dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, the transactional Web Feature Service (WFS-t) format could be used to send points out of the box, simply using a geo-server between the client and the point cloud server. This could be a revolution in point cloud availability, similar to what happened to geo-raster data (e. g. , google map WFS). Processing In-base processing offers many opportunities because it is close to the data and can be written with many programming languages. Yet, it is also intricately limited to one thread and the amount of memory allowed for PostgreSQL. The execution is also within one transaction. It may also be hard to control the execution-flow, during the execution. However, the Python access both from within and outside the database shows the possibility to write more ambitious processing methods with several parts executing in parallel as well as communicating, dealing properly with errors, etc.
Perspective for applications Patches and their generalisations are perfect candidates to perform fast and efficient registration  (cloud-to-cloud, cloud-to-raster, etc. ). Indeed, the classical solution for registering a massive point cloud is to sub-sample it. Using extracted primitives would be better. Having all the meta-data, the trajectory (or camera position matrices), and the raw data, it would be possible to change the trajectory (matrices) and regenerate the point cloud with updated coordinates, all of this from within the database. Processing of point clouds would extract landmarks, which could be matched with a landmark database. It would be possible to mix PostGIS Topology (2D partition of the space) for graph queries.
CONCLUSION In this paper, we presented a complete point cloud server system based on groups of points (patches). Using these patches as generalisations, we fulfil all point cloud user needs (loading, storing, filtering, exporting and processing). The system is fully open source and thus easily extensible and customisable using many programming languages (C, C++, Python, R, etc. ). Our system opens new possibilities because of intricate synergy with other geo-spatial data. Lastly, we proved through real-life uses that this system works with various point cloud types  (Lidar, stereo-vision) , not only for storing point clouds, but also for processing. As perspective, we could explore in-base re-registration from trajectory and raw data, in-base cloud-to-cloud registration, in-base classification, and point streaming, as well as scaling to thousands of billions of points.
Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demon-.
, 2011). Light detection and ranging (lidar) technologies can be helpful in this regard because they generate repeatable, precise three-dimensional information of the Earth's surface characteristics. Lidar allows for simultaneous measurements of aboveground vegetation structure and human infrastructure, as well as the topography of the Earth surface, including soils, exposed bedrock, stream channels, and snow/ice. Depending on the data collection system and platform, observations can be made at the landscape scale (> 1000 km 2 ) and at spatial resolutions capable of capturing fine-scale processes (< 10 cm). These unique measurement capabilities offered by lidar have the potential to help answer transdisciplinary research questions, which transcend a single discipline, capture greater complexity, and create new intellectual advances that are synergistic (across disciplines) in nature.
Fundamental CZ science questions often require transdisciplinary approaches that surpass what is possible in multidisciplinary (i. e. , collaborations across disciplines that pose their own questions) or interdisciplinary (i. e. , collaborations where information is transferred amongst disciplines) research set-tings. Because lidar can characterize geomorphic, ecologic, and hydrologic processes simultaneously across a range of scales, it is uniquely suited to address questions posed by CZ research. Lidar acquisition capabilities are increasing exponentially  (Stennett, 2004; Glennie et al. , 2013)  and new ground-based (terrestrial laser scanning -TLS), mobile platforms (airborne laser scanning -ALS, or other mobile platforms like trucks or boats), and space-based platforms (spaceborne laser scanning -SLS) are leading to increased availability of lidar data sets with CZ-relevant information content.
Data access, processing, and analysis The crux of successfully leveraging a flood of new lidar (and other high-resolution topographic information) data for CZ science (e. g. ,  Stennett, 2004)  will be the ability to extract meaningful information from these rich and voluminous data sets. These new lidar data sets require that data processing and analysis tools be optimized to handle increasingly large data sets with greater information content. Processing limitations are likely to reduce the usability and extent of very high information data sets, e. g. , waveform or multispectral data sets pose processing challenges at the continental scale but may be more manageable at the watershed scale. Further, new software and workflows need to be developed that enable scientists to incorporate lidar data into detailed models of the CZ without expertise in remote sensing.
The CZ science community must engage in a concerted effort to develop (and/or adopt from other domains) new open-source tools that leverage high performance computing resources available through programs such as the National Science Foundation (NSF) Extreme Science and Engineering Discovery Environment (XSEDE) (https://www. xsede. org/home). By increasing the scalability of CZ lidar-oriented processing and analysis tools, computationally intensive analysis and modeling at the highest resolution of the lidar data sets will be possible. In addition to increasing software scalability, new processing tools are necessary to take advantage of new data types, such as full waveform lidar  (Wagner et al. , 2008, Mallet and Bretar, 2009)  and hyperspectral laser technology  (Hakala et al. , 2012). Cloud computing and the "big data paradigm" that is increasingly common in both industry and academia (Mattman, 2013) present opportunities for the CZ lidar community.
One such opportunity for big data sharing is EarthCube (http://www. earthcube. org), a relatively new program that has potential to integrate lidar information (among other geospatial information) into data sharing efforts in the geosciences. Due to efforts such as NSF's OpenTopography  (Crosby et al. , 2011) , there is a large volume of CZ-oriented lidar online and freely available to the community. For example, OpenTopography already offers on-demand processing services  (Krishnan et al. , 2011)  that permit users to generate standard and commonly used derivatives from the hosted lidar point cloud. By coupling data processing with data access, users are not required to download large volumes of data locally or have the dedicated computing and software resources to process these data. Although many CZ-oriented lidar data sets are already available to the community through resources such as OpenTopography in the USA, there are numerous other lidar data sets globally that are not accessible because they are not available online or access is restricted.
Open lines of communication Develop communication within and among groups, including individual CZ disciplines, remote-sensing scientists, computer scientists, private industry, and funding agencies. Workshops have the potential to increase communication between "data users" and "data creators". CZ scientists must find ways to communicate their data acquisition specifications to the scientists and engineers who create lidar hardware and processing software through venues such as meetings with private industry, the development of advisory committees, and commentary pieces in trade journals that present a vision for the future needs of CZ scientists. Open communication among diverse CZ scientists is fundamental to developing collaborations capable of transdisciplinary advances. Working groups within CZ communities, like the critical zone exploration network (http://www.
Increase information extraction Advocate for lidar repositories that are interoperable and broaden data access, as well as open-source and communitycentric processing resources. Ultimately, enhanced and streamlined data processing and analysis tools will enable CZ researchers to concentrate on understanding fundamental science problems instead of struggling with data access, processing, and analysis. Specifically, recent efforts focused on cloud storage and computing resources, and open source software tools could greatly aid this effort. Efforts to improve the efficiency of processing will become more important as the acquisition of lidar expands to continental scales. Information extraction at larger extents will require judicious tradeoffs between acquisition parameters and costs that consider variability in local physiographic conditions (i.
Acknowledgements. The workshop forming the impetus for this paper was funded by the  National Science Foundation  ( EAR 1406031 ). Additional funding for the workshop and planning was provided to S. W. Lyon by the  Swedish Foundation for International Cooperation in Research and Higher Education (STINT  grant no. 2013-5261 ). A. A. Harpold was supported by an  NSF  fellowship ( EAR 1144894 ). The use of specific commercial or open-source packages and tools in this paper does not imply a specific endorsement. Edited by:  Y. Fan.
Attentive robots The tasks of the robot which involves visual attention might be classified roughly into three categories  [7] : • low-level category: uses attention to detect salient landmarks that can be used for localization and scene recognition, • mid-level category: considers attention as a frontend for object recognition, • highest-level category: attention is used in a human-like way to guide the actions of an autonomous system like a robot. In the first category robots use landmarks to compute their position in space. In  [20]  or  [27] , authors used static maps in which specific landmarks are located. In  [11] , the robot has to build a map of its environment and to localize itself inside. Salient regions are tracked over several frames to obtain a 3D position of the landmarks, and match them to database entries of all previously seen landmarks.
Initial model evaluation In  [22] , we have presented a very complete evaluation of our model. We used the cross-correlation, Kullback-Leibler divergence and normalized scanpath saliency measures between 6 efficient algorithms and an eyetracking ground-truth. In these evaluations, we have shown that our model is highly stable, robust, exploratory (we can easily define scene exploration strategy), dynamic, plausible, fast, and highly configurable. All measures were done on two image databases. The first one is proposed in  [4]. It is made up of 120 color images which represent streets, gardens, vehicles or buildings, more or less salient. The second one, proposed in  [17] , contains 26 color images. They represent sport scenes, animals, buildings, indoor scenes or landscapes. For both databases, eye movement recordings were performed during a free viewing task.
Regarding the numerous models that exist in literature, we have decided to benchmark our model. However, it is hard to make immediate comparisons between models. To alleviate this problem, T. Judd has proposed a benchmark data set containing 300 natural images with eye tracking data from 39 observers to compare model performances 2. She writes, this is the largest data set with so many viewers per image. She calculates the performance of 10 models in predicting ground-truth fixations using three different metrics: a receiver operating characteristic, a similarity metric, and the Earth Mover's Distance. We have downloaded the database, have runned our model to create saliency maps of each image and have submitted our maps. We present the results in Table. 1. References of algorithms can be found in the web page of the benchmark.
Experimentation For our experimentation we have decided to use a mobile system composed by a LEGO Mindstorm platform and a Kinect sensor (Figure  5 ). The LEGO Mindstorm allows motion, whereas Kinect allows video and depth acquisition. This system will be referred as CuriousMind in the rest of the paper. The LEGO Mindstorms series of kits contain software and hardware to create small, customizable and programmable robots. They include a programmable brick computer that controls the system, a set of modular sensors and motors, and LEGO parts from the Technics line to create the mechanical systems. For our test we have decided to link the LEGO Mindstorm and the Kinect to a computer thanks to a USB liaison rather than Bluetooth. Free tools in combination with the Robotics Developer Studio developed by Microsoft [9] enable programming the Mindstorm using the C# language.
Moreover, CuriousMind is able to cancel previous tasks/actions to respond to an unexpected distractor if this one is salient enough. The saliency algorithm provides a generic module capable of reacting in case of novel and unexpected situations for which the robot is not previously trained. This component is complementary to the specific taskoriented classical modules in robotics. It is able to respond to danger and provide reactivity in human-robot communication by a better adaptation to human actions despite an original task to be solved. This first step provides CuriousMind the ability to be distracted in the same way as humans by surprising scenes which can lead to a robot-human empathy improvement. We also look for novel behavior as artificial humor emergence from unexpected situations and unexpected robot behavior.
As a future work, we will implement our system inside a Nao (Figure  11 ), an autonomous, programmable humanoid robot developed by Aldebaran Robotics. Moreover, we will integrate a motion conspicuity map, to be reactive when a new element moves inside the robot field of view. A more in-depth validation will be conducted using the NUS3D-Saliency Dataset provided by Tam V. Nguyen  [16]  which includes depth maps and eye-tracking results on both 2D and 3D scenes.
The symbol , next to the task refine results, is used to indicate that this task is optional. Fig. 3  shows a further level of task decomposition with alternative ways to perform some tasks. As for the task formulate query, we can identify additional subtasks including provide keywords and select a database. The operator |=| indicates that these tasks can be performed in any order. It is worthy to notice that the task provide keywords is decomposed in alternative subtasks: type keyword and select word. The choice between tasks is indicated by the operator []. Some of the alternative tasks consider the case of manual versus automated execution. For example, the tasks select database can be done by prompting the user to explicitly identify a database. The automated alternative task assumes that the system uses a predefined database and the user cannot change it.
The set of predefined queries is large, and it aimed at to fit different users' goals for ancillary tasks, for example: finding co-authors, finding publications of a particular author, etc. The number of queries accessible from the client-side can be configured dynamically, but for the purposes of this paper we are just using a specific one, which returns the co-authors of a given researcher. These queries as well as the choice of DBLP database as information source of results are totally arbitrary but justified by the fact that they are related to our case study. Indeed, many different queries are possible to match an ancillary search with diverse users' goals. We suggest that the broker would embed some kind of intelligent behavior for suggesting ancillary searches according to the users' previous search.
Preliminary results In order to test our tools we have run a remote empirical study with end-users. The evaluation was designed around a scenario where, given a list of names of researchers in a web page, users should perform ancillary tasks for checking co-authorship at the DBLP. Users were allowed to perform the same tasks by visiting the DBLP web site (http://www. informatik. uni-trier. de/~ley/db/) and by using our tools. The list of coauthors was obtained by parsing data from DBLP and displaying it using IRIS as shown in Fig. 10 , so that users will get exactly the same data. Users should start at the Web page members of VIDAS' project shown in The study included an online survey that covers five main chapters as follows: i) presentation of the study, ii) occurrence of ancillary search in their daily life; iii) preference for formulating queries and interacting with results, iv) preference for the location of the search results in the display, and v) demographic data.
Capra et al. [1]  also proposed to augment the user interface of search results by adding images next to the ranking provided by search engines aiming at helping users to make better decisions. These few examples are illustrative of strategies for improving the design and display of the ranking of results from search engines. In the last decades, several information visualization approaches have been developed for presenting search results coming either from search engines or widely used databases, such as DBLP, ACM DL, IEEE Xplore, etc. Some search engines with built-in visualization tools have also been developed. The first reports presenting and/or discussing visualization of search results date from the late 90's and early 2000's. However, although along the years, many different techniques have been evaluated  [19]  with results favoring visualizations, the majority of web search engines still provide textual lists ordered by some user or tool specified criteria.
With the emergence of the Future Internet and the dawning of new IT models such as cloud computing, the usage of data centers (DC), and consequently their power consumption, increase dramatically. Besides the ecological impact, the energy consumption is a predominant criteria for DC providers since it determines the daily cost of their infrastructure. As a consequence, power management becomes one of the main challenges for DC infrastructures and more generally for large-scale distributed systems. In this paper, we present the EPOC project which focuses on optimizing the energy consumption of mono-site DCs connected to the regular electrical grid and to renewable energy sources.
However, if we consider an example, 32 servers per rack, the same operation would take about 53 hours, since now the bandwidth is being shared by the 32 servers. Consequently, increasing the bit rate of the interconnection network becomes a must. To overcome the aforementioned problem, classical dynamic consolidation system uses live migration with a Storage Area Network (SAN). In this case, the VM storage is shared between all servers and live migration is limited to transfer VMs memory. Nevertheless, adding a SAN impacts on the global DC energy consumption. EpoCloud proposes to suppress the SAN, which is a dedicated network providing access to consolidated data storage. Among various components of a data center, storage is one of the biggest consumers of energy. An industry report  (Inc, 2002)  shows that storage devices account for almost 27% of the total energy consumed by a DC.
Server throughput capacity In EPOC project, the DC consolidates the VMs periodically by using the live migration technology. Each server owns a 100 Gb/s transmission capacity and the live migration migrates the whole VM including the storage. It requires huge network resource to maintain the performance level therefore less degradation is employed during the process. In order to achieve a read/write speed of 100 Gb/s, we prefer SSD (Solid-State Drive) over HDD (Hard Disk Drive), since SSD is faster and less energy consuming than HDD. A single SSD of 800 GB capacity with a of PCI-E 2. 1 x8 (Peripheral Component Interconnect Express) interface, can achieve 2 GB/s reading speed and 1 GB/s writing speed. This implies that we still need several SSDs in RAID (Redundant Array of Inexpensive Disks) technology in order to attain the 100 Gb/s-data rate.
Therefore it is not an attractive solution for small-scale data centers. In order to avoid using storage or batteries in small-scale DC, in the EPOC project, we propose to virtualize the green energy. Virtualization of energy implies nullifying the degraded interval (lack of green energy) with the surplus interval (excessive green energy than demand), whenever the availability of green energy is over the demand. From clients or SaaS providers perspective, they realize both the interval as ideal interval (when supply meets the demand), though the green energy was not present instantaneously rather present virtually as shown in Figure  2. So, whenever the green/renewable energy is present, we use the whole portion of the available green energy and draw the other portion from the grid. In this way energy storage is not needed and neither of the portion of renewable energy is wasted.
EpoCloud Manager Architectural principles for small data centers were defined in the previous sections. They rely on innovative infrastructure where a limited number of servers (without SAN) are connected by a high speed optical network and supplied by local sources of renewable energy, composed of a limited number of server (without SAN) connected by a high speed network. To take advantage of this architecture, the EPOC project develops an innovative task management system: the EpoCloud Manager including a smart task scheduler (Section 4. 1), and an energy-aware SLA oriented management system (Section 4. 2).
Given a reliable prediction model, it is possible to design a scheduling heuristic that aims at optimizing resource utilization and energy usage, problem known to be NP-hard. So, the proposed heuristics will schedule tasks spatially (on the appropriate servers) and temporally (over time, with tasks that can be planed in the future). In order to achieve this energy-aware resource allocation, we distinguish two kinds of jobs to be scheduled on the data center: the web jobs which represent jobs requiring to run continuously (like web server) and the batch jobs which represent jobs that can be delayed and interrupted, but with a deadline constraint. The second type of jobs are the natural candidates of the opportunistic scheduling algorithm. Additionally for reducing further energy consumption in the EpoCloud, we are taking advantage of consolidation algorithms and on/off mechanisms to optimize the number of powered-on resources.
Conclusion In this paper, we have presented the EpoCloud principles, architecture and middleware components. EpoCloud is our prototype, which will tackle three major challenges: 1) To optimize the energy consumption of distributed infrastructures and service compositions in the presence of ever more dynamic service applications and ever more stringent availability requirements for services; 2) To design a clever cloud's resource management, which takes advantage of renewable energy availability to perform opportunistic tasks, then exploring the trade-off between energy saving and performance aspects in large-scale distributed system; 3) To investigate energy-aware optical ultra high-speed interconnection networks to exchange large volumes of data (VM memory and storage) over very short periods of time.