dataesr/scientific-paragraphs-categorization huggingface dataset:
Geodesic Multiscale LDDMM via Hamiltonian Control. The shape of Brainspace is given by its geodesics. We use Hamiltonian control to generate the geodesics. The Hamiltonian method reduces the parameterization of the vector field to the dynamics of the particles that encode the flow of states (17a). We write the dynamics explicitly as a linear function of the control, and define the flow of the measures indexed by the dynamical state: _ q t = ξ q t u t ð Þ ≔ ξ q ℓ t u ℓ t À Á ℓ≥0 , with _ q t = u t x i,t ð Þ, w i,t div u t x i,t ð Þ ð Þ i∈I , u t φ t x ð Þ ð Þ, w t x ð Þ div u t φ t x ð Þ ð Þ ð Þ x∈ℝ d , ( ð35aÞ 11 BME Frontiers μ q t ð Þ ≔ μ q ℓ t À Á À Á ℓ≥0 , with μ q t ð Þ ≔ 〠 i∈I w i,t δ x i,t ⊗ δ f i , ð ℝ d ρ x ð Þw t x ð Þδ φ t x ð Þ ⊗ δ f x ð Þ dx: 8 > > > < > > > : ð35bÞ The control problem satisfying  (16)  reparameterized in the states becomes, for α > 0, min u t ð Þ 0≤t≤1 ∈L 2 0,1 ½ ,V ð Þ _ q t =ξ q t u t ð Þ,μ q 0 ð Þ=μ 1 2 ð 1 0 Au t k k 2 V dt + α 2 μ q 1 ð Þ -μ obs k k 2 W * : ð36Þ Hamiltonian control for particles and tissues introduces the costates t ↦ p t via the Hamiltonian H q, p, u ð Þ≔ p ξ q u ð Þ À Á - 1 2 Au k k 2 V , ð37aÞ with p ξ q u ð Þ À Á = 〠 ℓ≥0 〠 i∈I ℓ p x,ℓ i , u ℓ x ℓ i À Á ℝ d + p w,ℓ i w ℓ i div u ℓ x ℓ i À Á À Á , 〠 ℓ≥0 ð ℝ d p φ,ℓ x ð Þ, u ℓ φ ℓ x ð Þ À Á ℝ d À + p w,ℓ x ð Þw ℓ x ð Þ div u ℓ φ ℓ x ð Þ À Á Þdx: 8 > > > > > > < > > > > > > : ð37bÞ Under the assumption V ℓmax ↪C k+2 0 ðℝ d , ℝ d Þ, the Pontryagin maximum  [22]  with k ≥ 1 gives the optimal control for all scales ℓ satisfying ∂ u ℓ H q, p, u ð Þ= 0, _ q ℓ = ∂ p ℓ H q, p, u ð Þ, _ p ℓ = -∂ q ℓ H q, p, u ð Þ: 8 > > < > > : ð38Þ Statement 1.
Acknowledgments This work was supported by the  National Institutes of Health (NIH)  (http://www. nih. gov)grants  R01EB020062  (MM),  R01NS102670  (MM),  U19AG033655  (MM),  P41-EB031771  (MM and Tward), and  R01MH105660  (MM); the  National Science Foundation (NSF)  (http://www. nsf. gov)16-569 NeuroNex contract 1707298 (MM); and the  Computational Anatomy Science Gateway (MM)  as part of the  Extreme Science and Engineering Discovery Environment  (XSEDE Towns et al. , 2014), which is supported by the  NSF  grant  ACI1548562 ,  Johns Hopkins University Alzheimer's Disease Research Center  with  NIH  grant  P50AG05146 , the  Dana Foundation 's (http://www. dana. org)clinical neuroscience research program, and the  Kavli Neuroscience Discovery Institute  (http://kavlijhu. org)supported by the  Kavli Foundation  (http://www.
Instructor Materials We developed an accompanying Excel workbook (Role play -main file. xlsx) that contains the key materials for the game. These include a role play structure table, the preassignment task, databases needed to facilitate the game, materials given to the students gradually during the play, and reflection guidelines. The databases are linked in Excel with the materials so that changing game stakeholders and negotiation outcomes can be done easily. Sheets 2 and 3 of the workbook present the structure of the game; sheet 2 is intended for instructor use, and sheet 3 can be handed out to each group. Sheet 4 presents the instructions for the preassignment, and sheet 5 contains the background data. By changing hotel, chain, key client, and student names (in blue), the game can be tailored for a desired hotel market and cohort.
With such widespread access to large digital music collections, there is substantial interest in issues of music engagement and the musical experience. The important question thus arises: How do people engage with the music that they hear? What music features do they attend to and what music structures do they perceive? Large-scale digital music datasets have spurred interests in how people engage with music collections, leading to research in hit song identification  (Pachet, 2002; Pachet and Roy, 2008) , where top Billboard positions have been correlated with audio features  (Herremans et al. , 2014) , instrumentation  (Nunes and Ordanini, 2014) , and lyric repetition  (Nunes et al. , 2015). In a first ever large-scale ecological study relating when users choose to send a Shazam query on a popular song in relation to its popularity and song structure,  Kaneshiro et al.
, 2015). Large-scale corpus studies of music structure as it relates to music experience have focused primarily on popular music, due in part to the available datasets, the computational challenges in automating the cognition of music structures such as beat in classical art music, and the time and labor required to create meaningful manual annotations. While the same can be said of many world musics, room remains for work aiming to address the gap in population-scale performed music research in classical music. The temporal nuances in classical (Western art concert) music, including its contemporary developments, make it particularly challenging for machine analysis. Automatic techniques that speed annotation like computer-based tempo tracking work poorly on classical datasets  (Grosche et al.
, 2010) , where movements in tempo are much more volatile than in popular music, which are frequently temporally constrained by the use of click tracks. The alternative, manual tapping to create beat annotations, does not scale, which is why initially beat annotations were available only for a small subset of the Mazurka dataset of the CHARM Project (www. mazurka. org. uk), which had amassed 2500 recordings of 49 Chopin Mazurkas  (Sapp, 2007 (Sapp, , 2008)). A workaround to this problem is to painstakingly manually annotate one recording of a piece, and transfer the annotations to others of the same piece after aligning all other recordings to the reference recording  (Wang et al. , 2016) , then manually checking the transferred annotations. Using this technique, my lab has produced beat and loudness information for 2,000 recordings in the Mazurka dataset to form the MazurkaBL dataset  (Kosta et al.
, 2018)  (github. com/katkost/MazurkaBL), which has been used in research on performed loudness  (Kosta et al. , 2015 (Kosta et al. , , 2016)). Not only is extracting the beats themselves difficult, assigning meaning to expressive nuances is also challenging. Sometimes, phrase arching can be clearly demarcated through increasing and decreasing beat rates, and sometimes the beat rate will be entirely meaningless with respect to phrase, with changes in loudness being the main indicator  (Cheng and Chew, 2008). This can be the result of performance conventions, or the performer's idiosyncratic decisions. Sometimes, a note elongation signifies a harmonically important note, and sometimes it marks the start of a motif; the start of a motivic grouping can also be demarcated using articulation rather than time.
And, sometimes, extreme elasticity of pulse denotes a major tipping point (turning point)  (Chew, 2016)  in the piece, and sometimes it is simply the slow down at the end of a phrase or piece. The reasoning can be inferred from data with the help of human annotations of music structure. The limited number of music datasets with structural annotations focus on compositional structure, rather than the felt or experienced structures, although the two are necessarily related. Leveraging basic skills of noting boundaries between sections and assigning labels that indicate which sections contain similar material, the SALAMI (Structural Analysis of Large Amounts of Musical Information) Project (DDMAL, 2019) (ddmal. music. mcgill. ca/research/salami) employed music students at the McGill Schulich School of Music to create 2,400 structural annotations of almost 1,400 musical recordings of popular, jazz, classical, and world music  (Smith et al.
, 2011) ; variability in listener annotations was addressed by having two students annotate each recording. Other datasets with structure annotations include Tampere University of Technology's TUTstructure07 dataset (www. cs. tut. fi/sgn/arg/ paulus/TUTstructure07_files. html), which contains 557 sectional annotations of mainly popular music, and AIST's 285 beatmelody-chorus annotations of the RWC Music Database  (Goto, 2006) , a quarter of which is jazz and classical music. The focus here is on structures felt and experienced, musical analogs of elemental categories such as thresholds and change points, stability and instability, to inform music engagement research. Music listening is almost universal amongst humanity. Sloboda (2008)  has argued that it is the experience of music that must be studied in order for us to discover its true impact.
, 2011). More recently, as Google Deepmind's AlphaFold program improves its predictions of protein structure, the Fold. it community has shifted its focus from structure prediction to protein design and to fitting electron density (fold. it/portal/node/2010912). Problems in music research, especially those having to do with ill-defined areas of music engagement and musical creativity in performance and in composition, are computationally difficult to solve; algorithmic solutions to these problems often yield partial and unsatisfactory results. Participation in citizen cyberscience, projects utilizing technology, come in the form of volunteer computing (sharing computing resources), volunteer thinking (assisting in cognitive tasks according to prescribed protocol), and participatory sensing (distributed data collection)  (Haklay, 2013).
A key component of these successful projects is their ability to effectively harness citizen science, in particular, volunteer thinking for scientific research. Most citizen science projects have been based primarily on visual recognition or on manipulation of graphics; audio centric projects are still rare. For example, on Zooniverse (zooniverse. org), the only two audio projects have been based on identification of animal sounds in WhaleFM  (Shamir et al. , 2014)  (github. com/zooniverse/WhaleFM) and Bat Detective  (Aodha et al. , 2018). Music science experiments frequently require empirical data from listeners and large empirical studies exist. The SALAMI (Structural Analysis of Large Amounts of Music Information) project (DDMAL, 2019) employed 8 music graduate students to provide the 2,400 structural annotations of approximately 1,400 recordings  (Smith et al.
Another example, Earsketch (earsketch. gatech. edu), is a software tool for learning coding by remixing music audio snippets  (Freeman et al. , 2014). Earsketch has been used by 60,000 students in over 100 countries. Widespread access to unprecedented computing power means that music (video) games with real-time control are now part of the digital landscape. While many music performance games exist in the form of Guitar Hero  (Egozy, 2016)  and Magic Piano (www. smule. com), etc. , these designs largely judge a user's game play by how well they fit a template performance. As such, they function effectively as target practice modules: the next note plays when a button is pressed or screen tapped at the right time. The latest in Harmonix's suite of games, Fantasia Evolved, adds an "expressive" element only in that it allows users to improvise melodies and rhythms from a limited control palette.
COSMOS RESEARCH OBJECTIVES To achieve the trans-disciplinary goals outlined above, the COSMOS project sets out three mutually-informing research directions. The first is to research problem solving skills in the perception and cognition of musical structures, taking advantage of crowdsourcing to amass data requiring human cognitive abilities. The second aims to investigate decision making in the act of shaping performed structures through software-based sandbox environments. The third objective is to develop a novel methodological framework for computational music structure analysis in the context of music performance, with extensions to cardiac signals. The research themes are given below, each described in greater detail in the subsections to follow: i) to find new ways to represent, explore, and talk about the musical structures in performance and performed music, and other music-like sequences such as cardiac signals; ii) to harness human problem solving skills through citizen science to understand musical structures created in performance and experienced in performed music; iii) to create sandbox environments to experiment with making performed structures; iv) to create theoretical frameworks to discover the reasoning behind or explain the structures perceived, sensed, and made; v) to foster community engagement by training experts to provide feedback on structure solutions.
Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here, we propose to solve it using StereoSpike, an end-to-end neuromorphic approach, combining two event-based cameras and a Spiking Neural Network (SNN) with a slightly modified U-Net-like encoder-decoder architecture. More specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It provides a depth ground-truth, which was used to train StereoSpike in a supervised manner, using surrogate gradient descent. We propose a novel readout paradigm to obtain a dense analog prediction -the depth of each pixel-from the spikes of the decoder. We demonstrate that this architecture generalizes very well, even better than its non-spiking counterparts, leading to state-of-the-art test accuracy.
In this context, we propose an ultra-low power spiking neural network for depth estimation, capable of dense depth predictions even at places without events and performing on par with the current state-of-the-art. Despite the dynamic nature of DVS data, we show that the problem of depth estimation from such neuromorphic event streams can be treated as a non-temporal task; therefore, our model is purely stateless in the sense that we reset all neurons to a membrane potential of zero at each timesteps. If this feature might not fully take advantage of the temporal processing abilities of SNNs, it drastically decreases the computational and energy footprint of our model. In section 2, we introduce a few related works that inspired our approach. We explain our methodology in section 3, including the data pre-processing, network architecture, and training details.
For instance, the authors of  [32]  implemented a spike-based algorithm on a FPGA to regress low-resolution depth maps on a small size dataset. Furthermore,  [13]  proposed a SNN for processing depth from defocus (DFD); this work targeted neuromorphic chips and was able of recovering depth at full resolution, but the reconstruction was not dense and did not include learning. Following the latter,  [40]  presented a neural network that jointly predicted camera pose and per-pixel disparity from stereo inputs. However, their reconstructed depth maps remained sparse as they restricted their analyses to pixels where events occurred. [34]  addressed this problem and pushed even further the state-of-the-art on indoor scenarios thanks to 3D convolutions exploiting a specific event embedding. In  [17] , dense metric depth was recovered from only one camera, and showed very good performances with a recurrent, monocular encoder-decoder architecture on outdoor sequences.
Method We used PyTorch and SpikingJelly  [6]  as our main development libraries. PyTorch is currently one of the most popular tools for deep learning and automatic differentiation, while Spikingjelly is an open-source framework for spiking neural networks, based on PyTorch and with rising popularity. Our codes will be released on GitHub upon publication of the paper. For the time being, a tentative anonymous repository can be accessed at the following address: https://github. com/urancon/StereoSpike.
Dataset We trained and tested our network on the Multi Vehicle Stereo Event Camera (MVSEC) dataset  [39]. Because of its large size and variability, it has become one of the most popular benchmarks for depth reconstruction from neuromorphic events. It was collected from two DAVIS346 cameras with a resolution of 346 × 260 pixels, mounted on several vehicles such as a car, a motorbike or a drone. The depth groundtruth was provided by a Velodyne Puck Lite LIDAR mounted on the top of the two event cameras and with a sampling frequency of 20 Hz, hence providing a ground truth depth map every 50 ms. We applied our method on the indoor flying sequences of MVSEC, which was recorded on a quadricopter flying inside a large room. We used the data splits that were defined in  [38]  and  [34]. We followed these previous works and removed take-off and landing parts of the sequence, because they contained very noisy event streams and inaccurate groundtruths.
Training Procedure Learning of parameter values in our model was done using surrogate gradient learning  [25] , as implemented in Spikingjelly Python library  [6]. Because our network is feed-forward and only processes one step for inference, we trained it with regular backpropagation, not Back-Propagation Through Time (BPTT) and on the shuffled dataset. We adopted the following procedure: 1) initialize all potentials (including output) to zero, 2) forward pass a sample or a batch of them, 3) calculate loss and back-propagate it, 4) update the weights and 5) start again with a new sample. We used Adam optimizer  [19]  with β 1 = 0. 9 and β 2 = 0. 999. We trained the network for 30 epochs with an initial learning rate set to 2. 10 -4 and divided by 10 at epoch 10, 25 and 40. Batch size was set to 1 and we did not use any weight decay.
Conclusion We have proposed StereoSpike, the first fully spiking, deep neural network architecture for large scale regression task with sparse activity. The key is expressivity and we tackle this problem by increasing the number of spikes generated by deeper layers, with a pool of perfect integrator neurons bearing the final prediction with their membrane potential. The same strategy could presumably be used for other dense regression problems with SNNs, for example optic flow prediction. We have shown that depth estimation from DVS data can be brought back to a stateless, static, non-temporal task. As a result, hardware implementations of StereoSpike could consume substantially less than recurrent versions. However, we are aware that our model neither takes advantage of this implicit recurrence of SNNs to capture temporal dependencies, nor of the very high temporal resolution of DVSs; these two aspects deserve investigation for further improvement of our algorithm.
Acknowledgements We would like to thank  Amirreza Yousefzadeh  for his help and expertise on digital neuromorphic hardware. Our thanks also go to  Wei Fang  for his wonderful SpikingJelly library, without which this work would not have been possible. This research was supported by a grant from the  'Agence Nationale de la Recherche ' ( ANR-16-CE37-0002-01 ,  ANR JCJC 3D3M ) awarded to  Benoit R. Cottereau.
Organization of the paper After a brief review of related research, Section 3 describes how the proposed framework allows us to learn anatomical models including the two types of information (visual and spatial) from a small training database. First, the learning of the local voxel classifiers modeling the visual information (shape and intensities) of each substructure of interest is described in detail. Second, the learning of the spatial relationships modeling the relative position and size between the substructures is explained. Section 4 analyzes the influence of different parameters on the segmentation results. A user experiment is also presented to describe the usability of the proposed approach.
Method This section describes how the proposed framework allows us to learn anatomical models including the two types of information (visual and spatial) from a small training database. First, the learning of the local voxel classifiers modeling the visual information (shape and intensities) of each substructure of interest is described in detail. Second, the learning of the spatial relationships modeling the relative position and size between the substructures is explained. We then describe the unified model that we defined to store both types of information simultaneously. The segmentation method using this new unified model incrementally and interactively is then presented. The automatic or manual positioning of the bounding boxes corresponding to the desired substructures within the entire image is explained, and the classification process provides the final local segmentation.
Experiments After a brief description of the implementation of SILA 3D, this section analyzes the influence of different parameters on the segmentation results. The size of the training database and the impact of the order of segmentation are evaluated. Experiments with sheep brain and human brain images highlight the effects of the method by providing satisfactory results within a short computation time compared to other methods. A user experiment is also presented to describe the usability of the proposed approach. Finally, the generic nature of the method is demonstrated by providing segmentations of heart images and intermodal cases.
Core kernel C++/ITK The main piece of software (Core/Kernel) has been designed and implemented in C++, in a modular and extensible way. This kernel includes not only a set of computational tasks, but also loading and saving functions. The main functionalities are exposed in a binary library to help further development and integration. For instance, the kernel includes DataInput, Registration, Segmentation and Results classes that are available to implement new registration methods, new segmentation methods, or new data input formats in order to build a versatile software tool. The current implemented registration methods are based on the ITK (Insight Toolkit Software Consortium) framework, which is powerful and well-known in the medical image community.
Client app, graphical user interface (GUI) Thanks to the wide open source framework provided by 3DSlicer  (Fedorov et al. , 2012) , and its python programming interface (SDK), 3DSlicer was chosen to build a standalone client GUI to communicate with the SILA 3D core functionalities. This client GUI reuses Slicer visual widgets to allow users to navigate through image slices and to drive the interactive segmentation process. The GUI provides access to all needed core functionalities such as loading modalities images, loading a new model, choosing structures and launching process on servers, multi-users session management (saving/exporting segmentation results). For instance, the proposed GUI allows a precise and easy positioning of the borders of the bounding boxes through few intuitive moves of 2D boxes in each image plans with the help of an additional global 3D preview (see Fig.
Datasets Two image databases were used during the experiments. The MIC-CAI'12 image dataset  (Landman et al. , 2012)  used for the multiatlas segmentation challenge dealing with brain images and coming from the OASIS project. This database consisted of 35 MRI T1 images of human brains, and each image was manually segmented with more than 140 anatomical structures; however, only 13 structures were used in this study, including 6 bilateral structures: the caudate nucleus, pallida, thalami, putamens, ventricles, hippocampi and brainstem. Fifteen images are used as training images, and 20 are used as test images. An image from the training database is shown in Fig. 5.
(left). NeuroGeoEx is an image database of T2 ex vivo sheep brains provided by Neurospin company  (Leprince et al. , 2015)  as part of the NeuroGeo 3 project. Six brains were acquired with 7T 50 mT/m MRI with a spatial resolution of 0. 3 × 0. 3 × 0. 3 mm. Each image was manually labeled with 13 anatomical structures composed of 6 bilateral structures: olfactory bulbs, caudate nucleus, amygdala, optic superior colliculi, motor superior colliculi, hippocampus and periaqueductal gray (PAG). Leave-one-out process was applied to the NeuroGeoEx dataset; five images were used to build the Anatomical Graph Model used to segment the remaining image. A typical image is shown in Fig. 5  (right).