dataesr/scientific-paragraphs-categorization huggingface dataset:
Micro scale relaxation experiments and generalized Maxwell model For our experiments, we use a customized compression test setup based on the commercial FemtoTools™ setup (Fig. 3(a) ). We perform displacement-controlled tests, by adjusting the x,y,z position of the capacitive force sensor and measuring the resulting force. To automate our relaxation tests, we program a custom LabView interface. In our experiments, we used two different sensors (FTS10000, FTS100000, with a maximum force of 10. 000 μN and 100. 000 μN) depending on the required force range. These two sensors have a flat tip area of 50 × 50 μm, which allows compatibility with the compression samples (Fig. 1  stiffness in our experiment. Therefore, there is a non-negligible displacement in the force sensors for the performed experiments.
Note that the GMM is in series with the experimental stiffness E exp. By transforming into the frequency domain, we can obtain the frequency dependent form of the storage and loss modulus. ∑ ∑ The loss factor is defined as the ratio between loss and storage modulus: η = E E ″ ′. For fitting the GMM to a continuous relaxation spectrum from experimental data, we fix the number of branches to 4 and set the relaxation times manually to avoid the ill-posed optimization problem  [40]. A complete analysis over a larger spectrum of temperatures and relaxation times would probably impose the use of a larger number of branches. We use the lsqcurvefit function of Matlab© to fit the 5 missing moduli to the experimental curves. E ω E E ω τ ω τ E ω E ωτ ω τ ′( ) = + 1 + ″( ) = 1 + i n i i i i n i i i ∞ =1 2 2 2 2 =1 2 2 (7).
Finite element modeling The experiments and the calculation of the effective viscoelastic properties of the microlattices have been complemented with detailed time-domain and frequency-domain analysis, using the finite element package Comsol Multiphysics©. We study only the unit cells of the lattices, with applied periodic boundary conditions on the side faces. The bottom surfaces were fixed and we applied either step (relaxation experiment) or harmonic displacement (frequency domain) on the top surface. The frequency domain simulations are performed to study the dissipated energy per cycle in the different lattices. The meshes were based on the sample's geometry as measured in a scanning electron microscope and contained typically 50 × 10 3 terahedral elements with approximately 1 × 10 6 degrees of freedom.
Acknowledgements The authors acknowledge the help of  Wei-Hsun Lin  for programming the experimental software. Funding for this research is provided by the Grant " MechNanoTruss-Mechanical response of polymer nanotruss scaffolds " ( Swiss National Science Foundation  Grant No. 164375 ,  French Agence National pour la Recherche Grant  no. ANR-15-CE29-0024-01 ). A. C. would like to show his appreciation for his sabbatical stay at  ETH  and to acknowledge the funding through the  DGA  mobility grant  ERE 2015 60 0009.
INTRODUCTION Worldwide corporations are mining their data to learn about client purchasing patterns, fraud, credit applications and health care outcome analysis. It appears that the worldwide business intelligence and data warehousing market had about a 60% year-overyear growth. As one expects, there has been a surge in the number of applications that provide DW creation, management and mining. A DW is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process  (Han and Kamber, 2000). During the last decade, significant research efforts have been devoted towards facilitating access to information. In this context, typical relational database systems were optimized for query processing and online transaction processing (OLTP), which minimizes the time needed for systematic daily operations of an organization  (Sawant et al.
Sixteen volunteers were instructed to look for information using an interaction technique involving either a Wiimote TM (monomanual technique) or both a Wiimote TM and a Nunchuck TM (bimanual technique). In the next section, we give a short survey about VR techniques used in the context of Visual Data Mining (VDM). Then we describe some works about 3D interaction techniques in Virtual Environments (VEs). In section 3, we describe the experimental data set and the proposed graphic encoding of these data. Section 4 presents the proposed interaction techniques based on the Nintendo Wiimote TM and Nunchuck TM. Section 5 is dedicated to the experiment and the results analysis. The paper ends by a conclusion and gives some tracks for future work.
Visual Data Mining Visual data mining (VDM) aims to integrate the human in the data exploration process, applying his perceptual abilities to the large data sets available in today's computer systems. The main idea of VDM is to present the data set in some visual form, allowing the human to get into the data, draw conclusions, and directly interact with the data. VDM techniques have proven to be of high value in exploratory data analysis and they also have a high potential for exploring large databases. According to  (Wong and Bergeron, 1997)  the exploratory analysis of data and VDM are not only a set of tools but also a philosophical manner to approach the problem of knowledge discovery. VDM methods have been implemented in VR in several occasions as well as traditional methods for data exploration  (Symanzik et al.
, 1997; Wegman and Symanzik, 2002). In 1999, a research project called 3D Visual Data Mining (3DVDM) was initiated at the VR Media Lab at Aalborg University to study how VR may be used in VDM  (Nagel et al. , 2001; Granum and Musaeus, 2002). Among the facilities of the VR Media Lab are a 3D Power Wall, a 160 degree Panorama, a 6-sided CAVE, and a 16 processor SGI Onyx2. Another project called DIVE-ON (Data mining in an Immersed Virtual Environment Over a Network), uses advance in VR, databases, and distributed computing to experiment with a new approach to VDM. For example, the DWs generated by DIVE-ON were N-dimensional data cubes  (Ammoura et al. , 2001).
DATABASE AND GRAPHIC ENCODING The aim of the graphic encoding is the rewriting of the data in the form of graphic objects by associating each variable in the data with a graphic ones (position, length, surface, color, luminosity, saturation, form, texture, etc. ). Graphic objects can be from zero to three dimensions, i. e. a point, a line, a surface, or a volume. Evolution of the objects in time can introduce an additional dimension. Several authors worked with the classification of the graphic encodings so as to determine those which are most effective according to the data to represent. For the statistical graphs (groups of dots, diagrams, etc), there are works from  (Cleveland and McGill, 1984) , and  (Wilkinson, 1999).
Description of the Database For the experiment, we modelled a simple management of client relationship (CRM) database that gather information enabling the describtion and characterization of customers purchase. This database is primarily characterized by: • the Customer table that is characterized by the identifier, first name, familly name, age, sex, marital status and the number of credits; • the Product, table where each product is identified by a single code and is indicated by the wording, the category of product (fruits, vegetables, drinks), the unit price and stock. This database is described under the Microsoft Access basic management system, and include 200 customers. The Figure  1  illustrates the selected database using the relational model.
Graphic Encoding Each element of the database was translated to a graphic representation (position and size). The proposed graphic encoding of each customer is illustrated in Figure  2. The height of the cone represents the number of purchased items, while the radius of the sphere represents the percentage of the expenditure relativelly to the other customers. The color of spheres and cones were randomly selected in order to easily distinguish each customer. More precisely, a large cone posed with the lower part of a sphere represents a customer whose expenditure is high, while a small cone posed with the lower part of a sphere represents an encoding of a low expenditure customer. Moreover, complementary text labels are posted above each object to give the first name and familly name of the customer.
Each customer has been placed on the right (females) or left (males) side of the VE according to his/her gender (Fig. 3 (a) ). As we can see, this graphic encoding strongly highlights the most active customers. Similarly, the customers have been placed at different depth according to their age, as illustrated in Table  1. The database contains all information about customers' purchases history (last twelves months only) which have been classified according to their categories (cloths, fruits, meat vegetable, yoghurts, fish, cheese, drinks). These data are visualized using three-dimensional histograms representing the amount of each product versus the month they have been purchased. The histograms are positioned similarly to the shelves of a supermarket (Fig. 3 (b) ).
Design and Procedure Sixteen participants (8 males and 8 females) were divided into two groups (G1 and G2). They were aged between 22 and 35 years old (72. 72% are righthanded and 27. 27% are left-handed) and had normal or corrected-to-normal vision capabilities. The experiment was conducted according to the two following conditions: • C1: navigation and selection using Wiimote TM only (Fig. 9  a); • C2: navigation using the Nunchuck TM , selection using the Wiimote TM (Fig. 9 b ). Four different VEs (D1, D2, D3 and D4) each corresponding to a different database were defined. The participants of the group G1 start the exploration of D1 and D2 by using Wiimote TM only (condition C1). Then, they explore D3 and D4 by using Wiimote TM and Nunchuck TM (condition C2). Similarly, the participants of the group G2 start the exploration of D1 and D2 by using Wiimote TM and Nunchuck TM (condition C2), and they also explore D3 and D4 by using Wiimote TM only (condition C1).
In order to facilitate the comprehension of the experience, we give a short description of the task and allow each participant to get acquainted with the system and perform in both conditions (C1 and C2). Each participant was placed in front of a large rear-projected stereoscopic screen (2x2. 5 m) as illustrated in Figure  9 , and equiped with passive (polarized) glasses. A Full HD Optoma HD142X (1080p) projector was used for displaying the images. The participants were instructed to start the task using either the Wiimote TM (C1) or both the Wiimote TM and Nunchuck TM (C2) according to the group they be-long to. At the end of the experiment, we gave each participant a questionnaire in order to get subjective data about the proposed interaction techniques and the graphic encoding of the data warehouse.
Results In this section we present the results of the experimental study. The collected data have been analyzed through a one-way repeated ANOVA. The description of the results is based on three criteria: (1) task completion time, (2) average number of errors (number of best custumers not selected or number of the three most purchased item not selected), and (3) participant's paths, which have been recorded in order to analyse his/her behaviour and strategy or any difficulties concerning the task. Number of Errors. The average number of errors for the condition C1 (monomanual) and C2 (bimanual) were 4. 27 and 3. 73 respectively. A statistical analysis (ANOVA) showed that the interaction technique, either monomanual or bimanual, has no significant effect on error rate.
Land surface models rarely incorporate the terrestrial phosphorus cycle and its interactions with the carbon cycle, despite the extensive scientific debate about the importance of nitrogen and phosphorus supply for future land carbon uptake. We describe a representation of the terrestrial phosphorus cycle for the ORCHIDEE land surface model, and evaluate it with data from nutrient manipulation experiments along a soil formation chronosequence in Hawaii. ORCHIDEE accounts for the influence of the nutritional state of vegetation on tissue nutrient concentrations, photosynthesis, plant growth, biomass allocation, biochemical (phosphatase-mediated) mineralization, and biological nitrogen fixation. Changes in the nutrient content (quality) of litter affect the carbon use efficiency of decomposition and in return the nutrient availability to vegetation.
Model description The land surface model used for this study, ORCHIDEE, is based on two different modules  (Krinner et al. , 2005 , their Fig. 2 ). The first module describes the fast processes such as the soil water budget and the exchanges of energy, water, and CO 2 through photosynthesis between the atmosphere and the biosphere. The second module simulates the carbon dynamics of the terrestrial biosphere and essentially represents processes such as maintenance and growth respiration, carbon allocation, litter decomposition, soil carbon dynamics, and phenology. Global vegetation is described by 13 metaclasses which correspond to plant functional types (PFTs) with a specific parameter set (one for bare soil, eight for forests, two for grasslands, and two for croplands). The major modifications since  Krinner et al.
The calibration of the Langmuir equation for global application represents a major challenge as global datasets on soil phosphorus content are limited  (Yang and Post, 2011)  and parameters cannot be derived with enough confidence. Given the high sensitivity of the dynamics of available phosphorus on the sorption dynamics, we choose a simple but sufficiently constrainable approach. Thus the dynamics of sorbed labile phosphorus (P sorb ) are given by dP sorb dt = k s dP sol dt. ( 20 ) The dynamics of dissolved labile phosphorus (P sol ) are given by (see Appendix A in  Goll et al. , 2012 , for details) dP sol dt = (1 -k s )(F weath -F up + F min + F bcm -τ sorb P sorb -F leach ) (21) where F weath (Eq. A1) is phosphorus release from primary minerals, F up (Eq. 4) is phosphorus uptake by plants, F bcm (Eq.
18) is biochemical mineralization, and F min is the biological mineralization of phosphorus  (Parton et al. , 1993). The fraction of adsorbed to total soil labile phosphorus is derived from a global dataset of soil phosphorus fractions  (Yang et al. , 2013)  and we use USDA soil-order-specific parameter values. Further, we assume a constant rate at which adsorbed mineral phosphorus becomes strongly sorbed (τ sorb ) and is subsequently fixed into secondary minerals. The turnover time of sorbed phosphorus with respect to occlusion is derived from the difference in occluded phosphorus among the sites of the Hawaii chronosequence (Violette, unublished data).
Forcing data The meteorological forcing data for ORCHIDEE are derived from Daily Surface Weather Data on a 1 km Grid for North America (DAYMET), version 3  (Thornton et al. , 2016). The data include meteorological information (short-wave radiation, maximum daily temperature, minimum daily temperature, daily precipitation sum) for the period 1980-2013. In DAYMET, the mean annual surface temperature and annual sum of precipitation for the actual locations of the two sites (Table  3 ) substantially deviate from the values reported at the sites  (Crews et al. , 1995). Therefore, we pick the closest site nearby, Thurston (16 km distance; lat = 19. 8318, lon = -155. 411), which has an annual sum of precipitation of 2500 ± 250 mm yr -1 and an average annual temperature 16 ± 1 • C as reported in  Crews et al.
(1995) , and use it for both sites (as the DAYMET data for the Kokee island did not include any grid point with an appropriate climate). We extract additional information which is needed to run ORCHIDEE, namely surface pressure, long-wave downward radiation, and wind, from a 0. 5 × 0. 5 • reanalysis dataset (CRU-NCEP)  (Harris et al. , 2014)  using the coordinates of the Thurston site. We correct surface pressure from CRU-NCEP with the actual altitude of each site using a lapse rate and derive specific humidity from water pressure, air temperature, and surface pressure. The annual inputs of nutrients by atmospheric deposition and weathering (Table  3 ) are kept constant and are evenly distributed throughout the year.
Conclusions Here, we present the implementation of a terrestrial phosphorus cycle and its interactions with the carbon and nitrogen cycle in the ORCHIDEE land surface model. The model accounts for effects of nutrient stress on tissue nutrient concentration, litter quality, root-to-shoot allocation, and photosynthesis. We further account for root phosphorus uptake and the movement of phosphorus in the soil volume as an additional constraint on soil phosphorus availability to plants, to reduce the sensitivity of plant phosphorus availability to the sorption dynamics which can only be poorly constrained from available data  (Goll et al. , 2012; Reed et al. , 2015). We evaluated the performance of the model at two sites of contrasting nutrient availabilities  (Crews et al. , 1995; Harrington et al.
Acknowledgements. D. S. Goll is funded by the  IMBALANCE-P  project of the  European Research Council  ( ERC-2013-SyG-610028 ). S. Zaehle was supported by the  QUINCY  project of the  European Research Council  ( ERC-2014-CoG-647204 ). We thank two anonymous referees and the editor  H. Sato  for their constructive comments. Further, we thank the TRY initiative and database, which is hosted, developed, and maintained by  J. Kattge  and  G. Boenisch  ( Max Planck Institute for Biogeochemistry, Jena, DE ), for additional leaf stoichiometric data on Metrosideros,  Ying-Ping Wang  and  Ben Houlton  for sharing their data compilation on the Hawaiian sites, and  Sebastiaan Luysseart  for the discussions related to biomass allocation. Edited by: Hisashi Sato Reviewed by:  Hisashi Sato  and one anonymous referee.
We show how an extension of an algorithm due to Howgrave-Graham and Szydlo can be used to recover the key from that relative norm, at least when the absolute norm is easy to factor (which happens for a significant fraction of secret keys). We describe how this leakage can be exploited in practice both on an embedded device (an 8-bit AVR microcontroller) using electromagnetic analysis (EMA), and a desktop computer (recent Intel CPU running Linux) using branch tracing. The latter attack has been mounted against the open source VPN software strongSwan. We also show that other parts of the BLISS signing algorithm can leak secrets not just for a subset of secret keys, but for 100% of them. The BLISS Gaussian sampling algorithm in strongSwan is intrinsically variable time. This would be hard to exploit using a noisy source of leakage like EMA, but branch tracing allows to recover the entire randomness and hence the key: we show that a single execution of the strongSwan signature algorithm is actually sufficient for full key recovery.
Our contributions. Our goal is to look at the security of BLISS against side-channel analysis. Most of the attacks we describe apply in particular to the original proof-of-concept implementation of Ducas et al. [15] , but we specifically target two implementations of a less academic nature: the 8-bit AVR microcontroller implementation of Pöppelmann et al. [46] , as well as the productiongrade implementation included in the open source VPN software strongSwan  [51]. The first source of side-channel leakage that we consider is the clever algorithm proposed in the original BLISS paper  [14]  to perform the rejection sampling, which is intervened in a crucial way in those embedded implementations. To achieve the correct output distribution, the signature generation algorithm has to be restarted with probability: 1 M exp - ∥Sc∥ 2 2σ 2 cosh ⟨z, Sc⟩ σ 2 , where (z, c) is the signature generated so far, S the secret key, σ the Gaussian standard deviation and M a scaling factor ensuring that this probability is always at most 1.
It turns out that the clever algorithm for rejection sampling, based on iterated Bernoulli trials, traverses the bits of the two values ⟨z, Sc⟩ and K -∥Sc∥ 2 (where K is defined such that M = exp K/(2σ 2 ) ) in much the same way as a square-and-multiply algorithm traverses the bits of its exponent: one can basically read those bits on a power or electromagnetic trace! This makes it possible to mount a simple power analysis (SPA) or simple electromagnetic analysis (SEMA) attack on the rejection sampling using either of these values. Similarly, on a desktop platform using a recent Intel CPU, one can similarly read out that sequence of bits from the list of branching instructions executed within the corresponding function, which is recorded in the CPU branch trace store. On Linux, this is accessible using perf_events, which are often available to all userland processes with the same user ID as the program running the BLISS computation (in our case, strongSwan).
Branch tracing attack against strongSwan Experimental setup. As discussed in the introduction, our other target is the open source VPN software strongSwan  [51] , which includes a production grade implementation of BLISS for use in the IKEv2 authentication protocol, as well as for the postquantumsecure signing of X. 509 certificates. The architecture of the BLISS implementation in strongSwan is described in  [50]. We attack the latest stable version of strongSwan as of this writing (5. 5. 2) when compiled and run on a Linux x86-64 desktop platform. We ran our experiments on an Intel Core i7-3770 CPU from the Ivy Bridge family, but they should apply to all Intel CPUs from the past ten years at least, with very recent architectures like Sky Lake providing even greater control. We used Linux kernel version 4.
Access to those hardware functionalities is normally privileged, but can be exposed to userland processes by the operation system. This is in particular the case under Linux through the perf_events interface and the perf command. Branch tracing of a process (belonging to the same user), in particular, can be carried out using the following instruction: perf record -e branches:u -c 1 -d -p ⟨pid⟩ which produces a large binary file (perf. data) containing information about all branching instructions executed in the process with PID ⟨pid⟩ between user space addresses, in the correct order (even if the CPU may execute out-of-order or mispredict branches, the data in perf. data corresponds to the logical execution, so it is in the right order and does not include branch misses). The binary file can then be analyzed using the perf report and perf script commands.
Specifically, perf script -F ip,addr prints a list of lines of the form: 7fd0a2a48884 => 7fd0a2a484a8 indicating the source and destination address of each branching instruction. The use of perf_events by non-privileged users can be restricted, according to the kernel. perf_event_paranoid sysctl setting: if that parameter is set to 2 (most paranoid), the perf record command above will fail without root privileges, which makes our attack rather meaningless. However, at any level below that (1, 0 or -1), the command succeeds, and so does our attack. And the parameter is often not set to the most paranoid level by default. Moreover, there can actually be security reasons not to disallow user space perf_events by ordinary users: for example, the use of PEBS counters has been recommended as a possible way of detecting cache attacks  [27]  and other attacks causing large numbers of cache misses (such as rowhammer  [36] ): see for example the discussion in  [30].