dataesr/scientific-paragraphs-categorization huggingface dataset:
We did not include these earlier implementations, which are gone from the final artifact, in our work estimates. ) First we wrote the interpreter; we tested it against the compiler codebase (we compile interp with the reference compiler for testing) and tried to support all necessary OCaml features with a simple, readable codebase. Then we looked at the features we used in the interp codebase that were not supported by minicomp -outside the MiniML subset of the time. For each such feature, we had the choice to add it to MiniML and implement it in minicomp, or to rewrite the interpreter to stop using this feature, at the cost of uglier code. In some cases features were motivated not directly by the interpreter codebase, but by their usage in the OCaml standard library for modules we wanted to use in the interpreter.
In particular, we implemented functors to be able to use the Set and Map modules from the OCaml standard library. We want to be able to reuse the OCaml standard library in our interpreter code as much as reasonably possible, in the interest of maintainability. An alternative would be to implement our own specialized sets/maps data structures to not use functors (and in fact we did this in an intermediate version of our code). But while this makes debootstrapping slightly easier, it increases the maintenance burden on the project, duplicating code from the standard library, and it makes it "weird" as an OCaml project. We care very much about the fact that our interpreter can be taken as-is as a "good reference interpreter for OCaml", easy to maintain and usable by other people not interested in bootstrapping.
interp: Interpreting OCaml in MiniML Our first software contribution is interp, an interpreter for OCaml written in MiniML, a reasonably small subset of OCaml. It takes lines of code, took about four humanweeks of work to write, and it covers all features of OCaml used in the standard library and the compiler, which is almost all the features of the language. ⁰ : Nathanaëlle Courant, Julien Lepiller, and Gabriel Scherer The interpreter is written to be reusable in other projects: it strives to be easy to read and maintain. To get a sense of it, we have shown its definition of OCaml "values". Let us describe some technical details that show up in this type definition. We find them (mildly) interesting in that they show that writing an interpreter reveals interesting aspects of the programming-language semantics.
: Debootstrapping without Archeology: Stacked Implementations in Camlboot Note: interp does not embed a parser for the OCaml surface language; it works directly from a parse tree as produced by the compiler parser. This is a natural choice for a reference evaluator, using the compiler as a library to parse in a compatible way, but it also works well in our debootstrapping scenario, where we can execute the compiler parser produced by ocamlyacc and interpret the resulting parse trees. In the rest of this section, we go into the details of two technical issues we had to solve (among others). They support our impression that writing a reference for debootstrapping is full of insights on the language and its implementations.
Foo, or should it use the ugly internal name Lib__Foo? Using Lib from Lib__Bar was originally rejected, as Lib itself mentions Lib__Bar and OCaml does not allow circular dependencies among files / toplevel modules. But users found it inconvenient, so the language semantics was changed slightly to allow it: the -no-alias-deps option, now widely used by default, decides that mentioning a module in the right-hand side of a module "aliasing" (rebinding), such as module Bar Lib__Bar, does not introduce a strong dependency on this module: it is okay if the module is only present "later" in linking order. Of course, any actual usage of the Bar module introduces a strong dependency on its definition Lib__Bar, but merely mentioning Lib from Lib__Bar does not create a circular dependency.
: In the object files (library archives or executables) produced by the bytecode compiler, there is not only the bytecode instructions for the program, but also various side-data such as tables of constant values and debug information. Most of these file formats defined (in the compiler implementation) in a precise way, to be parsed by the bytecode interpreter ocamlrun: a header with a magic word and section tables in a precise binary format, bytecode instructions in a precise byte encoding, etc. But the constant tables and debug information are serialized using OCaml's builtin polymorphic pickling/marshaling operation (input_value and output_value), and inserted as-is in the bytecode object file. This works fine with the reference implementation of the language, which uses the same implementation of (un)marshalling as ocamlrun: they are both using the implementation provided by the OCaml runtime system.
But this does not work with our interpreter, which interprets the marshaling primitives differently: it calls the marshalling function on the interpreter representation of the value, which differs from the native representation, and thus we get different rules. If we use our interpreter to interpret the code of ocamlc, then the call to output_value in ocamlc source code (producing the bytecode artifact) will produce a serialized value in a different format, and ocamlrun will crash when trying to deserialize this part of the bytecode executable and consume it. To work around this issue, we could have modified the implementation of ocamlc and ocamlrun to stop using OCaml's built-in marshaling functions, and instead use a precisely defined binary serialization format for constant tables and debug information.
But this would be a non-trivial change from the reference implementation, and it is unclear that the upstream maintainers would have been willing to integrate it. We would like our debootstrapping process to be maintainable alongside the reference implementation, rather than requiring the application of nontrivial patches to get a debootstrappable compiler. So we decided to give up interpreting the bytecode compiler ocamlc, and interpret the native compiler ocamlopt instead. The native compiler produces binaries in its standard system format (ELF on Linux), without any implementation-defined parts. We had to extend our interpreter with a sizeable amount of language features used in the ocamlopt codebase but not in ocamlc, notably OCaml objects. The backend uses class inheritance to factorize code over several architectures -for example, instruction selection is defined in a main class, that is inherited in each backend to implement architecture-specific refinements.
Build times You will not be surprised to hear that naively interpreting a large, complex program is slow, and that running that interpreter as a naively compiled program becomes really slow. Therefore, we iterated over several build plans in order to make the experiment fast enough to be reproducible. All our build plans start by building ocamlrun from the C sources of the OCaml compiler distribution, which takes around one minute, and then run minicomp to compile interp into interp. minibyte. This step takes around two minutes -in any case, these times are negligible compared to the time taken for the other steps. The complete timings for the build plans are summarized in Table. All times were measured on a machine equipped with an Intel(R) Core(TM) i -U CPU @. GHz CPU ( cores, threads) and GB of RAM.
Improved build plan We improved the code generation of minicomp slightly, which gave us a -% performance improvement in interp. minibyte, and we added an extra step: instead of using interp. minibyte(ocamlopt) to compile the standard library then ocamlc, we would use it to compile interp. opt first, and then use that faster interpreter for the remaining compilation steps. This improved build plan is as follows:. Run interp. minibyte(ocamlopt) to compile the interp into interp. opt. Run interp. opt(ocamlopt) to compile the standard library. Run interp. opt(ocamlopt) to compile ocamlc. This extra step shrinks the total build time from around hours to around hours.
Performance analysis We compare compile times for the interp codebase, without any parallelism: with ocamlopt. opt:. s with ocamlopt. byte:. s (. x slower) with interp. opt(ocamlopt): h mn ( x slower than. s with ocamlopt. byte) with interp. minibyte(ocamlopt): h (. x slower than h with interp. opt) This suggests that the performance gap between interpretation and compilation is much larger than between bad compilation and better compilation. On other (smaller) compile targets, we observed that interp. byte is around. x slower than interp. opt, and interp. minibyte is in turn. x slower than interp. byte. It is interesting that the performance cost of bytecode compared to native code is similar to the performance cost of our "naive bytecode generation" compared to the decent optimization and code-generation work of ocamlc.
This suggests that we could improve performance of our debootstrapping toolchain by improving the bytecode generated by minicomp. Unfortunately, the lack of convenient profiling tools for bytecode programs makes it hard to determine our main sources of inefficiencies in the produced bytecode. Implementing an interpreter in Scheme? Our approach implements both an interpreter and a compiler. Should we instead implement an interpreter directly in another, simpler programming language? To evaluate this design alternative, we implemented a naive Scheme interpreter for a small fragment of OCaml -essentially MiniML. Before doing performance measurements, we did not know whether it would turn out faster or slower than our compiled-interpreter approach: Both interpreters (in OCaml and in Scheme) are implemented in a similar, very naive "reference interpreter" style, without much care for performance; both could probably be optimized with some care.
Our MiniML interpreter is compiled using our naive minicomp compiler, introducing inefficiences compared to a production implementation. In contrast, our Scheme https://github. com/gasche/camlboot/tree/scheme-interpreter, visited on --. : interpreter is run with Guile, which is a reasonable implementation of Scheme. Guile compiles its programs to a bytecode internally (just as our compiler does), and even provides a JIT -performs Just-in-Time code generation. On the other hand, using a dynamic language typically incurs a performance overhead, due to extra dynamic checks and less compact, more self-describing representations of data structure. Some implementations use advanced speculative optimizations to remove this overhead, but Guile is not that sophisticated yet. On a small test program, we observed that interp.
minibyte is. x slower than interp. opt. (This is roughly consistent with our measurements interpreting the whole interp codebase. ) guile interp. scm, our Scheme interpreter running with Guile, is. x slower than interp. minibyte. One could try to run the interpreter with another Scheme implementation, or switch to an implementation language without dynamic overhead, such as C. In any case, our conclusion is that our compiler+interpreter design is in fact sensible, performance-wise. Note that performance was not the only guiding factor in our choice: Writing the interpreter in OCaml itself has value for OCaml programmers needing a reference interpreter. Targeting the OCaml bytecode lets us reuse the OCaml runtime system, which greatly simplifies the implementation of runtime primitives in our interpreter: we can let runtime primitives be implemented by themselves.
Parameter Optimization The SEAPODYM model parameters were estimated for yellowfin tuna for the historical period 1979-2010. Environmental forcings (water temperature and currents, dissolved oxygen concentrations, total vertically-integrated primary production and euphotic depths) were provided by the ocean model NEMO (www. nemoocean. eu/) coupled to the biogeochemical model PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies;  Aumont et al. , 2015). Ocean Acidification (pH) was not included in the environmental forcings for the optimization. The ocean physical model was driven by the ERA40-INTERIM atmospheric reanalysis (atmospheric temperature, zonal and meridional wind speeds, radiative heat fluxes, relative humidity, and precipitation) which has been corrected using satellite data  (Dee et al.
In contrast, the low-resolution model projected in  Matear et al. (2015)  showed a substantial reduction in phytoplankton concentrations and primary productivity. To account for this uncertainty, one additional Decreasing trends in dissolved oxygen concentration have also been detected from historical datasets  (Schmidtko et al. , 2017) , although their origin (observation errors, natural variability or climate change) is not yet clear. To evaluate uncertainty to dissolved oxygen, an additional member of the ensemble (SDO1) used climatological distribution of dissolved oxygen concentration instead of model projection. Taken together, these different simulations produce an ensemble of 18 members (Table  1 ). Impact of these futures was analyzed by extracting the mean and standard errors of biomass of yellowfin tuna for the adults and recruits from each of the simulation ensembles.
RESULTS Reference Simulation From Historical Hindcast  The SEAPODYM solution for Pacific yellowfin tuna was achieved with the full catch-and-effort dataset being raised to the total nominal catch (see Table  S2 ; Figures  S2; S3 ; S4 in Supplementary Material). There was contrast in the final solution for larval distribution with areas of less dense concentrations in the central equatorial region, and higher densities in the eastern and western regions (Figure  3 ). The effect of fishing was predicted to have reduced the adult (spawning) biomass from unfished estimates in 2010 by 45% from 6. 4 mt to 3. 6 mt (Figure  S5a  in Supplementary Material). This fishing impact was highest in the WTPO and ETPO for both the immature (purse-seine caught) and mature (purse-seine and longline caught) components of the population and less prevalent in the CTPO (Figures S5b; S5c in Supplementary Material).
ACKNOWLEDGMENTS We are grateful to  Peter Williams  and  Sylvain Caillot  for their support in processing fishing and tagging data. Darren Parsons ,  Tommy Moore  and  Michael Gillings  participated in a workshop where the initials concept around inclusion of ocean acidification within the SEAPODYM framework were first discussed. Their inputs to this discussion are acknowledged as are the contributions of  Jeanne Wexler  and  Maria Stein  to the implementation of the experiments conducted at the  Achotines Laboratory  (as described in Bromhead et al. , 2015). Graham Pilling  is thanked for his comments on a final draft of this manuscript. We thank the reviewers and editor for their thoughtful and constructive comments.
DATA AVAILABILITY STATEMENT Publicly available datasets were analyzed in this study. This data can be found here: The executable files for SEAPODYM, together with the input files, the outputs and the SEAPODYM manual, are available on a repository at https://osf. io/qa8w4/. The 3D ocean data from the ESMs in netcdf format used to inform the SEAPODYM modelling are available at http://data. umr-lops. fr/ pub/AFCM85/.
For instance, some researchers have developed methods to plan manufacturing system design changes over several ⋆ The present work was conducted within the project ASSISTANT (https://assistant-project. eu/) that is funded by the European Commission, under grant agreement number 101000165,H2020-ICT -38-2020, artificial intelligence for manufacturing. Corresponding author: S. Ehsan Hashemi-Petroodi (seyyedehsan. hashemi-petroodi@imt-atlantique. fr) generations of production  (AlGeddawy and ElMaraghy, 2012; Bryan et al. , 2013; Abbas and ElMaraghy, 2018). Studying the reconfiguration planning is an interesting research topic for an assembly line  (Boysen et al. , 2009) , which aims to design a line able to cope with several generations of the product families, to account for the market demand changes of the product variants and the new product variant added to the production at each generation.
For instance, some researchers have developed methods to plan manufacturing system design changes over several ⋆ The present work was conducted within the project ASSISTANT (https://assistant-project. eu/) that is funded by the European Commission, under grant agreement number 101000165,H2020-ICT -38-2020, artificial intelligence for manufacturing. Corresponding author: S. Ehsan Hashemi-Petroodi (seyyedehsan. hashemi-petroodi@imt-atlantique. fr) generations of production (AlGeddawy and ElMaraghy, 2012;  Bryan et al. , 2013; Abbas and ElMaraghy, 2018). Studying the reconfiguration planning is an interesting research topic for an assembly line  (Boysen et al. , 2009) , which aims to design a line able to cope with several generations of the product families, to account for the market demand changes of the product variants and the new product variant added to the production at each generation.
Software interface XMapTools is a MATLAB based Graphical User Interface developed by Lanari et al. [27, 28]. This software was created firstly to convert Xray qualitative electron microprobe or SEM maps (X-ray intensities) into quantitative maps, in oxide weights percentage, by using the Castaing approach (EPMA)  [26, 27, 43]. The quantification of X-ray intensity image in XMapTools is based on a calibration between the map intensity and electron microprobe quantitative individual analyses that will be used as internal standards. The required data to quantify EPMA X-ray intensity maps are (i) elemental maps files and (ii) a file that includes the available individual EPMA analyses (Standards) with their coordinates as well as the maps coordinates. This file will be used for the standardization process  [29, 30].
SEVIL supports a decentralized data certification and a centralized multi-signer data aggregated and batch verification. Indeed, SEVIL allows a signer, belonging to a trusted group, to generate anonymous group signature (i. e. , NIWI proof). Relying on this proof, the signer is able to prove to a verifier the integrity of the data without revealing the signing keys. For efficiency reasons, the verifier is given the capacity to proceed to the verification of multiple proofs at once. If the batch verification fails, the verifier proceeds to a divide and conquer verification. It splits the list of proofs into sub-lists and performs verification to each sub-list recursively until all invalid signatures are identified. In a nutshell, SEVIL satisfies several properties of interest. First, it ensures data integrity without revealing the data contents, thanks to the use of proofs of knowledge.
Globally, the architecture is composed of ephemeral and stable resources. Each of these two resource pools is formed by a set of hosts. The hosts forming the ephemeral pool deploy the following two modules: Forecasting Builder  [2] : it predicts the next 24 hours of future resource utilization at a host-level. The amount of available resources is then inferred from hosts capacities. QoS Controller  [3] ,  [5] : it ensures that the SLA of regular customers who have reserved resources on the host are respected. It does so by reducing the utilization of ephemeral resources if their owners (i. e. , regular customers) need them. The Operator deploys two modules (our contribution): Volatility Calculator: this module is used to reduce the complexity of the resource allocation process. It summarizes the multiple points (i.
B. Volatility Calculator The Forecasting Builder predicts future resource utilization for every host. Having the hosts' predictions for the next 24 hours as input for the RLAllocator is costly as the size increases with the number of hosts. Through the Volatility Calculator module, our goal is to reduce the verbose information and only provide a single value that represents the volatility rate of losing resources in the ephemeral pool. The volatility rate provides a summary of prediction errors to the RLAllocator. The latter can then make allocation decisions based on the provided information. The module receives as input both the past prediction and utilization of resources during a Δt window (e. g. , 24 hours). The module then computes the volatility rate p ∈ [0, 1] of the ephemeral pool.
Finally, the module outputs the value for the RLAllocator. Table  I  shows an example of traces with predictions from the Forecasting Builder for a time window of Δt=24h with a 3-minute sampling period (which makes 480 periods). It contains the following for both the CPU and memory metrics: • Actual measures of utilization: y metric • Predictions of future resource utilization: � y metric • Prediction errors: e metric = � y metricy metric The volatility rate represents the probability of underestimating the amount of resource utilization. In other words, resources are lost if � y metric < y metric and the amount of resources lost is proportional to the prediction error. To calculate this probability, a random variable z is used where z t represents whether the predictions underestimated the CPU or memory usage at time t.
III. EXPERIMENTAL EVALUATION We try to answer the following Research Questions (RQ): RQ1: What is the overall performance of RISCLESS in terms of resource utilization, SLA violations, and CPs' profits? RQ2: How many on-demand stable resources does RISCLESS use on top of the ephemeral ones to reduce SLA violations ? A. Experimental setup 1) Datasets: the traces used were extracted from three datacenters. One is from a University, and two from Private Companies labeled PC-1 and PC-2. The traces were recorded over 6 months with a 3-minute sampling period  [2] ,  [3] ,  [11]. 2) Resource allocation approaches: RISCLESS was compared to the following approaches: Fixed: [3]: This approach uses a static safety margin percentage of 5% selected empirically from different datasets. Scavenger  [4]. The mean and standard deviation of resource utilization history is used to compute a dynamic safety margin.
The contribution to the muon anomaly in this model is 15 a HVP µ (model) = - α π 1 0 dx (1 -x) Π model x 2 1 -x m 2 µ = 6992. 4 × 10 -11 , (5. 6) and the shape of the integrand is shown in figure  5. We next propose to fix the values of the parameters A(n, L) and B(2p -1) of the reconstruction approximants in eq. (4. 1), from a linear fit to "data-points" provided by the Π model (Q 2 ) function. We choose for that a set of x-points in the interval 0. 23 ≤ x ≤ 0. 93 in fifty equal steps; a choice motivated by the x-region where the MUonE experiment expects to have the best quality of measurements  [31]. We have used the Mathematica nlm code to do the linear fits, with the following results: JHEP05(2022)084.
elegans recombinant inbred lines  11  is due to unintended developmental variation and that almost 38% of the datasets that did not intend to include development in a C. elegans gene expression database  12  show substantial developmental variation in gene expression  10. Identifying hidden developmental variation and estimating developmental time of samples is important first to quantify the impact of the perturbation of interest on developmental speed (Fig. 1a ); second, to distinguish perturbation-specific from unspecific gene expression changes due to development (Fig. 1b ); third, to uncover time specific effects of the perturbations under study  13  by including estimated age as a covariate in differential expression analyses (Fig. 1c ). In yeast, analogous ideas successfully identified genetic and environmental perturbations impacting specific phases of the cell cycle  14  and direct and specific effects of 700 gene deletions on gene expression after removing the main source of variance (25%): a shared expression signature of cell cycle and growth rate  15.