dataesr/scientific-paragraphs-categorization huggingface dataset:
Neural network setup and training Thermal contrast (TC) is a key quantity for infrared remote sounding of the lower troposphere  (Clarisse et al. , 2010; Bauduin et al. , 2017). It is defined as the temperature difference between the surface and the air at a given altitude. Here we calculate the TC with respect to the 500 m air temperature (note that in  Whitburn et al. , 2016 , it was defined with respect to the 1. 5 km air temperature). The ANNI-NH 3 -v1 has a rather poor performance for observations with a thermal contrast larger than 10 K, above both land and sea (see Fig. 1a  and c ). This is somewhat surprising as exactly the opposite would be expected. A primary reason for this behaviour is that such high TCs are under-represented in the v1 training dataset. To remedy this, a large amount of simulations were added to the training set, in such a way that a uniform distribution in terms of TC was achieved.
Because TC varies much more over land than over sea, it was decided to make a separate training set (and neural network) for land and sea scenes. Additionally, it was observed that lower latitudes were under-represented in the sea training set due to the way the atmospheres were selected (from random IASI observations, for which the higher latitudes are overrepresented due to the Metop polar orbit). Consequently, over 36 000 simulations were added for the lower latitudes. All in all, the complete training dataset is now almost double compared to the previous set, with around 450 000 simulations (273 000 over land and 172 500 over sea). Another reason for the relatively poor performance of the higher TC observations in ANNI-NH 3 -v1 is related to the way the neural network was setup. As explained in  Whitburn et al.
The total cost function, defined as the mean squared error of the training dataset, will therefore be dominated by these. The end result is that the part of the training set corresponding to HRIs very close to zero leads to non-convergent training or a badly performing network. In  Whitburn et al. (2016) , this situation was remedied by excluding those observations of the training set with a ratio larger than 7 × 10 16 molec cm -2. However, the fact that observations with a poor sensitivity (lower HRI for a constant NH 3 column) have a higher weight in the training cost function than those with a high sensitivity still makes the training focus on that part of the training set with the lowest sensitivity. This would not be a problem if one could train the neural network perfectly for the complete input space, but this is clearly not the case.
The theoretically expected performance improvements outside the blind spot region of TC ≈ 0 are evident. In the v1, the average training error started from 15 %; while in the v2, this drops to around 5 %. Also, the biases mostly disappear: v1 was biased both inside and outside the blind spot. Note finally that these plots also demonstrate the much larger range of TCs covered by the v2. The overall training of the v2. 1 network is largely improved thanks to this simple change of output variable outlined above; however other changes also played a role in improving the performance on the training dataset. The main one is the addition of the HRI as an input parameter. While the ratio f is independent of the column in the linear regime, linearity is only valid for low to medium columns. The de-parture from linearity can actually be observed as gradients in the v1 bias plots of Fig.
Performance on real data and recommendation for use In  Whitburn et al. (2016) , a post-filter was applied to the retrieved data to remove the unphysical measurements (e. g. large negative columns associated with a large positive HRI). For ANNI-NH 3 -v2. 1, we have extended this post-filtering process to remove more of the blatantly erroneous retrievals. The current filtering procedure removes the observations for which 1. the cloud coverage exceeds 25 %, 2. the column is negative and HRI is larger than 1. 5 in absolute value, 3. [NH 3 ] HRI is larger than 1. 75 × 10 16 molec cm -2 in absolute value. The first two criteria were already present in this form in v1, but the third criterion was weaker. Example NH 3 total column retrievals are shown in Fig. 2  for IASI morning and evening overpasses on 17 June 2015 over south Asia, North America and the western part of north and central Africa for both v1 and v2.
1 datasets. Overall it can be observed that both retrievals highly correlate; in particular, the elevated and background columns occur in the same places. One noticeable difference is the effect of the extended post-filter in v2, which removes more of the larger negative columns over sea and over land on the evening overpass. When looking at the evening overpass over India, it could be argued that the filtering is too aggressive. However, the observations that were removed there are associated with extremely large uncertainties due to an almost-zero sensitivity (TC very close to zero). For these observations that were removed, all that one can realistically say is that the NH 3 columns are enhanced. The observations over Africa on the other hand suggest that the post-filtering procedure is still not strict enough.
We refer to  Whitburn et al. (2016)  for examples and a discussion on the pros and cons of weighting measurements. With the extended post-filtering introduced in v2, we no longer recommend using weighted averages. Instead, if averages have to be performed, we would now suggest to use unweighted averages. Using median values is also an option allowing to decrease the importance of outliers. However, it is always better wherever possible to use the individual measurements with their associated uncertainty and to avoid averaging. 3 An ERA-Interim reanalysis (ANNI-NH 3 -v2. 1R-I) Up to now, for all our retrieval algorithms, we have only relied on IASI L2 meteorological data  (August et al. , 2012)  to be used as input data (see e. g. Clarisse et al. , 2012; Hurtmans et al. , 2012; Van Damme et al.
3 ). In particular, a noticeable overall increase in the NH 3 columns was found to correspond with the change from v5 to v6, and a smaller decrease was observed with the introduction of v6. 2. As we will show below, these are a direct consequence of algorithmic changes to the retrieved temperature of the surface and lower troposphere. Following these findings, the need arose for a self-consistent IASI NH 3 dataset, which uses stable and uniform input data. The ECMWF ERA-Interim reanalysis  (Dee et al. , 2011)  is very suitable for this purpose, as it provides all the necessary meteorological parameters and covers the whole IASI time period. We now detail how the ERA data are prepared for the neural network and some of the assumptions that are made. Two separate datasets are used. The first set consists of 0.
With respect to cloud coverage, we decided to continue to use the cloud information provided in the IASI L2 Eumetsat data, but instead of the standard 25 % threshold value, we now filter out all observations with a cloud coverage above 10 % (this leaves about 30 % of all observations for the latest version of IASI L2). Fortunately, except between 14 September 2010 and 28 February 2012 where a lot more observations were flagged as clear (see Table 2), the IASI L2 algorithm seems to be consistent enough for this approach to make sense. This choice also only affects data filtering, not the retrieval itself. For the surface temperature, this is not the case, and so the decision was made to set up a secondary neural network, dedicated to the retrieval of the surface temperature. The inputs of this neural network consist of 105 IASI channels, the satellite zenith angle and the emissivity.
For the training and validation dataset, 37 days of the latest IASI L2 version (v6. 2) ranging from June 2016 to June 2017 were used. Overall, the performance of this secondary NN yielded a standard deviation of 1. 3 K and a mean surface temperature difference of -0. 02 K (considering observations between -60 • and 60 • latitude). This in-house retrieved surface temperature was therefore considered good enough to be used as a retrieval parameter in the neural network. Figure  3a  presents daily time series (5-day moving average) of the NH 3 columns for the reanalysed retrieval (ANNI-NH 3 -v2. 1R-I, blue) and the near-real-time retrieval (ANNI-NH 3 -v2. 1, red) over the Northern Hemisphere. Figure  4  shows morning distributions over south Asia for 3 days, corresponding to v5. 3. 1, v6. 0. 5 and v6.
5 K). In contrast, the air temperature offset becomes positive with v6 (and below 2 K), while the bias in surface temperature is close to 0, resulting therefore in a moderately low bias in TC. This implies, for land evening observations using L2 v5. 3. 1, a low bias of the v2. 1 columns compared to the reanalysis; after version 6 of the L2, we find a high bias. Over sea, differences between the L2 datasets are smaller, with respect both to the surface temperatures and to air temperature profiles, leading to smaller differences between the different products.
Concluding remarks This paper presents the ANNI-NH 3 -v2. 1 retrieval, an improved version from the v1 detailed in  Whitburn et al. (2016). The main changes are (1) a simplification of the input parameters and (2) the development of separate neural networks for land and sea observations, resulting in a better retrieval performance. As discontinuities are observed in the near-realtime processing, a reanalysis of this version 2 was also introduced, namely the ANNI-NH 3 -v2. 1R-I, which uses input generated from the ECMWF ERA-Interim dataset and a surface temperature retrieved by a secondary neural network. While further enhancements to the ANNI-NH 3 product are foreseen in the future (e. g. , improved NH 3 columns could be achieved by using a distinct HRI for land and sea scenes as an input parameter), the neural network design described here is not expected to undergo major changes.
The presented analysis illustrates well the large impact that the (meteorological) input data can have on the retrieved NH 3 column. In particular, small absolute errors in the TC can lead to very large inaccuracies on the retrieved columns, especially when the TC itself is small. Incremental improvements in IASI L2 temperature and cloud algorithms and/or ECMWF ERA-Interim data are therefore expected to have a positive impact on the quality of the NH 3 datasets. While certain biases might still be present, the ANNI-NH 3 -v2. 1R-I is self-consistent in time and is expected to be highly suitable to study long-term trends. Data availability. The near-real-time ANNI-NH 3 -v2. 1 data used in this work are freely available for all users through the AERIS database: http://iasi. aeris-data. fr/NH3/.
Acknowledgements. IASI has been developed and built under the responsibility of the Centre National d'Études spatiales (CNES, France). It is flown on board the Metop satellites as part of the EUMETSAT Polar System. The IASI L1c and L2 data are received through the EUMETCast near-real-time data distribution service. The research was funded by the F. R. S. -FNRS and the Belgian State Federal Office for Scientific, Technical and Cultural Affairs (Prodex arrangement IASI. FLOW) and EUMETSAT/AC-SAF project. Simon Whitburn is grateful for his PhD grant (Boursier FRIA) to the "Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture" of Belgium. Lieven Clarisse is a Research Associate (Chercheur Qualifié) with the Belgian F. R. S. -FNRS. Cathy Clerbaux is grateful to CNES for scientific collaboration and financial support.
In the following sections, firstly we review the GMR/TMR effect and the IEC effect in different material systems, concurrently we discuss the related basic and forefront key research issues. Then we revisit the development and principles of the optical manipulation of the MR effect. Such manipulation can be realized via different methods, including switching the relative magnetic alignment via the all optical switching (AOS) of the magnetic layer or via tuning the IEC effect; or otherwise modulating the electronic transport of the optical responsive spacer layer. The final section is devoted to the abundant applications of those effects, including data storage and sensing. At the end we present a perspective for applying the optical tunable MR to realize ultra-low-power optical date writing and high-speed, inter-chip connection.
The GMR effect rapidly found impactful applications in data storage, and attracted tremendous research interest. In the following sections, firstly we review the GMR/TMR effect and the IEC effect in different material systems, concurrently we discuss the related basic and forefront key research issues. Then we revisit the development and principles of the optical manipulation of the MR effect. Such manipulation can be realized via different methods, including switching the relative magnetic alignment via the all optical switching (AOS) of the magnetic layer or via tuning the IEC effect; or otherwise modulating the electronic transport of the optical responsive spacer layer. The final section is devoted to the abundant applications of those effects, including data storage and sensing. At the end we present a perspective for applying the optical tunable MR to realize ultra-low-power optical date writing and high-speed, inter-chip connection.
All Optical Switching Motivated by the demands of high-speed, large-volume storage applications, researchers have exploited using light instead of magnetic fields to manipulate magnetization in data recording materials. While laser has already been used as a heating source in the so-called heat-assisted magnetic recording (HAMR) to assist magnetic field-driven switching [115], early theoretical and experimental studies meanwhile confirmed light as an electro-magnetic wave can directly influence the magnetization  [116]. Major breakthroughs of AOS, which means deterministic magnetic switching triggered purely by femtosecond laser pulse, have been achieved during the last decade. Two types of AOS have been observed experimentally, namely all optical helicity-dependent switching (AO-HDS) and all optical helicity-independent switching (AO-HIS), depending on whether the magnetic switching relies on the helicity of light.
Application of OTMR Effect The light-tunable MR effect indicates a novel path for combining photonics with magnetic technologies  [112]. The first intriguing application consists of the data writing of magnetic memory. The optical writing of a novel AOS-material-based memory bit can be achieved with a single femtosecond laser pulse  [95, 121] , requiring the switching energy prospectively to be much lower than the current electrical switching approaches  [145]. Admittedly, before reaching a point of practical application, it still demands further research for AOS material systems with higher MR ratios, and efforts at the device engineering level to realize downscaled devices switchable by low-power laser. Different to the optically-switchable MR devices, which mainly provide higher speed and power-efficiency, the optically-sensitive MR devices feature other advantages.
Conclusions The collision and blending of magnetics, electronics and nanotechnology have triggered the birth of spintronics, which is marked by the discovery of the GMR and TMR effect. These magnetoresistance effects and other emerging effects, with abundant applications in the information industry, have kept changing our daily life for several decades. In this paper, we have reviewed the development of GMR, TMR and other related effects, from their mechanism to novel device applications. We first revisited the discovery and mechanism of GMR, TMR and IEC effects within various material systems. We then reviewed the optically tunable MR effect by different approaches. Finally, we discussed the abundant applications of these MR effects and presented a perspective to realize efficient data writing and inter-chip communication.
Acknowledgments: This work was financially supported by  National Natural Science Foundation of China  (No. 61571023 , No. 51602013 , No. 61627813 , No. 61504006 ), the  International Collaboration 111 Project  (No. B16001 ) and the  Beijing Natural Science Foundation  (No. 4162039 ). Acknowledgments: This work was financially supported by  National Natural Science Foundation of China  (No. 61571023 , No. 51602013 , No. 61627813 , No. 61504006 ), the  International Collaboration 111 Project  (No. B16001 ) and the  Beijing Natural Science Foundation  (No. 4162039 ).
Trial identification As part of the wider STOPCaP project, we regularly and systematically searched a number of trial sources to identify all published, unpublished and ongoing trials in men with mHSPC. This provides a comprehensive and up-to-date database of all RCTs eligible for all of our STOPCaP systematic reviews. We also requested regular updates from relevant trial teams on the status and reporting plans. Trials pertinent to this particular review of AAP were identified as part of this broader search. With no restriction on language, LHMR, SB and CLV searched MEDLINE, Embase, clinicaltrials. gov and the Cochrane Central Register of Controlled Trials to May 2017, using database-specific search strategies  [22, 23]  (Web Appendix 1). We also searched proceedings from relevant conferences, such as the American Society of Clinical Oncology, the European Society for Medical Oncology, the European Cancer Organisation, the American Urological Association and the European Association of Urology to May 2017 (Web Table  1 ).
Data collection For eligible trials, and for men with metastatic disease, we sought information on the following: trial accrual period, number of patients, patient age, PSA, performance status, T and N category, location of metastases, disease history, Gleason sum score and hormone therapy from publications, protocols and directly from investigators. We also sought results overall for OS, PFS and FFS as well as by patient subgroups, defined by age, Gleason sum score, nodal status, performance status, type of hormone therapy, location of metastases and disease history. We also requested results for all grades of acute treatment toxicity, as collected within trials, and for the main grade IIIeIV categories, which we tried to match across trials. To assess the risk of bias of included trials, based on the outcome of OS, we also sought information on the method of randomisation sequence generation, allocation concealment, blinding of participants, personnel and outcome assessment, completeness of outcome data and whether all key outcomes were reported/available.
Planning the meta-analysis Initial searches identified three eligible trials (Table  1 ). In 2016, through contact with investigators, we anticipated that by 2017, one trial, PEACE-1 (NCT01957436) would still be recruiting patients, but the two other trials (LATITUDE (NCT01715285) and STAMPEDE (NCT00268476)) would be reporting results  [11, 12]. Based on recruitment information available at that time, we predicted that the latter two trials would represent over 70% of metastatic patients eligible for this comparison. Given that these trials were large and adequately powered and that PEACE-1 is unlikely to produce results before 2020, this provided the trigger to initiate an early systematic review and metaanalysis. However, we also planned to take into account the potential impact of the results of PEACE-1.
Therefore, these meta-analysis results provide reliable and robust evidence to guide practice and future research. Using our collaborative FAME approach, we have been able to synthesise the effects of adding AAP to ADT in a more timely, reliable, and meaningful manner than is usually possible with aggregate data  [7]. We were able to identify all eligible trials, in advance of results being available, anticipate when results of STAMPEDE and LATITUDE were due to emerge and obtain up-to-date accrual information for the ongoing PEACE-1 trial  [11, 12]. The STOPCaP Project Management Group also gained access to prepublication results and additional unreported analyses. We obtained additional PFS results for men with metastatic disease from STAMPEDE, allowing us to examine the consistency of effect across the two trials and provide the best estimate of the sizes of the effect.
Given the improved OS seen with both AAP and docetaxel, together with their differing mechanisms of action, a major question is also whether the effects of these two agents are additive. This will take some years to assess, as only the second phase of the PEACE-1 trial will provide data on ADT plus docetaxel plus AAP. Therefore, we would actively encourage participation to this trial. Furthermore, as enzalutamide has been shown to have a similar clinical effect on androgen signalling in castration-resistant prostate cancer as AAP, the ENZAMET trial (NCT02446405) of enzalutamide plus ADT  [19] , which has recently completed accrual, and the ARCHES trial (NCT02677896) which is still recruiting  [20] , both of which are stratified by docetaxel use, will further augment our knowledge of such 'triplet therapy'.
IPD will also be valuable for tackling other important clinical and scientific questions arising, including the identification of surrogate outcomes by building on the Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) initiative in non-metastatic prostate cancer  [24]. Therefore, we are developing the STOPCaP/ICE-CaP M1 repository of contemporary trials in mHSPC. This work will be supported by the MRC  Fig. 4. Effect of adding AAP to ADT on overall survival by age group. Labelling and conventions as in Fig. 3. AAP, abiraterone acetate plus prednisone/prednisolone; ADT, androgen deprivation therapy; CI, confidence interval; HR, hazard ratio. report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Acknowledgements The STOPCaP abiraterone collaborators thank all patients who participated in the trials and contributed to this research. The meta-analysis would not have been possible without their participation or without the institutions and collaborative groups that carried out the trials, including a great many trial sites. The authors thank the LATITUDE and STAMPEDE trial teams for supplying pre-publication results and additional analyses to facilitate meta-analysis. They also thank  Mary Todd  for considerable help in establishing the collaboration with  Janssen  and  David Fisher  for help in conducting and presenting the analyses. The  STOPCaP Project Management Group  was funded by the  UK Medical Research Council  ( MC_UU_12023/25 ).
The early addition of leg bicycling on a cyclo-ergometer and of electrical muscle stimulation (EMS) is now part of common practice in the ICU. Whether it can preserve or improve muscle strength and further increase the beneficial effects of ESR is little known. Patients and methods Single-centre, randomised study comparing the effects of the combination of early and daily leg bicycling + EMS of the quadriceps + ESR (intervention group) versus ESR alone (usual care group) on the global muscle strength assessed by the MRC score at ICU discharge by a physiotherapist blinded to the randomization group (NCT02185989). All consecutive patients were potentially eligible if they were deemed to need more than 72 h of care in ICU. Main non-inclusion criteria were resuscitated cardiac arrest, presence of pacemaker or implantable defibrillator, acute cerebral disease requiring deep sedation for at least 72 h, known neuromuscular disease, and amputation of a lower limb.