dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides the addition of more contrapuntal pieces, future work includes extending MCMA via a tagged query-response subset (akin to SQuAD  [12] ) which will entail meticulous musicological effort in identifying variable-length musical phrases that correspond to each other. While this process can be partly automated via segmentation algorithms  (Tenney & Polansky, 1980; Cambouropoulos, 2001)  and strategies such as Dynamic Time Warping  (Gold & Sharir, 2018) , it still requires human intervention and editing. We also plan to build an interactive companion website to MCMA, whereby users who are not necessarily techno-affluent can sort, organize and explore the corpus's content. Such an interactive version of MCMA might include in-browser score rendition and database functionality (e. g. , aggregate functions) which can be used as a first port of call for basic statistical enquiry of the musical archive.
Signal chain with amplifier It should be noted that the output voltage (OV) of the PAR sensor is too low (under 100 mV) to be directly readable by an ESP32 card. Thus, an amplification circuit using a rail-to-rail amplifier was set up. The low power consumption ESP32 card, while providing Wi-Fi and Bluetooth connectivity, was also used as a data-logger. The choice of this low power card was partly linked to the low computing power of the card, which was sufficient for our study: few data and few calculations. This card is an easy-to-use solution without operating system to install. No peripherals, such as a monitor or a mouse, are required, as is the case with more powerful cards. As with Raspberry, the ESP32 programming language is based on that of Arduino. The open-source Arduino software (IDE) was used.
This constitutes the reference device. The data-logger operation was the same as for the ESP32 card: every five minutes, the average of 60 measurements was recorded in a data file. Moreover, the PQS1 sensor voltage was transformed according to Eq. (  3 ). The voltage values from the noncertified PAR/LE sensor (SOLEMS) were recorded by the PARADe acquisition chain (ESP32 board ans amplifier) and were plotted according to the PAR radiation values measured by the certified sensor, PSQ1. For this calibration, as well as for the other tests that were done, data with a PAR value less than 100 mol. m -. s -  were deleted. Indeed, it has been demonstrated that these values, generally have a high variability  [10]  , are not significant and represent only a small proportion of the daily PAR measurement.
Data validation First, the performance of the PARADe acquisition chain has been studied and compared to the Campbell Scientific Instrument data-logger (model CR1000). After the PAR/LE sensor output, a bypass has been installed to measure at the same time, the output voltage with both acquisition chains. A strong agreement between both acquisition chains is observed in Fig. 5. However, the signal seems to be slightly noisy suggesting structured fluctuations rather related to electronics but without being able to define the cause. Then the accuracy of the measurement performed with PARADe was evaluated. The percent error profile (RE, %) in PARADe measurements is presented in Fig. 6  as a function of the PAR true. In addition, for each PAR measurement, the solar elevation angle has been calculated.
Graphically, the points have been represented by different colors: the red color corresponds to low solar elevation angle values and the green color is associated with high solar elevation angle values. For intermediate cases, the points are in yellow. The RMSE and nRMSE between PAR calculated and PAR true are calculated. Table  3  summarizes the performance results of PARADe over the entire dataset. Regarding the shape of the percent error profile, different comments can be made. It reveals that depending on the PAR values, the RE values vary differently. Visually, in this profile three ranges of RE values (high, zero and low values) seems to be distinguished. This would therefore indicate that PARADe operates differently depending on the PAR measurement ranges. Area 1 is represented by a very high value of the average RE when the PAR radiation is low (less than 250 mol.
Indeed, if PARADe is used to obtain the daily PAR value, deduced from the integration of all the PAR data of a day. Thus, these low PAR values, where the RE is high (area 1), can be neglected as they represent only a very small proportion of the daily PAR. This reinforces the idea of pursuing the development of such a device, inexpensive for access to accurate and representative daily PAR values. In addition to the mean and standard deviation of the percent error, the Table  3  presents the RMSE and nRMSE for each area and for the entire dataset. Regarding the RMSE values obtained for each zone, they are quite similar and close to the value obtained on the global range of measurements. Looking at the normalized RMSE values, we can see that area 1 has the highest nRMSE. This again confirms that PA-RADe does not give reliable measurements for this area.
Objective: The objective of this study is to get an overview of the research trends that use VR in neuropsychological tests and to analyze the research trends using fully immersive VR neuropsychological tests with experimental articles. Methods: This review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were searched in three web databases using keywords related to VR, EFs, and cognitive abilities. The study was conducted in two steps, keyword analysis and in-depth systematic review. In the web database search from 2000 to 2019, 1167 articles were initially collected, of which 234 articles in the eligibility phase were used to conduct keyword analysis and a total of 47 articles were included for systematic review.
Search Strategy Initial articles were searched in three web databases: Scopus, Web of Science, and PubMed. These engines are adequate to cover the wide spectrum of topics in the target area. The articles were searched in March 2020, and the search query string was as follows: ("virtual reality" OR "VR" OR "virtual environment") AND ("executive function(s)" OR "EF(s)" OR "cognitive function(s)" OR "cognition"). The publishing years were limited from 2000 to 2019.
Study Selection The overall procedure of the study and the number of selected articles are shown in Figure  1. After searching the web database, 1167 articles were collected (Scopus 868, PubMed 102, and Web of Science 197). After removing duplicates and outliers, the remaining articles were used in the next phase. As a result of the screening phase, 702 articles were excluded by titles, keywords, and abstract review. With abstracts of 234 papers (data set 1), keyword analysis was conducted to examine the research trends. Then, we conducted a full-text review in the eligibility phase, and 187 papers were excluded based on the exclusion criteria. Finally, 47 papers (data set 2) were selected for the descriptive analysis.
Data Analysis Data analysis was conducted by two sub-analysis methods. First, a keyword analysis was conducted with data set 1. It was important to specify keywords for the analysis, so we conducted keyword extraction with Python NLTK. The Natural Language Toolkit (NLTK) is a Python package that has been developed for natural language processing and document analysis. The NLTK extracted top 200 keywords based on their frequency from each article's title, keywords, and abstract. Among the results of the extraction, we selected significant keywords in line with the study's purpose and categorized them according to measuring targets, names of mental disorders, and behavioral and physiological measures. Moreover, some keywords were added from the 47 full-text descriptive analysis. Second, a descriptive analysis was conducted in detail with data set 2.
Combining Behavioral and Physiological Data Measurement It seems that measuring behavioral data and physiological data is becoming increasingly important. These kinds of data are used to extend the concept of embodied cognition to measure the neuropsychological status and to overcome the weaknesses of VR. Embodied cognition is a broad notion from embodiment thesis in philosophy, and it states that an agent's cognition is powerfully affected by aspects of their body beyond the brain itself. This means that sensorimotor experiences and actions are crucial to cognitive processing  [106]. These aspects are thought to be crucial for cognitive performances in the elderly  [107]. In this context, measuring behavioral and physiological variables in immersive VR would be a key method of assessing the neuropsychological status.
Acknowledgments This work was supported by the  Institute for Information & Communications Technology Promotion (IITP)  grant funded by the  Korean Government  (MSIP; no. 2019-0-01569 ,  Development of VR and IoT-based Systems for Evaluation of Embodied Cognition ). In addition, this work has been partly funded by the  Pays de la Loire French region  through the  West Creative Industries program  and by the  European Union  through the  Hubert Curien French-Korean program  STAR.
When link failure occur, controller is informed about failure state and instructions to compute the backup path. During period of link failure, the routing database and flow table work as collaborator. Packets affected by link failure shall be process using the flow table. For the purpose , Controller find the alternative paths using the Floyd algorithm ignoring the failed links. But it's a post failure recovery or reactive action performer for recovery purpose not a proactive approach like Veriflow , a layer between controller and data plan to evaluate the flow rule proactively before installation[32]. SPIDER[33]is a pipeline packet processing that apply the stateful design for the recovery mechanism in switch locally. It provide the functionality using fully customizable and programmed techniques for recovery directly in switch.
Behavioral Model in this work is in form of Finite State Machine which is acted for per flow processing. Flow Table Compression in proposed in [34] to Utilize the OpenFlow switch memory efficiently. generated by OpenFlow itself. Switch join, leave , switch feature and stats request are examples [30]. Yu et. al. [31] Proposed a link failure framework. This framework provide the resilience against the link failure using OpenFlow in existing routers. Routing process generate the routing information database while OpenFlow controller configure flow table for communication. So, there are two layers of forwarding structure. OpenFlow enabled forwarder are to forward data by searching in flow table and taking the corresponding action of flow entry. If relative rule does not exist for source to destination in flow table then routing information database in followed.
Acknowledgments L. B. gratefully acknowledges hospitality and financial support from the  Laboratoire de Physique of the ENS Lyon. R. A. R. and L. B. acknowledge funding from the  CY Initiative of Excellence  (grant" Investissements d'Avenir "  ANR-16-IDEX-0008 ) where this work developed during R. A. R. 's stay at the  CY Advanced Studies. L. B. thanks the EPSRC for DTP funding. We thank  Warwick's Scientific Computing Research.
In this study, we address the problem of connecting the 5G Network Slice management systems with the TSN control plane. We propose a new network architecture that is able to support network slice life-cycle management in TSN-enabled transport networks. We elaborate on the TSN for the Xhaul concept, we introduce the notion of slice-aware TSN orchestration, while we still preserve compatibility with the 5GS as a "black box" (TSN bridge) approach, from both the IEEE TSN control and data plane perspective. Many research and standardization activities have started to deal with TSN-5G integration. From the 5G pre-standardization and standardization perspective, 5G-ACIA and 3GPP TS 23. 501, TS 23. 502, TS 23. 503 are describing the relevant architecture aspects and also the relevant mappings and processes.
• We investigate how to expose capabilities of the TSN transport network and requirements to the mobile network through a multi-layer transport network controller. • We handle the TSN-related network slice instance state at the transport network level. • We show how to preserve slice isolation over a TSN-based Xhaul data plane and how to support slice-aware TSN network life-cycle management. • We show how to perform coordinated actions between the TSN control plane and a multi-layer transport network controller. • We present implementation experience using the JOX open source orchestration software  [8] , Open Air Interface (OAI)  [9] , and TSN-enabled prototype devices. In contrast with related work, we describe in detail the way TSN technology can serve as an enabler for realizing the concept of network slicing under deterministic performance constraints.
We also report implementation experience over a real testbed using a prototype solution with TSN-enabled hardware, OpenAirInterface  [9] , and JOX slice orchestrator  [8]. An OAI-based disaggregated Radio Access Network (RAN) testbed was built, where TSN-enabled devices were used to interconnect the different components. We evaluate both the data plane and the control plane aspects under network slicing. However, because OAI does not support all the 5G functionalities, no TSN translation services (AF-TT, DS-TT) were implemented. This is planned for future work. On the data plane, our evaluation focuses on investigating the  VOLUME 9, 2021  ability of TSN to differentiate real mobile traffic on a per slice basis and preserve isolation. By means of the control plane, we investigate the interaction of the TSN control plane with the orchestrator and measure both TSN network and mobile network provisioning time.
More in-depth investigation of the relevant control plane aspects is planned for future work. In section II, we provide background information and related work. In section III, we describe the key principles for incorporating TSN technology in the transport network. Subsection III-A describes both the TSN control and data plane; Subsection III-B describes TSN for the Fronthaul 802. 1CM profile; Subsection III-C elaborates on how a 5GS can operate as a TSN bridge and Subsection III-D describes the case of using TSN in the Xhaul. In section IV, we describe our architectural proposal and develop methods to interconnect the TSN control plane with the network slice management system of the mobile network. In section V, we report implementation experience and performance evaluation results. In section VI, we summarize our findings and outline future research directions.
However, it cannot be used as a panacea to solve any transport network connectivity issue. For example, pure Layer 2 connectivity is impossible to scale. In this regard, it is expected that the outcome of the liaison activities between IEEE TSN and IETF DetNet will actually drive the TSN-aware Transport network, especially for large-scale deployments. In this context, relevant standardization and research activities are RFC 8655 (Deterministic Networking Architecture), RFC 8938 (DetNet Data Plane Framework), RFC 8939 (DetNet IP Data Plane), and  [27] ,  [28]  where a TSN-aware MPLS data plane is specified for DetNet. Control plane signaling, for DetNet covering distributed, centralized, and hybrid signaling scenarios in the TSN and SDN domain are investigated in  [51] , IP inter-networking with TSN is investigated in  [52] , management operations for DetNet are investigated in  [53].
From one implementation perspective, this module can operate inside CNC, from another perspective this could be implemented as a part of the TSN orchestrator (or even inside CUC). However, it could also be independent and expose service to both CUC/CNC. The different components of the architecture and their functionalities are detailed as follows: • TN-NSSMF: TN-NSSMF is responsible for the orchestration and management of the TN-NSSI counterpart. • E2e Slice-DB: TN Slice Config and State info: We assume that this is a database infrastructure with all the TN-NSSI information. This database is used to store all the information regarding NSI state, NSI templates, reserved resources, network functions, and configurations. • Multilayer Transport Network Slice Controller (MTNSC): This entity is defined by IETF in  [54] ,  [55].
• TSN-NetSiceDB: The database infrastructure with all the TSN TN NSSIs' state information. This database infrastructure is not controlled by 3GPP. It is used to store all the information regarding identification and mappings between NSI and NSSI, store the TSN TN NSSI state, templates, reserved resources, network functions, configurations, etc. It is also the entity where TSN TN NSSI operation and management information are stored. For every network element or network service, we consider that for each TSN TN NSSI, only specific Operations, Administration, and Maintenance (OAM) information is stored at the TN-NSDB that is relevant only to this TSN TN NSSI. This OAM filtering operation could be implemented by a domain controller, however, how this operation is made is out of the scope of the interface specification.
g. , resource reservation and allocation to the network instances). • Monitoring manager: Collect usage information of NFVI resources by VNF instances. JOX is operating on top of Canonical's JUJU, which serves as a Virtualized Network Function Manager (VNFM). Regarding the VIMs responsible for managing the infrastructure, we experimented with LXC containers, Kernel-based Virtual Machines (KVMs), and physical machines. JOX also supports a plugin architecture based on the microservice paradigm. By design, the interaction between plugins and the JOX core is made through a message bus (RabbitMQ), while each plugin serves a different purpose, like store periodic statistics into NoSQL elastic-search database, or interacting directly with the RAN using FlexRAN plugin. In JOX, slices and subslices are created as follows.
Thereby allowing access to enough TNBC data to sustain a complete response heterogeneity investigation. We show evidence that local ML models relying on Whole-Slide Images (WSIs) at diagnosis are able to predict the histological response to NACT as accurately as current clinical approaches, which rely on time-consuming expert annotations. We demonstrate that collaborative training further improves performance over single-center training outperforming clinical methods. Our ML model is interpretable by design, and we show that it is sensitive to specific histological patterns. While we identify known predictive biomarkers among them, this proof of concept for real-world collaborative FL paves the way for future biomarker discovery using unprecedently large datasets.
In the same line of work, it has been recently shown that TILs can be automatically detected in WSIs with high accuracy  [37, 38] , and that RCB can be computed on post-treatment WSIs with a DL model  [39]. To the best of our knowledge, a single work has investigated the use of ML for NACT response prediction based on WSIs  [40] , and shown that WSIs contain useful signal for NACT response prediction. Yet, this study only reaches the same accuracy as clinicians, and relies on a relatively small dataset of 122 WSIs from a single center. The potential of ML to improve prognosis and discover new predictive biomarkers has yet to materialize in the case of NACT response in early TNBC. Due to the relatively small prevalence of TNBC in the general breast cancer population (15% or 9, 000 new cases per year in France  [41] ), TNBC data is difficult to access in large quantities.
However the complexity of its deployment in a real-world network of hospitals has hindered its progress, and so far federated learning studies have mostly been limited to simulations  [44, 45, 46, 47, 48, 49, 50]. In this study, through an immense interdisciplinary effort across multiple institutions, we manage to perform the first real-world Federated Learning analysis on TNBC data, reaching for one of the first times a sample size of more than 500 TNBC patients between two hospitals (top 10% of studies in terms of patient counts according to Fig. 6  in Supp. Mat. ), which represents a milestone in TNBC research and more generally in showing the applicability of FL to biomedical research. Our main contributions are: • Performing the first real-world collaborative Federated Learning between histopathology datasets behind hospitals' firewalls, without ever sharing data itself, as displayed in Fig.
4  (a) the distribution of the tiles score on the entire distributed dataset and extract the top and minimum scoring tiles across the slides from both centers. We show that the scores distribution on all tiles is bimodal, and that tiles associated with high and low scores are presenting clear histological patterns. Thanks to an extensive expert annotation of pre-defined criteria available in Tab. 5 in Supp. Mat. , we are able to highlight specific pattern associated with response or non-response in the entire test sets. On Fig. 4  (b) we show that such patterns indeed appear clearly in the annotated tiles: presence of TILs (pvalue 5. 5 • 10 -12 ), hemorrhagic suffusion (p-value 1. 6 • 10 -5 ) along with necrosis are associated with overall high scores and probability to respond, while presence of apocrine tumor cells with abundant eosinophilic granular cytoplasm and prominent nucleoli (p-value 1.
It is known that TNBC comprises at least 6 molecular subtypes  [55]  and that treatment effects should be investigated from within sub-groups  [56]. Our model indeed underlines that such subgroups (Basal-like and Immunomodulatory versus LAR) were important in predicting the response and it is possible that finer grained analysis would be even more performant. Indeed it has been shown before that models can extract molecular information from WSI  [57]. However, this information is not always available in cancer centers nor is there a definitive agreement on sub-types classifications for TNBC, which limits the deployment of specialized models. This makes our models more general, but possibly hinders their performances. Furthermore, populations of a specific molecular subtype would necessarily contain fewer slides and even more unbalanced distributed datasets.
After NACT, at surgery the breast and the axillary lymph nodes are probed to measure the treatment effectiveness based on the Residual Cancer Burden (RCB). NACT response Responders are split into two groups according to a binarization of their RCB status after treatment. Details about the binarization of RCB to obtain the histological NACT response are provided in Tab. 4 in the Supp. Mat. Dataset After selecting records matching this pathway in each hospital, we end up with 102 slides in Cohort A associated with 99 patients and 427 slides in Cohort B associated with 420 patients. We treat each slide as an individual data point, but stratifications are done respecting the patient information. The complete list of clinical variables available in cohorts A and B are provided in Tab. 4 in the Supp.
Regarding scanners, in Cohort A, a Leica APERIO AT2 scanner was used producing SVS images, in Cohort B two scanners were used at different periods of time: a Philips Ultra Fast Scanner 1. 6 RA and a Hamamatsu Nanozoomer S360 producing TIFF and NDPI images. In both centers, slides were scanned with up to x40 magnification but in practice we only used x20 zoom-level in our experiments (see Section 5. 3) as we find it provides the best trade-off between the size of the context, the level of details of the tiles and the computational burden of the method, corroborated by the literature  [66, 30]. Train-test dataset split We reserve approximately 20% of the slides in each center for a sanctuarized test set where we evaluate only once the final models, and use the remaining ones for cross-validation purposes.