dataesr/scientific-paragraphs-categorization huggingface dataset:
From these selected tools, another difficulty is the license software (D4). A same FPGA vendor offers several FPGA tools that can be integrated in the design flow. Buying an FPGA development kit enables the hardware designer to get specific licensed FPGA software with unlimited time and some specific functionalities. e hardware designer can also buy FPGA software without buying hardware platforms. He obtains the software with limited use in time. As an example, Intel provides to their hardware designers a set of FPGA development kit, the Stratix 10 GX, with one year license software for the associated tools, the Quartus Prime Pro design software. For a design with multiple designers, a floating point license with several seats is mandatory. For selected tools, a huge number of Tool parameters (D5) must be set.
4. 1. FPGA Software Tools as a Service. FPGA designs require very often several iterations before achieving timing closure. Synthesis and place-and-route tools include many parameters to control area and timing performances, and there is no one approach to achieve an optimized design. As designs are getting more and more complex, hardware designers require high compute capability to reduce design cycle, even with powerful workstations. With the adoption of management software by the industry such as Load Sharing Facilities (LSF)  [48]  or Sun Grid Engine (SGE)  [49] , FPGA design tools allow native control of computer bays. Quartus Design Space Explorer (DES), Xilinx Vivado, or ISE SmartExplorer use LSF to distribute multiple compilations. Cloud vendors support many versions of FPGA tools  [50]  (Quartus-II 13.
0, Quartus Prime Standard/Pro Edition 15. 1, SDSOC SDACCEL 2017. 4, and Vivado 2017. 2. 1). We present major products that put the FPGA design flows and tools in the cloud without requiring any setup and maintenance for the cloud user. To speed up the synthesis phase, Synopsys' Synplify Premier Synthesis tool uses the Common Distributed Processing Library (CDPL). Synplify Premier splits the design into submodules and uses CDPL to synthesize them separately. e result of each submodule merges into a single netlist, and the global report is available on Synplify Premier. Figure  3  has an open view of all the components of the FPGA service. In local, the hardware designers design, select, and integrate IPs to design the FPGA architecture. ey upload the FPGA design to the cloud. In the cloud, the existing FPGA tools synthesize and place and route the designs with several parameters of tools and for several FPGA devices and platforms if needed.
As a result, the application designer uses one of these platforms remotely by using an Ethernet controller to benefit from highest performances and a low cost solution. Amazon provides an FPGA virtual machine image for its cloud FPGA instances, where users can easily develop and deploy FPGA acceleration applications  [71]. Recently, Microsoft company presented AccelNet  [72] , which is an FPGA-based platform for host SDN processing supported by the software and the hardware infrastructure of the previous catapult project. Microsoft Azure also offers an FPGA-based platform to enable application designers to deploy machine learning applications. A key challenge is the multitenancy to efficiently share the FPGA while enforcing strict data and performance isolation between tenants. Alibaba  [73]  cloud officially launched three generations of large-scale FPGA instances, respectively, Ali F1, Ali F2, and Ali F3 based on Intel and Xilinx FPGAs to achieve strong isolation between IP acceleration and the deployment environment.
Multinode Level. A multinode is defined as a cluster of more FPGAs. For this level, we consider techniques and architectures used to connect multiple FPGAs for accelerating a framework like InAccel  [80]. Kirchgessner et al. [39]  use a uniform hardware/software interface for multi-FPGA communications. e work is based on the portability of a set of open IP cores and tools across three platforms (GiDEL PROCStar III (4 FPGA Altera Stratix III), Pico Computing M501 (a single Xilinx Virtex-6), and Nallatech H101 (a single Xilinx Virtex-4)). is Virtual RC platform allows the application designer to synthesize the same design using ROCCC  [81] , Vivado HLS. is work is inappropriate for cloud data center because it is domain specific, and results require huge area and high delay overheads. In  [82] , Asghari et al.
propose a scalable infrastructure based on HLS as a Service  [83]  for multiapplication designers. In the cloud infrastructure, there is an FPGA pool of several FPGA platforms, FPGA synthesis tools that are managed by a hypervisor. Authors use the Openstack to manage the high-level programs. ere are several methods of FPGA virtualization to share FPGA resources between multiapplication designers to reduce cost and enable the flexibility of the system. Table  5  summarizes several studies and surveys on FPGA Resources used as a Service with relevant metrics. At the hardware resource level, the reconfigurable resource is based on the FPGA architecture and the mapping of accelerators. Hence, the nonreconfigurable resource is based mostly on the I/O resources that exist in the CPU/software domain.
Conclusions e paper presents an overview of the cloud FPGA concept that is based on the cloud computing paradigm. Using FPGA as a Service has emerged to become an active research which has attracted the attention of many researchers and industrials. In brief, we outline the starting of putting FPGA in data centers to support their infrastructure and to solve some cloud issues. Despite the major drawbacks of designing and using FPGA in local for one hardware designer, FPGAs present a compelling alternative for the cloud to be used as a service. e purpose of this work is to provide a general use of FPGA to any designer (not domain specific) and support the pooling of tools, platforms, and resources. ree main classifications for cloud FPGA are discussed. e first classification extracts the context of SaaS through FPGA software tools.
Inverse planning quality validation The planning quality of the proposed method (DVH-FSA) was evaluated on a retrospective clinical cohort study. Planning data of a population of 18 randomly selected patients who underwent LDR prostate brachytherapy were extracted from the commercial inverse planning system's database. All patients received treatment during the year 2016. DVH-FSA and clinical plans were compared to measure the expected performance of the proposed algorithm in a clinical setting. The clinical plans were calculated using the commercial inverse planning system VariSeed v9. 0 (Varian Medical Systems, Palo Alto, CA) (using the default surface optimization module). The quality of the clinical and the DVH-FSA plans was compared regarding diversion from the AAPM TG-137 report's guidelines.
0% for an edema magnitude of 27% AE 9%. 43 Our future objective is to consider the edemarelated postoperative dose variations within the inverse planning step. This will be addressed by introducing within the optimization process a previously developed biomechanical model for edema prediction. 44 Additionally, in future work, we aim to evaluate the proposed methodology in a clinical setting. Therefore, we have developed the inverse planning software TiTAN based on the proposed DVH-FSA algorithm.
D, Schematic of electrode decomposition. The data was represented as a matrix, where each row contains the full response timecourse of each electrode across all 165 sounds tested (the data matrix included responses from 271 sound-responsive electrodes, defined as having a test-retest correlation greater than 0. 2). For each sound, we measured responses from a three-second window time-locked to the onset of each sound. The data matrix was approximated as the product of two component matrices: a electrode weight matrix expressing the contribution of each component to each electrode, and a response matrix containing the response timecourse of each component to the sound set. E, Cross-validation was used to compare models  (Fig S2C)  and determine the number of components. The data matrix was divided into cells, with one cell containing the response timecourse of a single electrode to a single sound.
Using a subset of data, we identified electrodes selective for speech, music or song, and then measured their response in independent data. The electrode selection stage involved three steps (all performed on the same data and distinct from that used to measure the response). First, we measured the average response across time and stimuli to all sound categories with more than five exemplars. Second, we identified a pool of electrodes with a highly selective (selectivity > 0. 6) and significant (p < 0. 001 via bootstrapping) response to either speech, music or song. Selectivity was measured by contrasting the maximum response across all speech and music categories (English speech, foreign, speech, vocal music, instrumental music) with the maximum response across all other non-music and non-speech categories.
Preprocessing. Preprocessing was implemented in MATLAB. The scripts used to implement the preprocessing steps are available here (we reference specific scripts within these directories in describing our analyses): https://github. com/snormanhaignere/ecog-analysis-code https://github. com/snormanhaignere/general-analysis-code The responses from all electrodes were common-average referenced to the grand mean across all electrodes (separately for each subject). We excluded noisy electrodes from the common-average reference by detecting anomalies in the 60 Hz power (see channel_selection_from_60Hz_noise. m; a IIR resonance filter with a 3dB down bandwidth of 0. 6 Hz was used to measure the RMS power). Specifically, we excluded electrodes whose 60 Hz power exceeded 5 standard deviations of the median across electrodes.
m). The envelope was measured as the absolute value of the analytic signal after bandpassing. For the single-electrode analyses (Fig  4 ), we downsampled the envelopes to 100 Hz (from the 1200 Hz recording rate), and smoothed the timecourses with a 50 ms FWHM kernel to reduce noise and make it easier to distinguish the timecourses for different categories in the plots. For component analyses, we downsampled the envelopes to 25 Hz, because this enabled us to fit the data in the limited memory available on the GPU used to perform the optimization (no smoothing was applied since the model inferred an appropriate smoothing kernel for each component). Occasionally, we observed visually obvious artifacts in the broadband gamma power for a small number of timepoints. These artifacts were typically localized to a small fraction of electrodes; thus, we detected artifacts separately for each electrode.
We also manually excluded some or all of the runs from 11 electrodes where there were a large number of artifacts. For each 2-second stimulus, we measured the response of each electrode during a three-second window locked to sound onset (for the 4-second natural and modulation-matched stimuli we used a 5-second window). We detected the onset of sound in each stimulus by computing the waveform power in 10 ms bins (with a 2 ms hop), and selecting the first bin in which the audio power exceeded 50 dB of the maximum power across all windows and stimuli. Following standard practice, the audio and ECoG data were synced by sending a copy of the audio signal to the same system used to record ECoG signals. This setup allowed us to measure the time delay between when the system initiated a trial and the onset of sound (which we measured using pure tones).
Seven electrodes passed our conservative significance threshold. For these electrodes, we simply treated the data from different sessions as coming from different electrodes, since they likely sampled distinct neural populations. Electrode selection. We selected electrodes with a reliable response to the sound set. Specifically, we measured the test-retest correlation of each electrode's broadband gamma response timecourse across all sounds, measured in two splits of data (odd and even runs). We kept all electrodes with a test-retest correlation greater than 0. 2 (electrodes with a test-retest correlation less than 0. 2 were quite noisy upon inspection). Results were similar using a more liberal threshold of 0. 1. Electrode localization. We localized electrodes in order to be able to visualize the electrode weights for each component.
To minimize gross errors, we preferentially localized sound-driven electrodes to regions where sound-driven responses are likely to occur. Specifically, using a recently collected fMRI dataset, where responses were measured to the same 165 sounds in a large cohort of 20 subjects with whole-brain coverage (our prior published study only had partial brain coverage (Norman-Haignere et al. , 2015)), we calculated the fraction of subjects that showed a significant response to sound at each point in the brain (p < 10 -5 , measured using a permutation test  (Norman-Haignere et al. , 2016) ). We treated this map as a prior and multiplied it by a likelihood map, computed separately for each electrode based on the distance of that electrode to each point on the cortical surface (using a Gaussian error distribution; 10 mm FWHM).
, 2009). We investigated each of these statistical properties in broadband gamma responses to sound (Fig  S1 ). To evaluate non-negativity, we measured the percent of the total RMS power accounted for by positive vs. negative responses during the presentation of sound (measured relative to 300 ms of silence preceding the onset of each sound): 1 where  and  are shorthand for positive and negative values. We applied the above equation to the response of individual electrodes (summing over all timepoints for all sounds; Fig S1A  , B ), as well as to the pooled response of all sound-responsive electrodes (summing over all timepoints, sounds, and electrodes; Fig S1D). To minimize the effect of measurement noise, which will create negative values even if none are present (since measurement noise will not depend on the stimulus and thus noise fluctuations will be symmetric around the silent baseline), we measured the response of all electrodes in two splits of data (average across odd and even runs).
To investigate whether and how the distribution of responses might differ from a Gaussian, we measured the skewness (normalized 3 rd moment) and sparsity (excess kurtosis relative to a Gaussian) of the responses: 2 3 100 ⇤ s P p 2 P p 2 + P n 2 We applied the above equations to the response distribution of each electrode across all timepoints and sounds (i. e. concatenating the timecourses from all sounds into a single vector), denoised using the procedure described in the preceding paragraph. Fig S1F plots a histogram of these skewness and sparsity values across all electrodes. We found that all electrodes were skewed and sparse relative to a Gaussian, and relative to what would be expected given just noise in the data (p < 0. 001 via sign test; see Statistics for details). This observation implies that the response distribution of each electrode across time/stimuli has a heavy rightward tail, with a relatively small fraction of timepoints yielding large responses for any given electrode.
We also tested the skewness and sparsity of responses across electrodes by applying equations 2 and 3 to the distribution of responses across electrodes. Specifically, we measured the averaged response of each electrode to each sound, and then for each sound, we applied equations 2 and 3 to the distribution of responses across the 271 sound-responsive electrodes. Fig S1G plots the histogram of these skewness and sparsity measures for all 165 sounds. We did not apply our denoising procedure since we only had 271 electrodes which made the sorting/median-filtering procedure infeasible (in contrast, for each electrode we had 12,375 timepoints across all sounds); instead we time-averaged the response of each electrode to each sound to reduce noise. We again found that this distribution was significantly skewed and sparse relative to a Gaussian and relative to what would be expected given just noise in the data (p < 0.
Components that are stable should be consistently present for all solutions with low cost, which we quantified by correlating the component response profiles for the solution with the lowest cost with those for the 99 next-best solutions (using the "Hungarian algorithm" to determine the correspondence between components from different solutions  (Kuhn, 1955) ). As a measure of stability, we computed the median correlation value for each component across the 99 next-best solutions (Fig  1G ). The responses and weights shown are from the model with the lowest cost. We ordered components based on their total contribution to explaining the data matrix, measured by summing the response timecourse and electrode weights for each component, and then multiplying them together: 7 min {w k,i },{a k },{h k } X i e i (t) K X k=1 r k (t)w i,k 2 + ⇣ X i K X k=1 w i,k + K X k=1 X t a k (t) ⌘ ( X t r k (t))( X i w i,k ) Cross-validation analyses.
We thus subdivided the response to each stimulus into seven 500 ms segments (discarding the first 500 ms to account for the build-up in the response), and measured the average response to each segment. For both the song-selective component and the average response of song-selective electrodes, we found that for all fourteen 500-ms segments (7 segments across 2 stimuli), the response to natural vocal music was higher than the response to the modulation-matched controls, and thus is significant with a sign test (p < 0. 001). To determine whether the electrode responses were significantly more skewed and sparse than would be expected given noise (i. e. to evaluate the significance of the skewness/sparsity measures described in Response statistics relevant to component modeling), we computed skewness/sparsity using two data quantities: (1) the residual error after subtracting the response to even and odd runs; and (2) the summed response across even and odd runs.
Using a sign test across sounds, we found both the skewness and sparsity of the summed response to be significantly greater than that for the residual error (p < 0. 001). indicate an enhanced response to sound, and negative responses indicate a suppressed response (blue). The color scales shows values from 0 to the 99 th percentile of the response magnitude distribution for each electrode. B, Distribution of response magnitudes, measured in a cross-validated fashion to reduce effects of noise: using data from the odd runs, we sorted all of the bins of the raster on the left based on their magnitude (pooling across all timepoints and stimuli). The response of each bin was then measured using the even runs, and then smoothed using a median filter to suppress noise. Positive responses accounted for >99% of the RMS response power in all three electrodes.
Acknowledgements This work was supported by the  National Institutes of Health  ( EY13455  to N. G. K. ,  P41-EB018783  to G. S. ,  P50-MH109429  to G. S. ,  R01-EB026439  to G. S. ,  U24-NS109103  to G. S. ,  U01-NS108916  to G. S. , and  R25-HD088157  to G. S. ), the  U. S. Army Research Office  ( W911NF-15-1-0440  to G. S. ), the  National Science Foundation  (Grant  BCS-1634050  to J. H. M. ), the  NSF Science and Technology Center for Brains, Minds, and Machines  ( CCF-1231216 ),  Fondazione Neurone  (Grant to G. S. ), and the  Howard Hughes Medical Institute  ( LSRF Postdoctoral Fellowship  to S. N. H. ).
Background: Several reviews have noted shortcomings regarding the quality and reporting of network meta-analyses (NMAs). We suspect that this issue may be partially attributable to limitations in current NMA software which do not readily produce all of the output needed to satisfy current guidelines. Results: To better facilitate the conduct and reporting of NMAs, we have created an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis). This R package relies upon Just Another Gibbs Sampler (JAGS) to conduct Bayesian NMA using a generalized linear model. BUGSnet contains a suite of functions that can be used to describe the evidence network, estimate a model and assess the model fit and convergence, assess the presence of heterogeneity and inconsistency, and output the results in a variety of formats including league tables and surface under the cumulative rank curve (SUCRA) plots.
To address these limitations, we have developed an R package called "BUGSnet" (Bayesian inference Using Gibbs Sampling to conduct a Network meta-analysis) aimed at improving the reporting and conduct of NMA/ITC. BUGSnet improves over its two main competing software packages for conducting a contrast-based Bayesian NMA: GeMTC [15] and NetMetaXL  [16]. While NetMetaXL does produce much of the output necessary to satisfy reporting guidelines, it is limited in the types of analyses it can carry out. Specifically, one cannot use NetMetaXL to analyze outcomes that are not dichotomous, to conduct meta-regression, or to analyzing evidence bases with more than 15 treatments  [16]. While GeMTC provides an enhanced suite of functions for conducting NMA relative to NetMetaXL, its reporting capabilities are limited.
Information as to how to install JAGS can be found at the program's sourceforge homepage: http://mcmc-jags. sourceforge. net/. BUGSnet is hosted and can be accessed at the following URL: https://bugsnetsoftware. github. io/. We encourage users to submit feedback on existing code and to provide suggestions for additional functions that should be added to BUGSnet at the aforementioned homepage. Detailed vignettes describing the step-by-step use of BUGSnet to conduct an NMA on various types of outcomes are currently available in the R package documentation and on the BUGSnet homepage and additional applied examples are forthcoming.
Bayesian inference BUGSnet conducts NMA using Bayesian inference. There were several practical and theoretical reasons for choosing to implement the package within a Bayesian as opposed to a frequentist framework as noted by others: 1) Bayesian methods are more popular among researchers who conduct network meta-analyses; 2) Bayesian methods for network meta-analysis have been developed to a further degree; 3) Bayesian methods allow one to better handle data from trials with multiple arms and trials in which there are arms with zero events; 4) Bayesian methods are currently better suited for modeling uncertainty surrounding the heterogeneity between studies; 5) Bayesian methods present results as probabilities and are thus more suitable for ranking treatment efficacy and for incorporation into health-economic decision modeling  [1, 22].
NMA models BUGSnet can handle continuous, dichotomous, and count data (with or without varying follow-up times) as well as data from studies with more than two treatment arms. In what follows, we describe the NMA models that are implemented within BUGSnet. Suppose that we have data from studies i = 1, …, M. In arm k of study i, treatment t ik ∈ {1, …, T} was used. The set {1, …, T} represents the set of treatments that were assessed across the M studies, where treatment 1 is a reference treatment. Let a 1 , …, a M represent the number of arms in studies 1, …, M. Let R ik be the measured aggregate response in arm k of study i (e. g. proportion of individuals who were alive at one-year, average blood pressure, etc. ). Those responses are modeled as conditionally independent using an appropriate distribution F which is chosen based on the type of outcome at hand.