dataesr/scientific-paragraphs-categorization huggingface dataset:
The RUs are connected to the ECs. In fact, an EC mainly acts as an aggregation point where the data of a group of RUs is collected at this point. The fronthaul between the RUs and ECs is assumed to be mmWave links. Furthermore, the ECs transmit the aggregated data to  the CC via midhaul. Midhaul links are implemented using cost-efficient TWDM-PON. In this architecture, the CC and ECs are equipped with DUs which are able to perform function processing of requested content. Therefore, these DUs can serve any connected RUs by sharing their computational resources. For instance, in upstream, traffic from cells can be partially processed at the EC so that bandwidth requirement can be relaxed for midhaul links, then the remaining processing will be conducted at the CC. However, the EC is usually less energy-efficient than the CC, because the number of DUs at the CC is larger than that in each EC.
The main novelty of the polar code design in SooGREEN was the use of a hybrid decoding algorithm that combined successive cancellation (SC) decoding with majority logic (MJL) decoding. The hybrid method struck a balance between the low-complexity nature of SC decoding and the extreme speed of MJL decoding. The result was a soft IP core that was able to exceed 100 Gb/s throughput on an fieldprogrammable gate array (FPGA) test platform. Figure  16  shows the performance of some of the polar coding schemes that have been implemented in SooGREEN. The figure also shows the performance of Reed-Solomon RS(255,239) code, which has been used as a benchmark. The acronyms HD and SD stand for hard-decision and soft-decision processing of channel outputs. In HD, the channel output is quantized to 1 bit, while the channel output is quantized to 5 bits in SD.
Concerning the development tools, in addition to vendor-proprietary solutions such as Vivado (Xilinx) or Quartus (Intel), a bunch of open source high-level programming tools is emerging. These tools assist the developers in the HW accelerator programming task. We investigated the usability and the performance of two of them, Open Computing Language (OpenCL)  [46] ,  [47]  and Heterogeneous System Architecture (HSA)  [48]. The results showed that OpenCL is a mature solution providing code portability across different platforms (''write once, compile for different targets'') and offering simplicity in code generation. Performance is acceptable but does not match available target-specific libraries. On the other hand, HSA is an interesting tool but not enough mature yet for product development.
A. GREEN MOBILE NETWORKS Mobile networks are today responsible for about 20 -25% of the aggregate information technology network (ITN) consumption including the data center, and will dramatically increase in the next decade due to new networks deployments and traffic demand exponential increase (about 30 to 40% per year). In a short-term view, trends are moving towards making energy consumption more traffic load dependent. Actual macro BS is highly consuming even for low traffic load. A special care should be maintained for site-sharing initiatives (including outsourcing towards Tower Companies) to maintain assets in the green field. The technical environment (cooling, energy backup) shall not be neglected as they are highly consuming. Ventilation should be stated as a default installation configuration rather than active cooling.
E. USE OF ARTIFICIAL INTELLIGENCE To capitalize on the long exerted efforts in SooGREEN summarized in this article, another EU Celtic-Plus project has been approved and started this year focusing to further employ the recent AI advances for greener networks. The envisioned efforts in the new project, entitled AI for Green Networks or AI4Green for short, target core and access network segments alike. We briefly highlight in the following some directions where AI is seen to offer remarkable savings and smarter control of network energy consumption. The main objective of the AI4Green project is to leverage the available huge datasets of network measurements at the different network operators and equipment vendors, along with the rich literature and powerful tools of machine learning, big data and AI to optimally provision, operate, and maintain mobile networks.
ACKNOWLEDGMENT The authors would like to thank  Abdulrahman Alabbasi ,  Didier Marquet ,  Mamdouh El-Tabach ,  Mats Nilsson ,  Wilfried Yoro  and  Xinbo Wang  for their contributions and active participation in the  EU Celtic-Plus SooGREEN Project  and its associated publication, dissemination and standardization efforts. Authors P. Silva  and  N. T. Almeida  would like to address a special thank to  Dr. Rui Campos , from  INESC TEC , for his inestimable and continuous contribution to the developed work as well as to  Tiago Oliveira  and  Saravanan Kandasamy  for their valuable participation in the initial phase of the project. They also would like to thank  Pedro Rodrigues ,  José Fonseca , and  Diogo Costa , all from  Eurico Ferreira , for the helpful discussions and accomplished collaborations along the realization of this work.
However, very few of them attempted to investigate the preclinical phase of the disease, the very early asymptomatic phase. Indeed, this type of study is a very challenging task since it requires an imaging database starting before the appearance of the disease, and the corresponding long follow-up study. Therefore, in the past, such studies have been based on subjects with rare autosomal dominant mutations associated with a high risk of developing dementia 7-10 or on longitudinal studies with long follow-up in which brain imaging have been performed before the appearance of clinical symptoms (i. e. , memory impairment)  [11] [12] [13]. In these previous long follow-up studies, the starting point of the neurodegeneration was not determined since incident cases already present brain morphometric differences at baseline 7 or 10 years before the diagnosis  [13] [14] [15]  or 5 before the apparition of Mild Cognitive Impairment (MCI)  16.
Finally, the lifespan evolution of these imaging biomarkers many years before cognitive decline also remains unknown. Determining the timing of the onset of neurodegeneration would require longitudinal MRI datasets with several decades of follow-up. So far, due to obvious technical reasons, such imaging dataset does not exist yet. Consequently, in this paper, we propose to consider alternative questions such as -When does the AD model diverge from the normal aging model? How do the trajectories of biomarker models differ in AD from normal.
• For the CN model, we used the N = 2944 subjects from 9 months to 94 y of the cognitively normal dataset as done in  21. • For the AD/MCI model, we used N = 3262 subjects. We mixed AD patients, with MCI patients and with young CN considered as presymptomatic subjects. We used 426 AD patients (from 55 y to 96 y), 959 MCI patients (from 55 y to 92 y) of the AD/MCI dataset and all the CN younger than 55 y (i. e. , 1877 subjects). These subjects are included in the CN used for CN trajectory. • For the AD model, we used N = 2303 samples. We mixed AD patients with young CN. More precisely, we used 426 AD patients (from 55 y to 96 y) of the AD/MCI datasets and all the CN younger than 55 y (i. e. , 1877 subjects). • For the MCI model, we used N = 2836 samples. Here, 959 MCI patients (from 55 y to 92 y) of the AD/MCI datasets were mixed with all the CN younger than 55 y (i.
Datasets description. To study structural changes across the entire lifespan for CN and AD, we aggregated several open access databases to construct two datasets. In the following, CN and AD/MCI datasets will be described. The four previously described groups are built on these datasets. Cognitively normal dataset (N = 2944). The cognitively normal dataset is composed of the 3296 T1-weighted (T1w) MRI. The composition of the nine open access databases used to build the control dataset is provided in Table  1. As explained in  21  , after a quality control (see image processing section), only 2944 MRI were kept. The female proportion is 47% for the remaining subjects and the age range is [0. 75-94] years. AD/MCI dataset (N = 1385). The AD/MCI dataset is composed of 426 AD patients and 959 MCI patients extracted from OASIS, AIBL, ADNI1 and ADNI2 databases.
Details on clinical criterion for groups definition are provided in  28  for ADNI1, ADNI2 and AIBL and in  29  for OASIS. After a quality control, only 1385 MRI were kept. The female proportion is 44% for the remaining subjects and the age range is [55-96] years (see Table  2 ). In the following, more details are provided about acquisition protocols of the different datasets used in this study. • C-MIND: 266 images of control subjects from the C-MIND dataset (https://research. cchmc. org/c-mind/) are used in this study. All the 3D T1-weight (T1w) MPRAGE high-resolution MRI were acquired at the same site on a 3 T scanner with spatial resolution of 1 mm 3 acquired using a 32 channel SENSE head-coil. • NDAR: 415 of control subjects from the Database for Autism Research (NDAR) (https://ndar. nih.
gov) are used in this study. The T1w 3D MRI were acquired on 1. 5 T MRI and 3 T scanners. In our experiments, we used the NIHPD (http://www. bic. mni. mcgill. ca/nihpd/info/data_access. html) dataset and 197 images of control subjects from the Lab Study 19 of National Database for Autism Research. For the NIHPD dataset, the 3D T1w SPGR MRI were acquired at six different sites with 1. 5 Tesla systems by General Electric (GE) and Siemens Medical Systems with spatial resolution of 1 mm 3. The 3D T1w MPRAGE MRI from the Lab Study 19 were scanned using a 3 T Siemens Tim Trio scanner at each site with spatial resolution of 1 mm     Image processing. All the considered images were processed with the volBrain pipeline  30  (http://volbrain. upv. es). The volBrain system is a web-based online tool providing automatic brain segmentation and generating report summarizing the volumetric results.
The full processing time is around 10 minutes. In the past 2 years, vol-Brain has processed online more than 75. 000 brains for approximately 1800 users. In a recent work, we compared volBrain pipeline with two well-known tools used on MR brain analysis (FSL and Freesurfer). We showed significant improvements in terms of both accuracy and reproducibility for intra and inter-scanner scan-rescan acquisitions  30. The volBrain processing pipeline includes several steps to improve the quality of the input MR images and to homogenize their contrast and intensity range  30. The volBrain pipeline achieves the following preprocessing steps: (1) denoising using spatially adaptive non-local means 31 , (2) rough inhomogeneity correction using N4 method 32 , (3) affine registration to MNI152 space using ANTS software 33 , (4) SPM based fine inhomogeneity correction  34  and (5) tissue based intensity standardization  35.
This model selection procedure was applied to all the considered structures. In this study, we studied the following brain structures: lateral ventricles, hippocampus, amygdala, caudate, putamen, accumbens, globus pallidus and thalamus. Moreover, tissue classification was used to obtain the global volume of white matter and gray matter. All statistical tests were performed with Matlab© using default parameters. Afterwards, percentage of relative rate of change per year and percentage of abnormality were computed on the estimated models. The relative rate of change in percentage per year was computed as the first derivative of the model divided by the model  39  and the abnormality in percentage as the absolute difference between pathological models and control model divided by control model (i.
e. , absolute relative difference compared to control). Finally, we studied lifespan classification accuracy of several AD biomarkers. To this end, for each age, a classification was performed with a linear discriminate analysis (LDA) using all the samples in an interval of 10 y (i. e. ,+/-5 years). We used Area Under the Curve (AUC) as classification performance metric. The AUC was estimated through a cross-validation procedure based on a repeated K-fold using 10 iterations and 10-fold. Finally, the average AUC obtained over the 10 repetitions is reported for all ages. Classification experiments were performed with Matlab© using default parameters.
The importance of taking into account volume increase at advanced age in normal aging has been previously mentioned  26. In the present study, early divergence of the models has been also observed for thalamus around 42 y. Thalamic atrophy was previously reported in AD literature  73, 74. However, we found that thalamus abnormality was very small (4. 6% at 81 y). This may explain why only a small number of studies have mentioned that this structure could be affected by AD, because a large number of subjects are needed to detect such subtle atrophy. In this study, to overcome the absence of longitudinal datasets with several decades of follow-up, we processed a massive number of cross-sectional MRI to generate a model of the volume trajectory in several brain structures in AD across the entire lifespan.
Conclusion In this work, to analyze when AD brain model diverges from the cognitively normal model we build extrapolated lifespan models of AD brain structures by combining multiple large-scale databases. We found an early divergence of AD model from control model for hippocampus before 40 y followed by lateral ventricles and amygdala around 40 y. Moreover, we observed a similar abnormality evolution for the hippocampus and the amygdala. Finally, the model of ventricular enlargement shows that the volume of lateral ventricles reaches a peak of abnormality at 65 y before decreasing due to important enlargement in normal aging.
308 normal subjects from the International Consortium for Brain Mapping (ICBM) dataset (http:// www. loni. usc. edu/ICBM/)obtainedthrough the LONI website are used in this study. The T1w MPRAGE MRI were acquired on a 1. 5 T Philips GyroScan imaging system (Philips Medical Systems, Best, The Netherlands) with spatial resolution of 1 mm 3. •IXI: 588 normal control from Information eXtraction from Images (IXI) database (http://brain-development. org/ixi-dataset/) are used in this study. The MRI are T1w images collected at 3 sites with 1. 5 and 3 T scanners with spatial resolution close to 1 mm 3.
Acknowledgements This work benefited from the support of the project DeepVolBrain of the  French National Research Agency  ( ANR-18-CE45-0013 ). This study was achieved within the context of the  Laboratory of Excellence TRAIL   ANR-10-LABX-57  for the  BigDataBrain  project. Moreover, we thank the  Investments for the future Program IdEx Bordeaux  ( ANR-10-IDEX-03-02 ,  HL-MRI  Project),  Cluster of excellence CPU  and the  CNRS. This study has been also supported by the  DPI2017-87743-R grant  from the  Spanish Ministerio de Economia, Industria y Competitividad. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the  C-MIND Data Repository  (accessed in Feb 2015) created by the  C-MIND study of Normal Brain Development.
This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by  Cincinnati Children's Hospital Medical Center  and  UCLA  and supported by the  National Institute of Child Health and Human Development  (Contract #s  HHSN275200900018C ). A listing of the participating sites and a complete listing of the study investigators can be found at https://research. cchmc. org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the  NIH -supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the  National Institutes of Health  to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the  NIH Pediatric MRI Data Repository.
308 normal subjects from the International Consortium for Brain Mapping (ICBM) dataset (http:// www. loni. usc. edu/ICBM/)obtainedthrough the LONI website are used in this study. The T1w MPRAGE MRI were acquired on a 1. 5 T Philips GyroScan imaging system (Philips Medical Systems, Best, The Netherlands) with spatial resolution of 1 mm 3. •IXI: 588 normal control from Information eXtraction from Images (IXI) database (http://brain-development. org/ixi-dataset/) are used in this study. The MRI are T1w images collected at 3 sites with 1. 5 and 3 T scanners with spatial resolution close to 1 mm 3. • OASIS: 315 control subjects and 98 AD/MCI patients from the Open Access Series of Imaging Studies (OASIS) database (http://www. oasis-brains. org) are used in this study. The MRI are T1w MPRAGE image acquired on a 1.
5 T Vision scanner (Siemens, Erlangen, Germany) and resliced at 1 mm 3. • ADNI1: 228 control subjects and 587 AD/MCI patients from the Alzheimer's Disease Neuroimaging Initi- ative (ADNI) database (http://adni. loni. usc. edu) phase 1 are used in this study. These baseline MRI are T1w MPRAGE acquired on 1. 5 T scanners at 60 different sites across the United States and Canada with recon-structed spatial resolution of 1 mm 3 • ABIDE: 528 control subjects from the Autism Brain Imaging Data Exchange (ABIDE) dataset (http:// fcon_1000. projects. nitrc. org/indi/abide/)are used in this study. The MRI are T1w MPRAGE acquired at 20 different sites on 3 T image and the details of acquisition, informed consent, and site-specific protocols are available on the website. • ICBM:.
(53%) 39. 65 (26. 62) [0. 74-94]Table 1. 1 Dataset description for CN data. This table provides the name of the dataset, the MR acquisition configuration, the number of considered image before and after QC, the gender proportion after QC and the average mean, standard deviation in parentheses and the interval in brackets. Dataset Before QC After QC Gender after QC Age in years after QC C-MIND 266 236 F = 129 M = 107 8. 44 (4. 35) [0. 74-18. 86] NDAR 612 382 F = 174 M = 208 12. 39 (5. 94) [1. 08-49. 92] ABIDE 528 492 F = 84 M = 408 17. 53 (7. 83) [6. 50-52. 20] ICBM 308 294 F = 142 M = 152 33. 75 (14. 32) [18-80] IXI 588 573 F = 321 M = 252 49. 52 (16. 70) [20. 0-86. 2] OASIS 315 298 F = 187 M = 111 45. 34 (23. 82) [18-94] AIBL 236 233 F = 121 M = 112 72. 24 (6. 73) [60-89] ADNI 1 228 223 F = 108 M = 115 75.
96 (5. 03) [60-90] ADNI 2 215 213 F = 113 M = 100 74. 16 (6. 39) [56. 3-89] Total M = 1565 Dataset 3296 F = 1379 (47%) 2944. • AIBL: 236 control subjects and 112 AD/MCI patients from the Australian Imaging, Biomarkers and Lifestyle (AIBL) database (http://www. aibl. csiro. au/) are used in this study. The baseline MRI are T1w image acquired on 3 T MR scanners with the ADNI protocol (http://adni. loni. ucla. edu/research/protocols/mri-protocols) and with custom MPRAGE sequence on the 1. 5 T scanners.
Conflict of interest JAS has received consultant fees from Crealta/Horizon, Fidia, UBM LLC, Medscape, WebMD, the National Institutes of Health and the American College of Rheumatology. JAS is a member of the Veterans Affairs Rheumatology Field Advisory Committee. JAS is the editor and the Director of the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis. JAS served as a member of the American College of Rheumatology's (ACR) Annual Meeting Planning Committee (AMPC) and Quality of Care Committees, the Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee and the co-chair of the ACR Criteria and Response Criteria subcommittee. LM is a Principal Investigator on the Australian Rheumatology Association Database that has received arms-length funding from 8 companies.
The number of facilities of various types a city has (e. g. number of bars, nightlife spots more generally, bus stops, etc. ) is documented e. g. by FourSquare. The extent to which a national food culture relies on various food categories (e. g. beverages, alcoholic beverages more specifically, sugary snacks, etc. ) can be found in the OpenFoodFact database. Such data offers interesting opportunities to uniquely and objectively characterize concepts in terms of such counters. Most immediately, one could use the aggregate of these counters over all concepts, and contrast the counter of a particular concept with that average. The stronger it differs (positively or negatively), the stronger it should characterize that counter. In this way, one could obtain a set of unique aspects of each concept.
A challenge with this approach, however, is that counters are often organized in a hierarchy-a toy example is shown in Fig. 1  for illustration. Indeed, bars are examples of nightlife spots in the FourSquare taxonomy, and alcoholic beverages are particular kinds of beverages in the OpenFoodFact dataset. This means that the characterization can be highly redundant. Moreover, information about one counter can affect one's expectation about another (e. g. the fact that there are lots of bars can be explained by a high number of nightlife spots more generally), which may influence one's expectation about other counters (e. g. this may inflate one's expectation about night clubs). Hierarchies have been extensively investigated in the literature for decades  [1, 9, 16, 18]. Most studies have only considered hierarchies to restrain the pattern syntax (i.
These contrastive antichains pinpoint non-redundant concepts of the hierarchy that are at the same time compact and unexpected. In Figure  1 , the set {Food, Bar} is an antichain, because none of these two items is a predecessor of the other one (they are not comparable). This antichain informs the user about the number of Food and Bar venues in the studied dataset. This antichain is informative if the number of Food and Bar venues are different from the expected values by the user. The main contributions are as follows: • Section 2 introduces and formalizes the problem of mining contrastive antichains in hierarchically organized sets of counters. • Section 3 formalizes the interestingness of a contrastive antichain with respect to prior beliefs about the values of these counters. These prior beliefs can be derived from a concept with which the concept under investigation needs to be contrasted, or from an aggregate count over all concepts.
Algorithm 2: GreedySearch Input: H : the hierarchical dataset, values of conditional probabilities Pr( ŷi |O) for each y i Output: P: A greedily constructed antichain 1 P ← ∅, C ← E, Q ← ∅ 2 while C ∅ do 3 e ⋆ ← argmax e ∈C I C (P ∪{e }|O) D L(P ∪{e },Q ∪{e }) 4 f ⋆ ← argmax e ∈min(C ) I C (P |O) D L(P ,Q ∪{e }) 5 if I C (P ∪{e ⋆ }|O) D L(P ∪{e ⋆ },Q ∪{e ⋆ }) ≥ I C (P |O) D L(P ,Q ∪{f ⋆ }) then 6 P ← P ∪ {e ⋆ } 7 Q ← Q ∪ {e ⋆ } 8 C ← {e ∈ C | e ⪯̸ e ⋆ ∧ e ⋆ ⪯̸ e } 9 else 10 Q ← Q ∪ {f ⋆ } 11 C ← {e ∈ C | f ⋆ ⪯̸ e } 12 return P ontologies in different stages of the mining process (data understanding, task design, etc. ). The generic problem of Semantic Data Mining has been defined in  [16, 18] : given a set of objects annotated with ontology terms, the goal is to find hypothesis, expressed by domain ontology terms, explaining the given empirical data.
Specifically in  [15, 18, 19] , the Semantic Subgroup Discovery problem is studied: given a dataset where each object is annotated with ontology terms and belongs to a specific class, the goal is to find a conjunction of ontology terms (a conjunctive rule) that corresponds to a set of object discriminating a specific class. To evaluate the discriminativity of a conjunctive rule, these approaches mainly use the wW RAcc heuristic based on the W RAcc 1  measure. This heuristic has been initially proposed for the generic task of Subgroup Discovery  [14]. A similar method has been proposed in  [1]  to take benefit from ontology structures in order to optimize and reduce the redundancy in the search of conjunctive rules that satisfy some user specified constraints. In  [2] , this problem of rule learning is tackled based on ILP (Inductive Logic Programming), and ontology structures are integrated by providing additional clauses to the ILP solver.