dataesr/scientific-paragraphs-categorization huggingface dataset:
We also demonstrate some limits on language sentiment measures. Second, we apply the instrumental variables approach to test whether the sentiment expressed in judicial rulings' have actual consequences in the development of the law. In particular, we show that more positive/warm (rather than negative/cold) case sentiment increases the likelihood that the Supreme Court reviews the Circuit Court decision, and the chances that the decision is eventually reversed. Moreover, we find that expressed sentiment increases the probability of an opinion to be cited in subsequent cases. Expressed sentiment in judicial opinions matters for the responses of other judges and for the path of the law. This paper adds to the emerging literature using machine learning methods to overcome limitations of standard datasets -in our case, isolating variation in judicial sentiments.
Data We have assembled data from a range of sources. To create the judges' sentiment measure, we use the complete collection of United States Courts of Appeals opinions from 1961 to 2013. The corpus includes all published cases and comes from Bloomberg Law. For each case we also use additional information such as whether the Supreme Court reviewed the case, if the Supreme Court reversed the decision, the number of citations, if the case was reversed by the Circuit Court, and general category labels. 2 e use these data in the empirical application section. Further, we collected the biographical information of judges that have been assigned to at least one case in the same period. We match each judge with data from the Federal Appeals and District Court Attribute Data. 3  We integrate this information with data from the Federal Judicial Center's biographies of judges and previous data collection  (Chen et al.
Our contribution is more empirically oriented because we look at the impacts of language variation on the law and society. Concretely, to create our measure of judicial sentiment we use the collection of opinions of all published cases of United States Courts of Appeals. First, we parse the raw text into Python and use the Python module nltk to tokenize sentences. Next, we map sentences into vectors using the Python module Doc2Vec  (Mikolov et al. , 2013; Le and Mikolov, 2014). This algorithm represents words and sentences in a shared vector space (in our case, 200 dimensions). As already suggested, words that tend to have similar contexts are located near each other (we used window size of five). 5  Similarly, sentences with comparable language tend to locate close to each other and tend to locate close to words contained in the sentence.
6% per week, based on IPS data. For the share of tradable and non tradable goods, γ N,t and γ T ,t , I use the sectorial decomposition of the GDP in the Eurostat database. In IPS, the average share of non tradable production for the period 1996:2007 is 71 per cent. 29 Focusing on the tradable goods sector I find that the share of imported goods is around 33. 9 per cent for IPS countries, displaying a relevant home bias. In Table  1  I also report the values of the estimated exogenous VAR used to proxy the behavior of the rest of the monetary union and the values of the estimated deterministic trend using Eurostat data between 1995 and 2007. 30.
Sample period Q1:1996Sample period Q1: -Q4:2007. For computing these moments I generate an artificial sample of the observable variables with same length as my dataset (48 observations) after discarding 100 initial observations. It shows standard deviation and autocorrelation at t-3 for Greece. Variable listed are, in order: current account with respect to steady state GDP ( C A Y ss Table A1 Table A2 Posterior Mean -different models. Data and Model Moments -Greece. sa Data No ζ 4,t Model No ζ 8,t No ant Model No cons Govern Spread Reduced Estimated Parameters Mean Std dev Mean Std dev Autocorrelations μ v Lab supply wealth eff F r i s c h e l a s t ( μ=0) C A Y ss ReR 0. 767 4. 248 -7. 23 -0. 26 5. 35 1. 76 0. 768 4. 252 0. 00 0. 43 0. 754 4. 253 8. 52 3. 39 0. 743 4. 464 0. 68 -0. 18 0. 838 3.
Design of Paediatric Investigational Plans (PIPs) and Clinical Trial Protocols Some PIPs and protocols do not succeed because they do not fit with clinical practice or because assumptions about participant availability are flawed. In addition, methodology may be suboptimal: extrapolation, adaptive designs and innovative methodologies are underutilized. In order to address these problems, c4c has set up a database of > 300 experts in methodology and clinical subspecialties, as well patient and parent representatives. This database is managed by the advisory group secretariat, which reports to the c4c Network Management Committee (Fig. 2 ). The clinical experts are organized in subspecialty groups, as much as possible in connection with the relevant learned society or existing research network.
c4c is also ready to conduct face-to-face activities with patients. PPI experts contribute during all of the advice process and facilitate appropriate PPI activities. Young adult patients, caregivers, experts from POs and facilitators of YPAGs are part of a dedicated patient involvement database of experts that is consulted once the c4c advisory group secretariat receives a request for patient involvement. Liaison with disease-specific and umbrella POs is also done with the aim to select the right experts to be involved in these activities. All the results are shared with the sponsor who will decide how or whether to include the advisory outcomes in the final design of their project (protocol design, patient information documents, etc. ). A dedicated analysis of the return on investment and the return conect4children (c4c) and European Paediatric Research Education team Apply to SPoC, which will then route the request to the relevant team on engagement will be performed during the project to help in the final definition of the services portfolio of the future c4c legal successor entity in the field of PPI activities.
1 News media interest also shows significant recent growth as seen in Fig. 1. Trade in NFTs matches the news growth, with a popular transaction website 2 estimating lifetime trading volumes to 17th March 2021 at $440m, but that $200m of those sales were just in the single month up to 17th March. No previous study has examined NFT pricing, and this is important to address given the size of this new asset market, particularly the growth rate. In this study, we provide a first exploration of how these digital assets are being traded. We take trade in Decentraland LAND NFTs as a dataset as the LAND NFT is reasonably uniform and therefore amenable to assessment as a price series. We also consider the mental association between the virtual asset LAND and the long-established asset of physical land as interesting, as traders may have implicit pricing frameworks in mind when trading this NFT.
Methodology Our dataset is all secondary market trades in Decentraland LAND tokens from the period March 2019 to March 2021. 3 We have 4936 trades of LAND in our dataset, with trades on 720 out of the 749 days in our time frame. The average LAND price is $1311, as per the descriptive statistics in Table  1. Fig. 2  shows the strong upward price trend in recent months and highlights that daily data is subject to extreme volatility. We, therefore, primarily use weekly data in our testing, something which is suited to a low transaction volume market. We rely on a suite of tests informed by initial cryptocurrency markets studies by Khuntia and Pattanayak (2018) and Urquhart (2016) for our market efficiency tests. As a first step, we are interested in whether there is evidence of martingale market efficiency in LAND pricing.
The NIH U. S National Library of Medicine was also searched through clinicaltrials. gov using the keywords "placebo controlled" OR "best supportive care controlled", "completed", "terminated studies", "interventional studies", "advanced cancer", "phase 2, 3 00 in order to identify missing trials. Abstracts of references that appeared potentially eligible for inclusion were examined independently by two reviewers (C. M. B. and E. C. ) and, if deemed relevant, full-text articles including supplementary materials were retrieved and included if appropriate. Disagreements between the two reviewers were resolved by consensus with one of us (C. L. T. ). Only papers published in the English language were considered.
Data analysis Trial characteristics included tumor type, line of therapy, type of anticancer drug evaluated in the experimental arm, mode of administration, the type of control, phase of the clinical trial, number of patients in each arm, year of publication, type of sponsor, and crossover permission. Efficacy outcome data included overall response rates (ORR), criteria used to assess efficacy, overall survival (OS), progression-free survival (PFS) (or time to progression [TTP] when PFS was not reported), and hazard ratios (HR) for survival outcomes. Safety data included all-grade, grade III, IV and V (death) adverse events according to the National Cancer Institute Common Terminology Criteria for Adverse Events guidelines used by the authors. Since adverse events related to study drugs were infrequently reported in the trials, we only reported emerging adverse events.
, 1984). Further stratification by amyloid status was performed whenever needed (see section Neuroimaging data processing). We also included 130 older adults who were cognitively unimpaired (eHC) from the Age-Well randomized controlled trial of the Medit-Ageing European Project  (Poisnel et al. , 2018) , sponsored by the French National Institute of  Health and Medical Research (INSERM). Those were recruited from the general population, older than 65 years, native French speakers, retired for at least 1 year, educated for at least 7 years, and able to perform within the normal range on standardized cognitive tests. The main exclusion criteria were safety concerns associated with magnetic resonance image (MRI) or PET scanning, evidence of a major neurological or psychiatric disorder (including alcohol or drug abuse), history of cerebrovascular disease, presence of a chronic disease or acute unstable illness, and current or recent medication usage that may interfere with cognitive functioning.
All 191 participants from both Projects included in the present study had, within a maximum period of 3 months, structural MRI, dual-phase [ 18 F]AV45 PET and [ 18 F]FDG PET scans, along with a neuropsychological examination (including the mini-mental state examination (MMSE) and the free and cued selective reminding test (FCSRT)  (Grober and Buschke, 1987) ). Participants' demographics are displayed in Table  1. The IMAP Study was approved by a regional ethics committee (Comité de Protection des Personnes Nord-Ouest III) and is registered with http://clinicaltrials. gov (number NCT01638949). The Age-Well randomized controlled trial was approved by the ethics committee (Comité de Protection des Personnes Nord-Ouest III, Caen, France; trial registration number: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.
Neuroimaging data acquisition All participants were scanned on the same MRI and PET cameras at the Cyceron Center (Caen, France): a 3 T Philips Achieva scanner (Philips Healthcare, Best, the Netherlands) and a Discovery RX VCT 64 PET-CT device (GE Healthcare, Milwaukee, WI, USA), respectively. The MRI sequences and parameters associated to the IMAP Study were described previously  (Mutlu et al. , 2017). For the Age-Well Study, high-resolution T1-weighted anatomical volume was first acquired using a 3D fast-field echo sequence (3D-T1-FFE sagittal; repetition time = 7. 1 ms; echo time = 3. 3 ms; flip angle = 6 • ; 180 slices with no gap; slice thickness = 1 mm; field of view = 256 × 256 mm 2 ; in-plane resolution = 1 × 1 mm 2 ). A high-resolution T2-weighted FLAIR anatomical volume was then acquired using a 3D inversion recovery sequence (3D-IR sagittal; repetition time = 4800 ms; echo time = 272 ms; inversion time = 1650 ms; flip angle = 40 • ; 180 slices with no gap; slice thickness = 1 mm; field of view = 250x250 mm 2 ; in-plane resolution = 0.
Semi-automatic quality check A semi-automatic quality check of both reconstructed surfaces and smoothed normalized preprocessed PET surfaces was applied. Euler number and defect size were computed for each reconstructed surface to estimate the number and size of topology defects, while correlations between all participants' preprocessed PET surfaces for each tracer were calculated to assess the homogeneity of our data sample. Visual inspection of native participant T1 MRI was executed for reconstructed cortical surfaces with high Euler number and/or defect size. In the same manner, visual inspection of native participant PET data was performed for preprocessed PET surfaces with global correlation lower than two standard deviations. Following these inspections, no participant had to be discarded from the statistical analysis.
Brief description of the results based on PVE-corrected PET data Results were in accordance with those obtained from the PVE-non corrected PET data, despite (i) [ 18 F]FDG PET had slightly nonsignificant better performance in the binary classifications of eHC vs. Alz-CS and eHC vs. aMCI than eAV45; (ii) eAV45 and lAV45 had similar performance in the binary classification of eHC vs. Alz-CS; and finally (iii) combined eAV45 and lAV45 had slightly non-significant better performance in the binary classification of eHC vs. AD-d than [ 18 F]FDG PET (data not shown).
, 2016). Our dataset is optimal for addressing this issue with a large number of dual-phase [ 18 F]AV45 PET exams acquired on the same scanner in a population ranging from healthy young to elderly volunteers with no cognitive impairment to Alzheimer's clinical syndrome with MCI or dementia. Validating the results on a mixed study population provides further support that optimal early time frame and preprocessing methods of eAV45 are not dependent on clinical diagnosis, suggesting wider applicability of our methodology. While previous studies  (Asghar et al. , 2018; Hsiao et al. , 2012; Kuo et al. , 2017; Lin et al. , 2016; Ottoy et al. , 2019)  chose the optimal early time frame of eAV45 based solely on the best within-or inter-subject correlation with [ 18 F]FDG PET SUVR values, this study considers for the first time many complementary quantitative analyses to robustly optimize both the early time frame and the preprocessing methods of eAV45, by comprehensively assessing the similarities and differences between eAV45 and [ 18 F]FDG PET.
Acknowledgements We would like to warmly thank the patients and healthy volunteers of the IMAP and  Age-Well  studies. We are also grateful to  C. André ,  E. Arenaza-Urquijo ,  A. Bejanin ,  A. Cognet ,  J. Dayan ,  M. Delarue , B. Desgranges,  S. Egret ,  F. Felisatti ,  M. Fouquet ,  M. Gaubert ,  J. Gonneaud ,  E. Kuhn ,  R. La Joie ,  M. Leblond ,  A. Manrique ,  K. Mevel ,  F. Mézenge ,  I. Moulinet ,  J. Mutlu ,  V. Ourry ,  L. Paly ,  A. Pélerin ,  A. Perrotin ,  A. Quillard ,  G. Rauchs ,  C. Schupp ,  C. Tomadesso ,  E. Touron ,  F. Viader ,  N. Villain ,  C. Ware  and the  Cyceron  staff members for their help with data acquisition.
Z ik comprises the economic development from various income groups under study (LIE, LMIE, MIE, UMIE, HI), type of crop plants (Crops1, Crops2, Crops3, Crops4, Crops5, Crops6, Crops7, Crops8, Crops9), cross-sectional data (Type), number of observations (Obs), model speci cation based on Data Envelopment Analysis (DEA) and speci cation of the production function (Other, CD, TL). Based on World Bank (2016) country classi cation by income level, we employ dichotomous variables to capture the national economic development under study. We use a set of ve dummy variables, low income economy (LIE), lowmiddle-income (LMIE), middle-income (MIE), upper middle-income (UMIE) and high-income (HIE). The distribution of the regional origin is illustrated in Supplementary Materials (Figure  7 ). With the largest proportion of studies in our sample coming from LMIE, we choose this category as the reference in the meta-regression.
Finally, our paper is related to the recent rationalization of the failure of the UIP in an Asset Pricing Theory framework as proposed by  Lustig, Roussanov and Verdhelan (2011)  and  Menkhoff et al. (2012). In particular, we show that when the currencies are not attractive to investors in the carry trade, they do not comove significantly with the global SDF. Our sample contains 26 currencies and covers the period January 1985 to January 2017. The data are extracted from both Bloomberg and Datastream. Our main sample is extracted from Bloomberg and covers the period January 1999 to January 2017. This sample exhibits interesting characteristics for our study, especially when we consider the dynamic of the bid-ask spreads which is central for the definition of the time-varying set of attractive currencies.
The same sample extracted from Datastream does not exhibit these characteristics. We relate the differences between the two sets of data to the way providers process information. However, as the coverage is sparse in Bloomberg before 1999, we complement our sample with data for the G10 countries only available in Datastream. Taken as a whole, our findings might be particularly interesting for policymakers and forecasters as they suggest that the status of the currency being attractive or non-attractive may contain information about its future return. In particular, our result shows that non-attractive currencies may impair returns to the carry trade and simply add noise to the asset pricing relationship. As a consequence, the classical equally weighted portfolio which mixes long positions in all the currencies which pay a discount and short positions in all the currencies which pay a premium does not produce high enough risk-adjusted returns to be appealing to the investors.
Data Analysis We use data collected by Bloomberg and both by Barclays and Reuters and available on Datastream (WM/Reuters). These data sets cover the period from January 1985 to January 2017 for 26 different currencies: Australia, Brazil, Canada, Czech Republic, Denmark, Euro Area, France, Germany, Hong Kong, India, Italy, Japan, Mexico, the Netherland, New Zealand, Norway, Philippines, Poland, Singapore, South Africa, South Korea, Sweden, Switzerland, Taiwan, Thailand and the UK 3. The euro series starts in January 1999 replacing in our dataset the currencies of the countries which joined the Euro (i. e. France, Germany, Italy and the Netherland). While forward rates may be available for a larger basket of currencies, there would have been virtually no liquidity in many of them  (2019 BIS Triennial Survey).
Those illiquid currencies might suffer from measurement error especially concerning the bid-ask spread (e. g. Cochrane, 2005). All currencies are quoted as the number of Foreign Currency Unit (FCU) per dollar. We use only data collected by Bloomberg for our main tests because the bid-ask spreads are much more dynamic than in the dataset available in Datastream (see Appendix A for a comparison). These data extracted from Bloomberg cover the period January 1999 to January 2017. In a second step and for robustness tests only, we consider data for G10 countries from 1985 to 1998 which are available in Datastream. We start from daily data including the spot exchange rates and the one-month forward exchange rates with bid and ask rates for each observation (we use NDF quotes for the Brazilian and the Indian currencies).
We estimate 10. 000 vectors B of 22 currency returns that minimize the absolute value of Equation  5. We consider several values for parameter β ranging from -0. 2 to 0. 2. Initial vectors are generated randomly by drawing in the uniform distribution 16. Simulated returns data are obtained by adding generated iid errors of mean zero and variances matching those of the idiosyncratic errors in the actual data. We consider only economically interesting results 17. Figure  3  shows that the mean annualized currency returns of the funding currencies should be 0. 042 when they are non-attractive against 0. 015 in the observed sample. Similarly, the mean annualized currency returns of the investment currencies should be -0. 061 when they are non-attractive against 0. 0007 in the observed sample. In this case, the non-attractive currencies do not conform to the UIP either but the beta to the regression of their mean returns on their forward discounts is negative against positive for the attractive currencies.
g. Tornell and Yuan, 2012). The CFTC issue the number of open future contracts (open interest) held by registered traders each week. We gather data for 10 currencies for the period January 1999 to January 2017: Euro-area, Japan, UK, Australia, Canada, New-Zealand, Switzerland, South-Africa, Brazil and Mexico. These data report long positions and short positions for each foreign currency (FCU). The CFTC also differentiate between positions held for commercial purposes (hedging of an underlying asset) and non-commercial purposes (speculation). Being long a contract in one FCU is equivalent to being long this currency (i. e. short USD/FCU). While the COT data have been used extensively by academics, it is important to note that they are not without limitations. In particular, the data contains about 25% of points for which the information is not available.
Also, they aggregate positions for different contract maturities and various type of traders (hedgers/non hedgers) for which the designation might not be accurate (e. g. Tornel and Yuan, 2012). We define market's net position as the difference between the number of contracts associated to long positions (Positions long all in the COT report) and the number of contracts associated to short positions (Positions short all). To compare positions across time and currencies, we standardized this difference by the total number of positions (Open Interest all). The net position variable (NP) for currency i in month t is defined as: N P it = LongP ositions it -ShortP ositions it OpenInterestall it (8) We collect weekly data of futures-only positions and transform them into monthly data by calculating their monthly average.
After imposing data availability requirements, the final sample consists of 1442 currency-month observations of the NP variable. In Table  VII  we report descriptive statistics for traders' net positions in the foreign exchange future market. We see that, on average, speculators have negative net positions in most currencies, that is, they hold larger short positions than long positions. However, we also can see that speculators are, on average, long NZD and BRL, two prototypical carry trade currencies. When we look at the commercial positions, not surprisingly, we see the opposite: on average hedgers exhibit positive net positions except again in NZD and BRL. However, it is important to note that these mean net positions might not be significantly different from zero when we consider the associated standard-deviations (columns 2 and 6).
Our results indicate that policy makers should be particularly careful when their domestic currencies near the limit of being attractive and non-attractive. Pushing the currency through this limit by revising the reference interest rate may change the future return of the domestic currency significantly. In particular, we show that when the currencies become attractive they tend to exhibit greater crash risk both under physical or risk-neutral measures. Finally, the fact that only when currencies are attractive to investors do they breach UIP suggests that the failure of this theory is possibly due to the activity of carry traders in the foreign exchange market. Our analysis of CFTC-COT data would tend to support this conclusion.