dataesr/scientific-paragraphs-categorization huggingface dataset:
A common approach to measuring the similarity between the query q embeddings and candidate paper p embeddings. The papers with the highest similarity scores are considered the most similar to the query and are thus returned at the top of the search results. Among the recent research directions, there has been a focus on learning representations of sci-entific documents that can be used as rich input features for downstream tasks, thus alleviating the need for further fine-tuning  (Cohan et al. , 2020; Parisot and Zavrel, 2022; Singh et al. , 2022). Zero-shot robustness directions continued to show promising results as well, with state-of-the-art being dominated by models optimized to resist to natural dataset shifts  (Yu et al. , 2022).
Datasets TREC-COVID  (Voorhees et al. , 2021 ) is a test collection leveraging the TREC framework that aimed to address the search needs of clinicians and biomedical researchers during the COVID-19 pandemic, thus laying the groundwork for improved search systems in future public health emergencies. The document set is the one provided by CORD-19  (Wang et al. , 2020) , which consists of new and historical publications on coronaviruses. TREC-COVID aims to rank papers in response to textual search queries and contains 50 queries with 69,318 candidate papers cumulatively judged by relevance. NFCorpus  (Boteva et al. , 2016 ) is a Medical Information Retrieval data set on nutrition facts where queries are harvested from Nutrition-Facts. org site and candidate documents are medical research papers mostly from PubMed.
Results & Discussion We report in Table  1  the average nDCG@10 of the different models on both NFCorpus and TREC-COVID, as well as our use case. We experiment with three strategies of searching: based on title relevance, on abstract relevance, and titles and abstracts appended 6. NFCorpus BM25 is leading on the three strategies, followed by scientific LMs mostly dominating the general robust models. The low scores (compared to the other datasets) can be partially explained with the fact that the percentage of relevant articles is smaller for most queries (≤1-2%). All models, with the exception of BM25 SPECTER, perform better on titles rather than assessing abstracts' relevance. TREC-COVID On titles and titles+abstracts, COCO-DR is the best-performing model, whereas ASPIRE slightly outperforms it on abstracts.
Models' performances are quite consistent on this benchmark, with scientific LMs having close scores and mostly best performing on titles. TREC-COVID's queries are more detailed than the other two datasets. This might explain the coherence of results between models; there is more relevant information to judge on. Vitamin B & health Models' performance in our case seems to be divergent from what can be observed on the other two benchmarks. BM25's performance entirely drops with abstracts, which might be caused by the nomenclature of vitamin B (Appendix A) present in titles and abstracts. On the other hand, monoT5 outperforms all other models on strategies that include abstract relevance, whereas COCO-DR achieves perfect nDCG@10 on titles. Overall, our results show that models perform differently on datasets and suggest that there is an inconsistency in performance and difficulty in identifying the best model for seeking biomedical information in publications: if starting from NF-Corpus, one would suggest using BM25 as a decent model for the vitamin B case, whereas if comparing on TREC-COVID, one would prefer COCO-DR and entirely leave out monoT5.
Discussion We further discuss in this section the misalignment between performance measures from the perspective of an expert seeking information in papers. Expert search strategy preferences Our expert expressed that they sort on titles for more speed, but abstract relevance remains the reference. The reason for this is that titles usually provide information on the study domain as a whole, and can be used to classify into big categories. The abstracts however are used when the title does not allow for immediate classification, since they contain the main question of the paper, the methodology, and the main results. Intuitively, models would find more relevance "hints" in abstracts, and thus have greater performance on search strategies that include them. This was rarely the case for all datasets, suggesting that many models might be better at matching shorter contexts (titles being closer to query length compared to abstracts).
Category We provide a datasheet for the data that we created as supplementary material. B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank. B5. Did you provide documentation of the artifacts, e. g. , coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc. ? Not applicable. Left blank. B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results.
Flow of stimulus and choice information across the human visual system We analyzed a previously published dataset (  [57] , see also SM3. 1 for details) of source-level MEG data recorded while human participants performed a visual decision-making task. At the beginning of each trial, a reference stimulus was presented (contrast 50%), followed by a test stimulus that consisted of a sequence 5 of 10 visual samples with variable contrasts (Fig. 3A ). After the test stimulus sequence, participants reported their choice of whether the average contrast of the samples was greater or smaller than the reference contrast. The previous study on these data (  [57] ) analysed the encoding of stimulus and choice signals in individual areas but did not study information transfer. We focused on gamma-band activity (defined as the instantaneous power of the 40-75Hz frequency band), because it is the most prominent band for visual information encoding  [21; 41; 16]  and information propagation  [6; 3]  in the visual system.
Eye-specific interhemispheric information flow during face detection We next tested the ability of FIT to detect feature-specific information flow between brain hemispheres. We analyzed a published EEG dataset recorded from human participants detecting the presence of either a face or a random texture from an image covered by a bubble mask randomly generated in each trial (  [43] ; see SM3. 2. 1 for details). Previous analysis of these data  [23]  showed that eye visibility in the image (defined as the proportion of image pixels in the eye region visible through the mask) is the most relevant 7 image feature for successful face discrimination. It then showed that eye-specific information appears first at ∼120ms post-image presentation in the Occipito-Temporal (OT) region of the hemisphere contralateral with respect to the position of the eye, and then appears ∼20-40ms later in the ipsilateral OT region (Fig.
Stimulus-specific information flow in a thalamocortical network We finally used FIT to measure the feature-and direction-specificity of information flow in the thalamocortical somatosensory and visual pathways. We analysed a published dataset in which multi-unit spiking activity was simultaneously recorded in anaesthetized rats from the primary visual and somatosensory cortices, and from first-order visual and somatosensory thalamic nuclei (  [7] , see SM3. 3. 1 for details), during either unimodal visual, unimodal tactile, or bimodal (visual and tactile) stimulation. This analysis tests FIT on another major type of brain recordings (spiking activity). Moreover, due to the wealth of knowledge about the thalamocortical network  [48; 15] , we can validate FIT against the highly-credible predictions that information about basic sensory features flows from thalamus to cortex, and that somatosensory and visual pathways primarily transmit tactile and visual information, respectively.
We confirmed the utility of FIT in applications to neural data. In three brain datasets spanning the range of electrophysiological recordings (spiking activity, MEG and EEG), FIT credibly determined the directionality and feature specificity of information flow. Importantly, in most of these datasets this happened in the absence of variations in the overall flow of activity between the same brain regions (measured with TE). The partial dissociation between overall activity flow and feature-specific flow found consistently in simulations and data has important implications. First, it highlights the need of introducing a specific measure of feature information transfer such as FIT, as it resolves question unaddressed by content-unspecific measures. Second, it establishes that measuring feature-specific components of information flow between brain regions is critical to go beyond the measurement of overall neural activity propagation and uncover aspects of cross-area communication relevant for ongoing behavior.
As an example, in Fig. S1B , we demonstrate that I(S; Y pres ) is the sum of atoms belonging to both decompositions, which together consist of 36 atoms (18 per decomposition). In general, each quantity in eq. S21 imposes constraints between two sets of many atoms from the two decompositions. To study numerically the solutions of S19 for these 36 atoms, we wrote a MATLAB script named FIT_nullB. m. This script computes the four quantities in eq. S21 as the sum of atoms from either the decomposition with S or Y pres as target. It then constructs the 4 × 36 matrix B (Fig. S2 ) in a similar way to how we derived the 2 × 8 matrix in eq. S20 from the eqs. in S17. From Fig. S2 , it is clear that in this four-variable case, some atoms, such as I ∂ (Y pres ; {X past }{Y past }), are not constrained by B and can vary independently (analogously to how for N = 2 the two terms I ∂ (S; {X}) and I ∂ (Y ; {X})) were unconstrained).
SM1. 6 Permutation-based non-parametric null hypotheses for FIT and TE To test for significance of the information theoretic quantities, we used non-parametric permutation tests, described below. To test for the significance of mutual information values about the stimulus feature, we used established non-paramametric procedures  [7; 11]. We constructed surrogate datasets in which we destroyed any stimulus information by randomly permuting across trials the values of S, and then we recomputed information on the surrogate data to obtain a null-hypothesis distribution of null information values. To test for the significance of FIT, we developed a permutation test in which we created surrogate data in which we preserve the stimulus feature information in the past of X and the present of Y while destroying the communication of this information between X and Y.
(To compute the null-hypothesis distribution, we generated 500 different realizations of the permuted average information for each test we conducted. ) In some analyses (e. g. Fig. 3C  and 4B,C ) we had to identify the cluster of post-stimulus times and transmission delays for which FIT or TE were significantly different from zero (e. g. in Figs. S6C, S7A, B ). We individuated these clusters of points in the time-delay space using a cluster-based permutation test  [21; 7]  using as null hypothesis values those obtained from the permutation test described above. We computed the cluster forming threshold as the 99th percentile of information values in the surrogate data. We created information clusters in the original and shuffled datasets by summing together all adjacent information values above the cluster forming threshold.
SM3. 1. 1 Behavioral task and MEG recordings We analyzed a publicly available MEG dataset  [33] , with source-reconstructed data available at [https://doi. org/ 10. 6084/m9. figshare. 12770366]. Full details (including details of approvals from Ethical Committees) are reported in the original publication and are briefly summarized here. The MEG data were recorded from the brain of participants while they performed a visual decision-making task. N=15 participants took part in the experiments, performing 4 experimental sessions each with on average 429 trials per session. At the beginning of each trial, a reference sample was shown with a contrast of 50%. Each sample lasted 100ms and its contrast was drawn from a Gaussian distribution whose mean was randomly selected in each trial. At the end of each trial, participants reported whether the average contrast of the 10 sample gratings was higher or lower than the reference contrast.
SM3. 1. 2 Parameters and details of the Information theoretic analyses For the analysis FIT and TE we used gamma-band instantaneous power obtained by computing the time-frequency representations of single-trial data via the multi-taper method and then averaging the obtained powers in the [40 -75] Hz band, exactly as described in the original publication  [33]. To estimate the joint probability distributions of the neural activity and the stimulus (or the choice) used to compute the information theoretic quantities, we binned the MEG gamma power from each ROI into 2 equally populated bins and then computed empirically the frequency of occurrence of each response bin across all available trials. The stimulus features used for the information analyses was the average contrast of the 10 samples presented on each trial, discretized into two values.
This is of interested because in the main text analyses (Figure3E-H) we compared FIT and TE using a time-delay region around the peak of FIT. The TE panel (Fig. S6D ) shows that TE peaks in a different time-delay region with respect to stimulus FIT (Fig. 3C ). Taking a DI-specific box centered around the TE peak in time-delay to select information values we could not assess the direction nor the behavioral relevance of information transmission (Fig. S6E ). We next analyzed a publicly available EEG dataset  [27]. Data are available at [https://datadryad. org/stash/dataset/doi:10. 5061/dryad. 8m2g3]. Full details (including details of approvals from Ethical Committees) are reported in the original publication. Here we summarize them briefly. The EEG data were recorded while participants (N=16) performed a face detection task.
Participants were presented with an image hidden behind a bubble mask that was randomly generated in each trial. The presented image was a image of a face in half of the trials and a random texture in the other half of the trials. Participants were instructed to report whether a face was present or not. In our analyses, we only considered correct trials where the face was correctly detected by the participants (approximately 1000 trials per subject). Following the recommendations of the original publications analysing these data  [27; 16] , we excluded one participant from the analysis due to a poor EEG signal that did not contain significant eye visibility information in any of the electrodes. All analyses in our paper are based on the N=15 selected participants. EEGs were recorded by fitting participants with a Biosemi head cap comprising 128 EEG electrodes.
EEG data were re-referenced offline to an average reference, band-pass filtered between 1 Hz and 30 Hz using a fourth order Butterworth filter, down-sampled to 500 Hz sampling rate and baseline corrected using the average activity between 300ms pre-stimulus and stimulus presentation. ICA was performed to reduce blink and eye-movement artifacts, as implemented in the infomax algorithm from EEGLAB  [9]. Components representing blinks and eye movements were identified by visual inspection of their topographies, time courses, and amplitude spectra.
SM3. 4 Applications of cFIT to real neural data We tested the effectiveness of the conditioned FIT (cFIT) in the analysis of neurophysiological data by applying it to perform further analyses on the EEG and the MUA datasets (Fig. S9 ). For simplicity, when computing cFIT from X to Y conditioned on the stimulus information of Z, we always considered the same communication delay (that is, the time difference between the present and past activity used to compute the information theoretic measures) for the past activity of the sender X and the past activity of the third region Z. However the definition of conditional FIT holds for an arbitrary representation of Z past , potentially including multiple time points or a communication delay that is different from the one of X past. We first applied cFIT to the EEG dataset.
Next, we analysed the spiking activity dataset. We examined tactile-discriminative information flowing through the somatosensory pathway, and the visual-discriminative information flowing though the visual pathway. We first used cFIT to test whether the FIT from the somatosensory thalamus VPM to the somatosensory cortex S1 could have actually been relayed through the visual thalamus LGN. The neurophysiological expectation is that all tactile-discriminative information flows within the somatosensory pathway, without contributions from visual stations. Consistent with this expectation, we found that the tactile-discriminative cFIT from VPM to S1 conditioned on the visaul thalamus LGN was equal to the unconditional tactile-discrminative FIT from VPM to S1. We then used cFIT to test whether the visually-discriminative FIT from the visual thalamus LGN to the visual cortex V1 could have actually been relayed through the somatosensory thalamus.
The 2DECOMP&FFT library is a software framework written in modern Fortran to build largescale parallel applications. It is designed for applications using three-dimensional structured meshes with a particular focus on spatially implicit numerical algorithms. However, the library can be easily used with other discretisation schemes based on a structured layout and where pencil decomposition can apply. It is based on a general-purpose 2D pencil decomposition for data distribution and data Input Output (I/O). A 1D slab decomposition is also available as a special case of the 2D pencil decomposition. The library includes a highly scalable and efficient interface to perform three-dimensional Fast Fourier Transforms (FFTs). The library has been designed to be user-friendly, with a clean application programming interface hiding most communication details from application developers, and portable with support for modern CPUs and NVIDIA GPUs (support for AMD and Intel GPUs to follow).
The first version of the library was released in 2010 as a tar. gz package, with a Makefile approach, and could only make use of CPUs. It has not been modified since its release. The new version of the library can now leverage NVIDIA GPUs, modern CPUs, and various compilers (GNU, Intel, NVHPC, CRAY). It has CMAKE capabilities as well as a proper continuous integration framework with automated tests. The new library was designed to be more appealing to the scientific community, and it can now be easily implemented as an independent library for use by other software.
GPU porting An initial port of 2DECOMP&FFT to GPUs was performed within the solver AFiD-GPU  (Zhu et al. , 2018) , which was mainly based on CUDA-Fortran for some kernels and CUDA-aware-MPI for communications. A second library, named cuDECOMP, which was directly inspired by 2DECOMP&FFT, takes full advantages of CUDA and uses NVIDIA's most recent libraries for communications, such as NVIDIA Collective Communication Library (NCCL), is presented in  Romero et al. (2022). Indeed, cuDECOMP only targets NVIDIA GPUs. The updated 2DECOMP&FFT mainly uses a mix of CUDA-Fortran and openACC for the GPU porting together with CUDA-aware-MPI and NCCL for the communications. In addition to previous work, the FFT module is ported to GPUs using cuFFT. The next step is also to implement OpenMP for GPU porting to support both AMD and Intel GPU hardware.
How to use 2DECOMP&FFT The 2D Pencil Decomposition API is defined with three Fortran modules which should be used by applications as: use decomp_2d_constants use decomp_2d_mpi use decomp_2d where use decomp_2d_constants defines all the parameters, use decomp_2d_mpi introduces all the MPI related interfaces, and use decomp_2d contains the main decomposition and transposition APIs. The library is initialised using: call decomp_2d_init  (nx, ny, nz, p_row, p_col)  where nx, ny, and nz are the spatial dimensions of the problem, to be distributed over a 2D processor grid 𝑝 𝑟𝑜𝑤 × 𝑝 𝑐𝑜𝑙. Note that none of the dimensions need to be divisible by p_row or p_col. In the case of p_row=p_col=0, an automatic decomposition is selected among all possible combinations available. A key element of this library is a set of communication routines that perform the data transpositions.
As mentioned, one needs to perform 4 global transpositions to go through all 3 pencil orientations (i. e. , one has to go from x-pencils to y-pencils to z-pencils to y-pencils to x-pencils). Correspondingly, the library provides 4 communication subroutines: call transpose_x_to_y(var_in,var_out) call transpose_y_to_z(var_in,var_out) call transpose_z_to_y(var_in,var_out) call transpose_y_to_x  (var_in,var_out)  The input array var_in and output array var_out are defined by the code using the library and contain distributed data for the correct pencil orientations. Note that the library is written using Fortran's generic interfaces so different data types are supported without user input. That means in and out above can be either real or complex arrays, the latter being useful for applications involving 3D Fast Fourier Transforms.
Acknowledgements The first version of the library was initially designed thanks to several projects funded under the  HECToR Distributed Computational Science and Engineering (CSE)  Service operated by  NAG Ltd. The new library has been designed thanks to the support of  EPSRC  via the  CCP Turbulence  ( EP/T026170/1 ) and work funded under the embedded  CSE programme  of the  ARCHER2 UK National Supercomputing Service  (http://www. archer2. ac. uk) ( ARCHER2 eCSE03 -2).
Off-grid electrical energy systems based on renewable energy sources (RESs) have become increasingly popular for their ability to generate low-carbon electricity in remote areas without access to traditional power grids. These systems rely on the effective management of RESs and storage solutions. Designing and sizing these systems can be a complex task, requiring careful consideration of various parameters such as energy demand, solar irradiance, storage capacity of batteries and State of Charge, power management of fuel cells, and hydrogen production and storage. This study presents an adaptive power management tool that facilitates the sizing of energy equipment for standalone low-carbon microgrids. The proposed simulation tool, implemented in Matlab/Simulink, is based on mathematical models for each energy unit and incorporates a specific power management strategy to determine the optimal size of each component in the system.
In this context, this study aims to present an adaptive power management tool that achieves this goal. The main contributions of this work are based on an innovative power management strategy using state machine principles and an iterative sizing optimization of the energy equipment from a power study conducted hour by hour, on a yearly basis. Moreover, proven mathematical models  [3, 9]  and a detailed specification extraction methodology are used to represent the energy units (sources and storage systems). Furthermore, since users have full access to each part of the system, the presented tool can be easily modified to integrate other energy units, as well as costs or environmental constraints. In this work, the proposed sizing tool is developed in Matlab/Simulink. It is applied to a fictive off-grid district, named New Era thereafter, that is supplied by 100% of green electricity through a PVbattery-hydrogen power system.