dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, for existing methods, splitting data among more machines generally increases communication requirements  [1, 58] , which can severely affect overall runtime. Primal-Dual Convergence. We additionally strengthen the rates by showing stronger primal-dual convergence for both algorithmic frameworks, which are almost tight to their dual-only (or primal-only) counterparts. Primal-dual rates for CoCoA had not previously been analyzed in the general convex case. Our primal-dual rates allow efficient and practical certificates for the optimization quality, e. g. , for stopping criteria. Experimental Results. Finally, we give an extensive experimental comparison highlighting the impact of using various arbitrary solvers locally on each machine on several real-world, distributed datasets. We compare the performance of CoCoA and CoCoA + across these datasets and choices of solvers, in particular illustrating the performance on a massive 280 GB dataset.
2 below, it holds that if α is an optimal solution of (2), then w(α ) is an optimal solution of (1). In particular, strong duality holds between the primal and dual problems. If we define the duality gap function as Gap(α) := P(w(α)) -D(α), (4) then Gap(α ) = 0, which ensures that by solving the dual problem (2) we also solve the original primal problem of interest  (1). As we will later see, there are many benefits to leveraging this primal-dual relationship, including the ability to use the duality gap as a certificate of solution quality, and, in the distributed setting, as a tool through which we can effectively distribute computation. Notation. We assume that to solve problem (2) we have a network of K machines at our disposal. The data {x i , y i } n i=1 is residing on the K machines in a distributed way, with every machine only holding a part of the whole dataset.
Numerical Experiments In this section we explore numerous aspects of our distributed framework and demonstrate its competitive performance in practice. Section 6. 1 first explores the impact of the local solver on overall performance, by comparing examples of various local solvers that can be used in the framework (the improved CoCoA + framework as shown in Algorithms 1 and 2) as well as testing the effect of approximate solution quality. The results indicate that the choice of local solver can have a significant impact on overall performance. In Sections 6. 2 and 6. 3 we further explore framework parameters, looking at the impact of the aggregation parameter ν and the subproblem parameter σ , respectively. Finally, Section 6. 5 demonstrates competitive practical performance of the overall framework on a large 280GB distributed dataset.
We conduct experiments on three datasets of moderate and large size, namely rcv1test, epsilon and splice-site. t  5. The details of these datasets are listed in Table  2. For solving subproblems, we compare numerous local solver methods, as listed in Table 3. Also, we apply Euclidean norm as regularizer g(x) = x 2 for all the experiments. All the algorithms are implemented in C++ with MPI, and experiments are run on a cluster of 4 Amazon EC2 m3. xlarge instances. Our open source code is available online at: https://github. com/optml/CoCoA.
Comparison of Different Local Solvers Here we compare the performance of the various local solvers listed in Table  3. We here show results for quadratic loss function i (a) = 1 2 (a-y i ) 2 with three different values of the regularization parameter, λ=10 -3 , 10 -4 , and 10 -5 , for g(. ) being the default Euclidean squared norm regularizer. The dataset is RCV and we ran the framework for a maximum of T := 100 communication rounds. We set ν = 1 (adding) and choose H which gave the best performance in CPU time (see Table  4 ) for each solver. From Figure  2 , we find that the coordinate descent (CD) local solver always outperforms the other solvers, even though it may be slower than L-BFGS at the beginning. The reason for this is that CD, as compared to the other methods, does not need to spend time evaluating the full (batch) gradient and function values.
Averaging vs. Adding the Local Updates In this section, we compare the performance of our algorithm using two different schemes for aggregating partial updates: adding vs. averaging. This corresponds to comparing two extremes for the parameter ν, either ν := 1 K (averaging partial solutions) or ν := 1 (adding partial solutions). As discussed in Section 4, adding the local updates (ν = 1) will lead to less iterations than taking averaging, due to choosing different σ in the subproblems. We verify this experimentally by considering several of the local solvers listed in Table  3. We show results for RCV dataset, and we apply quadratic loss function with three different choices for the regularization parameter, λ=1e -03, 1e -04, and 1e -05. The experiments in  indicate that the "adding strategy" will always lead to faster convergence than averaging, even though the difference is minimal when we apply a large number of iterations in the local solver.
The Effect of the Subproblem Parameter σ In this section we consider the effect of the choice of the subproblem parameter on convergence (Figure  12 ). We plot duality gap over the number of communications for RCV and epsilon datasets with quadratic loss and set K = 8, λ = 10 -5. For ν = 1 (adding the local updates), we consider several different values of σ , ranging from 1 to 8. The value σ = 8 represents the safe upper bound of νK, as given in Lemma 3. 2. Decreasing σ improves performance in terms of communication until a certain point, after which the algorithm diverges. For the rcvtest dataset, the optimal convergence occurs around σ = 5, and diverges fast for σ ≤ 3. For epsilon dataset, σ around 6 is the best choice and the algorithm will not converge to optimal solution if σ ≤ 5. However, more importantly, the "safe" upper bound of σ := νK = 8 has only slightly worse performance than the practically best (but "un-safe") value of σ.
Performance on a Big Dataset As shown in Figure  14 , we test the algorithm on the splice-site. t dataset, whose size is about 280 GB. We show experiments for three different loss functions , namely logistic loss, hinge loss and least squares loss (see Table  1 ). We set λ = 10 -6 for the squared norm regularizer. The dataset is distributed across K = 4 machines and we use CD as the local solver with H = 50, 000. In all the cases, an optimal solution can be reached in about 20 minutes and again, we observe that setting the aggregation parameter ν := 1 leads to faster convergence than ν := 1 K (averaging). Also, the number of communication rounds for the three different loss functions are almost the same if we set all the other parameters to be same. However, the patterns of duality gap decrease for the three loss functions are different.
Also, a subproblem is to be solved in each iteration to obtain updates. For all these algorithms, all the hyper parameters are tuned manually to optimize their performance. The experiments are conducted on a compute cluster with K = 4 machines. We run all algorithms using two datasets. Since not all methods are primal-based in nature, it is difficult to continue using duality gap as a measure of optimality. Instead, the norm of the gradient of the primal objective function (1) is used to compare the relative quality of the solutions obtained. As shown in Figure  15 , in terns of number of communications, CoCoA + usually converges more rapidly than competing methods during the early iterations, but tends to get slower later on in the iterative process. This illustrates that the Newton-type methods can accelerate in the vicinity of the optimal solution, as expected.
Versions of these models are integrated in more complete forest ecophysiological models simulating the ensemble of forest water and carbon fluxes: (1) MAIDEN (Modeling and Analysis In DENdroecology)  (Gea-Izquierdo et al. , 2015; Misson, 2004) , which contains the isotopic module MAIDENiso  (Danis et al. , 2012) , and (2) MUSICA  (Ogée et al. , 2003 (Ogée et al. , , 2009)). Both account for important postphotosynthetic factors and are able to link photosynthesis and carbohydrate allocation to stem growth. In this paper, we use the MAIDENiso model to decipher the 18 O TR variability in American temperature-sensitive species (Picea mariana in northeastern Canada and Nothofagus pumilio in western Argentina). The selected sites are of special interest for palaeoclimatology given that their 18 O TR chronologies carry strong temperature signals.
Sampling sites and tree ring data Two high-latitude American native species were studied here: (1) Picea mariana (Mill. B. S. P. ; black spruce), which is a conifer widely distributed over the American boreal forest  (Viereck and Johnston, 1990) , and (2) Nothofagus pumilio  (Poepp. et Endl. Krasser; lenga) , which is an angiosperm deciduous species dominating the high-elevation forests along the Patagonian Andes from 35 to 55 S  (Donoso, 1981; Schlatter, 1994). We selected two sites of P. mariana in the centre of the Québec-Labrador Peninsula in northeastern Canada (L01 and L20; from 53 51 0 N-72 24 0 W to 54 33 0 N-71 14 0 W; ⇠ 480 m elevation; see  Gennaretti et al. , 2014, and Naulier et al. , 2014, for details)  and three sites of N. pumilio in northern Patagonia, western Argentina (NUB, ALM and CHA; from 41 09 0 S-71 48 0 W to 41 15 0 S-71 17 0 W; 1270-1610 m elevation; see  Lavergne et al.
2. 4 ). This estimated set of data is referred to in the following as the estimated 18 O P dataset. Second, we run the model with the series of the daily 18 O P derived from two general circulation models (GCMs) with different spatial resolutions and enough available data at our site locations: (1) the Melbourne University model (MUGCM;  Noone and Simmonds, 2002)  forced by varying sea surface temperature (SST) from the HadISST dataset for the 1950-2003 period (2 ⇥ 2 resolution; hereafter referred to as the MUGCM 18 O P dataset), and (2) the Laboratoire de Météorologie Dynamique Zoom model (LMDZ5A;  Hourdin et al. , 2013; Risi et al. , 2010)  with the horizontal winds guided by those of the National Centers for Environmental Protection 20th Century Reanalysis (NCEP20) for the 1950-2008 period  (Compo et al.
, 2011)  (2. 5 ⇥ 3. 75 resolution; hereafter referred to as the LMDZ-NCEP20 18 O P dataset). The final 18 O TR time series are the annual average of the 18 O TR daily values (Eq. 1) weighted by the daily simulated stand gross primary production (GPP), assuming a proportional allocation of carbon to the trunk. For the northeastern Canadian sites, the GPP simulated by MAIDENiso was optimized using observations from an eddy covariance station (see  Gennaretti et al. , 2017a). Unfortunately, such observations were not available for N. pumilio, and therefore the parameterization obtained for the GPP of P. mariana was also used for the western Argentinian sites but constraining the simulations with phenological observations extracted from the literature. For example, to respect the annual cycle of the leaf area index (LAI) for N.
Estimated versus modelled and observed 18 O P values The modelled 18 O P series from the GCMs are similar to the GNIP datasets, with mean values ranging from 12 to 8 ‰ over June-September in northeastern Canada (Fig. S2a ) and from 7 to 3 ‰ over December-April at the western Argentinian sites (Fig. S2b ). In general, 18 O P series from LMDZ-NCEP20 model in western Argentina are slightly displaced toward higher values (+1 ‰) in comparison with the GNIP and MUGCM data. The estimated 18 O P values based on plausible values of coefficients a and b agree well with those of the models and observations in northeastern Canada. For the western Argentinian sites, they are 2-3 ‰ lower from April to October, i. e. late spring-early autumn (Fig. S2 ). O variations   1 ). The horizontal dot lines in (b) are the mean values of the observed 18 O TR.
This suggests that the imprint of the precipitation amount on 18 O P in western Argentina is low and that 18 O P variations are mainly controlled by seasonal changes in temperature, which is in agreement with previous work  (Rozanski et al. , 1995). However, due to the strong west-to-east precipitation gradient in this region (orographic rain shadow), large 18 O P variations occur over short distances  (Rozanski et al. , 1995; Smith and Evans, 2007; Stern and Blisniuk, 2002). Therefore, the daily precipitation dataset extracted from the gridded reanalysis data, which has a low spatial resolution (> 200 km), may not represent the daily variations in precipitation at a local scale O variations faithfully. Therefore, the model may underestimate the contribution of precipitation on 18 O P variability in this particular area.
In contrast, in northeast Canada, both temperature and precipitation amount equally control the 18 O P variations. The high amount of precipitation falling in summer (⇠ 46 %) should have a strong effect and decrease the 18 O P values in the condensed water, while high temperatures counteract this effect by increasing this ratio. Before reaching northeastern Canada, the air masses pushed by the dominant westerly winds discharge most of their humidity over the land, leading to a depleted 18 O P signal at our sites (for the same reason, 18 O TR values at L20, which is located 110 km northeast of L01, are ⇠ 1 ‰ lower). Moreover, the 18 O P signal in the Canadian sites is comparatively more depleted than in the Argentinian sites, because of their higher latitude. It is worth noting that the resolution of the gridded meteorological dataset used for the Canadian sites is relatively high (⇠ 10 km), which means that the local processes are likely well represented.
, 2010) , results at specific sites, especially in mountainous regions such as at our western Argentinian site, can be less accurate (Fig. S2 ; see the offset between GNIP stations and LMDZ-NCEP20). Ideally, daily 18 O P longterm records from meteorological stations in the study region should be used as an input of MAIDENiso. Simulations from high-resolution regional circulation models, such as REMOiso, which has a 0. 5 ⇥ 0. 5 (⇠ 55 km) horizontal resolution  (Insel et al. , 2013; Sturm et al. , 2005 Sturm et al. , , 2007)) , may produce reliable local 18 O P values. Such a dataset has proven to be quite helpful with MAIDENiso in the Fontainebleau forest (France)  (Danis et al. , 2012). However, up to now, measured or REMOiso 18 O P datasets in our regions of study do not exist, which is the case for most regions of the world.
Moreover, early data (1970  from GNIP stations may have been compromised by pan evaporation and therefore isotopic enrichment. Therefore, we recommend that daily GNIP stations are set up in various forested ecosystems, that an effort is accomplished to homogenize older GNIP time series, and that high-resolution simulations of 18 O P are performed in wider regions. The modelling of 18 O TR values based on the estimation of 18 O P is relatively more accurate for northeastern Canada than for western Argentina (Fig. 3 ). As the mean levels of the measured 18 O TR values are high at the western Argentinian sites (mean value of about 30 ‰ ), the Bayesian optimization tends to increase the biochemical (" 0 ) and kinetic (" k ) fractionations as well as the coefficient a, while reducing the dampening factor (f o ) to reach more representative mean levels of the 18 O TR simulation.
Acknowledgements. This study has been co-funded by the research federation ECCOREV. Aliénor Lavergne has been supported by a research associate/lecturer position at the  Aix-Marseille University (France). Fabio Gennaretti  has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 656896. We acknowledge all data providers: the  Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA, Argentina)  for providing the daily temperature data from  La Almohadilla  site; the  National Meteorological Service from Argentina  for providing the monthly temperature data from Bariloche meteorological station (Argentina); the  Department of Natural Resources Canada  for providing the daily climatic data used for Québec; the  US Department of Energy, Office of Science Biological and Environmental Research (BER)  and the  National Oceanic and Atmospheric Administration Climate Program Office  for providing the daily climatic data used for Argentina; and the  SWING  project for providing the daily 18 O P data from MUGCM model.
Code and data availability. The code of the model can be found at https://doi. org/10. 6084/m9. figshare. 5446435. v1 (recent version developed by Gennaretti et al. , 2017). The daily 18 OP data from the MUGCM model were extracted via the SWING project webpage (SWING project, http://paos. colorado. edu/~dcn/SWING/ database. php). The daily climatic data used for Québec were retrieved from the webpage of the Natural Resources Canada products (Natural Resources Canada, http://cfs. nrcan. gc. ca/projects/3/ 4), while those for Argentina were extracted from the Earth Sys-A. Lavergne et al. : Modelling tree ring cellulose 18 O variations tem Research Laboratory (ESRL) of the National Oceanic & Atmospheric Administration (ESRL-NOAA) webpage (ESRL-NOAA, 20th Century Reanalysis V2c, https://www. esrl.
noaa. gov/psd/data/ gridded/data. 20thC_ReanV2c. html). The atmospheric CO 2 concentration data derived from the Mauna Loa station were extracted from the ESRL-NOAA webpage (ESRL-NOAA, Mauna Loa data, http://www. esrl. noaa. gov/gmd/ccgg/trends/). The Supplement related to this article is available online at https://doi. org/10. 5194/cp-13-1515-2017-supplement.
Introduction The rigid airfoil geometries of today's aircrafts are usually the result of a design compromise optimizing the shape only for some parts of flight. Control surfaces are used to adapt the shape for different parts of the mission profile. However, due to the introduction of discontinuities in the shape these control surfaces usually have poor aerodynamic performance and efficiency. The use of deformable or morphing structures has the potential to resolve this issue. Smart-materials hold great potential in achieving the goal of an entirely deformable airfoil structure  [1]. The RTRA supported research program DYNAMORPH and the research platform SMARTWING aim at improving the performance of micro-air-vehicles in realistic environments via electro-active morphing. During the course of this project a prototype NACA4412 wing was developed with embedded Shape memory alloys (SMAs) and trailing-edge Macro-fiber composite actuators enabling both large deformations (~10% of the chord) at limited frequency (<1Hz) and small deformations at high frequencies (≤100Hz)  [2].
This allows us to define an equivalent spring constant keq for the use in the simulation model. To simulate the prototype's response due to the SMA's actuation the previously described structural model will be combined with the phenomenological SMA model as defined by Elahini  [11]. The simulation is created using the Matlab/Simulink platform. The block diagram of the combined SMA-structural model is shown in Figure  3. The model forms an algebraic loop as the SMA's behavior is dependent on both stress and temperature of the actuator. Hence, an iterative solution has to be conducted. The parameters were mainly taken from previous experiments as well as from the literature  [11, 12]  and can be seen in the table above. The equations describing the heat transfer, the phase transformation, the structural dynamics as well as the relationship between stress, strain and temperature in the SMA will only be briefly repeated below for a thorough explanation please refer to Elahini  [11].
Introduction The Internet of Things (IoT) enables physical devices not only to be connected to the virtual world but they can also remotely receive their order from the Internet and act as access points to various Internet services. 1 In that context, things are entities with computing and communication capabilities, so they can be seen as devices having some level of intelligence able to interact with other devices, themselves connected to the global data network. Interconnecting devices together as well as devices to the Internet is a challenging task due to the large number of devices involved. Moreover, the heterogeneity of technologies and hardware require the definition of common languages and standards which allow them to interoperate. Wireless sensor networks (WSNs) are important building blocks of IoT.
Design considerations This section describes the network architecture and presents some major design considerations. The gateway selection procedure is formulated as an integer linear program (ILP) problem under several constraints. Figure  1  shows the envisioned network architecture. Gateways depicted as routers are more robust nodes than those scattered in the target area. They are constantly powered and have sufficient memory and processing resources. In the proposed model, the third version of Raspberry Pi platform 10 is used, thanks to its good hardware features and low acquisition cost. So, it is easier (in terms of investment cost) to deploy a large number of such devices in a given area of interest. Sensor nodes acquire and relay data through multihops wireless links to the border router performed by the gateway.
Let V n 1 , V n 2 , and V n 3 being WSNs, respectively, denoted by color codes gray, red, and blue with the set cardinality jV n 1 j = 14, jV n 2 j = 13, and jV n 3 j = 10. All network nodes (gateways and sensors) are assumed to be stationary once deployed. Every gateway g i i2 1, m ½ ½ has a limited capacity C(g i ) = a i known at the setting up step. This indicates how much traffic the gateway can handle given its bandwidth to access the Internet. For simplicity, all nodes are assumed to produce the same amount of data, thus, each gateway capacity is seen as ''the number of sensors for which the latter can relay data to the Internet. ''.
As a result, it must be opened by the algorithm of selection. Network deep. Due to unreliability of data transmission when using wireless medium, the packet loss rate increases with distance to the base station. To increase the throughput, an additional constraint limiting the length of paths during the routing topology building step is introduced, namely, the maximum hop-count noted MAX Deep. Hence, a given node only uses a gateway if the latter is located at most MAX Deep hops from it. Under the aforementioned constraints, two optimization objectives are targeted: 1. Minimize the number of opened gateways. Some sensor nodes may be in the service area of many gateways. The selected gateway(s) must allow connection of those at the best service costs by optimizing the routing metric (e. g. hop-count, delay, or reliability).
Finally, both inequalities referenced by equation (  7 ) use binary values (0 or 1) for the ILP output variables, X and Y. There are several heuristics  11, 12  to solve the proposed ILP. We opted to use the IBM ILOG CPLEX v 12. 6. 3 libraries. 13 In the latter, the CPLEX optimizer looks for exact solutions for the ILP and restricts the search space using the branch and cut method. The model has been coded in Java programming language.
In the backbone side, the Raspberry Pi 3 is used and acts as both, the gateway and the RPL DODAG root. Indeed, the third version of this platform is a real computer that includes a quad-core ARM Cortex-A53 CPU clocked at 1. 2 GHz with 64-bit data path, 1 GB memory, 1 Ethernet, and IEEE802. 11n wireless local area network (LAN) interfaces. Moreover, it incorporates Bluetooth 4. 1 and the new BLE convenient for IoT usages, in addition to 4 USB, HDMI ports, and more than 40 general-purpose input/output (GPIO) pins. The main reason of this choice is the small size of the device (the scale of a credit card), its powerfulness compared to its low cost and the ability to fully run the gateway interface program without size and other resource restrictions. Figure  4   Network operations WSN operations.