dataesr/scientific-paragraphs-categorization huggingface dataset:
The PyLEMS library returns an expression tree consisting of a large Python dictionary which contains all the aforementioned model components. During the rendering of the model, the elements in the dictionary are projected onto the placeholder expressions of the Mako templates. There are Mako templates for Python, Cuda and the GPU driver module, consisting of dynamic for-loops responsible for generating the code. If a Python conversion is executed, the TVB framework is updated with the latest model. The user can specify a folder in which to save the generated model.
The -n sets the simulation length to 400 resulting in 400/0. 01 simulation steps. The Montbrió model produces a high time-resolution neuroelectric signal, namely the firing rate r and the membrane potential V. Then, a low time-resolution simulated BOLD activity is obtained, by filtering the membrane potentials through the Balloon-Windkessel model. A sliding window approach was applied to obtain the dynamical Functional Connectivity (dFC); inside each time window, a static Functional Connectivity (FC) was computed as the correlation matrix of the BOLD activities. The entries of this windowed-dFC (dFC w ) are defined as the correlation between the FCs at different windows. Figure  4  shows the results of parameter simulation validated against the standard Python TVB version. It shows the variance in dFC for each parameter combination.
The memory copying can be masked by asynchronously starting a new integration step in a new stream, in parallel to the memory movement. When a GPU instance is still busy with copying data to disk another stream can start the next integration step. The more simulation steps performed the larger the benefit from asynchronously copying data to disk, resulting in an increase in the iterations per second. Finally, for reference we present current performance of the Python TVB library using the Numba backend (Figure  6 ). For this we have configured the simulation with the Montbrió model driven by noise and a connectome of 68 nodes and delays induced by a propagation speed of 2 m/s. The benchmark was executed on a single Cray XC40 node (Intel Xeon E5 -2,695 v4, 2 × 18 cores, 2. 10 GHz, 128 GB RAM) of the multicore partition located in Piz Daint.
The execution of different parameters was orchestrated by the multiprocessing Python package using all 36 cores of the compute node. The results presented in Figure  6A  show that the performance increases with the length of the simulation suggesting better amortization of the simulation initialization overhead and better data locality. We explored two simulation lengths with this model: a 4 000 iteration step microbenchmark and a longer experiment of 100000 integration steps corresponding to 10 s of simulated time which spans several BOLD time points (usual sampling frequency 1Hz) and is more representative of real workloads. The results presented in Figure  6B  indicate that the peak performance is already reached for small parameter space sizes. Longer simulations achieve better performance due to amortization of simulation launch overhead and increased data locality.
DISCUSSION In this work we presented RateML, a model code generator based on LEMS for defining neural mass models succinctly. TVB simulations vary greatly in simulation time, number of nodes used and which parameters to explore within the different research topics and science groups. One thing is clear, in order to have the simulation results fit subject data for clinical research or have an optimal resolution for, e. g. , the cohort studies for aging, the number of parameters that need to be explored are vast; and it is safe to say that the more parameters explored the better. RateML enables the user to generate complex Python and CUDA neural mass models and to do fast parameters sweeps on the GPU, with identical results as when done with TVB. We introduced the interface and its elements, the inner workings of the generator and the relation of the elements to the placeholder templates which generate the code for neural mass models and the GPU driver.
At the moment some of the variables within the LEMS components used in RateML have been modified to fit specific functionality and match the TVB simulation strategy. Even though at the moment models produced by RateML can not be directly ported to other simulators which support LEMS and are able to simulate BNMs or NMMs, work is being done in collaboration with the LEMS development community to fit all the requirements and perform any required modifications to RateML or extensions to the standard. In the use case section we demonstrated how to define the XML file for the Montbrió model, generate the CUDA code and driver, and do fast and efficient parameter sweeps over the global speed and global coupling parameters using a GPU. Several other models are already currently available in XML format at the TVB repository.
We showed that the output is very close to the TVB simulator. This verifies that the code generation process is faithful to the reference  implementation in TVB. For simplicity and clarity in the validation procedure, noise has been neglected from these tests. However, RateML can include noise within the model description. We have benchmarked the code and shown that the GPU version is memory bound and the runtime for the models generated with RateML scales linearly to the number of explored parameters, up to 144,000 parameters on a single GPU. Results also show that the GPU performs even better with larger parameter spaces, due to the fact that it can then hide memory latency more effectively. In Figure  5  it is also visible that the amount of iterations per second in the GPU continues to increase as the number of parameter combinations grows.
CONCLUSION AND FUTURE WORK While the work on performant code generation in RateML has focused on NVIDIA GPUs, because of their prevalence in HPC centers, future work in RateML will address better code generation for CPUs, from generating full simulations for CPU instead of just the model derivatives and ensuring Single Instruction Multiple Data (SIMD) 4  instruction sets are correctly used, instead of relying on Low Level Virtual Machine (LLVM) (Lattner and Adve, 2004) autovectorization, to sizing parameter space exploration work arrays to stay within CPU level 3 cache. These improvements allow for end users to do significant interactive prototyping prior to sending a model for full sweeps or optimization. To further streamline computational workflows involving brain atlas data, RateML will add data specifications, which enable fetching the requested data from an online atlas, transformed into a model on which a parameter sweep is performed to fit the model against, e.
Combining that with the void due to the unavailability of datasets encompassing these adversities to effectively train and validate the deep learning (DL)-based machine learning (ML) models leads to an improbable solution to the outdoor robot perception problem. Theoretically, an approach to outdoor perception can be termed as generic if the dataset on which the models are evaluated are rich in diversity that ensure generalization to varying objects and scenarios and environmental conditions  [11]  and the same model is able to yield stable, reliable predictions over time. Usually the CNN layers are trained on such a data distribution by minimising a given loss function on the training set. Consequently they will perform well if the evaluation domain is similar to the training domain and is carried out on a per frame basis.
To correspond with our distinctive use case we produce our own synthetic dataset with a blending target distribution to ablate, train, and validate our method while performing scene perception for our autonomous EPW. The remainder of this paper is structured as follows. In Section 2, we review the related works in smart assist features for EPW and video semantic segmentation. In Section 3, we introduce our method that perform fast semantic segmentation (FSS). In Section 4, we present and explain our dataset, the benchmark metrics, and the analysis of the results we obtained. Finally, the conclusions and future directions are outlined in Section 5.
Dataset Construction The native GSVNET model uses the Cityscapes  [51]  and CamVid  [52]  datasets for training and benchmark. The distribution of these datasets are in the context of vehicles in a road, a dissimilar collection of scenes from our target distribution. Figure  1  shows some sample snippets of synthetic scenes we develop by using the CARLA  [53]  simulator, with similar specifications as Cityscapes. CARLA is an open-source simulator for supporting, training, and validating autonomous driving systems in urban environments with a wide variety of sensor suits. We generate video snippets of fixed distribution in the sidewalk preview with image capture geometries set as per our camera properties and its mounting location in our EPW. The total volume is split as 11, 904/8020/10, 076 for training/validation/testing.
Each snippet has a resolution of 2048 × 1024 with 30 frames for each sequence with the 19th frame finely annotated for 22 classes. These 22 classes include all the group of classes from the Cityscapes dataset with additional crucial classes such as road lines, guard rails and static objects, (such as fire hydrants, fixed benches, fountains, and bus stops). To further enrich diversity and generalization, the scenes are captured in various visibility, weather, and intricate scenarios. Figure  1  shows some sample frames from our dataset.
At each step, the segmentation maps from GSVNET of previous frames are subject to dilated convolution to refine and enhance the segmentation maps. The models are trained end-to-end with the training objective that involves an ordinary cross-entropy loss imposed on the final segmentation output. Similar to  [20] , we use stochastic gradient descent (SGD) with a momentum of 0. 9 and a learning rate of 0. 002, which is decreased by a factor of 0. 992 every 100 iterations and set the batch size to 8 and the weight decay to 0. 0005. The complete training and validation dataset are generated with CARLA. We train our models on the MYRIA  [56] , with the AI-Deep Learning framework. MYRIA is composed of 366 Broadwell dual-processor compute nodes (28 cores at 2. 2 GHz, 128 GB RAM) including 20 nodes, each equipped with either 4 GPU Kepler K80 (12 GB VRAM per GPU) or 2 GPU Kepler P100 (12 GB VRAM per GPU).
Conclusions and Outlook This paper presents a light and efficient video segmentation method for visual perception in urban sidewalk environments for robotic EPWs. We propose a self-supervised approach that facilitates cross-domain applications of popular CNN models into distinct use-cases. Our approach is innovative for at least two significant reasons. First, we use an open source simulator to develop a dataset of synthetic video sequences in diverse sidewalk environments with matching target distribution to succeed data unavailability. Second, we prove the feasibility of reusing available models and possibility to alter its accuracythrough output intrinsic characteristics depending on requirements at no additional cost of computational resources. We select GSVNET, a lightweight semantic segmentation framework, and institute the necessary process without overhead to obtain better accuracy and throughput trade-off for our application.
Our initial results with GSVNET as a propagation framework and SwiftNet/BiseNet as a base segmentation model prove the feasibility of performing semantic segmentation for the new sidewalk domain. But we found this setup has an exigent accuracy-throughput characteristic that can be manipulated to improve the accuracy further by moderately taxing speed. Being a mobile robotic application, it is crucial to preserve the lightweight nature of the initial setup. Therefore, we incorporate two additional process that perform dilated convolution to refine the feature map from GSVNET with a better receptive field and a low-rank bilinear upsampling technique to obtain prediction maps with better object boundaries and detailed contextual information. With our custom dataset, our approach achieves an mIoU of 46.
Acknowledgments: This research is supported by  ADAPT  project which is carried out within the  INTERREG VA FMA ADAPT "Assistive Devices for empowering disAbled People through robotic Technologies " http://adapt-project. com/index. php(accessed on 2 June 2022) project. The  Interreg   FCE program  is a European Territorial Cooperation Program that aims to fund high-quality cooperation projects in the Channel border region between France and England. The Program is financed by the  European Regional Development Fund (ERDF) ). We would like to thank the engineers of the  Autonomous Navigation Laboratory (ANL) of IRSEEM  for their support. This work was performed in part on computing resources provided by  CRIANN  ( Centre Régional Informatique et d'Applications Numériques de Normandie, Normandy, France ).
g. , adaptive scaffolding vs. no scaffolding of self-regulatory behavior) on learning outcomes. First, we focus on findings from self-reports, learning outcomes, and multimodal data (e. g. , log files, eye tracking, facial expressions of emotion, and screen recordings) and their contributions to our understanding of SRL with an ITS. Second, we elaborate on the role of embedded PAs as external regulators designed to scaffold learners' cognitive and metacognitive SRL strategies. Third, we highlight and discuss the contributions of multimodal data in measuring and understanding the role of cognitive, affective, metacognitive, and motivational (CAMM) processes while unpacking the challenges these data pose for designing real-time instructional interventions that scaffold SRL. Fourth, we present existing theoretical, methodological, and analytical challenges and briefly discuss lessons learned with MetaTutor and open challenges.
Learners could track and accomplish their subgoals and learning objectives using MetaTutor's interface features (e. g. , SRL palette to indicate to the system which SRL processes they were planning on using) and with the support of the four PAs. Self-regulating in the context of learning with MetaTutor meant learners had to use cognitive and metacognitive processes to accomplish their sub-goals such as making inferences, summarizing, making hypotheses, and others while metacognitively monitoring their learning by engaging in judgments of learning (JOL), feelingsof-knowing (FOK), monitoring progress toward goals, and evaluating the relevance of content, such text and diagrams, given their current learning goal. While self-regulating with MetaTutor, we expected learners to also experience emotional and motivational states that were captured by multimodal data using cameras, physiological devices, and embedded selfreport measures.
The effectiveness of the system has been extensively tested and published widely in several cognitive, learning, instructional, and computer science refereed conference proceedings, journals, chapters, and widely disseminated at national and international conferences  (Azevedo et al. , 2010 , Azevedo et al. , 2013 , 2018 , 2019). MetaTutor was originally designed to be both a learning tool to foster self-regulation and a research tool to collect trace data on CAMM processes as they unfolded during learning. The system supports several learning strategies through its user interface including features that prompt learners to activate prior knowledge about content, goal setting, evaluating learning strategies, integrating information across diagrams, evaluating content, summarizing key information, note-taking, and drawing.
It also scaffolds specific metacognitive monitoring processes, such as JOLs and FOKs. The unique contribution of this paper is its comprehensiveness and synthesis of all the studies conducted by our team and collaborators over more than a decade that emphasizes empirical findings across CAMM processes. The central research questions addressed in our research on MetaTutor with predominantly Caucasian female college students, include: (1) How do different scaffolding methods influence students' learning about human biology and their SRL performance? (2) How do different scaffolds influence students' deployment, effectiveness, and quality of SRL processes during learning with MetaTutor? (3) What is the temporal and dynamic nature of students' CAMM processes while using MetaTutor to learn about complex biology topics with MetaTutor? (4) Do process-oriented multimodal trace data (e.
Fourth, a session is characterized by learner-generated subgoals). Fifth, several types of trace, self-report, and product data are identified as critical for examining the complex nature of SRL. In our studies, trace data included think-alouds, eye tracking, log files, and physiological recordings. Product data represented three individual pretest measures that assessed different types of knowledge including declarative, procedural, and mental models; equivalent measures were also given as a posttest. We included self-report measures of motivation and emotions that were also presented at pretest, during learning, and at posttest. Figure  3  illustrates the experimental procedure for all MetaTutor studies. To experimentally test the effectiveness of the scaffolding provided through the system, MetaTutor features two experimental conditions, i.
Additionally, we should consider how a learner's needs may change over the learning session (i. e. , they may require prompt and feedback support early, but not later on) and over multiple learning sessions as they learn more strategies. Finally, the abovementioned studies demonstrate the usefulness of using multimodal multichannel data to investigate metacognitive processes during learning with MetaTutor  (Azevedo et al. , 2018 (Azevedo et al. , , 2019)). These studies predominantly used log files, but also eye tracking and videos of facial expressions. Using more data channels provides greater insight into how students engage in metacognitive processes (in terms of the what, when, and how), and how metacognition interacts with cognitive, affective, and motivational processes when investigating self-regulatory behaviors during learning with MetaTutor.
Synthesis of Key Findings From Published Studies Our first question concerning emotions in MetaTutor was the incidence of different emotions. A deceptively simple question that has layers we endeavored to tackle. The first general layer was a temporal one: How is the time period that emotional occurrence is evaluated and defined, and how stable are emotions over time? A second layer was: Which emotion expression component is supplying us with the data we are using to answer our question and does using different channels provide us with a different answer? We developed a single-item self-report measure to assess 19 different concurrent state emotions and help explore these lines of inquiry: The emotion-value (EV) questionnaire  (Harley et al. , 2013 (Harley et al. , , 2015)). The EV questionnaire was administered on five occasions during a study with MetaTutor which provided us with five snapshots of learners' present in-the-moment emotional experiences.
Mastery-approach learners, in particular, may also benefit from less frequent agent interaction or from fading scaffolds over time  (Belland, 2013). A key feature of the MetaTutor studies described is that they illustrate how selfreport data of motivation can be examined alongside trace data to provide a richer understanding of both the motivating goals (questionnaires) and resultant learning processes (eye-tracking, log files). This was examined further in  Cloude et al. (2021b)  by investigating trace data and its relation to different sub-goals over the course of the learning session, which proved useful in identifying signatures of goal pursuit, potentially providing future direction for implicit measures of motivation in action rather than as a one-point-in-time assessment, which we discuss next.
However, MetaTutor captures other types of trace data (e. g. , time on task via log files) that can serve as a proxy to track other facets of motivation not addressed here, such as effort, persistence, and choice, especially to study its relation to different sub-goals over time during the learning session. This could provide more information about the degree of motivation, whereas data analyzed to date has focused on the type of motivation. Additionally, think-aloud data could also be examined for indicators of curiosity, interest, and selfefficacy. Together, these traces could provide insight into the dynamic nature of motivation. Finally, limited research has examined the regulation of motivation  (Wolters, 2003; Schwinger and Stiensmeier-Pelster, 2012) , which includes understanding how learners monitor and deploy strategies to boost or sustain motivation.
However, this also requires us to make inferences that students are, in fact, engaging in metacognitive processes. For example, when students select a JOL or FOK from the SRL palette, are they really judging their understanding or familiarity with the text, or are they selftesting (i. e. , want to take the 3-item quiz)? Also, through the SRL palette, we only measure processes that are externalized either verbally or behaviorally or both by the student. For instance, a JOL selected from the palette on one page might be followed by further "internal" (i. e. , not uttered or behaviorally enacted) JOLs on subsequent pages, that we can't measure without triggering, prompting, or interfering with the process. Additionally, when we used eye-tracking data as indicators of monitoring behaviors (e. g.
, 2018 (Lallé et al. , , 2021;; Wortha et al. , 2019; Wiedbusch and Azevedo, 2020 ) complex eyetracking data and facial expressions of emotions during learning with MetaTutor. We continue to use non-traditional statistical techniques, including dynamical systems modeling  (Dever et al. , in press ) to examine learners' emergent SRL behaviors, and MLAs to predict performance at the end of the learning session  (Mu et al. , 2020; Saint et al. , 2020; Chango et al. , 2021; Fan et al. , 2021). Despite our ability to continuously adapt and use contemporary analytical techniques that emerge from the computational, engineering, psychological, statistical, and data sciences, we as a field are still faced with a major barrier that continues to impact the educational effectiveness of intelligent systems such as MetaTutor.
The issue is that these analyses are all conducted in a post-hoc fashion (i. e. , after a student learns with MetaTutor), thus moving forward, it would be beneficial to analyze these processes in real-time, and provide truly intelligent, adaptive personalized support of CAMMs. Machine learning approaches are particularly promising in this regard as their focus lies in the prediction of behavior rather than (post hoc) explanations  (Yarkoni and Westfall, 2017). Further, they generally are capable of addressing issues of traditional statistical analyses with regards to adequately handling large amounts of data (e. g. ,  Dwyer et al. , 2018) , such as the multichannel data collected with MetaTutor. Thus, machine learning models, trained on multichannel data, can serve as the basis for increasingly adaptive systems that can intervene in the learning processes as or before issues arise.