dataesr/scientific-paragraphs-categorization huggingface dataset:
Furthermore, the invacost package is an ideal tool to help achieve novel scientific assessments of invasion impacts, not merely the reported costs. Specifically, the continued improvement of the invacost package as well as the interoperable nature of the dynamic InvaCost database will enable: (a) the linkage of cost estimates to established indicators of alien impacts world-wide (e. g. GRIIS: Global Register of Introduced and Invasive Species, Pagad et al. , 2018; SEICAT: Socio-Economic Impact Classification of Alien Species, Bacher et al. , 2018) to ensure a standardized assessment of IAS impacts across regions and socio-economic sectors over time; (b) investigation of the relationships between invasion costs, ecological traits and phylogeny of IAS to better understand and predict impacts; (c) movement towards predictive approaches to support, evaluate and prioritize cost-efficient management strategies according to various scenarios of invasions in our changing global environment (Lenzner et al.
This paper introduces the multi-freq-ldpy Python package for multiple frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is a gold standard for achieving local privacy with several real-world implementations by big tech companies such as Google, Apple, and Microsoft. The primary application of LDP is frequency (or histogram) estimation, in which the aggregator estimates the number of times each value has been reported. The presented package provides an easy-to-use and fast implementation of state-of-the-art solutions and LDP protocols for frequency estimation of: single attribute (i. e. , the building blocks), multiple attributes (i. e. , multidimensional data), multiple collections (i. e. , longitudinal data), and both multiple attributes/collections. Multi-freq-ldpy is built on the well-established Numpy package -a de facto standard for scientific computing in Python -and the Numba package for fast execution.
The single frequency estimation task has received considerable attention in the literature (e. g. ,  [3, 11, 19, 20, 22] ) as it is a building block for more complex tasks dealing with temporal and/or multidimensional aspects. More recently, in  [1]  we have investigated the frequency estimation task of multiple attributes and proposed a solution named Random Sampling Plus Fake Data (RS+FD) that outperforms the state-of-the-art solution (divide users into groups to report a single attribute) commonly adopted in the literature  [14, 18]. In addition, our work in  [2]  optimized state-of-the-art LDP protocols  [9, 11, 20]  for longitudinal studies (i. e. , multiple frequency estimation over time), which are based on the memoization framework from  [9]. In this paper, we introduce multi-freq-ldpy 1  , which is the first open-source Python package providing an easy-to-use and fast implementation of state-ofthe-art solutions and LDP protocols for the task of private multiple frequency estimation.
$ pip install multi -freq -ldpy The multi-freq-ldpy package is mainly based on the standard numpy  [17]  and numba  [13]  libraries, as the goal is to enable an easy-to-use and fast execution toolkit. The source code, documentation, several (Jupyter notebook) tutorials as well as an introductory video are available at the GitHub page (https: //github. com/hharcolezi/multi-freq-ldpy). Released under the MIT open source license, multi-freq-ldpy is free to use and modify, and user contributions are encouraged to help enhance the library's functionality and capabilities.
Main Modules (Tasks Covered) The first task multi-freq-ldpy covers is single-frequency estimation under the pure_frequency_oracles module, which is a building block for the other tasks. The package currently features six 2  state-of-the-art LDP protocols, namely: Generalized Randomized Response (GRR)  [11] , Binary Local Hashing (BLH)  [3, 20] , Optimal Local Hashing (OLH)  [20] , Subset Selection (SS)  [19, 22] , Symmetric Unary Encoding 3  (SUE)  [20] , and Optimal Unary Encoding (OUE)  [20]. Secondly, for multidimensional frequency estimation (i. e. , multiple attributes), three solutions are implemented from  [1]  with all aforementioned LDP protocols. These solutions, under the mdim_freq_est module, are: SPL) a naïve solution that splits the privacy budget ϵ over the total number of attributes; SMP) a state-of-the-art solution that randomly samples a single attribute and report it with ϵ-LDP  [2, 14, 18, 20] , and RS+FD) a state-of-the-art solution that randomly samples a single attribute to report with an amplified (ϵ ′ > ϵ)-LDP as it also generates one uniformly random fake data for each non-sampled attribute.
Thirdly, for single longitudinal frequency estimation, multi-freq-ldpy features Microsoft's dBitFlipPM  [5]  protocol and all the longitudinal LDP protocols developed in  [2]  based on the Google's RAPPOR  [9]  memoization solution (i. e. , two rounds of sanitization). These protocols, following the long_freq_est module, are: Longitudinal GRR (L-GRR) that chains GRR in both rounds and four Longitudinal Unary Encoding (L-UE) protocols that chains SUE and/or OUE in both rounds of sanitization (i. e. , L-SUE, L-SOUE, L-OUE, and L-OSUE). Indeed, L-SUE refers to the utility-oriented version of RAPPOR that chains SUE twice (a. k. a. basic RAPPOR  [9] ). Finally, for longitudinal multidimensional frequency estimation, the package features both SPL and SMP multidimensional solutions with all the longitudinal protocols from  [2]  and Microsoft's dBitFlipPM  [5] , under the long_mdim_freq_est module.
Worked Example: Single Frequency Estimation For example, the following use case demonstrates how easy it is to perform single frequency estimation with the GRR  [11]  protocol. In this example, there is a single attribute A = {a 1 ,. , a k } with domain size k = |A|, n users, and the privacy guarantee ϵ. The complete code to execute this task is illustrated in Listing 1. 1 with the resulting estimated frequency for a given set of parameters and a randomly generated dataset. One can note that after the import functions, we essentially need two lines of codes to simulate the LDP data collection pipeline through applying the GRR_Client and GRR_Aggregator functions.
Worked Example: Longitudinal Frequency Estimation In this second example, we demonstrate how to perform single longitudinal frequency estimation with the L-SUE protocol  [2]  (i. e. , RAPPOR  [9] ) using multi-freq-ldpy. In this specific example, there is a single attribute A = {a 1 ,. , a k } with domain size k = |A|, n users, and the privacy guarantees ϵ perm (upper bound for infinity reports, a. k. a. ϵ ∞ in  [9] ) and ϵ 1 (lower bound for the first report  4  ). The complete code to execute this task is illustrated in Listing 1. 2 with the resulting estimated frequency for a given set of parameters and a randomly generated dataset.
Worked Example: Multidimensional Frequency Estimation In another example, we demonstrate how to perform frequency estimation of multiple attributes with the RS+FD  [1]  solution and the GRR protocol  [11]  using multi-freq-ldpy. In this setting, there are n users, the privacy parameter ϵ, and each user's profile is a tuple composed of d attributes A = {A 1 ,. , A d } in which each attribute A j has a discrete domain of size k j = |A j |, for j ∈ [1, d]. The complete code to execute this task is illustrated in Listing 1. 3 with the resulting estimated frequencies for a given set of parameters and a randomly generated dataset.
Conclusion In this paper, we have showcased the first open-source Python package named multi-freq-ldpy for private multiple frequency estimation under LDP guarantees. More specifically, we presented the modules of Version 0. 2. 4 of the library, but also its easy-to-use essence, requiring two lines of code to simulate the LDP data collection pipeline. In addition to the standard single frequency estimation task, multi-freq-ldpy features separate and combined multidimensional and longitudinal data collections, i. e. , the frequency estimation of multiple attributes, of a single attribute throughout time, and of multiple attributes throughout time. As an open source project, we welcome and encourage code contributions from the community to help grow and improve the library in all of its forms.
Multi -Freq -LDPy functions for GRR protocol from m ul ti _ fr eq _ ld p y. p u r e _ f r e q u e n c y _ o r a c l e s. GRR import GRR_Client , GRR _Aggre gator#NumPy library import numpy as np # Parameters for simulation eps = 1 # privacy guarantee n = int (1 e6 ) # number of users k = 5 # attribute 's domain size # Simulation dataset following Uniform distribution dataset = np. random. randint (k , size = n ) # Simulation of data collection reports = [ GRR_Client ( user_data , k , eps ) for user_data in dataset ] # Simulation of server -side aggregation est_freq = GRR_A ggrega tor ( reports , k , eps ) >>> array ([0. 199 , 0. 201 , 0. 199 , 0. 202 , 0. 199])Listing 1. 1: Code snippet for performing single frequency estimation with the GRR [11]  protocol.
Multi -Freq -LDPy functions for L -SUE ( RAPPOR ) protocol from m ul ti _ fr eq _ ld p y. long_freq_est. L_SUE import L_SUE_Client , L _ S U E _ A g g r e g a t o r # NumPy library import numpy as np # Parameters for simulation eps_perm = 2 # longitudinal privacy eps_1 = 0. 5 * eps_perm # first report privacy n = int (1 e6 ) # number of users k = 5 # attribute 's domain size # Simulation dataset following Uniform distribution dataset = np. random. randint (k , size = n ) # Simulation of data collection reports = [ L_SUE_Client ( user_data , k , eps_perm , eps_1 ) for user_data in dataset ] # Simulation of server -side aggregation est_freq = L _ S U E _ A g g r e g a t o r ( reports , eps_perm , eps_1 ) >>> array ([0. 199 , 0. 201 , 0. 2 , 0. 198 , 0. 202]) Listing 1. 2: Code snippet for performing single longitudinal frequency estimation with the L-SUE [2] (i.
e. , RAPPOR [9]) protocol. # Multi -Freq -LDPy functions for RS + FD solution with GRR from m ul ti _ fr eq _ ld p y. mdim_freq_est. RSp FD_sol ution import RSpFD_GRR_Client , R S p F D _ G R R _ A g g r e g a t o r # NumPy library import numpy as np # Parameters eps = 1 # privacy guarantee n = int (1 e6 ) # number of users k = 4 # attribute 's domain size d = 3 # number of attributes lst_k = [ k for _ in range ( d ) ] # attributes ' domain size # Simulation dataset following Uniform distribution dataset = np. random. randint (k , size =( n , d ) ) # Simulation of data collection reports = [ R S p F D _ G R R _ C l i e n t ( user_tuple , lst_k , d , eps ) for user_tuple in dataset ] # Simulation of server -side aggregation est_freq = R S p F D _ G R R _ A g g r e g a t o r ( reports , lst_k , d , eps ) >>> array ([0.
255 , 0. 246 , 0. 248 , 0. 251] , [0. 252 , 0. 247 , 0. 249 , 0. 252] , [0. 252 , 0. 255 , 0. 244 , 0. 249]) Code snippet for performing multidimensional frequency estimation with the RS+FD[GRR] [1] protocol. # Multi -Freq -LDPy functions for SMP solution and L -GRR protocol from m ul ti _ fr eq _ ld p y. l o n g _ m d i m _ f r e q _ e s t. L _SMP_S olutio n import SMP_L_GRR_Client , S M P _ L _ G R R _ A g g r e g a t o r # NumPy library import numpy as np # Parameters for simulation eps_perm = 2 # longitudinal privacy eps_1 = 0. 5 * eps_perm # first report privacy n = int (1 e6 ) # number of users k = 4 # attribute 's domain size d = 3 # number of attributes lst_k = [ k for _ in range ( d ) ] # attributes ' domain size # Simulation dataset following Uniform distribution dataset = np. random.
randint (k , size =( n , d ) ) # Simulation of data collection reports = [ S M P _ L _ G R R _ C l i e n t ( user_tuple , lst_k , d , eps_perm , eps_1 ) for user_tuple in dataset ] # Simulation of server -side aggregation est_freq = S M P _ L _ G R R _ A g g r e g a t o r ( reports , lst_k , d , eps_perm , eps_1 ) >>> array ([0. 253 , 0. 255 , 0. 242 , 0. 250] , [0. 248 , 0. 253 , 0. 25 , 0. 249] , [0. 25 , 0. 245 , 0. 25 , 0. 255]) Listing 1. 4: Code snippet for performing longitudinal multidimensional frequency estimation with the SMP solution and L-GRR [2] protocol. with the SMP solution and the L-GRR protocol [2] using multi-freq-ldpy. In this setting, there are n users, the privacy parameters ϵ perm and ϵ 1 , and each user's profile is a tuple composed of d attributes A = {A 1 ,. , A d } in which each attribute A j has a discrete domain of size k j = |A j |, for j ∈ [1, d].
Acknowledgements This work was supported by the  European Research Council (ERC)  project  HYPATIA  under the European Union's  Horizon 2020 research and innovation programme. Grant agreement n. 835294. The work of Jean-François Couchot was supported by the  EIPHI-BFC Graduate School  (contract " ANR-17-EURE-0002 "). Sébastien Gambs  is supported by the  Canada  Research Chair program as well as a  Discovery Grant  from  NSERC.
# Multi -Freq -LDPy functions for GRR protocol from m ul ti _ fr eq _ ld p y. p u r e _ f r e q u e n c y _ o r a c l e s. GRR import GRR_Client , GRR _Aggre gator#NumPy library import numpy as np # Parameters for simulation eps = 1 # privacy guarantee n = int (1 e6 ) # number of users k = 5 # attribute 's domain size # Simulation dataset following Uniform distribution dataset = np. random. randint (k , size = n ) # Simulation of data collection reports = [ GRR_Client ( user_data , k , eps ) for user_data in dataset ] # Simulation of server -side aggregation est_freq = GRR_A ggrega tor ( reports , k , eps ) >>> array ([0. 199 , 0. 201 , 0. 199 , 0. 202 , 0. 199])Listing 1. 1: Code snippet for performing single frequency estimation with the GRR [11]  protocol.
#Listing 1. 3 : 13 Multi -Freq -LDPy functions for L -SUE ( RAPPOR ) protocol from m ul ti _ fr eq _ ld p y. long_freq_est. L_SUE import L_SUE_Client , L _ S U E _ A g g r e g a t o r # NumPy library import numpy as np # Parameters for simulation eps_perm = 2 # longitudinal privacy eps_1 = 0. 5 * eps_perm # first report privacy n = int (1 e6 ) # number of users k = 5 # attribute 's domain size # Simulation dataset following Uniform distribution dataset = np. random. randint (k , size = n ) # Simulation of data collection reports = [ L_SUE_Client ( user_data , k , eps_perm , eps_1 ) for user_data in dataset ] # Simulation of server -side aggregation est_freq = L _ S U E _ A g g r e g a t o r ( reports , eps_perm , eps_1 ) >>> array ([0. 199 , 0. 201 , 0. 2 , 0. 198 , 0. 202]) Listing 1. 2: Code snippet for performing single longitudinal frequency estimation with the L-SUE [2] (i.
The ENIGMA-TS working group is an international collaborative effort bringing together a large network of investigators who aim to understand brain structure and function in TS and dissect the underlying neurobiology that leads to observed comorbidity patterns and clinical heterogeneity. Previously collected TS neuroimaging data will be analyzed jointly and integrated with TS genomic data, as well as equivalently large and already existing studies of highly comorbid OCD, ADHD, ASD, MDD, and AXD. Our work highlights the power of collaborative efforts and transdiagnostic approaches, and points to the existence of different TS subtypes. ENIGMA-TS will offer large-scale, high-powered studies that will lead to important insights toward understanding brain structure and function and genetic effects in TS and related disorders, and the.
Disorderfocused ENIGMA working groups have identified brain profiles for multiple psychiatric disorders and conditions based on an analysis of large worldwide datasets  (32). These include the largest neuroimaging studies for multiple disorders that are highly correlated to TS: OCD (N = 5,423)  (35) (36) (37) (38)  ADHD (N = 3,762)  (39, 40) , ASD (N = 3,222)  (41) , and MDD (N = 10,327)  (42, 43)  (Figure  1A ). The ENIGMA-TS working group was formed to address the need for large-scale studies to understand brain structure and function in TS and tackle clinical heterogeneity. The effort was motivated not only by ENIGMA but also TS-EUROTRAIN, an earlier collaborative consortium funded by the European Union (2012-2016), which represented an international network of researchers from 12 different sites in academia and industry aiming to understand the neurobiology of TS  (5, (44) (45) (46) (47) (48) (49) (50).
With a strong basis of collaboration, investigators from 23 sites and 12 countries (namely, Australia, Germany, Denmark, France, Italy, the Netherlands, Norway, Poland, Spain, the United Kingdom, China, and the United States) have already joined ENIGMA-TS and are currently working to pool and harmonize previously collected TS neuroimaging data with an initial goal of analysis for 1,000 TS individuals and a corresponding number of controls. The data mostly represent diverse European and European American ancestry and one cohort from China. There is an open and ongoing call for additional investigators to join ENIGMA-TS and information on how to join is available on the ENIGMA-TS website. 1  Indeed, through our publications, conference presentations, our website, and access to the ENIGMA network, our goal is to draw additional membership, further expanding our sample size and representation of diverse populations in our studies.
The ENIGMA-TS working group uses standardized protocols for the processing and analysis of imaging data, to determine the reliability of effects across datasets, and identify common trends of low effect size that may not reach statistical significance in any individual study (37, 51, 52). We will use previously standardized methods to harmonize structural MRI, diffusion tensor imaging (DTI), and rsfMRI data across different sites, scanners, and imaging protocols  (41, (53) (54) (55). In addition to greatly enhanced statistical power, data pooling allows novel comparisons across demographics (e. g. , sex). In particular, our proposed analyses will have sufficiently large sample sizes to examine the effects of comorbidity and uncover different TS brain phenotypes or dimensions. Integrating with genetics, we will shed light on the links between TS and related disorders and underlying the brain structure and function (Figure  1B ).
0132 to -0. 0138). ENIGMA-TS will pursue further analysis based on the most up-to-date TS GWAS and also the latest ENIGMA and UKB MRI GWAS datasets. In ENIGMA, more than 50,000 people from diverse populations from around the world have been assessed and analyzed with GWAS and whole-brain MRI, including over 10,000 with DTI and over 10,000 with rsfMRI. UKB data on approximately 10,000 individuals have also been recently integrated with ENIGMA  (34, 81) , while an even larger UKB dataset is now available. ENIGMA has now identified multiple genetic variants determining brain structure, namely, intracranial volume (ICV)  (82)  and subcortical volumes  (33, 34, 83, 84). Our recent work also provides genetic determinants for regional and global measures of cortical surface area and thickness  (34, 85).
Each part was measured separately in the left hemisphere and the right hemisphere of the brain. Linear regression was performed with age, sex, genotyping batch, and top 10 PCs used as covariates in the analysis. The asterisk (*) indicates a significant association after multiple testing correction using the FDR method (p < 0. 05). (C) Exploring the genetic architecture of TS and related comorbidities via genomic structural equation modeling. The path graph shows loads and corresponding standard errors in parenthesis. the shared pathophysiology of the studied disorders. Given the high comorbidity of TS to MDD and AXD, ENIGMA-TS will extend the cross-disorder analysis to include these additional comorbidities. Figure  2C  shows our exploratory factor analysis of the genetic correlation matrix produced from multivariable LD Score Regression (LDSR), across the TS, ADHD, ASD, and OCD datasets described in Yang et al.
Structural T1-weighted brain MRI scans of controls (n = 5,827) and individuals with ADHD (n = 2,271), OCD (n = 2,323), and ASD (n = 1,777) from 151 datasets worldwide were analyzed using standardized ENIGMA processing protocols. Subcortical volume and regional cortical thickness differences were examined in a mega-analytical framework  (54). Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models controlling for age, sex, and site (and ICV for subcortical measures). Lifespan dynamics were found in the pairwise findings: Children with ADHD compared with those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared with controls and other clinical groups.
No OCD-specific alterations across age groups-or surface area alterations among all disorders in childhood and adulthood-were observed. Furthermore, differences between medicated and unmedicated patients, and effects of duration of illness and age of onset were identified  (54). Through collaboration with the relevant ENIGMA working groups, ENIGMA-TS will extend this work across TS, and ADHD, OCD, ASD, MDD, and AXD, analyzing a combined worldwide dataset of unprecedented power. Our work will yield brain maps of the main effects of each disorder, in children, adolescents, and adults, ranking brain metrics for effect sizes, and detection of metrics with common and disease-specific brain alterations.
We aim to identify biomarkers for disease subtypes that cut across diagnostic boundaries, lifting a major barrier in truly understanding factors that drive high comorbidity in patients with TS. Although our initial studies include mostly European datasets, we will make every effort to extend analyses to include representation from more diverse datasets through ENIGMAavailable resources, public databases, and through our open call for additional collaborations. To date, all major progress in understanding the genetics of TS and other neuropsychiatric disorders has been realized thanks to international collaborative efforts. Through ENIGMA-TS, we will seek to replicate this success to understand brain structure and function in TS and related disorders bringing together investigators working on the genetics and neuroimaging of these common disorders.
Introduction Language is a unique trait of the human species. Although its genetic origins are broadly accepted, they remain largely unknown. Since the seminal study that revealed the major role of FOXP2 in language processing  ( Fisher et al. , 1998 ) , several candidate genes related to language disorders were identified  ( Landi and Perdue, 2019 ). Human language is a complex system -both structurally and functionally. As such a complex and multifactorial trait, it is unlikely to be associated with only a few genes but rather with many genes that are also interact-Abbreviations: eQTL, expression Quantitative Trait Locus; FA, Fractional Anisotropy; FC, Functional Connectivity; FDR, False Discovery Rate; GWAS, Genome-Wide Assocation Study; ICA, Independent Component Analysis; ICVF, IntraCellular Volume Fraction; ISOVF, Isotropic Volume Fraction; LD, Linkage Disequilibrium; MD, Mean Diffusivity; MO, Tensor Mode; MOSTest, Multivariate Omnibus Statistical Test; mvGWAS, multivariate GWAS; OD, Orientation Dispersion index; ROI, Region Of Interest; rsfMRI, resting-state functional MRI; SNP, Single Nucleotide Polymorphism; STAP, Superior Temporal Asymetrical Pit; tbfMRI, task-based functional MRI; dMRI, Diffusion Magnetic Resonance Imaging; DW-MRI, Diffusion-weighted Magnetic Resonance Imaging.