dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All seafloor images collected with the OFOBS system are available from the data publisher PANGAEA. No preprocessing or processing stages were applied prior to upload, with no colour correction or light-vignetting algorithms applied. These images are provided with georeferenced positions for each image, as derived from the Posidonia system (https://doi. pangaea. de/10. 1594/PANGAEA. 936205 (Purser et al. , 2021b ) and https://doi. pangaea. de/10. 1594/PANGAEA. 932827 (Purser et al. , 2021c)  as indicated on Table  1 ). Acoustic data for all deployments is also available from PANGAEA, with the raw side scan data available at: https://doi. pangaea. de/10. 1594/PANGAEA. 939322 (Hehemann et al. , 2021b) , forward facing acoustic camera data at: https://doi. pangaea. de/10. 1594/PANGAEA.
939341 (Purser et al. , 2021a). Seafloor topographic maps derived from the side scan data are available at: https://doi. pangaea. de/10. 1594/PANGAEA. 939087 (Hehemann et al. , 2021a). The datasets are published under the Creative Commons Attribution 4. 0 International (CC BY 4. 0) license. Video data collected via the OFOBS system are available from the authors on request. https://doi. org/10. 5194/essd-2021-444 Open Access Earth System Science Data Discussions Preprint.
Acknowledgements The captain, crew and scientific party of RV Polarstern expedition PS124 are thanked for their enthusiastic support and interest during this cruise, made during the second year of the global COVID-19 pandemic. The complexities of conducting research in Antarctica during epidemic were smoothed by the  AWI  logistics and ship co-ordinations teams. Steffen Hain  is thanked for assisting in insuring we fulfilled our environmental commitments and commitment to the Antarctic Treaty during the expedition. Ulrich Hoge  is thanked for onshore support of the OFOBS system. Stefanie Schumacher  and  Janine Felden  from the PANGAEA team are thanked for their helpful and efficient work in uploading this data to archive. The following AWI grant numbers are acknowledged for supporting the work incorporated into this data set:  AWI_PS124_02 ,  AWI_PS124_03 ,  AWI_PS124_05  and  AWI_PS124_06.
Data Collection Electronic medical records were retrospectively studied then queried for demographical, clinical, and biochemical data, which were standardized in a report file. We recorded primary epidemiological factors such as age and sex, along with essential comorbidities such as obesity, (body weight mass over 30 kg/m 2 ), history of cardiovascular or respiratory disease, diabetes, pre-existing renal failure, and any history of malignancy or immunodeficiency. Different aspects of ED management were documented, such as clinical parameters and the call for early organ support strategies, including endotracheal intubation. Laboratory results such as arterial blood gas, creatinine, and C-reactive protein, were also collected. Radiological findings were documented, mainly reporting on the extension of lesions.
We empirically show that training a model to output discriminative features improves its resilience to quantization. Results on CIFAR10, CIFAR100 and ImageNet datasets show the clear advantage of our approach, with significant performance gains, especially for very low-bit settings. This paper is organized as follows. Section 2 presents some previous work on QAT as well as the foundation of disentangled loss functions. Section 3 introduces our method that takes advantage of both AMS and GML to improve the QAT procedure. Section 4 presents our experimental setup and the results obtained on relevant datasets.
Training setups All experiments use a Resnet-18  [7]  with the CIFAR10, CIFAR100  [10]  and ILSVRC 2012 ImageNet dataset  [3]. The batch size is 768 for CIFAR and 1024 for ImageNet. We use the same learning strategy as  [9]. When training on CIFAR, the learning rates are 0. 01 for SAT using CEL & DL-QAT using AMS loss and 0. 2 for DL-QAT using GML. When training on ImagNet, the learning rate is 0. 02 for both SAT using CEL and DL-QAT using GML. All networks are trained over 150 epochs. Finally, we use m = 0. 35 from Eq. (  3 ) and α = 0. 7 from Eq. (  4 ) for CIFAR and α = 0 for ImageNet as they give best results. As it is common practice in the previous quantization approaches  [4, 9] , the precision of filters from the first convolution, the weights of the last layer and the activation preceding the last layer are fixed to 8 bits.
ImageNet In this section, we evaluate the performance of our method using the ImageNet dataset. Considering the results on CI-FAR and our hypothesis on the losses, we chose to focus on the GML for ImageNet experiments. We report the top-1 test accuracy on ImageNet of our method DL-QAT using the GML and the SAT method  [9]  using CEL and other state-of-the-art approaches in Tab. 2 for low-bit settings and in Tab. 3 for binary weights settings. As we read Tab. 2 and Tab. 3 from left to right, the quantization is more and more aggressive. Considering our experimental results only (DL-QAT using GML and SAT using CEL), the gap between the disentangled loss GML and the CEL is getting bigger as the settings reach more extreme quantization. Ultimately, in the binary weights and 2-bit activation setting, our approach reaches an accuracy of 64, 0%, improving by 0.
CP-CML (OPTIC) trials for this patient population. PACE (NCT01207440) evaluated ponatinib 45 mg/day in CML patients with resistance to prior TKI or T315I. In OPTIC (NCT02467270), patients with CP-CML and resistance to ≥2 prior TKIs or T315I receiving 45 or 30 mg/day reduced their doses to 15 mg/day upon achieving ≤1% BCR:: ABL1 IS or received 15 mg/day continuously. Efficacy and safety outcomes from patients with CP-CML treated with ≥1 2G TKI (PACE, n = 257) and OPTIC (n = 93), 45-mg starting dose cohort, were analyzed for BCR::ABL1 IS response rates, overall survival (OS), progression-free survival (PFS), and safety. By 24 months, the percentages of patients with ≤1% BCR::ABL1 IS response, PFS, and OS were 46%, 68%, and 85%, respectively, in PACE and 57%, 80%, and 91%, respectively, in OPTIC.
| BACKGROUND Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the constitutively active tyrosine kinase, BCR::ABL1, generated as the result of a reciprocal translocation between chromosomes 9 and 22. 1  Although tyrosine kinase inhibitors (TKIs) targeting BCR:: ABL1 mutations have improved the therapeutic outcomes in patients with CML,  1, 2  resistance to BCR::ABL1 TKI treatment can occur. 3 [7]  Ponatinib is a third-generation TKI designed to inhibit BCR::ABL1, including the T315I mutation. 8 The efficacy and safety of ponatinib have been evaluated in two phases 2 trials: Ponatinib Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) and CML Evaluation (PACE, NCT01207440) and Optimizing Ponatinib Treatment in CP-CML (OPTIC, NCT02467270). 9,10 PACE was a pivotal phase 2 trial that evaluated the safety and efficacy of ponatinib at an initial dose of 45 mg in patients with heavily pretreated CML or Ph+ ALL with resistance or unacceptable side effects to 2G TKI treatment or with the BCR:: ABL1 T315I mutation.
| Statistical analysis Efficacy and safety analyses were conducted in a subset of 257 patients from the CP-CML cohort of PACE and a subset of 93 patients from OPTIC who received the starting dose of ponatinib 45 mg/day; both subsets had exposure to ≥1 2G TKIs (dasatinib, nilotinib, or bosutinib). The efficacy and safety data were analyzed separately for each study population. Efficacy data through 12, 24, and 60 months are presented for PACE and through 12 and 24 months for OPTIC. Categorical data were summarized by the number and percentage of patients. All data were summarized descriptively. Exposure-adjusted AOE rates were calculated as (number of first events in interval)/(total exposure for interval in patient-years) Â 100.
| RESULTS The data cutoff dates were February 6, 2017, for PACE and May 31, 2020, for OPTIC. For this analysis, 257 patients from the PACE CP-CML cohort and 93 from the OPTIC 45 mg starting dose cohort were analyzed. The median follow-up time for patients in PACE was 57 months (0. 1-73); the median follow-up time in OPTIC was 32 months (1-57). 10, 11 The median dose intensity at 24 months for the post 2G population was 30 mg for PACE and 15 mg for OPTIC.
Data from the current analysis indicate that efficacy was similar, if not better, with response-based dosing in OPTIC compared with patients with similar demographics in PACE. Sustained responses were observed in both studies in patients with CP-CML resistant to ≥1 2G TKI regardless of T315I status or number of prior 2G TKIs. Although the definition of an acceptable response is not clearly formalized for third-line treatment and beyond, achieving ≤1% BCR::ABL1 IS response is considered an important endpoint with meaningful survival implications, further supporting the robust efficacy results observed with ponatinib treatment in third-line therapy. 12 Es have been identified as a notable AE for ponatinib in the PACE trial. 11 4] [15] [16] [17] [18]  This analysis showed that in both trials, patients treated with ≤2 prior 2G TKIs had fewer TE-SAEs than patients treated with ≥3 prior 2G TKIs.
In OPTIC, which employed the responsebased dosing regimen strategy, patients had a reduced incidence of exposure-adjusted TE-AOEs and AOEs when compared with PACE. Together, these data demonstrate improved and sustained outcomes and highlight the clinical benefit of ponatinib treatment in this difficult-to-treat population of patients with CP-CML who had disease resistant to 2G TKIs, regardless of the line in therapy and mutation status. These results are consistent with previous studies showing that patients with CP-CML with resistance after second-line treatment with prior 2G TKIs are approximately twice as likely to achieve CCyR when treated with ponatinib rather than a 2G TKI  7  and that ponatinib is an optimal TKI in this patient population. 19 A B L E 3 Safety by number of prior second-generation TKIs Abbreviations: AOEs, arterial occlusive events; CP-CML, chronic-phase chronic myeloid leukemia; OPTIC, optimizing ponatinib treatment in CP-CML; PACE, positive acute lymphoblastic leukemia (Ph+ ALL) and CML Evaluation; TEAEs, treatment-emergent adverse events; TE-AOEs, treatment-emergent arterial occlusive events; TKI, tyrosine kinase inhibitor.
and responses in the T315I mutation patient population were seen at doses much higher (5Â) than in those without the T315I mutation. 21  Head-to-head, randomized trials of asciminib versus ponatinib would be needed to compare the efficacy and safety of these agents. However, our analysis represents the largest population of patients with CP-CML studied in the difficult-to-treat post-2G TKI setting. Data from the PACE and OPTIC trials highlight the important clinical benefits obtained with ponatinib in this difficult-to-treat population of patients with disease resistance to 2G TKIs. Limitations to this analysis include that PACE and OPTIC were open-label trials, the differences in dosing strategy between the 2 trials, and patient baseline characteristics. The present study was an analysis of 2 separate trials, and therefore the results could not be directly compared.
ACKNOWLEDGMENTS The authors would like to thank the patients and their families, and the investigators and staff at all clinical sites, for their participation in the study. The study is sponsored by  Takeda Development Center Americas, Inc. Professional medical writing assistance was provided by  Corey Burgin, PhD , of  Peloton Advantage, LLC , an  OPEN Health company, Parsippany, NJ, USA , which was funded by  Takeda Pharmaceuticals, USA, Inc. , and complied with the Good Publication Practice 3 ethical guidelines. 22.
The formation of amorphous KNO3 is because of the rotational disorder of the oxy-anions of the NO3 units and the positional disorder of potassium ions. Strikingly, to date, it could be the first report of amorphization on KNO3 considering both the static pressure and temperature experiments. Due to the ability of potential switching between crystalline and amorphous states, the title material is a good fit for the applications of optical data storage and molecular switching.
Study Designs The LTI/SIOPEN study (EudraCT 2009-018077-31) is a prospective Phase II trial in patients with relapsed and refractory high-risk neuroblastoma (Figure  1A, B )  [10]. The trial started as a single-arm study, where patients were treated with DB in combination with scIL-2, and was later amended to a randomised study, where patients received DB with or without scIL-2  [10]. The treatment regimen in the LTI/SIOPEN study included scIL-2 given once a day for five days (Days 1-5, 6 × 106 IU/m 2 /day), followed by combined administration of scIL-2 once a day (Days 8-12, 6 × 106 IU/m 2 /day) and a 10-day continuous i. v. infusion of DB (Days 8-17 10 mg/m 2 /day)  [10]. Oral isotretinoin was given for 2 weeks following DB infusion (Days 19-32, 160 mg/m 2 /day)  [10]. Patients received up to five 35-day treatment cycles  [10].
In the randomised phase of the trial, the treatment schedule for DB, scIL-2 and isotretinoin remained unchanged but was tested against a treatment arm without scIL-2  [10]. The HR-NBL-1/SIOPEN study (EudraCT 2006-001489-17) is a prospective Phase III trial in patients with newly diagnosed high-risk neuroblastoma and includes four completed randomisations (R1-4), which are described elsewhere  [1, 8, [20] [21] [22]. In the randomisation phase 2 (R2, Figure  1C, D ), patients received DB as short-term infusion (STI) consisting of 20 mg/m 2 /day given as 8 h infusion on 5 consecutive days (Days 8-12) either with or without scIL-2 (Days 1-5 and Days 8-12; 6 × 10 6 IU/m 2 /day)  [1]. Oral isotretinoin was given for 2 weeks (Days 15-28; 160 mg/m 2 /day). All patients received up to five 35-day cycles  [1].
Assessments All data on Grade 3/4 adverse events classified as neurotoxicity according to the Common Terminology Criteria for Adverse Events (CTCAE) were collected from both trials and analysed. A subgroup of patients who experienced severe CNS neurotoxicity underwent more detailed analysis. Criteria for severe CNS neurotoxicity are shown in Table  1. For the purpose of this report, we focused on severe CNS neurotoxicity with substantial and prolonged neurological deficits observed among patients with Grade 3/4 toxicity and causal relationship to DB. Severe DB-related CNS neurotoxicity was defined by.
Assessments All data on Grade 3/4 adverse events classified as neurotoxicity according to the Common Terminology Criteria for Adverse Events (CTCAE) were collected from both trials and analysed. A subgroup of patients who experienced severe CNS neurotoxicity underwent more detailed analysis. Criteria for severe CNS neurotoxicity are shown in Table  1. For the purpose of this report, we focused on severe CNS neurotoxicity with substantial and prolonged neurological deficits observed among patients with Grade 3/4 toxicity and causal relationship to DB. Severe DB-related CNS neurotoxicity was defined by the occurrence of clinical symptoms of the CNS without any other detectable reason. If radiological imaging was performed, results had to be consistent with the clinical findings, including the presence of CNS inflammation and/or demyelination (Table  1 ).
| Questionnaires and data collection Sociodemographic and clinical data were collected from counselees or medical records. Outcome variables were assessed with the following questionnaires. Genetic clinicians' confidence in counseling was assessed at T2 using a study-specific three-item questionnaire addressing confidence in: (1) estimating breast cancer risk, (2) communicating breast cancer risk, and (3) communicating clinical recommendation for breast cancer risk, on an eight-point scale from 0 (not confident at all) to 7 (very confident). As internal consistency (Cronbach's alpha) for the three items was 0. 94, a single variable was created. As this variable was not normally distributed, for multivariate analyses, a Box-Cox transformation was applied, leading to scores ranging from À0. 5 to 24.
ACKNOWLEDGEMENTS The work was supported by the  SLOWD  project which received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  815044. This work was performed by using HPC resources of the  Centrale Nantes Supercomputing Centre  on the cluster Liger. The authors acknowledge  Paolo Colagrossi ,  CEO of Punkt. inkcompany , for the post processing and the rendering of 3D SPH data.
[8] [9] [10]  After having been widely used in everyday clinical practice, BBT was abandoned with the advent and widespread of ultrasound technology and hormone assays. The neglect of the importance and use of BBT as simple and costless confirmation of ovulation has been quite sudden and excessive. Nevertheless, it still presents and undeniable advantage in case a relationship is found between the timing or the magnitude of the BBT shift and any of the menstrual cycle hormones. Previous studies that identified the day of ovulation by ultrasound and timed it with reference to the course of BBT and daily progesterone (or progesterone derivative) levels used small sample sizes and were not sufficiently powered to take into account the natural physiological variability. [11] [12] [13] [14] [15] [16]  It seemed therefore appropriate to analyse a large database of cycles that informs on daily BBTs, daily urinary levels of progesterone derivatives, and ultrasound-confirmed days of ovulation.
Women and menstrual cycles Women were recruited from 1996 to 1997 in eight family planning clinics in France, Italy, Germany, Belgium, and Spain. A database was built immediately but some analysis data, especially those relative to PDG, were published much later (past 2015),  [17, 25]  because of legal/ commercial disclosure agreements with the financing company (Quidel Corporation, San Diego, CA, USA). The inclusion criteria limited the study to women 19 to 45 years old with previous menstrual cycles of 24 to 34 days and with no known histories of anovulatory cycles, infertility treatment in the last 3 months, hormone or hormonal contraception use in the previous 3 months, abnormal cycles (polycystic ovary syndrome), hysterectomy, tubal ligation, or pelvic inflammatory disease. The study excluded athletes and breastfeeding or postpartum mothers (less than3 months).
Decreased governmental income forces the government to reduce investments in production, which is required to retain economic growth in the long term. Consequently, reduced investment negatively impacts economic growth. We used the dynamic PEP 1-t model developed by  Decaluwé et al. (2013)  and modified it to reflect the Tanzanian economy. The model database is a social accounting matrix (SAM) which represents a snapshot of the Tanzanian economy for 2015. Indeed, this matrix is a consistent framework that retrieves all the flows recorded in the economy for a given year and provides the structure of the economy  (Round, 2003). The SAM used in this study was developed by  Randriamamonjy and Thurlow (2017). In line with the matrix, our model has 55 activities and 56 commodities. Of these, 25 are agricultural, 19 belong to the industrial sector, and 11 are in tourism-related sectors such as accommodation and restaurants, retail, and transportation.
e. the business as usual. We computed these data as the linear interpolation between the historical level in 2019 and 2030, which we simulated with the CGE modal according to the business as usual assumptions. The dashed lines represent the development in the mild and the severe COVID-19 scenarios. We compute these data by applying the relative change simulated by the CGE model to the historical data of the year 2019 as the last year before COVID-19. The impacts cause by the COVID-19 pandemic exacerbate the historical black swan events. The 9/11 Attacks in 2001 and the 2003-SARS epidemic flatten the curve of tourist arrivals compared to the increasing trend from 2000 to 2002  (Aldao et al. 2021 , Gössling et al. , 2021). The 2008-2009 global economic crisis causes a drop in tourism arrivals and a slight decrease in the value-added of the service sector.
Ex-post comparison For 2020 we obtain data for the historical development and the development simulated with the CGE model based on observed data. In Figure  2 , the differences between the historical and the simulated data appear as a gap between the end of the solid line and the beginning of the dashed line. Table  6  compares the data numerically and presents a slight deviation between historical and simulated data of 1 to 3 percentage points. The underestimation of labour demand, private consumption and value-added of the service sector explain the overestimation of GDP. The CGE model underestimates the negative impact of COVID19 on sectors, consumption and labour market and consequently simulate a less negative impact for the economic development, i. e. the GDP. This ex-post comparison presents a relatively small deviation between simulated and historical results and thus a relatively good model-based reality replication.
Tropical cyclones may form locally in the southwestern Caribbean or originate in the eastern Caribbean/Atlantic Ocean. The latter tend to be stronger and cause more damage when they reach San Andrés. The HURDAT2 dataset shows that the frequency of storm events affecting San Andrés has increased in recent decades, with six storms over the past 20 years, including three category 4-5 hurricanes since 2007. Increasing storm frequency and intensity may be linked to increasing sea surface temperatures caused by anthropogenic global warming, although the changes described here may be limited to a relatively small geographical region, as opposed to representing basin wide tropical cyclone behavior. The growing population density since the 1950s has augmented the potential for disaster.
Even though tropical storms and hurricanes are less common in the southwestern Caribbean Sea than in the eastern Caribbean Sea (e. g. ,  Vecchi and Knutson, 2008) , San Andrés Island was affected by storm events on average once every ten years between the 1911 and 2020, as shown in Figure  8a , based on the HURDAT2 dataset of the National Hurricane Center of the United States. The tropical cyclones affecting the island can be divided into two groups depending on the area of origin of the storm systems. Whereas some tropical cyclones come from the eastern Caribbean Sea or Atlantic Ocean, about half of the storm systems start as tropical depressions or storms in the southwestern Caribbean Sea near San Andrés and develop later into hurricanes of different category of the Saffir-Simpson scale (Tabs.
3 and 4, Fig. 8a ). San Andrés is mainly affected by tropical cyclones between the months of September and November (Fig. 8b ). The average sea surface temperature (SST) today of the whole Caribbean Sea ranges between 27 and 28. 5 °C (Fig. 9a ). The SST data available for the southwestern Caribbean Sea is rather limited, as only buoy Station ID 42057 currently located about 480 km to the north of San Andrés Island at 16. 908°N and 81. 422°W, provides data for the western Caribbean Sea, according to the National Oceanic and Atmospheric Administration (NOAA) National Center of Environmental Information (NOAA Marine Environmental Buoy Database). Looking at the monthly average SST recorded by buoy 42057 for the years 2009 and 2020, an increase in mean annual SST from 28. 11 °C to 28. 65 °C with measured maximum and minimum SST of 30.