dataesr/scientific-paragraphs-categorization huggingface dataset:
MATLAB graphics performance depends mainly on the processor speed, rather than on the speed of the graphics card. With new processors adding more cores and increasing the clockrate, the display performance of MANTA will increase as well, as many core algorithms in MATLAB are multithreaded. Thus, the results could be improved if the latest generation of processors were used. Displaying the voltage traces of many hundreds of channels will be close to impossible at the moment, both with respect to FIGURE 4 | The update interval remains usable up to a high number of channels. The time between successive updates of the display depends on computer hardware, the analyses displayed and the parameters of the acquisition. The dependence of update rate on the number of channels is close to linear, only flattened at the lower limit by the maximum frame rate (25 Hz = 40 ms).
The update interval is strongly influenced by whether only the raw trace (red), only spikes (red), or both are displayed (blue). The dependence on the sampling rate was only slight, since only a fraction of the points are displayed in each step (data not shown). display space, as well as update rates in MATLAB (or human perception). However, the Mathworks is currently overhauling MATLAB's graphics abilities to support specialized graphics hardware. Alternatively, a more efficient visual representation of such large datasets may be in order, e. g. , 2D heatmaps/3D vector maps of instantaneous rate, which can be displayed rapidly (>30 Hz for 1000 × 1000 map) in MATLAB.
COMPARISON WITH OTHER PACKAGES The Neurorighter project  (Rolston et al. , 2009; Newman et al. , 2013)  is another open-source DAQ system based on C#, targeting similar usage scenarios, i. e. , multichannel recordings. While the update rate of the display is expected to be higher in Neurorighter, extensibility is more involved due to the choice of a compiled, rather than an interpreted language. Many changes in MATLAB can be implemented, tested and improved even while acquisition is running, and thus allow for a rapid iteration cycle for code development. The Ephus system  (Suter et al. , 2010)  is another project based on MATLAB which is targeting in-vivo/in-vitro, few channel recordings integrated with optical methods and stimulation. While in principle suitable for array recordings, Ephus is not optimized for this purpose, and would thus require substantial modifications to serve the same purpose as MANTA.
Acknowledgments: This work was supported by the  CCES (Competence Center Environment and Sustainability of the ETH Domain, Switzerland)  as part of the  MOUNTLAND  project and by the  State Secretariat for Education and Research  ( SER C07. 0112 ) in the framework of the  COST Action FP0603. We also thank  Konstantin Gavazov ,  Andreas Rigling ,  Rober Huber ,  Kathryn Lannas ,  Jean-Bruno Wettstein , and  Michael Kalbermatten.
Cities all around the world are in constant evolution due to numerous factors, such as fast urbanization and new ways of communication and transportation. Since understanding the composition of cities is the key to intelligent urbanization, there is a growing need to develop urban computing and analysis tools to guide the orderly development of cities, as well as to enhance their smooth and beneficiary evolution. This paper presents a spatial clustering approach to discover interesting regions and regions which serve different functions in cities. Spatial clustering groups the objects in a spatial dataset and identifies contiguous regions in the space of the spatial attributes. We formally define the task of finding uniform regions in spatial data as a maximization problem of a plug-in measure of uniformity and introduce a prototype-based clustering algorithm named CLEVER to find such regions.
Moreover, polygon models which capture the scope of a spatial cluster and histogram-style distribution signatures are used to annotate the content of a spatial cluster in the proposed methodology; they play a key role in summarizing the composition of a spatial dataset. Furthermore, algorithms for identifying popular distribution signatures and approaches for identifying regions which express a particular distribution signature will be presented. The proposed methodology is demonstrated and evaluated in a challenging real-world case study centering on analyzing the composition of the city of Strasbourg in France.
Different types of urban patches support different needs of people's lives and "serve as a valuable organization technique for framing detailed knowledge of a metropolitan area"  [18]. Improvement in scanning devices, gps, and image processing leads to an abundance of geo-referenced data. For example, tracking devices are now available to capture movement of human and animals in form of trajectories  [19]. Furthermore, more and more Point of Interest (POI) databases are created which annotate spatial objects with categories, e. g. buildings are identified as restaurants, and systems, such as Google Earth, already fully support the visualization of POI objects on maps. As more and more data become available for a spatial area, it is desirable to identify different functions and roles which different parts of this spatial area play; in particular, it is desirable to identify homogeneous regions in spatial data and to describe their characteristics, creating high-level summaries for spatial datasets which are valuable for planners, scientists, and policy makers.
3. Popular signatures are proposed which are frequently occurring distribution signatures in the subspaces of a spatial area of interest. A novel approach which summarizes the composition of a spatial dataset by annotating regions with popular signatures is presented and algorithms to identify regions which match particular signatures are introduced. 4. The proposed framework is evaluated in a case study involving the building structure of the city of Strasbourg, France; in particular, the city is partitioned into uniform regions which are annotated with signatures and the benefit for domain experts of having such summaries is discussed. Section 2 formally defines the problem of finding uniform regions in spatial dataset and introduces the spatial clustering approach for this task. Section 3 gives an experimental evaluation of the methodology.
So far we did not clearly discuss what distinguishes a uniform region from a non-uniform region in a spatial dataset. More formally, we are interested in obtaining spatial clusters which are uniform with respect to their signatures, using the following maximization procedure: Input: a dataset O containing spatial objects belonging to p classes Task: Find a spatial 1 clustering X = {c 1 ,. ,c k } of O such that (1) c i ⊆ O for i =1,. , k (2 ) c p  c q = for pq which maximizes the following objective function ϕ(X) where b is the number of pairs of neighboring clusters in X, s(c) denotes the signature of cluster c and d is a distance function which assesses the similarity of two signatures. 1 A spatial Cluster is assumed to be contiguous in the space of the spatial attributes. In summary, we are interested in obtaining a spatial clustering in which the average Euclidian distance between the signatures of neighboring clusters is as large as possible.
CLEVER-a Spatial Clustering Algorithm Supporting Plug-in Interestingness Measures In order to employ these two approaches outlined in section 2. 1, we need a spatial clustering algorithm capable of finding contiguous spatial clusters by maximizing a plug-in reward function which captures a particular notion of uniformity. A spatial clustering algorithm named CLEVER  [3, 8]  will be 2 If prototype-based clustering algorithms, such as K-medoids or K-means are used, a Voronoi tessellation can be used to derive cluster models from the set of cluster prototype which are convex polygons; unfortunately, it is not computationally feasible to compute Voronoi cells in higher dimensional spaces, as the complexity of the algorithm is exponential with respect to the dimensionality of the dataset. Consequently, it is only feasible to compute the Voronoi tessellation in 1D, 2D, and for small datasets in 3D.
For density-based clustering algorithm the situation is even worse; for example, we are not aware of any methods which are capable of producing cluster models from a DBSCAN clustering. 3 Popular signatures are distribution characteristics which occur frequently in contiguous subspaces of a spatial dataset. adapted for this task. In general, CLEVER is a prototype-based, k-medoid-style  [10]  spatial clustering algorithm which employs randomized hill climbing to maximize a plug-in reward function. Reward functions are assumed to have the following form when assessing the quality of a clustering X = {c1,. ,ck}: where |c| denotes the number of objects in a cluster c, i(c) is an interestingness function which assesses how interesting the cluster c is, and β ≥ 1 is a parameter which determines how much reward is put on cluster size; β indirectly controls the numbers of clusters in X.
As cluster size is rewarded using a non-linear function, usually fewer clusters are obtained when larger values for β are used. Moreover, the rewarding scheme encourages the merging of neighboring clusters with the same or similar signatures. The reward function assesses the quality of a clustering as the sum of the rewards of the individual clusters; two such interestingness functions will be introduced in Section 3. The pseudo-code of CLEVER is given in Algorithm 1. Algorithm 1: CLEVER. Input: Dataset O, distance-function d or distance matrix M, k', i(c), β, sampling rate p Output: Clustering X, quality q(X), rewards for clusters in X 1: Randomly create a set of k' representatives 2: Sample p solutions in the neighborhood of the current representative set 3: If the best solution of the p solutions improves the clustering quality of the current solution; its representative set becomes the current set of representatives and search continues with Step 2; otherwise, terminate returning the current clustering.
CLEVER maintains a current set of representatives which are objects in the dataset and forms clusters by assigning the remaining objects to the closest representative in the representative set. It samples p representative sets in the neighborhood of the current representative set by adding, deleting, and replacing representatives. This process continues as long as a better clustering with respect to q(X) is found. The algorithm begins its search from a randomly created set of k' representatives, where k' is an input parameter of the algorithm. To give an example, let us assume we cluster a dataset O= {o1,. ,o200} with k' set to 3; in this case, the algorithm starts with a random representative set, let us say {o3,o9,o88}, and forms clusters by assigning the remaining 197 objects to the closest representative which takes O(k (n -k)) where n is the number of objects in the dataset and k is current number of representatives.
IDENTIFYING UNIFORM REGIONS IN A CITY Since understanding the evolution of cities is the key to intelligent urbanization, there is a growing need to develop urban planning and analysis tools to guide the orderly development of cities, as well as to enhance their smooth and beneficiary evolution. However, it is a big challenge for urban planners to come up with methodologies to analyze how cities are changing. Partitioning a city into uniform regions facilitates this task, as change can be analyzed based on higher level of granularity instead on the raw data. In this section, we present a set of experiments which use the methodology, which was introduced in Section 2, to extract urban patches from a building dataset. In this context, metrics for evaluating the homogeneity of a group of buildings are very important as they impact how a city is partitioned into urban patches characterized by signatures.
In particular, two such metrics, one based on purity and one based on popular signatures will be introduced in this section. In particular, we report the results of a series of experiments in which the CLEVER spatial clustering algorithm is used in conjunction with those two uniformity metrics to obtain interesting, uniform regions for the city of Strasbourg, France. As part of the GeOpenSim project, a temporal topographic database of the city of Strasbourg has been acquired  [12]. As buildings are represented as polygons, we use Hausdorff distance  [5]  to compute the distance between buildings in the experiments.
Building Type Purity Experiments This section introduces a purity interestingness function which measures uniformity by the degree of dominance of instances belonging to a single category and discusses spatial clustering results obtained with this interestingness function. The purity interestingness function is used for analyzing interestingness with respect to a categorical non-spatial attribute. Purity interestingness iPUR(c) of a cluster c is computed using the following formula: Let 0 where cl(O) is the set of classes in the dataset O and p t is a function that computes the proportions of the objects of class t belonging cluster c; η> 0 is the scaling factor and th > 0 is the threshold. For example, assuming that th = 0. 4, η = 1, and s(c)=(0. 6, 0, 0, 0, 0. 4, 0) indicating that 60% of the objects belong to the first category, and 40% of the objects belong to the fourth category, we obtain: iPUR(c) = 0.
6 -0. 4 = 0. 2 for cluster c. In general when using the purity interestingness function, we are interested in obtaining clusters which are dominated by instances of a single category. There are six different building types in the dataset: single house, garage, commercial building, light building, collective house, and school. In year 2008 78% of the buildings are single houses; commercial buildings take 7%; collective houses take 8%; 4% of the buildings are garages and 3% of the buildings are light buildings; finally, 1% of the buildings are schools. Building type signatures describe the characteristics of each urban patch which can help domain experts to better understand the composition of a city. Figure  1  visualizes and lists the building type signatures of 12 clusters for the year 2008; they were generated by CLEVER using the purity interestingness function with th = 0.
5, η = 2 and β =1. 2. Cluster 0 contains 92% commercial buildings; therefore, cluster 0 is labeled as a business urban patch. Cluster 10 is a residential area because 97% of the buildings in cluster 10 are single houses. There are 76% of collective houses in cluster 5, which indicates a living area with a lot of apartment complexes. Both garages and schools have very small percentage in the whole dataset, but garages and schools are more frequent in the collective housing areas in clusters 4 and 5, but surprisingly are not present in cluster 2. Figure  1  verifies that our approach is able to identify contiguous urban patches dominated by buildings of a single type.
To accomplish that, we need a spatial clustering algorithm to partition the spatial dataset into regions whose signatures are a good match with respect to a given set of popular signatures. In the remainder of this section we will propose a framework for annotating regions with matching popular signatures. It first collects signatures using a sampling approach; second, it identifies a set of popular signatures from the collected signatures using a clustering approach; third, it uses a spatial clustering algorithm to identify regions with a good match with the set of popular signatures. As step 1 and 2 are kind of straightforward, we will not discuss those further. As far as the third step is concerned, we run CLEVER using the following popular signature interestingness function i POP (c): Let cld = d(s(c),.
To obtain a set of popular signatures, we first randomly created 1000 small spatial clusters and extracted their building type signature. Next, we apply a distance-based outlier detection technique to remove 10% of the building type signatures as outliers-signatures were sorted by their 3-nearest neighbor distance to the other signatures in the set. Signatures with the largest 3-nearest neighbor distance were removed from the signature set. Next, we clustered the remaining signature set using K-means with different k values ranging between 6 and 10 several times, and identified the clustering with the lowest squared average distance of the objects in the dataset to the cluster centroid they belong to. Finally, we extracted the centroids from the best clustering as popular signatures. Table  1  lists nine popular building type signatures that were obtained as the result of this process.
Querying a Spatial Dataset with Signatures Although the presented popular signature mining algorithm has been originally developed to determine the scope of a set of popular signatures, it can be used in conjunction with any signature set P. This enables us to use the same algorithm for querying spatial datasets for the presence of particular "query signatures". For example, in the experiment summarized in Table  2 , we came across cluster 3, which was dominated by light buildings and it might be interesting to see if its signature Q1=(29%,2%,9%,45%,15%,0%) occurs in other areas of the city; along the same line we might want to see, if there are regions with a high density of schools in a residential area captured by signature Q2=(70%,0%,0%,0%,0%,30%). Finally, we like to see if the popular signature Q3=(2%, 6%, 0%, 0%, 92%, 0%) (named S3 in Table  1 ) occurs anywhere in the dataset, as it did not match any cluster signature.
One key idea of this paper is to use signatures to annotate spatial clusters and to propose a framework to mine cluster signatures in spatial datasets. We are not aware of any work that uses signatures in conjunction with clustering; however, signatures have been used for other purposes. Applegate et al. [1]  state that "signatures are compact representations…that capture important characteristics of massive datasets" and then investigate a special family of signatures for multidimensional distributions that represent the distribution of probability mass over a manifold and introduce a novel distance function for such signatures. Cortes et al. [4]  discuss the use of signatures for mining massive telecommunications data to find communities of interest, and for fraud detection. Wong et al. [16]  demonstrate the benefits of using data signatures to guide the visualization of complex scientific datasets.
SIGNIFICANCE AND IMPACT This paper introduces a spatial clustering methodology which identifies contiguous regions in the space of the spatial attributes which are uniform with respect to their signatures, which represent statistical summaries for the objects belonging to a particular cluster. The second idea advocated in the paper is to mine spatial data for the presence of particular signatures. We claim that these two types of signature-based spatial clustering have broad applications in urban computing. The proposed methodology defines the task of finding uniform regions formally as a maximization problem. Various objective functions and corresponding algorithms are introduced. In particular, we introduce a prototype-based clustering algorithm named CLEVER, which identifies uniform regions in a spatial dataset by maximizing a plug-in measure of uniformity, relying on a randomized hill climbing approach.
Moreover, polygon models which capture the scope of a spatial cluster and histogram-style distribution signatures are used to annotate the content of a spatial cluster; both play a key role in summarizing the composition of a spatial dataset. We claim that the presented approach is novel and unique as existing clustering algorithms are not suitable for this task as they minimize distance-based objective functions, whereas assessing uniformity relies on non-distance based uniformity measures. The efficacy of the proposed methodology is demonstrated by a challenging real-world case study centering on analyzing the composition of the city of Strasbourg in France based on building characteristics. Applying the methodology, presented in this paper, faces several challenges, such as sensitivity to initialization, finding more suitable algorithms to compute the scope of a set of spatial clusters, providing a better theoretical foundation for signature mining, the capability to identify spatial clusters of arbitrary shape, and the need to run spatial clustering algorithms multiple times.
Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values. We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule.
For example, an implementation of one recent algorithm, local Laplacian filters  [3, 22] , is a graph of 99 different stages (Fig. 1 ), including many different stencils and a large data-dependent resampling. As a result of this structure, the performance difference between a naive implementation of a given pipeline and a highly optimized one is frequently an order of magnitude or more. With current tools, often the only way to approach peak performance is by hand-writing parallel, vectorized, tiled, and globally fused code in low-level C, CUDA, intrinsics, and assembly. Simple pipelines become hundreds of lines of intricately interleaved code; complex pipelines, like Adobe's Camera Raw engine, become hundreds of thousands. Tuning them requires immense effort by expert programmers, and the end result is not portable to different architectures, nor composable with other algorithms, without sacrificing much of this painstakingly earned performance.
Contributions Halide is an open-source domain-specific language for the complex image processing pipelines found in modern computational photography and vision applications  [26]. In this paper, we present the optimizing compiler for this language. We introduce: • a systematic model of the tradeoffs between locality, parallelism, and redundant recomputation in stencil pipelines; • a scheduling representation that spans this space of choices; • a DSL compiler based on this representation that combines Halide programs and schedule descriptions to synthesize points anywhere in this space, using a design where the choices for how to execute a program are separated not just from the definition of what to compute, but are pulled all the way outside the black box of the compiler; • a loop synthesizer for data parallel pipelines based on simple interval analysis, which is simpler and less expressive than polyhedral model, but more general in the class of expressions it can analyze; • a code generator that produces high quality vector code for image processing pipelines, using machinery much simpler than the polyhedral model; • and an autotuner that can infer high performance schedules-up to 5× faster than hand-optimized programs written by expertsfor complex image processing pipelines using stochastic search.