dataesr/scientific-paragraphs-categorization huggingface dataset:
In some cases (e. g. , multi-level architecture), an internal ECC, totally transparent to the user, may be present. NOR devices typically have separate buses for addresses and data  [10]. • NAND architecture: The organization of the NAND gate flash is shown in Fig. 3(b ). In this configuration, several groups of floating-gate transistors are connected in series. These groups are then connected via some additional transistors to a NOR-style bit line array in the same way that single transistors are linked in NOR flash. Due to this arrangement, the bit line is pulled low only if all word lines are pulled high (above the threshold voltage of the transistors). This organization of elementary transistors is called NAND flash because transistors are connected in a way which is similar to a NAND gate.
Compared to NOR flash, replacing single transistors with serial-linked groups adds an extra level of addressing. As explained in  [10] , the series arrangement and the great level of parallelism, which is achieved with this organization thanks to the low program/erase currents, give rise to a poor random access time but a very good serial access. Programming of the NAND flash is performed by FN tunneling at the page level (which is typically a few kBytes) and is carried out in about 0. 2 ms  [10]. The erasing operation is performed at the block level (typically a few MBytes) and takes about 2 ms  [10]. Block erasure is carried out also by FN tunneling, but by using opposite polarity. Thanks to these characteristics flash NAND is adapted better for the data storage, where the problems of latency are minor and the random access time is not very important.
Generation/Recombination/Transport in Oxide: The ion strike produces hot holes in the tunnel oxide or inter-poly dielectric and a certain fraction of these hot holes are not recombined in the prompt recombination phase. This model considers that the notrecombined holes  [15]  may drift into the floating gate. This may be possible since the negative electron charge stored on the floating gate itself produces an electrical field across the oxide which attracts holes. These holes that drift in FG are recombined with electrons stored in FG and cause a reduction in the negative charge on the floating gate. At the same time, the electrons produced by the ionizing particle are quickly transported to the silicon bulk or to the control gate due to their high mobility. However, this model lacks sufficient experimental validation because it does not agree quantitatively with data loss measured in FG Flash memory cells  [16].
Trap Assisted Tunneling (TAT): This mechanism is one of the most important causes of oxide wear out due to electrical stress of program/erased cycles of a FG cell. When an ionizing particle strikes the cell, defects are created in the tunnel oxide. These defects may provide a percolation path for the electrons which can thus pass by tunneling effect through the tunnel oxide; therefore, this mechanism is called trap assisted tunneling (TAT). It has been shown that TAT is responsible for retention problems in at least a certain percentage of irradiated devices  [14]. However, a very long time is required to discharge a FG cell by TAT mechanism (a few hours to a few weeks)  [16]. Therefore, the TAT mechanism cannot be responsible for SEU in FG cell taking into account that SEU data are taken immediately after the cell irradiation and then do not change with time  [14].
Description of TIARA-G4 NVM platform The Tool Suite for Radiation Reliability Assessment (TIARA) platform has been developed these last years conjointly at Aix-Marseille University (IM2NP laboratory) and at STMicroelectronics (Central R&D, Crolles). The last version of the code has been called TIARA-G4 in reference to the fact that it is totally rewritten in C++ using Geant4 classes and libraries and compiled as a full Geant4 application  [19]. This major evolution of TIARA allows us to consider now all the complexity of a given integrated circuit in terms of materials, doping and 3D geometry, using the Virtual Geometry Model (VGM  [20] ) factory and interface with both Geant4 for calculation and Root  [21]  for visualization. Up to now, TIARA-G4 has been used to simulate the interaction of Geant4 particles (including high energy and thermal neutrons, protons, muons, alpha-particles and heavy ions) with various SRAM and Flip-Flop architectures  [9].
To test the capability of the code to consider a real geometry, we based our developments on a NOR floating-gate flash memory architecture designed and fabricated by STMicroelectronics using a 90 nm CMOS process. This process is based on a Boro-Phospho-Silicate Glass (BPSG)free Back-End Of Line (BEOL) which eliminates the major source of 10 B in the circuits and drastically reduces the possible interaction between 10 B and low -thermal energy neutrons  [22]. Figure  7 (a) shows a TEM cross-section of the floating-gate devices along the transistor channel and Figure  7 (b) shows a portion of the cell array layout at metal1/metal2 level. The elementary memory cell has an area of 0. 18 µm 2. In TIARA-G4 NVM, the different transistor domains have been modeled as simple axis-aligned box volumes (Geant4 elements) of different materials (silicon, silicon dioxide, ONO and back-end-of-line stack), as illustrated in Figure  7 (c) for a portion of the memory array.
Physical model considered In complement to geometrical aspects, we also implemented in TIARA-G4 NVM a new module describing the charge loss from floating gates after single radiation particles strikes. From the review of the different available models in literature presented in Section 3, our initial choice was to adopt the full physical model of the Transient Carrier Flux (TCF) proposed by Butt and Alam  [14]. The original approach of these authors is therefore based on complex simulations, in particular for the computation of carrier relaxation in the low energy (< 10eV) range, using coupled hydrodynamic and Monte Carlo simulations in order to correctly account for energy relaxation due to phonon scattering and impact ionization. This requires outsourcing from the main code the calculation of the charge loss from FG as a function of the incident particle properties.
  An example of Butt and Alam's simulations is illustrated in Figure  8. Simulated curves very well reproduce experimental data without any fitting parameter (data extracted from Fig. 12  of Ref. [14] ). From a practical point-of-view and in absence of a relatively simple computational solution to implement the Butt and Alam's model, we adopted a pragmatic approach assuming that an ionizing particle of LET striking the FG produces a Number of Electron Loss given by the following analytical function: 2 = ´+ ŃEL A LET B LET (1) Figure  8  shows that Eq. (  1 ) is able to very well reproduce data. Of course, fitting coefficients A and B must be carefully evaluated for each device considered for the simulation from experimental measurements (heavy ion irradiation) or complementary numerical simulation using the Butt and Alam's complete computational procedure.
The next release of TIARA-G4 NVM will integrate such an external dedicated module to confer to the code the capability to simulate a wide variety of NVM devices. [16]  under an oxide electric field of 3. 5 MV/cm. Simulation results from Butt & Alam (Ref. [14] ) are also reported. The full line corresponds to the fitting function (  1 ) on experi-mental data. In the particular case of the present study and by chance, Figure  8  is based on data from Cellere et al. who precisely worked on STMicoelectronics FG arrays. It has been found that device T3 in Ref. [16]  is technologically very close to our circuit, with the same thicknesses for the different layers composing both the FEOL and BEOL stacks. In order to consider values given by Eq. (  1 ) to our memory devices, we introduced a scaling factor coefficient to take into account the difference in the dimensions of the floating gate polysilicon electrodes between devices considered in Fig.
Results and discussion This last section presents the numerical simulations performed with TIARA-G4 NVM for the 90 nm NOR floating-gate flash memory architecture previously described. In a second part, we report experimental measurements obtained from the direct exposition of a large number of circuits to natural radiation. These two sets of data are finally compared and discussed in the last part of this section. It is important to notice that, in the following, all numerical results concerning the characterization and the simulation of the 90 nm flash circuit have been normalized by a common arbitrary scaling factor for confidentiality reasons imposed by the semiconductor manufacturer.
Discussion In this last paragraph, we conclude by comparing these experimental results with predictive values obtained using the TIARA-G4 NVM simulation platform. Figure  20  summarizes this comparison for the different defined SER components. A good agreement is found for the neutron-SER taking into account all experimental and simulation uncertainties, in the first instance, the relatively weak statistics of the experiment in terms of number of events detected. Indeed, despite the duration of the experiment (18 months) and the huge quantity of data to manipulate (the individual V T evolution of more than 50 Gbits of memory cells has been stored and processed), the statistics of this first experiment remains relatively weak because of the extremely low rate of cell flips in this kind of memory.
Conclusion In conclusion, we developed in this work a numerical simulation code (TIARA-G4 NVM) capable of computing the soft-error rate of floating-gate flash memories induced by the two main natural radiation components at ground-level: the atmospheric high-energy neutrons and the alpha-particles emitted from ultra-traces of radioactive contaminants in circuit materials. Based on Geant4 geometry classes, elements and materials, the code is able to reproduce the circuit geometry from silicon substrate to back-end-of-line levels with fidelity. In complement to geometrical aspects, TIARA-G4 NVM also integrates a new module describing the charge loss from floating gates as a function of the properties (LET) of the incident ionizing particles. Using this code, we performed extensive Monte Carlo simulations on large arrays of memory cells (up to 10 5 cells) related to a 90 nm NOR floating-gate flash memory architecture designed by STMicroelectronics.
Acknowledgements The authors would like to acknowledge  G. Just ,  A. Regnier  and  J. L. Ogier  from  STMicroelectronics (Reliability and Electrical Characterization Laboratory, Rousset Plant, France ) for their contribution to the electrical characterization of the flash memory wafers and  Sébastien Sauze  ( ASTEP platform ) for his technical and support. They also sincerely acknowledge their past post-doc students  Sébastien Martinie  (now at  CEA-LETI, Grenoble ) and  Sébastien Serre  (now at  TRAD, Toulouse ) for their different contributions to this work. The logistical support of the  Institute for Radio astronomy at Millimeter Wavelengths (IRAM)  is finally gratefully acknowledged.
2006, Köhler and Huth 2007). Alternatively, in LPJ-GUESS and aDGVM propagule production is, at least partly, a function of assimilated carbon or net primary productivity. In both cases, larger or more productive plants produce more seeds than smaller plants. As growth is determined by environmental conditions, reproduction is only indirectly influenced by external factors. It would be relatively simple to replace the existing reproduction parameters with functions that more directly relate seed production to plant characteristics such as age, and environmental factors such as temperature and precipitation, provided the required empirical data are available. For example, a dynamic carbon allocation approach has already been adopted in aDGVM  (Scheiter and Higgins 2009)  for partitioning among roots, stems and leaves depending upon envi ronmental conditions, and could be adapted to include reproduction.
To reduce the simulation effort for disturbances, the GAPPARD upscaling method was developed  (Scherstjanoi et al. 2013). GAPPARD uses the output of a single patch simulation with no disturbances from bare ground to determine the succession of patch states after a disturbance. Then, together with the probability distribution of the times since disturbance (on the basis of the disturbance frequency), the expectation value of the disturbed forest's state is calculated at each point in time. To account for temporal changes in model forcing (e. g. as a result of climate change), GAPPARD performs a series of non-disturbed simulations under different environmental conditions and interpolates between the results. Applying parameterization methods and the increasing availability of larger databases will help alleviate some of these limitations.
Bayesian methods for model fitting  (Purves et al. 2008 , Hartig et al. 2012 ) provide a framework to estimate parameter values or probability distributions of parameter values. They allow the inclusion of field data of different types in the estimation process  (van Oijen et al. 2005 , Hartig et al. 2012)  which increases the quantity of data that can be used for such purposes. In addition to Bayesian methods, further approaches of inverse modelling are available to identify parameter values for which no information or not enough direct information is available. For example, demographic rate parameters in forest gap models can be tuned so that the simulated mature forest corresponds in its biomass, tree density and species composition to real forests  (Groeneveld et al. 2009). Model parameterization will also benefit from the establishment and expansion of large vegetation databases.
One example is the TRY database that includes information on life history and physiological attributes of plants  (Kattge et al. 2011). Forest inventories are also becoming more available on internet platforms (e. g. USDA forest inventory, French NFF, Smithsonian Inst. , Swiss NFI) offering new possibilities to estimate important plant attributes  (Purves et al. 2008 ) and testing model predictions  (Hurtt et al. 2010). These databases can also be used to re-evaluate the parameters currently being used in DVMs. A different approach to parameterization is that adopted by aDGVM2  (Scheiter et al. 2013 ). Here the model focuses on parameterizing general biophysical processes, such as how transpiration rate is influenced by leaf size and each plant has a trait that evolves within the model that defines its leaf size.
Either human impact processes will have to be added to the model  (Heiri et al. 2009)  or human impact removed from the observational data. The second step is to evaluate potential range shifts simulated by DVMs. This step is more difficult because the involved processes are slow and operate over large temporal and spatial scales. Independent observational data should ideally represent several time-steps. Pollen-reconstructed landscapes are one option  (Williams et al. 2004 ), as are phenomenological observations of recent altitudinal  (Jump et al. 2012)  or latitudinal  (Walther et al. 2005 ) shifts of species. Data requirements have always been a challenge for modelling efforts, but recent developments in Marie Curie IEF (FORECOFUN-SSA PIEF-GA-2010-274798) and CONICYT-PAI-82130046. We extend a special thanks to Signe Normand for her very helpful comments on an earlier version of this manuscript.
Acknowledgements -This paper arose from two workshops entitled 'Advancing concepts and models of species range dynamics: understanding and disentangling processes across scales' for which funding was provided by the  Danish Council for Independent Research -Natural Sciences  (grant no. 10-085056  to S. Normand). JEMSN and MS have been supported by grants of the  Swiss National Science Foundation  ( SNF 315200-122434 ) and the  COST section of the Swiss State Secretariat for Education, Research and Innovation  ( SBF C07. 0123 ). AGG was funded by grants.
By extending SPAWN  [1] , CarTorrent was born. CarTorrent is a BitTorrent-style file swarming protocol applied to the vehicular environment. CarTorrent clients use k-hop limited scope gossiping to disseminate their chunk availability. Gossip messages are propagated up to k hops from the originator. This allows peers to collect information about piece availability as well as local topology. Topology and availability info is used to select the piece to request next. For example, if A and B own a rarest piece that C desires, and A is at a shorter hop distance than B, the query is sent to C. CodeTorrent  [3]  is a further extension of CarTorrent where source and intermediate nodes can encode pieces before sending them out. In CodeTorrent, a peer can decode a file consisting of k chunks as soon as it receives k linearly independent coded chunks.
Related work and future directions In this section, we review previous work in the VANET content distribution area, and discuss future research directions. One of the early contributions to cooperative data retrieval was the system COMBINE by Ananthanarayanan et al. [24]. COM-BINE uses multiple radio access technologies and is specifically designed for collaborative downloading. It integrates neighboring nodes' Wireless Wide Area Network (WWAN) inputs to down-load resources on behalf of an active node. After downloading, the neighboring nodes deliver the data to the active node using WLAN links. Other researches have focused on adaptive video coding as a means to provide good quality video streaming. Leung and Chan proposed a protocol called Collaborative Streaming among Mobiles (COSMOS) using Multiple Description Coding (MDC) in wireless networks  [25].
In-depth interview of the subject by assessors b. Observer rated task including orientation, mobility and day to day activities c. A case study narrative written by the assessors after the assessment. This represented the totality of the assessor's judgment and opinions about the effects of the ARGUS II system on patient's daily life. The data from 30 eligible RP patients implanted with the Argus II Retinal Prosthesis were used to calculate the transition probabilities for our decision analytic model. The model assumed to have Argus II fitted patients moving from low visual acuity to higher visual acuity health states at an annual rate of 10%. This transition was deemed reasonable after personal communication with experts. The Markov model transition probabilities are shown in the Table  1.
Costs The cost of the patient screening for eligibility to Argus II implant, cost of Argus II device, cost of the surgical procedure including medications, instruments and supplies, costs of clinical follow-up and rehabilitation and cost of annual upgrade of the device were provided by the implant manufacturer Second Sight Medical Products Inc. The costs assigned to each Markov state were taken from an article published by Frick et al. [14]. In this article, annual health care cost for RP patients are published for the year 2012. Total mean annual costs per patient were estimated to be 14,988 USD in United States. These costs were converted to 2012 Eurozone costs by using purchasing power parity (PPP) from the Organization for Economic Cooperation and Development (OECD) database  [15]. The total mean annual costs for RP patients in the Eurozone were estimated to be 11,789 Euros for the year 2012.
Simulator-independent descriptions of connectivity in neuronal networks promise greater ease of model sharing, improved reproducibility of simulation results, and reduced programming effort for computational neuroscientists. However, until now, enabling the use of such descriptions in a given simulator in a computationally efficient way has entailed considerable work for simulator developers, which must be repeated for each new connectivity-generating library that is developed. We have developed a generic connection generator interface that provides a standard way to connect a connectivity-generating library to a simulator, such that one library can easily be replaced by another, according to the modeler's needs. We have used the connection generator interface to connect C++ and Python implementations of the previously described connection-set algebra to the NEST simulator.
INTRODUCTION The central nervous systems of vertebrates and many invertebrates have complex patterns of connections. In developing neuronal network models of such systems, there are two main tasks related to connection patterns. The first is to express the connectivity in a machine-readable format, e. g. , in code or in a configuration file. The second is to explain the connectivity unambiguously in prose in an article or book, so that the model can be understood and reproduced by someone else  (Nordlie et al. , 2009). For expressing connectivity in code, three methods are commonly used: (1) procedural code written by the modeler, using low-level operations such as connecting pairs of neurons or connecting a single neuron to a group; (2) a library of pre-defined, parameterized connection routines; (3) an explicit list of connections.
Each of these have their limitations. Procedural code and libraries limit the connectivity descriptions to a single language and often a single simulator, making it hard to port models from one simulator to another. Procedural code takes time to write, and will have more bugs than a library, since library code is likely to be more thoroughly tested and reused. On the other hand, the connectivity patterns available from a library are inevitably more limited than those that can be achieved with user-written code. Using an explicit list of connections is largely independent of a particular simulator or programming language, but may cause problems of storage and input/output efficiency, and does not enable conceptual descriptions of connectivity. All of these methods make it difficult to explain the connectivity in a scientific text.
For a fuller discussion of these issues see  Crook et al. (2012). These problems can be reduced or avoided by using a general purpose connectivity-generating software library such as the connection-set algebra (CSA;  Djurfeldt, 2012)  or the NineML graph library  (Raikov and De Schutter, 2010). Such libraries allow simulator-independent specification of connectivity and enable high-level, declarative descriptions which do not constrain how the connectivity should be realized in code and hence give scope for optimization and parallelization within the simulator software. The CSA in addition supports succinct, unambiguous descriptions of connectivity in text, using a mathematical notation. The adoption of new, simulator-independent methods for expressing connectivity, such as CSA, is hindered by the effort needed to add support for a given connectivity-generating library to a simulator.
This effort must, in general, be repeated for each simulator that aims to support the library. The exception to this is if using a simulator-independent modeling interface such as PyNN (http://www. neuralensemble. org/PyNN; Davison et al. , 2009) , which supports multiple simulators, and could also support multiple connectivity-generating libraries without the need to support it in each simulator. PyNN, however, is written in Python, which means that processing the CSA description will be slower than if using C/C++ (the languages in which many simulators are written). Furthermore, whether using a simulator-independent interface or not, a new programming effort is needed if another new method for expressing connectivity is developed. To minimize the effort required of simulator software developers, and allow modelers to flexibly choose among simulators and libraries which generate connectivity, we have developed a generic connection generator interface, which enables the use of CSA and similar libraries in different neuronal simulators.