dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015; Woodland et al. , 2015). In this paper, all ASR systems were built in HTK using 200 h of training data from the MGB challenge. We designed the experimental setup carefully to use only British English speech and reduce the channel difference caused by different recording devices. Of particular importance for the present study is the inclusion of a low-dimensional bottleneck layer in the DNN structure of our initial model. Each of the first five hidden layers contains 1,000 nodes, while the sixth hidden layer has just 26 nodes. Our choice to include six hidden layers in the DNN is not arbitrary. The performance of different DNN structures in the MGB challenge has previously been studied. Empirically, having a fewer hidden layers result in worse WERs, while more hidden layers result in unstable training performance due to the increased difficulty when optimizing deeper models.
Similar structures were often adopted on different datasets and by different groups (e. g. ,  Karafiát et al. , 2013; Doddipatla et al. , 2014; Yu et al. , 2014; Liu et al. , 2015). Since the layers in our DNN are feed-forward and fully connected, each node in each layer is connected only with the nodes from its immediately preceding layer, and as such the acoustic feature representations of the input speech are forced to pass through each layer in turn to derive the final output probabilities of the context-dependent phonetic units. The bottleneck layer representations are highly compressed and discriminative, and are therefore widely used as an alternative type of input features to acoustic models in ASR literature  (Grézl et al. , 2007; Tüske et al. , 2014; Woodland et al. , 2015). In addition, the inclusion of this bottleneck layer greatly reduces the number of DNN parameters without significantly diminishing the accuracy of word recognition  (Woodland et al.
, 2017) , we focus our present analysis within language-related brain regions: STC and HG. The neuroimaging data used in this study comes from electroencephalography and magnetoencephalography (EMEG) recordings of participants listening to spoken words in a magnetoencephalography (MEG) brain scanner. High-resolution magnetic resonance imaging (MRI) was acquired using a 3T MRI scanner for better source localization. As in our previous studies  (Fonteneau et al. , 2014; Su et al. , 2014; Wingfield et al. , 2017) , the data (EMEG and MRI) has been combined to generate a source-space reconstruction of the electrophysiological activity which gave rise to the measurements at the electroencephalography (EEG) and MEG sensors. Using standard minimum-norm estimation (MNE) procedures guided by anatomical constraints from structural MRIs of the participants  (Hämäläinen and Ilmoniemi, 1994; Gramfort et al.
After the DNN was first trained on the data from BBC TV programs, it was further adapted to fit the characteristics of the speaker and the recording channel of the stimuli data using an extra adaptation stage with 976 isolated words (see  Zhang and Woodland, 2015b  for details of the approach). This is to avoid any potential bias to our experimental results caused by the differences between the DNN model training set and the stimuli set, without requiring the collection of a large amount of speech samples in the same setting as the stimuli set to build a DNN model from scratch. There are no overlapping speech samples (words) between the adaptation and stimuli sets. This guarantees that the model RDM obtained using our stimuli set is not over-fitted into the seen data, and guarantees our results and conclusions to be as general as possible.
Computing model RDMs from incremental machine states To encapsulate the representational space of each of the DNN's hidden layer representations through time, we computed model RDMs from the activation of each layer using the following procedure, illustrated in Figure  1. RSA computations were performed in Matlab using the RSA toolbox  (Nili et al. , 2014). As described previously, the input layer of the DNN had access to 125 ms of audio input at each time step, to estimate the triphone-HMM-state likelihoods. Since we can only compute model RDMs where the DNN has activations for every word in the stimuli set, only the activations corresponding to the frames whose ending time is smaller than 285 ms (the duration of the shortest word) are used in our experiments Since each frame has a 25 ms duration and a 10 ms shift, only the activations of the first 27 frames of each word are reserved to construct our model RDMs (as the frame index t is required to satisfy 10 × t + 25 285).
EMEG data collection Sixteen right-handed native speakers of British English (six male, aged 19-35 years, self-reported normal hearing) participated in the study. For each participant, recordings of 400 English words, as spoken by a female native British English speaker were presented binaurally. Each word was repeated once. The study was approved by the Peterborough and Fenland Ethical Committee (UK). Continuous MEG data were recorded using a 306 channels VectorView system (Elektra-Neuromag, Helsinki, Finland). EEG was recorded simultaneously from 70 Ag-AgCl electrodes placed within an elastic cap (EASYCAP GmbH, Herrsching-Breitbrunn, Germany) according to the extended 10/20 system and using a nose electrode as the recording reference. All data  Fonteneau et al. (2014).
EMEG source estimation In order to track the cortical locations of brain-model correspondence, we estimated the location of cortical sources using the anatomically constrained MNE  (Hämäläinen and Ilmoniemi, 1994)  with identical parameters to those used in our previous work  (Fonteneau et al. , 2014; Su et al. , 2014; Wingfield et al. , 2017). MR structural images for each participant were obtained using a GRAPPA 3D MPRAGE sequence (TR = 2250 ms; TE = 2. 99 ms; flip-angle = 9 deg; acceleration factor = 2) on a 3 T Trio (Siemens, Erlangen, Germany) with 1 mm isotropic voxels. From the MRI data, a representation of each participant's cerebral cortex was constructed using FreeSurfer software (https://surfer. nmr. mgh. harvard. edu/). The forward model was calculated with a three-layer boundary element model using the outer surface of the scalp as well as the outer and inner surfaces of the skull identified in the anatomical MRI.
Computing brain RDMs in a spatiotemporal searchlight To match the similarity structures computed from each layer of the DNN to those found in human participants, in the ssRSA procedure, RDMs were calculated from the EMEG data contained within a regular spatial searchlight patch and fixedwidth sliding temporal window. We used a patch of vertices of radius 20 mm, and a 25 ms sliding window to match the 25 ms frames used in ASR. The searchlight patch was moved to center on each vertex in the masked source mesh, while the sliding window is moved throughout the epoch in fixed time-steps of 10 ms. From within each searchlight patch, we extracted the spatiotemporal response pattern from each subject's EMEG data. We computed word-by-word RDMs using Pearson's correlation distance (1r) on the resulting response vectors.
Systematic brain-model RDM comparisons The model RDMs computed from the DNN layer activations describe the changing representational dissimilarity space of each layer throughout the duration of the stimulus words. We can think of this as a dynamic model timeline for each layer; a collection of RDMs indexed by time throughout the stimulus. Similarly, the brain data-derived RDMs computed from brain recordings describe the changing representational dissimilarity space of the brain responses at each searchlight location throughout the epoch, which we can think of as a dynamic data timeline. It takes non-zero time for vibrations at the eardrum to elicit responses in auditory cortex (Figure  3A ). Therefore, it does not make sense to only compare the DNN RDM from a given time window to the precisely corresponding brain RDM for the same window of stimulus: to do so would be to hypothesize instantaneous auditory processing in auditory nerves and in the brain.
Instead, we offset the brain RDM's timeline by a fixed latency, k ms (Figure  3B ). Then, matching corresponding DNN and brain RDMs at latency k tests the hypothesis that the DNN's representations explain those in auditory cortex k ms later. By systematically varying k, we are able to find the time at which the brain's representations are best explained by those in the DNN layers. Thus, for each such potential processing latency, we obtain a spatial map describing the degree to which a DNN layer explains the brain's representations at that latency (i. e. , mean Spearman's rank correlation coefficient between DNN and brain RDMs at that latency). Varying the latency then adds a temporal dimension to the maps of fit. This process is repeated for each subject, and data combined by a t-test of the ρ values across subjects at each vertex within the mask and each latency.
Acknowledgments The authors thank  Anastasia Klimovich-Smith ,  Hun Choi ,  Lorraine Tyler ,  Andreas Marouchos , and  Geoffrey Hinton  for thoughtful comments and discussions. RSA computation was done in the RSA toolbox for Matlab (Nili et al. , 2014) using custom EMEG and ssRSA extensions, to which  Isma Zulfiqar ,  Fawad Jamshed , and  Jana Klímová  also contributed.
Funding This research was supported financially by a  Senior Research Fellowship  to LS from  Alzheimer's Research UK  ( ARUK-SRF2017B-1 ), an  Advanced Investigator grant  to WM-W from the  European Research Council  (AdG 230570 NEUROLEX), by  MRC Cognition and Brain Sciences Unit (CBSU)  funding to WM-W (U. 1055. 04. 002. 00001. 01 ),and by a  European Research Council   Advanced Investigator grant  under the European Community's  Horizon 2020 Research and Innovation Programme  ( 2014-2020 ERC Grant  agreement no  669820 ) to  Lorraine K. Tyler.
Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: The 200 h Multi-genre Broadcast (MGB) dataset used to train the ASR model was only available to the 2015 MGB-1 Challenge (http://www. mgb-challenge. org/) participants with copyright restrictions from BBC. The 26-dimensional hidden layer representations extracted from the L7 layer of the DNN-BN 7 model can be found in (http://mi. eng. cam. ac. uk/~cz277/stimuli). Masked, preprocessed human neuroimaging data used for this analysis is available from figshare (https://doi. org/10. 6084/m9. figshare. 5313484. v1). The DNN-based ASR system was created using an open-source toolkit, the HTK toolkit version 3. 5 (https:// htk. eng. cam. ac. uk/). The RSA procedure for this paper was performed using the open-source RSA toolbox (https://github.
Science drivers for APIS The aforementioned missions and studies have significantly improved our understanding of small-scale physical processes in the magnetosphere, such as magnetic reconnection and plasma currents  [31]. Despite the numerous heliophysics missions that have flown, plasma turbulence and the formation of plasma structures are still elusive and can only be understood with large-scale multi-point measurements  [28, 32]. In 2004, NASA stated that MagCon's database of dispersed measurements would allow us to "emerge from a long and frustrating hiatus"  [33].
The APIS mission shall measure the plasma flows and turbulence in the magnetotail using radio tomography and in-situ measurements. The use of a swarm architecture to produce high-resolution, small-scale tomographic maps, as well as large-scale observations will help explain key plasma processes that occur, not only in the magnetosphere, but also in magnetized plasmas across the universe  [1]. These processes, such as turbulence in a magnetized plasma, require multi-scale multi-point measurements to be fully understood  [32]. • Goal 2: Determine the Dynamics and Coupling of Earth's Magnetosphere and the Response to Solar Inputs. The operational architecture of the APIS mission shall allow simultaneous plasma density measurements of both the magnetotail and the Sun-facing magnetosphere. The data provided by the APIS mission will uncover relationships between plasma density between different parts of the magnetosphere.
Our region of interest is 8-12 R E , where key physical processes such as magnetospheric instabilities, plasma flows, morphological changes associated with geomagnetic storms, and turbulence occur  [34, 31, 32]. Thus, this region has been intensely studied by previous missions, although at smaller spatial scales than we propose. Initial science observations will provide the big-picture data required to understand the region and the processes happening in this region. The high-resolution follow-up observations will then study these processes in more detail. Polar plane: A secondary plane of the APIS mission swarm satellites will orbit on the same scale (8-12 R E ), nearly perpendicular to the first plane. This plane will sweep through the magnetotail and the Sunward magnetosphere over the course of a year.
The APIS mission The APIS mission will address key physical processes in the magnetosphere, including: how plasma enters the magnetosphere; the formation and dynamics of the plasma sheet; the formation of plasma structures in response to solar wind variability; and turbulence in a magnetized plasma  [28]. By exploiting the swarm architecture, the APIS mission will provide large-scale, high-resolution tomographic maps that exceed the targets set out in the 2013 decadal survey on heliophysics  [28]. The novel scientific feature of the APIS mission is the swarm-enabled ability to vary the spatial and temporal resolution of the tomography measurements, which will provide the precise data needed to understand key heliophysics processes. The APIS mission requirements -as discussed at length in this article -are summarized in Table  3 , and are derived from the science requirements as described in Table  1  and Table 2.
Navigation One of the key challenges for the APIS swarm lies in navigation, i. e. , in ensuring accurate position, time and orientation estimation and control. A single satellite in the APIS mission has the visibility of GNSS satellites only for a few hours per orbit. In the absence of GNSS, all the satellites rely on their respective on-board Attitude Determination and Control Systems (ADCS), and the two-way communication with the other satellites in the swarms using Inter-satellite Links (ISLs). In this section, we discuss the clocks and time synchronization, the ADCS system onboard the satellite, and localization strategies with and without GNSS. All the data processing pertaining to the ADCS system will be done by the OBC (Section 9). In addition to the space and time estimation of each satellite, the propulsion system on-board each satellite used for orbital correction is also presented.
For an immobile network, in the absence of a reference such as GNSS, the relative position of the satellites can be estimated using multi-dimensional scaling like algorithms  [62]. Furthermore,  [63]  proposed a distributed relative position algorithm, which offers a solution to solve for the relative position of satellites cooperatively on a sphere domain. When the satellites are mobile, the relative kinematics (e. g. , relative velocity and acceleration) need to be estimated by solving relative kinematics models  [64]. A large consideration for the APIS team is distributed control for the swarm in terms of position and attitude. Path-planning feedback control for autonomous and distributed position control of satellite swarms will be implemented, using local sensor data to coordinate individual satellite tasks, with the assumption that each satellite is able to locally process 3D attitude and inter-satellite distances from the on-board sensors.
Communication Communication is a crucial aspect of the APIS mission for autonomy, satellite navigation, and science payload processing. A summary of all the communication and operational requirements is listed in Table  8. We discuss the Inter-satellite and Earth-based communication in this section. Individual satellites need inter-satellite links (ISLs) to share information with each other and to transmit the information collected by scientific exploration to the ground center. Thus, the satellites also utilize satellite-to-ground communication. A simple block diagram in Figure  9  shows the communication system loop for the ISL in the satellite swarm. For the scientific goals of the APIS mission, every satellite must establish a high data rate radio connection with the other satellites, and include a transceiver.
The antenna must have sufficient gain and transmission power, and the transceiver must meet the data requirements. According to the existing patch antennas and transceivers available on the market, we investigated five possible antenna systems for the APIS satellites, which are listed in Table  6. Considering the size of the satellite and the data transmission requirements, the S-band patch antenna produced by Endurosat is the chosen antenna for the APIS  mission. Along similar lines, we considered four types of transceivers for the APIS mission as shown in Table  7. The transceiver made by ECM Space Technologies GmbH company is attractive in terms of power, volume and mass, which is, therefore, the chosen transceiver for the APIS mission. The ECM transceiver will employ QPSK for transmission and BPSK reception, and offers a data transmission rate of up to 20 Mbps.
Uplink and Downlink to Earth APIS mission satellites are small in size and restricted in power supply, so at higher orbits the satellites will not be able to communicate directly with the ground-based antennas in full bandwidth. At these orbits, Mobile Ad-Hoc Network (MANET) technology can be used to send information to ground stations. The swarm uses ISLs to hop data from satellite to satellite until the satellite closest to the Earth can send the data to the ground  [70]. During Earth fly-by at the perigee, the satellites employ multi-hop communication for data downlink, as illustrated in Figure  7b. The MANET system is highly dynamic, fault-resistant, and autonomous, which is advantageous for APIS satellites to communicate data to ground stations. To reduce design complexity, the communications from satellite-to-Earth and ISLs will be combined.
Delay Tolerant Networking (DTN) technology will store and transmit data back to ground stations, following processing, once satellite have reached a low-altitude location, providing reliable communication in case of network connection interruption  [71]. Using the THEMIS mission as a reference, we estimate a data rate of up to 500 bytes/second from all of the science payloads  [31]. The APIS satellites can have up to 20 Mbit/s of data downlink. To manage thermal issues and data packet loss we assume a guaranteed data rate of 1 Mbit/s. With one hour of contact to the ground station, each satellite can transmit up to 400 MB. Assuming a 50% success rate of package transmission, as commonly used  [31] , the data transmission per perigee pass reduces to 200 MB. Furthermore, assuming half the data consists of telemetry and housekeeping data, we can then assume a successfull transmission of up to 100 MB of science data per perigee pass.
Changes in trust levels will complicate mission design and may necessitate the creation and installation of safeguards that further increase system complexity. In addition to exploiting autonomy, the APIS mission will facilitate distributed functionality, enabling emergent behavior of the satellite swarm. To enable the emergent behavior of the swarm satellites, we need to partner an appropriate operating system with the OBC. NASA conducted research on the feasibility of using COTS technology for the Command and Data Handling (CDH) system of a swarm of satellites  [77]. The study used an android operating system with specific programming to relay data from both satellite-to-satellite and satellite-to-ground stations, demonstrating the feasibility of the satellite swarm behavior using COTS software.
Acknowledgement The APIS mission design study was part of the International Space University Space Studies Program in 2019 (ISU-SSP19) as the Next Generation Space Systems: Swarms Team Project, which is partially funded by NASA. This 9-week intense project was conducted by a team of 32 members from 16 nationalities, who are drawn from diverse professional backgrounds within the space sector, which include but are not limited to engineers, scientists, lawyers and managers. The authors of this publication include the APIS team, the Chairs Dr. Jacob Cohen, Anh Nguyen and Andrew Simon-Butler, and the support staff Ruth McAvinia. The name of the project is adopted from the genus name Apis, meaning 'Bee' in Latin, which was an inspiration for the APIS mission patch designed during this study (see Figure  12 ).
Data-independent acquisition (DIA) based strategies have been explored in recent years for improving quantitative analysis of metabolites. However, the data analysis is challenging for DIA methods as the resulting spectra are highly multiplexed. Thus, the DIA mode requires advanced software analysis to facilitate the data deconvolution process. We proposed a pipeline for quantitative profiling of pharmaceutical drugs and serum metabolites in DIA mode after comparing the results obtained from full-scan, Data-dependent acquisition (DDA) and DIA modes. using open-access software. Pharmaceutical drugs (10) were pooled in healthy human serum and analysed by LC-ESI-QTOF-MS. MS1 full-scan and Data-dependent (MS2) results were used for identification using MS-DIAL software while deconvolution of MS1/MS2 spectra in DIA mode was achieved by using Skyline software.
There is limited open-access software available for efficient use of DDA and DIA datasets for untargeted metabolomic analysis that majorly targets feature generation, normalization, alignment, quantification, transition list generation, and statistical analysis. It is also difficult to perform all these steps on a single open-access software due to a separate algorithm required for each mode. In this context, the aim of this study is to evaluate the performance of MS1 full-scan, DDA, and DIA modes using the easy-to-use pipeline of open-access software for profiling of serum metabolites and quantification of pharmaceutical drugs. For this, a two-step strategy was used. In the first step, MS1 full-scan and data-dependent (MS2) modes were used for feature generation, annotation, and identification using MS-DIAL software from the data acquired on pooled samples.
Results We present a simple and effective workflow based on open-access software for simple and comprehensive untargeted analysis including feature generation, identification, quantification, and statistical analysis using MS-DIAL, Skyline, and Perseus. We used 10 drug standards of different concentration levels pooled in the QC sample for three test modes. We suggest a two-step strategy. In the first step, feature generation, annotation, identification, and the transition list generation of serum metabolites and drug standards in each diluted point in MS1 full-scan and DDA methods were achieved in MS-DIAL. In the second step, a comprehensive transition list containing precursor ions, product ions, and retention time of each feature imported in Skyline software for processing DIA mode and extracted MS1/MS2 ion chromatograms for deconvolution and quantification.