dataesr/scientific-paragraphs-categorization huggingface dataset:
The main objective in a cooperation between algorithms is to improve the quality of the Pareto front. One can improve both the search time and the convergence/diversification of the Pareto front. A parallel model for MOEAs may be more efficient than a sequential one even on a single machine. • Resolve large instances of MOPs: parallel MOEAs enable to resolve big instances of MOPs. Those big instances cannot be resolved on a sequential computer. One can also resolve more accurate and then more complex models of MOPs. Enhancing the accuracy of models increases the complexity of the associated problems. Additionally, many MOPs need the handling of big databases such as machine learning of big data. Parallel MOEAs can be used with systems such as Hadoop and Spark which provide significant computing power in the mining of big databases  [2].
• Enhance the robustness: robustness may be measured in terms of solving in an effective manner different MOPs and various instances of a given MOP. It can also represent the sensitivity of the MOEA to its parameters. In this taxonomy a clear difference is made between the design issue and the implementation issue of parallel MOEAs. A unified description of parallel MOEAs is proposed. In the implementation issue, we will focus on the efficiency of parallel MOEAs on various types of parallel machines. The impact of parallel languages and programming environments, such as message passing and shared memory are analyzed. Several criteria from the architecture point of view are taken into account such as: sharing of memory, homogeneity of resources, multiplicity of users, and network locality. Those considered criteria will influence on the development of efficient load balancing and fault-tolerant approaches.
Solution-based model for MOEAs In this parallel model the main goal is to reduce the search time of a MOEA. This model focus on the parallelisation of the functions treating a single solution such as the calculation of objective function and constraints. Two different decomposition strategies may be applied  [26] [9][27] (Fig. 1 ): • Decomposition of data: many big MOPs can be decomposed according to the data involved in the problem. Hence the objective functions and/or the constraints can be evaluated in parallel for each partition  [28]. Depending on the target MOP, a partition can be a geographical area, a data structure or a database. • Decomposition of functions: the objectives or the contraints of a MOP can be decomposed in many sub-functions. Those sub-functions can be handled in parallel by a set of workers.
Implementation issues of parallel MOEAs This section will focus on the efficient implementation of the proposed parallel models of MOEAs on different types of high-performance machines. In a distributed memory machine, the processors have their local memory. The processing elements are coupled by a communication network using various topologies such as rings, hypercubes and fat-trees. This type of machines is much more complicated to program but is more scalable. Communication of information must be handled by message passing. The most popular example of distributed memory machines is the clusters of processors (COWs). Homogeneous/heterogenous machines: the homogeneity of resources (e. g. processors, networks) is an important characteristic that has to be taken into account. One can qualify clusters of processors by the homogenity of the processing elements.
For shared-memory HPC machines, there are three main options: • Multi-threading: a thread is a light process. Multiple threads composing a process share the same memory space. An important issue in multi-threading is the recovery between computation and communication. Multi-threaded programming may be used within libraries (e. g. Pthreads)  [41]  or programming languages (e. g. Java threads)  [42]. • OpemMP and CUDA: OpenMP (Open Multi-Processing) and CUDA are the most popular programming environment based on shared memory concepts. Various programming langages (e. g. Fortran, C and C++) are interfaced with OpenMP and Cuda  [43]. CUDA is used for the GPUs and its portability is limited to Nvidia GPUs. The CUDA framework can be accessed by a directive based language such as OpenACC (Open Accelerators).
Performance assessment of parallel MOEAs Speedup and efficiency are the most popular performance measures to evaluate the scalability of parallel MOEAs  [44]. They define the gain obtained by the parallelization of the program. The speed-up S N is the ratio between the sequential time T 1 on a single processor and the execution time T N on N processors S N = T 1 T N The wall-clock time can be used instead of the CPU time. The wall-clock time can be defined as the whole program time which includes the input and output processes. The CPU time is defined as the time which corresponds to the execution of the program. A super-linear (resp. linear) speedup is achieved if S N > N (resp. S N = N )  [45]. In most of the cases a sub-linear speedup S N < N is performed, caused by the overhead of the communications costs.
This paper illustrates the unified parallel models on evolutionary algorithms. However, the proposed framework can be extended to any population-based metaheuristic such as scatter search, particle swarm, and ant colonies. In the next years, the main perspective is to attain Exascale performance. The emanation of heterogeneous and hybrid HPC machines build of multi-cores and many-cores will accelerate the accomplishment of this goal. In terms of parallel programming environments, cloud computing will become an important alternative to traditional high performance computing for the development of MOEAs that harness large scale computational resources. This is an important challenge as nowadays cloud software frameworks for parallel MOEAs are just emerging. In the future design of high-performance computers, the ratio between power and performance will be important for sustainable supercomputing.
Indeed, no existing diagrams include all of its known components and the interactions between components are often poorly defined. Perhaps the best example available is present in the Reactome database  (16, 17)  (ID: R-HSA-400253), but this still lacks many details of the known biology of the system as described in the literature. There are also numerous mathematical models describing the circadian clock  (18) (19) (20) (21) (22) (23) (24)  and including a recent a kidney clock model  (25). In design, these tend to be even simpler, at least in terms of their inclusion of the known molecular components and representation of the interactions between them, although the mathematics behind the models is often sophisticated. Given the lack of a detailed model for this system, we set out to construct a pathway model of the mammalian circadian clock that reflects the consensus view of all known components of the system in man and the relationships between them.
In order to achieve this aim, we undertook an extensive review of the literature, employing the mEPN modelling language  (26, 27)  to generate a graphical model of the clock, based on the principles of the process diagram  (28). An advantage of the mEPN scheme over other graphical modelling languages is that it also directly supports the use of models for computational modelling, using a stochastic Petri net-based approach  (29). In order that the model reflected the kidney clock, we then examined the CircaDB transcriptomics data (2) to provide us with a list of genes that are expressed in the kidney and identify those that are under circadian control. This information was then used to parameterise the model. The outputs from this study include a detailed analysis of the circadian biology of the kidney transcriptome and a model of the peripheral mammalian circadian clock, which summarises the current knowledge of interactions between its components.
Transcriptomic analysis Gene expression data relating to the murine kidney were taken from CircaDB (2) and examined using a gene correlation network (GCN)-based approach. Clustering of the GCN identified a number of clusters of genes whose expression appeared to be under the regulation of the circadian clock, i. e. their expression coincided with the 12 h night/day cycle. When isolated from non-circadian regulated transcripts, the GCN was made up of two main groupings of genes. One set of genes being comprised of three main clusters (379 transcripts) whose expression was upregulated during the inactive period and another three main clusters (617 transcripts) whose expression was upregulated during the active period. The mouse being nocturnal, their active period is during the night. In both cases, clusters were comprised of genes whose expression peaked at different times during the day and night cycle.
Parameterisation and validation of the model Parameterisation of the model included the addition of delay motifs and input nodes, with the assignment of tokens representing the amount/activity of given components. The final parameterised model contains the same number of molecular species as the descriptive but a significantly increased node and edge count, i. e. 2679 nodes and 2761 edges. Gene expression data from the kidney taken from the CircaDB database was compared to the token accumulation patterns of the mRNA nodes during simulation of the model (Figure  4 ). Simulations, resulted in a similar pattern of activity to the gene expression profiles of the mouse kidney. In a number of cases, the peaks of expression were slightly offset between that predicted and observed, suggesting extra levels of regulation not captured by the model.
the so called classical 'main' transcription-translation feedback loop, the accessory loop and a third system that features the activation of TEF, HLF and DBP and their subsequent regulation of the REV-ERB and PER genes. Where appropriate, generic processes were explicitly depicted, e. g. the transcription, translation and translocation (albeit not in any detail), components were shown in the context of their sub-cellular location, i. e. nucleus or cytoplasm, and manual layout of the model was optimised for 'readability'. All components included in the model were named using standard human nomenclature, although common names are also given, and component nodes were hyperlinked to the NCBI Gene database. Whilst there have been numerous efforts to model the circadian clock previously  (13) (14) (15) (16) (17) , we believe this to be the most comprehensive model ever constructed of this system to date.
The mEPN language supports this through the introduction of nodes, motifs and tokens into a mode l that converts it into a form suitable for running Petri net-based simulations. When loaded into the network modelling software, Graphia Professional, it is possible to quickly run simulations using a stochastic Petri algorithm (SPN)  (46)  and visualise the activity of the system as a whole, or individual components of it. Through empirical testing of the model using an arbitrary number of tokens, we first tested the logic of the system, as inferred through the flow of tokens throughout all parts of the model. This involved ensuring the bipartite structure of the model was maintained throughout, i. e. alternating component nodes (places) and processes nodes (transitions), a perquisite for Petri net-based simulations.
The model currently includes the glucocorticoid receptor, NR3C1, where it binds with CIART and HDAC3 to inhibit the activation E-box containing genes. Glucocorticoids likely activate this receptor rhythmically and this could be one route that the steroids could tune the clock but further experiments are required to address how this may work and plasma glucocorticoid rhythms could be an important future input to add to the model. Furthermore, the model is primarily parameterised based on with mRNA levels but many renal transporters require posttranslational modifications for activation and kinase, phosphatase and ubiquitinylase activity appear to also be rhythmic. It is currently unclear how these acquire their rhythms and indeed how or if they connect and integrate with the clock. These could be added to the model as and when the experimental data becomes available.
Transcriptome analysis of the circadian clock of mouse kidney The CircaDB mouse tissue transcriptomics dataset (2) was downloaded from GEO (GSE54652) and data pertaining to the kidney isolated. A gene correlation network (GCN) analysis using the free network analysis software, Graphia Professional (Kajeka Ltd. , Edinburgh, UK) was performed. Briefly, a. csv file of normalised expression values derived from the kidney collected every 2 h over a 48 h period were loaded into the tool. A correlation threshold of r = 0. 85 was used to construct a GCN, where nodes represent transcripts and edges represent correlations above the set threshold. The network was clustered using the MCL clustering algorithm using an inflation value of 1. 6 that defines the granularity of clustering. Clusters of genes exhibiting circadian regulation (a total of 1061 transcripts), were then reloaded into the Graphia and re-clustered (MCLi 1.
In these cases, the paralogues were 'wired' into the model to reflect their better-characterised counterparts. For example, PER3 regulation has not been extensively studied but it is thought to be important in peripheral tissues  (54)  and was integrated into the core clock in a similar way to PER1. ARNTL2, is thought to be a functional paralogue of ARNTL but is expressed at negligible levels in the kidney (2) and was thus excluded from the model. Nodes representing individual proteins were hyperlinked to the relevant NCBI gene page via the URL field in the 'Data Tab' of yEd and similarly PubMed IDs relating to the evidence supporting a given interaction are included in the description field. All gene and protein components are labelled using their current human genome nomenclature committee (HGNC) symbol, but where a common name is widely used, e.
It was also noted that activation of certain genes took slightly longer than others for reasons as yet unknown. For example, with reference to the CIRCA database, Nr1d1/2 mRNA levels peak considerably earlier than Per1-3 and Cry2 mRNA despite in principle being activated by the same clock-regulated elements (E-BOX and D-BOX). Cry1 and Rorc mRNA peak levels are even further delayed. This is supported by the literature: whereas Per2 promoter and Per2 mRNA have near identical activation profiles, Cry1 expression is delayed by 4 h relative to its promoter  (57, 58). We have added this delay to the model. Rorc and Cry1 have near identical expression patterns and were therefore 'wired up' in exactly the same way. Parameterization of the model was also informed through extensive empirical testing and through further reference to the literature.
Enough time blocks are needed to move the tokens all the way through the pathway network. For example, ~250 time blocks are required for this model to complete a full oscillation of the clock, i. e. one day. For all simulations 1000 time blocks and 200 runs were used as standard, with a uniform distribution and consumptive transitions. Following a simulation, activity flow was visualized using the Graphia's animation control tool. This represents flow through the nodes/places by changing both the colour (through a spectrum) and size (small to large) in line with the accumulation of tokens present over time. Activity flow was also visualized by use of Graphia's built-in plot window providing a static graph of token flow over time in selected nodes (See video, Supp. Data 3). For more details on creating mEPN models and running simulations through them see  (26, 27).
First, we examined the literature to define the major clock genes, referencing the CircaDB kidney expression dataset (above) to define their qualitative and quantitative expression in this organ. Guided by the literature as well as existing models (13) (14) (15) (16) (17) , we then sought to define known interactions between components in order assemble a comprehensive model of the human peripheral circadian clock. In this model we aimed to include all known 'core' clock genes, including their lessercharacterised paralogues, e. g. PER3 and RORB/C and molecules known to be specifically required in the post-translational modification of clock proteins or complexes of them. The use of the modified Edinburgh Pathway Notation (mEPN) scheme allowed us to describe graphically the interactions between all known clock components in unprecedented detail; the final model describing the interactions between 115 individual molecular species (promoters genes, proteins, complexes, biochemical) represented by 178 component nodes, and 163 interactions reported in the literature (Supp.
This is determined by trading off the sensitivity increase realized through sampling more bandwidth at higher precision (see Equation (1)) with the cost of media required to store the data (for a detailed explanation, see  Thompson et al. 2017). For the EHT, a maximum aggregate bandwidth of 16 GHz is set by the characteristics of ALMA receivers. When bandwidth is limited in this way, one examines the increase in sensitivity per additional unit media. For 1-bit recording (two-level sampling), the digital efficiency (η Q in Equation (1)) is 0. 64. Moving to 2-bit recording (four-level sampling) increases η Q to 0. 88 at the expense of doubling the required recording media, for a 38 % sensitivity increase per additional unit media-this is approximately equivalent to doubling the front-end bandwidth and keeping 1-bit sampling.
High-speed Data Recorders The Mark 6 VLBI Data System  (Whitney et al. 2013 ) is a packet recording system used to store the R2DBE output streams to hard drives. The recorder captures the two 8 Gbps streams from the R2DBE using commercial 10 Gbps Ethernet network interface cards. It strips the internet packet headers and stores the payload containing VDIF data frames. For sustained recording at 16 Gbps, each Mark 6 recorder writes the data in time slices across 32 hard drives with a round-robin algorithm. The disks are mounted in groups of eight in four removable modules for ease of handling and shipping. For the EHT, four such recorders are configured in parallel to achieve an aggregate data capture rate of 64 Gbps. Mark 6 recorders and modules are commercially available and in use in other VLBI applications.
Phased Arrays To use the total collecting area available at connectedelement interferometers that participate as single stations in the EHT VLBI network (ALMA, SMA, and, in the future, the Northern Extended Millimeter Array (NOEMA) these arrays coherently add the signal received from the target source by each antenna and record as if from a single antenna. Practical constraints on the maximum data rate that can be recorded at each site require that this summation be performed in real time. Forming a coherent sum requires correcting for deterministic delays, such as geometric and known instrumental delays, as well as non-deterministic delays, which at EHT frequencies are significantly affected by the distribution of water vapor in the atmosphere. The atmospheric delay varies over time, antenna location, and direction so that accurate compensation can only be achieved through the use of in situ calibration methods.
Phasing systems were developed for the SMA and ALMA (see Appendix), and these observatories participated as phased arrays since 2011 and 2017, respectively. A phasing system for NOEMA is in the process of being implemented and commissioned for VLBI. Figure  10  shows the SMA phasing efficiency, i. e. , the fraction of beamformed power compared to ideal phasing, estimated using Equation (2), achieved over the course of several scans during one night of the 2017 EHT campaign. For most of the scans, the efficiency is well above 0. 9 (see inset). Typical phasing performance of the ALMA array can be found in  Matthews et al. (2018). After requantization of the samples to 2 bits, the data are distributed in VDIF packets over a 10 GbE network to Mark 6 recorders. The maximum FPGA clock speed is too slow to process the ADC output stream rate of 4096 megasamples per second in series, so 16 ADC samples are transferred from the sampler in parallel on each FPGA clock cycle and are processed in parallel though the FPGA, which is clocked at a sixteenth of the sample clock, or 256 MHz.
While possible, this requires robust data transfer connections. The most complete in situ full-system check consists of the injection of a test tone at a known frequency in front of the receiver. A short data segment is recorded, and the   autocorrelations and zero-baseline cross-correlations are inspected to verify that the test tone appears at the correct frequency and with the correct profile. Further, the tone in the baseband is mixed down to 10 kHz using a third LO and compared to a 10 kHz tone derived from the maser 10 MHz reference. The two should be phase locked when examined on an oscilloscope, which verifies the phase stability of the whole system. This test is exquisitely sensitive to small disturbances in the system. At the JCMT an equivalent test is done by verification of local connected-element fringes with the SMA.
Monitoring and VLBI Backend Control The EHT developed a centralized monitoring tool called VLBImonitor to visualize observing status, collect ancillary calibration and weather data from each site, and provide realtime and predicted weather information from meteorological services. The information collected, logged, and displayed includes atmospheric and local weather conditions, observatory metadata such as telescope coordinates and on-source status, system temperature measurements, opacity measurements, digital backend and recorder state information, and comments from on-site observers and operators. Communication with the metadata server (by the software clients at each site or via a web interface) uses the JSON-RPC (remote procedure call) protocol over HTTPS. A "masterlist" defines metadata parameters that are accepted (white-listed) by the server and all their properties (e.
PolConvert (Martí-Vidal et al. 2016) is a software routine developed for this purpose as part of the program to phase the ALMA array for VLBI operation  (Matthews et al. 2018; Goddi et al. 2019). This routine forms linear combinations of the cross-polarization-basis correlator products (XR, YR) and (XL, YL) with ±90°phase shifts introduced to the visibility phase to produce complex visibilities in the circular-polarization basis, for each integration time. The input correlator products to PolConvert are equalized in gain and phase, and the X-Y channel phase differences are removed before polarization conversion. This X-Y channel equalization is performed using standard calibration techniques for connected-element ALMA operation (for details see Paper III). To reduce data volumes and increase S/N, the crossproducts computed in the correlator are averaged both in time and frequency.
For EHT observations, the typical averaging bandwidth is 0. 5 MHz and the averaging time is 0. 4 s, allowing residual delays up to ±1 μs and residual rates up to ±2. 5 Hz. These settings enable the isolation and identification of instrumental spectral features, and also the ability to track atmospheric phase variations. This averaging in both time and frequency, results in a decimation of data volumes by more than a factor of 1 million. After the initial correlation, the data are further processed through a pipeline that results in final data products for use in imaging, time-domain analyses, and modeling  (Blackburn et al. 2019; Janssen et al. 2019). During fringe detection, the excess in correlated signal power due to the source on a VLBI baseline is identified, and the complex Fourier component of source brightness distribution is measured.