dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets The different methods of reduction where applied to the neuromorphic dataset DVS128 Gesture widely  used as a benchmark in event data processing, and we assessed the results according to the criteria described in the following sections. Hand gesture recognition is a skill used daily in the human society and is tightly integrated with verbal communication, hence the need of computational learning of such data. Since it relies heavily low latency, this task is well suited for event-based computation. Building on this notion, Amir et al. presented at CVPR 2017 a complete hand gesture neuromorphic dataset called DVS128 Gesture  (Amir et al. , 2017). To this end, 29 subjects were recorded performing 11 different hand gestures under 3 kinds of illumination conditions, by a DVS128 camera (see  Fig 6).
A total of about 133 samples are available for each gesture, each composed roughly of 400,000 events and of dimension 128 × 128 pixels, for a duration of 6 seconds approximately. The dataset is split in two sub-datasets to facilitate machine learning training: the train subcategory received 80% of the recorded samples and the test 20%, with an even distribution of the 11 gestures between them. This dataset was chosen according to its relevance to our motivations: DVS128 Gesture represents dynamic information to be processed temporally, which would be the case in most embedded systems.
Number of events The mean number of events per sample was calculated over the whole DVS28 Gesture dataset at fullscale and downscaled by the 7 methods mentioned in Table  1. Temporal density Events temporal density D corresponds to the activation probability of pixels averaged over the whole sensor: D = ∑ w x=0 ∑ h y=0 P x,y w • h (5) with w and h respectively the width and height of the sensor. The activation probability P x,y is calculated as the number of events (positive or negative) occurring at a given pixel divided by the time length of the sample: P x,y = ∑ t max t=t min δ(x t , x) • δ(y t , y) t max -t min (6) with P x,y the activation probability of one pixel of coordinates (x, y), t min and t max respectively the minimum and maximum timestamp of the sample, and δ the Kronecker delta function, which returns 1 if the variables are equal, and 0 otherwise.
Information entropy If we consider the information brought by positive (or negative) events according to Shannon's information theory, we can estimate the entropy H of the dataset as the average over all pixels and polarities p of the entropy H x,y,p : H = ∑ p∈{-,+} ∑ w x=0 ∑ h y=0 H x,y,p 2 • w • h with H x,y,p = ∑ P e ∈{P x,y,p ,P x,y,p } -P e • log 2 (P e ) (7) where P x,y,p is the probability of event (probability for the pixel of being active), and P x,y,p = 1 -P x,y,p is the dual probability of no event, for the polarity p at location (x, y). H x,y,p becomes lower when the pixel is either never active or always active. Entropy values in Table  4. 2 are averaged from both polarities.
Computation time The computation time of each method presented in this paper was calculated on a panel of over 500,000,000 events. The values presented in the Table  1  correspond to the average calculation period for the downscaling of 100,000 events, expressed in seconds. It should be noted that the values produced for the SNN Pooling method are not obtained from the similar panel of event data as the other. As a matter of fact, since the computation time for one sample using either technique exceeds 10 min, we chose to assess our criteria on a small set of downsampled events using SNN Pooling. The values of these criteria confirm the hypotheses we outlined above. Tonic's "Downscale" method has the highest number of events, thus the highest event density; this spatial reduction does not lead to a significant drop of event flow, thus does not answer our need for reduced data to handle.
The same goes for the Simple Event Funneling, which does ignore all event duplicates. Even though the computation time of those two methods is low, the output data is too important to be processed more efficiently than the original one. We should also point out that their information entropy is quite low, due to the constant activation of the sensor's pixels. The log-luminance techniques are the most consistent with the optical reality behind event data recording. It drops a large amount of data, thus significantly improving any embedded computation, while maintaining a relatively high level of information entropy. SNN Pooling is also promising by its behavior closely related to one of an event-camera, but it is extremely sensible to the weight of its connection: here the weight is probably too high hence its poor results.
Conclusion Event cameras take an increasing part in current trend in computer vision, with a number of possible applications in robotics and autonomous driving. While standard frame-based data (grayscale and RGB) are easily downscaled -which is a very common preprocessing step, there currently exists no straightforward equivalent for event data. In this paper, we introduce and compare 7 methods for event data downscaling for embedded computer vision applications. Our results show that the choice of the reduction methods greatly influences the event features of the reduced dataset, namely the number of events, the density, and information entropy. The proposed methods offer a panel of solutions to choose from depending on the target task  (classification, detection, tracking, etc. )  Our future work include the assessment of the proposed methods on a classical computer vision tasks such as classification, in order to evaluate the accuracy loss brought by the proposed methods.
We also present findings from running three separate iterations of an energy data science course we developed in light of this feedback -one of these iterations was offered only in KU Leuven (Belgium), while the other two were accessible to students at all four universities. In the paper, we also discuss challenges and opportunities arising from designing and delivering courses in a cross-university context. This foundational course and others like it are seen as a necessary means to enable students to take more specialized courses in data science, and eventually contribute towards realizing a sustainable energy transition and meeting climate change mitigation objectives.
applying data science in practice. Likewise, as with any other fast-moving discipline, there is a considerable dearth of teaching materials and expertise. In 2019, EIT Innoenergy helped set up an international working group comprised of participants from four partner universities across Europe (KTH Stockholm, KU Leuven, UPC Barcelona and Grenoble-INP) to help create the blueprint for a new data science program for energy engineers. This partnership was intended to create a targeted data science offering while reducing replication of work and enabling sharing of best practices across universities. An additional benefit of proceeding in this way was to free up instructor time to focus on developing more advanced courses dealing with local research competences. As part of its activities, the working group evaluated both the curricula and students' current knowledge in four different cross-university graduate programs in energy.
The results confirmed our initial hypothesis: students self-rated their knowledge of data science related skills as low or very low, while considering it important or very important to their future career plans. The curriculum in place in all four programs likewise paid little attention to data science for energy holistically, with only some exceptions. In light of these findings, the working group has worked on developing course content and delivery methods to cover the most critical elements of data science for energy engineers. Using the specific case of demand response, students have had the opportunity to explore the end to end life cycle of data science as it applies to energy projects in practice. The course was offered twice in 2020, once to a cohort of 13 students at KU Leuven, and once in an online setting to 108 students from all participating universities.
This is in stark contrast to workplace requirements, where data-related skills have clearly become a necessity to make the energy transition happen. Consequently, graduates from energy engineering programs joining the workforce today have large gaps in their data literacy, and as such are often required to either learn on the job, or make use of outdated programming languages, tools and skills. Companies continue to suffer from this lack of skills, with many lacking even basic automation and visibility due to a continuing reliance on spreadsheet software such as Excel. In the recent past, there have been a number of isolated efforts to address these shortcomings. Some of these have been specifically focused on energy education  [2]  [3], while others have been more generally aimed at STEM (Science, Technology, Engineering and Mathematics  [14] ) education  [10].
Some universities, such as University College London, Exeter University and Offenburg University, have developed entire one-or two-year MSc programs on energy data science. These typically cover a broad spectrum of relevant topics, but are usually seen in the narrow context of a stand-alone program rather than integrating with the broader energy engineering education. Other universities, such as National University of Singapore (NUS), have likewise developed a number of modules on applying data science in the built environment, however the focus in many such courses is not on the holistic energy system which also includes grid-side considerations. A number of similar initiatives have emerged in recent years in North America and other parts of the world. However, since there is no inter-university coordination in the development of almost all such programs, they remain fragmented and there is frequent replication of instructor work.
A number of practical initiatives, such as the Data Science Connector courses from UC Berkeley, have attempted to make data science accessible to different fields by fostering communal collaboration in both teaching and research  [18]. These courses also shine a light on how computational and inferential thinking must be interleaved for an effective education in data science: either component on its own is arguably not sufficient for students to fully leverage the advances in the field. Such collaborative programs that allow sharing of best practices and learnings, ranging from course design to delivery, do not exist to the best of our knowledge in the narrower domain of energy data science. More generally, even though most educational programs on data science are quite recent, there is a large body of literature on innovations and challenges in STEM education which can be drawn upon to develop best practices.
Over the years, there has been significant debate on what this entails in practice but there is sufficient consensus in the field to (1) emphasize integrated approaches to teaching and learning, where discipline specific content is treated as 'one dynamic, fluid study', rather than being divided and treated separately, and (2) further investigate integrative STEM education in a way that helps teachers understand the effect of teaching methods on student engagement etc. [19]. Moreover, based on a number of studies analyzed in a recent survey, it is evident that the integration in STEM education is most commonly associated with the engineering discipline and real-world contexts  [20]. This is a theme we will revisit repeatedly in our own course design as well. To reduce the gap in energy data science education while keeping in mind broader In this article, we document key findings of the working group so far, and share some key lessons learned while offering a course on energy data science over three iterations in very different settings.
The carbon footprint data for a large number of locations is available from system operators or through third parties such as electricitymap. org, which can be useful for short-and long-term planning. ENTSO-e, an umbrella association for European Transmission System Operators, provides both historic and future forecasts for different energy sources in the grid as well as energy demand for most European countries through its transparency platform. These datasets can be used to quickly build a better understanding of the current energy mix and its sustainability in a large number of countries across the world. Finally, data science is also being increasingly employed in demand side management programs, either through automation or through engaging end users and providing them with feedback to minimize their carbon footprint.
While engaging users creates a bottom-up movement of citizen empowerment, which ties in well with Europe's focus on citizen energy communities  [4] , automation is likely to provide the largest gains in using demand flexibility to help stabilize the grid. Finally, it is important to mention that the synergistic relationship between data science and energy has evolved considerably in recent years. From energy engineers using data science to reduce the energy consumption of consumers and data centers  [5] , the focus has slowly evolved to encompass green-ML methods, which ensure that machine learning (ML) models are deployed in an eco-conscious manner keeping in mind their potentially enormous footprint  [6] ,  [7]. The easy availability of energy data from numerous online sources means that it can be correlated with real-world events to show students their impact on energy supply and demand.
Some obvious examples of this includes the impact of an eclipse on solar production and the EURO football competition on electricity loads. In light of these applications, we shortlisted three skills as the building blocks for evidence-based, datadriven decision making that underpin energy data science: (1) programming skills, (2) machine learning knowledge, and (3) optimal decision-making expertise. We assume that machine learning knowledge also includes data wrangling, visualization skills, and probability and statistics. Optimal decision making, in the context of energy engineering, refers more to optimal control and design challenges, but it can also include using optimization algorithms for training machine learning models and optimizing their hyperparameters.
State of Curricula InnoEnergy administers eight different MSc programs. Each of these MS programs has a different focus, and some are more amenable to data science techniques than others as we will show in this section. All of these programs are offered at a number of different European universities, mostly ranging between four and seven per program. In many cases, the same university is involved in multiple MSc programs, and students 1. At KU Leuven, students in the selected programs had the possibility to take a course in smart distribution systems, which covered a wide spectrum of topics ranging from sensing to automation and machine learning to control. More concretely, in one of the lab sessions, students were required to train a neural network to predict electricity spot prices. However, besides this course and the very general introduction it provided, there was no specific course which addressed data science for energy.
Students could optionally take courses such as Industrial control and automation, optimization, building simulations, and experimental design but most of these were in general settings, and either not targeted at the energy domain or not adequately focused on data science. 2. At KTH, students could study a course on communication and control in electric power systems and an additional course on computer applications in power systems. However, these courses provided students only with an introduction to machine learning and how it could be applied to power systems providing exercises in Java. Furthermore, in two of the four programs we considered, these were optional From this summary, it became quite obvious that data science focused courses for energy engineers were mostly absent in KU Leuven, INP and UPC.
State of Students' Knowledge Concurrently with analyzing the current curriculum, we also approached students studying in these programs directly to better understand their current data science related skills in the three categories we discussed earlier: (1) programming skills, (2) machine learning knowledge, and (3) optimal decision-making know-how. Based on survey responses from over 150 current students and alumni in the four InnoEnergy programs, we drew the following conclusions: In light of the current curriculum, none of these conclusions came as a huge surprise. The students' perceived importance of data science and their motivation to study it mirrors industrial professionals' opinions. For instance, in an independent survey of over a hundred energy utilities, professionals and managers considered energy forecasting, data analytics and automated energy trading among the most important skills for the future  [2].
Course Design, Iterations and Intended Learning Outcomes (ILOs) In light of the above discussion, it was obvious that there was a need for a specific energy data science related curriculum which was open to students from all MS programs, irrespective of the university. The course, since its inception, has therefore had a number of clearly defined intended learning outcomes. These include  (1)  students should be able to access energy datasets from a variety of different sources, and perform exploratory analysis on these datasets; (2) students should be able to create models explaining the patterns they observe in the data, and predict them in the future; (3) students should be able to use these models in optimal decision making frameworks to improve control or design of energy resources; and (4) students should be able to appreciate the practical challenges with deploying these solutions at scale.
In addition, it is important for the students to realize the importance of always benchmarking ones' results against simpler alternatives, as well as the idea of significance testing when comparing the performance of different algorithms. These ILOs were chosen based on the results reported in  [2]  as well as interactions with European academics and energy companies. However, the primary focus during the course is on an understanding of the tools rather than the context, which can be exchanged for a different use case without loss of generalization. The first session provides students with an introduction to energy data science as well as useful pointers to brush up on their programming skills using existing notebooks taken from the University of Cambridge's excellent introduction to Python programming.
2. The second session on exploratory data analysis session allows students to explore the provided dataset in greater detail using summary statistics, filtering and resampling. They were also able to extract general trends in electricity demand as a function of time of day and day of year here. Additionally, visualization tools and techniques to deal with missing data were also introduced at this point. 3. In the following session on modelling and forecasting, students explore time series analysis concepts such as correlations (auto-, partial auto-and cross-correlation), as well as time series decompositions into trend, seasonality and noise components. Using demand data from the previous session, students are then able to create forecasts using three different families of algorithms for an entire year (baseline persistence, multi-linear regression, time series methods).
These techniques are then used to introduce more advanced concepts such as transfer learning, and auto-ML. Furthermore, with the latest iteration, we have decided to host at least two practitioner talks. This includes a top-down presentation from an aggregator of energy demand which provides the big picture view. The second talk, from a practitioner at a service provider company, builds from the ground up and takes students through the model deployment process i. e. how to deploy machine learning and optimization models in practice. In addition to making the Kaggle competition more realistic in the latest iteration, a new assignment on exploratory data analysis was introduced where students are asked to tell a story using an energy dataset of their own choice. The Jupyter Notebooks used in the course are available at https://github.
In the afternoons, there were open office hours for students to discuss any questions regarding theory or practice (or the provided Jupyter Notebooks). The second week was dedicated entirely to course readings, interactive question and answer sessions, and the Kaggle competition. The Jupyter notebooks, which form an important part of the course, have seen different hosts over the three iterations. For the first two iterations of the course, we used a custom-built cloud platform based on JupyterHub that allowed students to run their Python scripts on the cloud. With all the packages pre-installed, this removed a frequent point of failure for new Python users, smoothing the learning curve. Students could now focus on developing their algorithms. The cloud platform also granted students greater computational resources than available on a typical laptop, and therefore allowed students to explore implemented algorithms in greater detail (or implement their own from scratch).
From InnoEnergy's perspective, this also gave the instructors greater control on content. In the third, most recent iteration of the course, we have migrated to Deepnote, a cloud platform tool for collaborating on Jupyter notebooks. This was easier (and cheaper) to maintain than a custom implementation, and provided a lot of the functionality we needed to successfully run the course out-ofthe-box. To get students up to speed with Python programming, we have made use of two existing resources. To start with, students are provided an introductory "Lecture 0" with Jupyter notebooks taken from University of Cambridge's excellent self-study introduction to computing with Python (https://github. com/CambridgeEngineering/PartIA-Computing-Michaelmas). Additionally, till last year, we also provided interested students with licenses for DataCamp, an online resource for learning programming and foundational data science skills.
Evaluation of ILOs To evaluate how well the course does in meeting the ILOs, we introduce an assignment on story-telling (i. e. exploratory data analysis on a dataset of students' choice) as well as a Kaggle competition on forecasting energy demand in a group of buildings given both historical data and some exogenous variables. The assignment on exploratory data analysis is open-ended and the students are expected to choose their own energyrelated dataset for analysis. They then receive feedback on how the analysis can be further improved or how it would be carried out in practical settings. Most students were able to choose an interesting dataset, and run exploratory analysis on it, leading to a coherent report on what the dataset could be used for in practice. These datasets ranged from energy demand to generation, but some were more obscure as well dealing with the performance of electric vehicle batteries etc.
A vast majority of students (80 -90%) found the course readings very useful and felt that the lectures provided them with enough theoretical background to data science. More than half the students (both in 2020 and 2021) felt that the pace and level of the course was just about right. On the course delivery side, most students rated their experience of the interactive notebooks very positively (median rating of 9/10, n=34). Both the Kaggle competition and the Deepnote platform were likewise rated positively (median rating of 8/10, n=32). Students also found the practitioner talks useful (both talks received a median score of sessions. One thing that we felt provide positive reinforcement was that both speakers were alumni from different InnoEnergy programs, and students could relate to their experiences in the field.