHS category were provided then the GHS category was used when justified by the collaborator  • When an experimental value of >2000 mg/kg was used, a "Category 5 or Not Classified" entry was used.
Generating summary statistics The results were consolidated (as shown in Fig. 3 ), and a series of summary statistics were generated for the entire dataset as well as subsets including collections from the pharmaceutical industry, plant protection product industry and other chemical industries. These summary statistics use an assessment of whether the experimental in vivo GHS category exactly matched the predicted GHS category. In cases where the experimental category was assigned to the category "Category 5 or Not Classified", a correct match was recorded if the prediction was Category 5 or Not Classified. A series of summary statistics were calculated to support an assessment of whether the (Q)SAR test is fit-for-purpose for classification and labeling, that is it predicts either the correct or a more potent category.
This analysis was performed on both the entire data set as well as subsets of the data as explained below. (1) The proportion of compounds correctly or more conservatively classified (for example, if the in vivo GHS category was 3, then a prediction of GHS 1, 2 or 3 would be a match) (2) The proportion of compounds correctly predicted or one category more conservative (for example, if the in vivo GHS category was 3, then a prediction of GHS 2 or 3 would be a match) Two additional summary statistics were computed to assess the accuracy of the models. (3) The proportion of compounds correctly predicted (for example, if the in vivo GHS category was 3, then only a prediction of GHS 3 would be a match) (4) The proportion of compounds correctly predicted or one category higher/lower (for example, if the in vivo GHS category was 3, then a prediction of GHS 2, 3 or 4 would be a match) For each of these statistics, an overall assessment (i.
e. , the proportion across all test compounds) as well as a balanced assessment (based on the average proportion for each experimental in vivo GHS category) was calculated. Whilst the values derived from the overall assessment are more intuitive, the fact that the dataset was skewed towards a higher proportion of low toxicity chemicals (see below) makes the latter values more appropriate to consider. In addition, a baseline was computed using a random model (i. e. , a random uniformly distributed assignment to category 1 through 5 and  not classified) and the same balanced summary statistics generated. This was used for comparison purposes.
Results Results were provided from 3M, Abbvie, Bristol Myers Squibb (BMS), DSM, Genentech, Gilead Sciences, GlaxoSmithKline (GSK), Johnson and Johnson (J&J), Syngenta and Vertex. Information on 2568 chemicals was provided and, after processing the results, 2290 chemicals were used in the analysis. Given that the identities of the chemicals were not shared, it is not possible to determine whether any of the chemicals provided were duplicates; however, since these chemicals represent proprietary lead compounds, candidate active ingredients, intermediates, etc. from different companies, as well as additional marketed plant protection products and metabolites from a single database  (Lewis et al. , 2016) , we can reasonably assume there is limited overlap because of the diverse proprietary chemical space being assessed.