dataesr/scientific-paragraphs-categorization huggingface dataset:
Image data set The data set we have selected in this work is well-known in the remote sensing community. It corresponds to the hyperspectral image of the Washington DC Mall area in the USA acquired by the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor on 23 August 1995. 2, 18, 19 HYDICE is a push broom aircraft sensor system providing spectral information from 400 nm to 2500 nm with an approximate resolution of 10 nm. However, because of strong water absorption and noise, three spectral subdomains (1364. 8-1407. 2 nm, 1814. 48-1926. 31 nm and 2481. 4-2506. 0 nm) have been removed prior to data exploration, reducing the total number of spectral variables to 191. This data cube consists of 1280 × 307 pixels with a spatial resolution of 2. 8 m. 7] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]  In our case, the whole data set of almost 400,000 pixels has been used to show the good scalability of the proposed concept.
Using topological data analysis in remote sensing In topological data analysis, we generate a topological network which represents the shape of the explored data set. In this section, we will discover the way we construct such a network and how to obtain classification maps from it. Figure  1  gives us a global view of the concept. First, each pixel of the image is observed through what we call a "lens". In fact, all functions that produce a value from a pixel can be a lens. It can be selected from different domains such as statistics, geometry, chemometrics and much more. A lens value is then obtained for each pixel. Given the lens value scale, we divide it into overlapping subsets. In this way we partition the data set. We then apply a cluster analysis to each pixel's subset. The single linkage algorithm  42  is often used for this task.
We are thus beginning to construct the topological network. Indeed, each cluster from each pixel's subset is represented by a node. Nodes are also connected by edges when they share at least one pixel. A hot colour node indicates a high number of pixels in the considered cluster. In a final step, the network is split into different groups of pixels according to the node's density and/or particular features of the network shape. Classification maps are then generated. For this work, the Ayasdi software platform (ayasdi. com, Ayasdi Inc. , Menlo Park, CA, USA) was used for TDA. Final classification maps were generated with an additional Python script in connection with the Ayasdi Python SDK.
For obvious reasons, it is impossible to find an optimal look-up table in order to encode each group of pixels in one classification map with good contrast. We thus decided to generate a classification map with the first ten most important groups, a second one with the following ten and so on. It is also a good way to potentially observe features corresponding to very small groups with low numbers of pixels. Figures  3B-F  show classification maps for all considered groups. Corresponding Matlab fig files can be downloaded from the Supplementary Material section of this paper's online abstract at https://doi. org/10. 1255/jsi. 2018. a1 for closer observation. Figure  3A  presents an image of the considered Washington DC Mall area using the 60 th variable (i. e. 758. 89 nm) of the data cube.
The author advises readers to download the Matlab fig files in order to observe such details in the images. We can go even further by detecting smaller fountains, such as the one in front of the National Gallery of Art (R6, 3 pixels) or two others in the courtyard of the US House of Representatives (V2, 2 pixels for the first one and 1 pixel for the second). In this first part of exploration, we observe that TDA is able to discriminate water areas with different depths, but also extract groups with very low numbers of pixels. In Figure  3F , pixels of group 19 seems to be linked with pixels of group 30 (Figure  3B ) with the highest contributions observed on the bank of the Potomac River (A2, B1), the lower part of the Tidal Basin (G2:H2) and to a lesser extent on the border of the Constitution Gardens Pond (E4:5).
AFM observations AFM experiments were carried out using a Bioscope II operating with the NanoScope V controller. For morphological characteriza tions, we used the tapping mode in air and in the repulsive domi nant mode, in order to prevent any damage to the samples. Images were obtained with commercial cantilevers with a spring constant around 40 N/m, at a scan rate of 0. 5 1. 0 Hz and with a resolution of 512 Â 512 pixels. For imaging in liquid (ultrapure water), we used the contact mode, with an applied force around a few nN to pre vent any damage or indentation of the sample. Data were obtained with commercial cantilevers with a spring constant of 0. 12 N/m, at a scan rate of 1. 0 Hz. Different areas were systematically imaged and results shown thereafter are representative of the cells in a given set of colonies and of all the tested samples.
In parallel, we developed a Matlab routine to transduce these curves into those expressing the inden tation force, F, as a function of the deformation, d. These two parameters were calculated according to the following relation ships  [62] : d 0 z z 0 ð Þ d d 0 ð Þ z 6 z 0 z P z 0 F 0 k d d 0 ð Þ z 6 z 0 z P z 0 where (z 0 , d 0 ) are the coordinates of the contact point and k is the cantilever stiffness. Once the curve F = f (d) was obtained, the Young's modulus of the sample was calculated by adjusting this curve with the above formula. Generally, it is estimated that beyond 10% thickness of the cell, the measurement is biased by the intracellular part, which might be modified by the indentation, and potentially the sub strate properties  [62]. In our case, this thickness corresponds to the height of the bacteria with a maximum value around 700 nm (see below).
Acknowledgment The authors thank the  Région Aquitaine and CNRS (France)  for supporting this work through the equipment of the  NanoSpectroI magerie (NSI LOMA ) platform used in this work ( CPER COLA2 ). They are grateful to the  Direction Générale de l'Armement (DGA, Ministère de la Défense, France) , the  Région Aquitaine (France)  and LOMA for their financial support through the  Ph. D. grant of  M. Mathelié Guinlet. Finally , authors thank  NSI LOMA platform  and the  JPK Instruments Company (Berlin, Germany)  for technical.
Algorithms In this paper, we focus on the problem of grouping objects in all snapshots into clusters in an active interactive feedback scheme as described in Sect. 1. To summary, our goals are (1) reduce the number of constraints thus reducing the constraint annotation costs (2) make the algorithm scale well with large datasets and (3) smooth the gap between clustering results of two consecutive snapshots, i. e. , ensure temporal smoothness. The technical details of our methods will be described in Sect. 3.
Experiments Experiments are conducted on a workstation with 4. 0Ghz CPU and 32GB RAM using Java. We use 6 datasets Iris, Ecoli, Seeds, Libras, Optdigits, and Wdbc acquired from the UCI archives. 1 The numbers of clusters k are acquired from the ground truths. Constraint queries are also simulated from the ground truths by adding a must-link if two objects have the same labels or a cannot-link if they have different labels. We use Normalized Mutual Information (NMI)  [33]  (13) sim(C i , C j ) = |C i ∩ C j | |C i ∩ C j |.
Performance of CVQE+ Figure  5  shows comparisons among CVQE+ and existing techniques including kmeans, MPCK-means  [3] , CVQE  [10]  and LCVQE  [36]  over different sets of randomly selected constraints. CVQE+ consistently outperforms or acquires comparable results to CVQE and others for most datasets (except the Libras dataset where it is outperformed by LCVQE), especially when the number of constraints is large. This can be explained by the way CVQE+ assigns objects to clusters. By considering all related constraints while assigning cluster labels for objects, it can better optimize the overall cost function, thus leading to better clustering quality. Compared to its predecessor algorithm CVQE or LCVQE, it deals well with constraint overlap (constraints that share the same objects), which increases with the number of constraints.
Noise Robustness For studying the effect of noisy constraints on CVQE+, we randomly choose some constraints and change them from must-link to cannot-link and vice versa. Figure  6  shows the clustering quality of different algorithms w. r. t. the percentages of noisy constraints from 2 to 8% for real datasets. As we can see, for all algorithms, when the number of noisy constraints increases, the clustering quality decreases accordingly. However, CVQE+ tends to be more affected by noise than its related techniques CVQE and LCVQE. Though its point assignment scheme helps to increase the clustering quality as discussed above, it makes CVQE+ more sensitive to noise since one noisy constraint will affect the assignment cost for all of its related constraints. Nevertheless, in our experimented data, CVQE+ still acquires better (or equivalent) clustering results than CVQE and LCVQE under the same noisy conditions in most cases as seen in Fig.
Active Constraint Selection Figure  8  shows comparisons between Border, NPU  [43] , Huang  [43]  (a modified version of  [17]  for working with non-document data), min-max  [32] , Explorer-consolidate  [2] , and a randomized method (Huang and Consolidate are removed from Fig. 8  for readability). Border acquires better results than others on Libras, Wdbc and Optdigits, comparable results on Iris and Ecoli. For the Seeds dataset, it is outperformed by NPU. The difference is because Border tends to strengthen existing clusters by fortifying both the cluster borders and inter connectivity for groups of objects rather than connecting a single object to existing components like NPU and Huang. Moreover, since it iteratively studies the clustering results for selecting constraints, it has better performance than non-iterative methods like Consolidate and min-max.
Runtime Comparison For studying the runtime of Border on large-scale datasets, we create five synthetics datasets of sizes 2000-10,000 consisting of 5 Gaussian clusters and measure the time for acquiring 100 constraints. The results are shown in Fig. 9. Border is orders of magnitude faster than other methods in selecting pairs to query. For 1000 objects, it takes Border 0. 1 s while NPU and min-max need 439. 4 and 3. 0 s, which is 4394 and 30 times slower than Border. For 10,000 objects, Border, NPU and min-max consumes  Fig. 9  Runtimes of different techniques 0. 18, 5216. 3 and 18. 2 s, respectively. It is due to the fact that Border does not evaluate all pairs of objects at each iteration. Thus, it does much less works than others and faster. Besides, NPU and min-max are implemented in MATLAB which is slower than Border in Java.
Effect of the Constraint Inheritance Scheme Figure  12  shows the effect of the parameter on our algorithm Border via the inheritance scheme. Typically, its performance will increase with until it reaches the peak and then decreases as shown for the dataset Iris. This can be explained by the neighborhood influence scheme of Border. When is large enough, the number of wrong constraints will be increased, thus lower down the performance Fig. 12  The effect of the neighborhood size on the performance of Border 1 3 of Border. However, the peak value of is actually dataset dependence and thus is very hard to predict. Taking the dataset Optdigits as an example, the performance of Border still increases when = 5. However, with = 3 , Border starts perform worse on the dataset Seeds. Unfortunately, the value of is highly data dependent and is hard to select.
Clustering Quality We divide the datasets into different snapshots and measuring the clustering quality using the ground truths of the full datasets. Figure  13  shows the active temporal clustering results for three snapshots of the Optdigits dataset (with = 0. 2 ). As we see, our active learning scheme can help to boost clustering quality inside each snapshot compared to kmeans or CVQE+. With the constraint propagation scheme (Border), the clustering results are further boosted. For example, in Snapshot 2 and 3, Border performs much better than Border without the constraint propagation scheme. Since we only consider forward propagation, the clustering result in Snapshot 3 will be more affected than Snapshot 2 and Snapshot 1. For example, in Snapshot 3 the difference between Border and Border without Propagation is much higher than in Snapshot 2.
Border Fig. 14  The effect of on the smoothness of results on the dataset Optdigits where (C i , S s+1 ) = max C j ∈S s+1 sim(C i , C j ) is the closest clus- ter C j ∈ S i+1 of C i. By summing up all the similarities between two consecutive snapshots, we can measure how smooth the whole clustering results is. Obviously, higher sum means the more consistent the clustering results across snapshots are. Figure  14  shows the overall smoothness of the clustering results of kmeans, CVQE+, and Border wrt. different values of from 0 to 1 on the dataset Optdigits with three snapshots. The linear best fit lines (dotted) indicate that when the value of increases, the overall smoothness among snapshots increases, i. e. , the more consistent the clustering results between two consecutive snapshots. However, the trend is much clearer for kmeans compared to CVQE+ and especially Border.
However, it is limited to a probabilistic document clustering algorithm. NPU  [43]  also uses connected-components of must-link constraints as a guideline for finding most uncertain objects. Constraints are then collected by querying these objects again existing connected components like the Consolidate phase of  [2]. Though more effective than pre-selection ones, these techniques typically have a quadratic runtime which makes them infeasible to cope with large datasets like Border. Moreover, Border relies on border objects around clusters to build constraints rather than must-link graphs  [2, 43]. The inheritance approach is closely related to the constraint propagation in the multi-view clustering algorithm  [13, 14]  for transferring constraints among different views. The major difference is that we use the -nearest neighbors rather than the -neighborhoods which is limited to Gaussian clusters and can lead to an excessive number of constraints.
OSFP Data Our OSFP dataset is acquired from the French national registry of sleep apnea (OSFP). 2  It consists of longitudinal clinical information of many patients suffering from OSA collected from private practices, general hospitals, and university hospitals in France. At each medical visit, data are recorded such as demographic characteristics, comorbidities, and OSA symptoms as well as some environmental risk factors such as smoke, alcohol, and sedentary. In our study, all patients that are over 18 years old and has medium to severe OSA symptoms [characterized by Apnea/Hypopnea Index (AHI) > 15 events/h or Oxygen Desaturation Index (ODI) > 15 events/h] are included. Moreover, patients with incomplete or aberrant data are excluded. At the end, we have 22,568 patients for analyze.
Discussion Throughout this section, we present how our algorithm Border can be used for finding or tracking the evolution of clinical meaningful groups of patients. Currently, our study is done on the largest and generalized collection of patients in the field with wide range of attributes. However, there are still some limitations and potential future works that we are aiming at. First, while we use the whole dataset for studying the heterogeneity of OSA patients, examining more specific patient cohorts may help to reveal more interesting information, e. g. , patients with Oxygen therapy or patients with hypertension and nocturia  [12]. Second, other environmental factors such as air pollutions is known to be related to OSA, especially for children  [24]. Studying how the disease evolve over time wrt.
Conclusion We introduce a scalable novel framework which incorporates an iterative active learning scheme, instance-level and temporal smoothness constraints for coping with large temporal data. Experiments show that our constrained clustering algorithm, CVQE+, performs better than existing techniques such as CVQE  [10] , LCVQE  [36]  and MPC-kmeans  [2]. By exploring border objects and propagating constraints via nearest neighbors, our active learning algorithm, Border, results in good clustering results with much smaller constraint sets compared to other methods such as NPU  [43]  and min-max  [32]. Moreover, it is orders of magnitude faster making it possible to cope with large datasets. Finally, we revisit our approach in the context of evolutionary clustering adding a temporal smoothness constraint and a time-fading factor to our constraint propagation among different data snapshots.
Acknowledgements This work is supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creat iveco mmons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Effect of constraint types on CVQE+ (left), CVQE (middle) and LCVQE (right) for the Ecoli and Libras datasets 0. 9 Ecoli (CVQE) 0. 65 Ecoli (LCVQE) 0. 8 0. 7 0. 7 0. 75 0. 6 0. 8 2 4 6 8 1 0 2 4 6 8 10 Number of constraints (x50) Number of constraints (x50) 0. 9 Libras (CVQE+) 0. 7 Libras (CVQE) 0. 65 Libras (LCVQE) 0. 8 0. 65 0. 6 NMI 0. 7 0. 55 0. 6 0. 6 0. 5 0. 5 0. 55 0. 45 2 4 6 8 1 0 2 4 6 8 1 0 2 4 6 8 10 Number of constraints (x50) Number of constraints (x50) Number of constraints (x50) Must-link 20% Must-link 40% Must-link 60% Must-link 80% Fig. 7 that of LCVQE decreases. This can be explained by the ways they calculate the constraint violation costs for the must-link and especially the cannot-link constraints. LCVQE treats violated cannot-link constraints more properly than CVQE and CVQE+.
Cyber-Physical Systems (CPSs) are complex systems comprising computation, physical, and networking assets. Used in various domains such as manufacturing, agriculture, vehicles, etc. , they blend the control of the virtual and physical worlds. Smart homes are a peculiar type of CPS where the local networking fundamentals have seen little evolution in the past decades, while the context in which home networks operate has drastically evolved. With the advent of the Internet of Things (IoT), the number and diversity of devices connected to our home networks are exploding. Some of those devices are poorly secured and put users' data privacy and security at risk. At the same time, administrating a home network has remained a tedious chore, requiring skills from un-savvy users. We present Future Spaces, an end-to-end hardware-software prototype providing fine-grained control over IoT connectivity to enable easy and secure management of smart homes.
Access control and monitoring mechanisms relying on advanced programmable control points and augmented intelligence are therefore crucial properties for future smart home networks to address the growing concerns in terms of security and privacy while coping with the general lack of expertise of most end-users. A recent trend in network automation is to rely on Software-Defined Networking (SDN). A key feature of SDN is the possibility to support network "slicing", i. e. , creating, on the same physical network infrastructure, a logical separation of traffic into isolated "slices" with differentiated characteristics. For example, FlowVisor  [16]  and GENI  [17] , proposed network virtualization mechanisms for campus and research networks to support different test beds along with production traffic on the same network infrastructure.
The Future Spaces solution addresses these different aspects, by providing simple user controls and automation to pilot the home network through slicing. It goes further by supporting complex distributed and even cross-home scenarios, a feature not proposed by the state-of-the-art to our knowledge-especially for unmodified devices running no additional software (e. g. , VPN client) or specific network configuration (e. g. , VLAN), possibly assigned to multiple slices simultaneously. Some approaches have focused on the security advantages of SDN in home environments through isolation slicing, fine-grained flow control and advanced monitoring and mitigation support  [24, 25]  as well as the possibility to delegate the security of home networks to third parties thanks to programmability  [26].
Problem Statement and Working Assumptions As stated in Section 1, our goal is to provide better control to the user, with easy-to-use concepts and tools to both facilitate usage and management of smart home digital assets, while drastically improving their security. To achieve this, we focus on the fine-grained control of the underlying network through group-based slicing, i. e. , the definition of multiple independent virtual networks on top of the common home network infrastructure to interconnect groups of devices. The Future Spaces solution aims at supporting slices that are multi-location (i. e. , where digital assets can be distributed geographically) and multi-user (i. e. , shared between different users who desire to share their resources, with safeguards). To support commercial, off-the-shelf devices, our working assumption was that no specific software or networking configuration should be expected from the end devices.
The slices realizing the virtual spaces are isolated from each other in terms of data traffic, meaning no traffic is allowed between e. g. , a device in a Multimedia vSpace and a device in a Home Security vSpace. Furthermore, policies may be enforced to prevent unwanted communication when for example an untrusted device is added to a slice containing one or more sensitive devices; • Cross-domain resource sharing, enabling easy discovery of and interaction with third party resources, and securely sharing resources with others. Through simple mechanisms, an authorized user can discover resources of a visited domain and request the establishment of a cross-domain Virtual Space, combining resources of the other domain with some of her own resources, de facto leading to the sharing of devices across authorized users of both domains; • Ease-of-use & automation, allowing intuitive and secure management of all user connected resources and the deployment of task-specific automation processes to support users in different application contexts.
The Future Spaces Solution To enforce the vSpaces, the Future Spaces solution enables the seamless creation of a dynamic connectivity layer, implementing a LAN-like connectivity between components of a vSpace by programming virtualized network functions connecting the corresponding physical devices. We call such slices "Software-Defined LANs" (SD-LANs) and each vSpace definition is ultimately embodied as a SD-LAN. To support this type of connectivity, the solution relies on three core functional components: • a set of Virtualized Network Functions (DHCP, DNS, Internet Gateway and multiple Open vSwitches or OVS  [27] ), running either inside Future Spaces Gateways (FSGW) or the Future Spaces cloud, • a SDN Controller (SDN-C) to pilot the virtual switches,.