dataesr/scientific-paragraphs-categorization huggingface dataset:
Baseline characteristics were unavailable for the registry datasets; therefore, it is difficult to explain the differences in survival projections. AJCC version 8  [17]  survival was very optimistic but may have considered patients who had adjuvant therapy, and the mean age of the patients is unknown. Plausible model projections would have been between the version 7 and 8 AJCC datasets, given that newer metastatic treatments should have improved survival since the 2009 dataset. The PSM and STM using the same data sources produced different long-term estimates, indicating that-in this case-structure had an impact on survival estimates (21. 0 life-years for nivolumab and 14. 0 life-years for observation vs. 18. 4 life-years for nivolumab and 11. 1 life-years for observation over the 60-year time horizon, respectively).
g. , the data acquisition box, most of the aerial robots used in research are not capable of flying with these sensors on board. However, recently some light-weight 6D F/T sensors appeared on the market. For the experiments of this paper, we have decided to use the FTSens 6D F/T sensor, developed by the Italian Institute of Technology (IIT), as introduced in  Fumagalli et al. (2012a). This sensor weights 0. 122 [kg] including all the electronics and its costs is relatively low w. r. t its pairs in the market. All these factors make this sensor a suitable candidate to be used on board of an aerial robot. This paves the way of using them as direct measurement sources, which feed the wrench measurements back to the control algorithms (e. g. , the one sketched in Fig. 2 ). The details of the hardware and software for the FTsens F/T sensor is given in Section 4.
Experiments The experiments are managed using a stationary PC with Linux 14. 04 on it, which communicates with the experimental setup (robot in Fig. 4 ) through serial channels. Besides the codes embedded on board of the quadrotor setup, all experiments are programmed in this PC in ROS-Indigo environment and TeleKyb framework: an open-source end-toend ROS-based software for general purpose of mobile robot control developed at Max Planck Institute for Biological Cybernetics, Tübingen (for details, see  Grabe et al. (2013) ). Using a custom joystick high level decisions are indicated to the robot, e. g. , hovering, trajectory assigning or landing. The experimental setup consist of a quadrotor equipped with various sensors including F/T sensor on board. This sensor is connected to a rigid interaction tool (see Fig.
F/T Sensor In our experiments we used the FTSens F/T sensor, produced by (Italian Institute of Technology) IIT originally for the ICub humanoid robots (see  Fumagalli et al. (2012a) ). There are two reasons why we chose this sensor; first it was relatively cheaper than its peers in the market, and second it weights 0. 122 [kg] together with its electronics. This is definitely in the load range of our quadrotor. The sensor is provided with proper calibrations by IIT for the measurement range of interest. A challenge of using this sensor was implementing the software for acquiring the meaningful force/torque measurements, from scratch. The FTSens communicates through the Controller Area Network (CAN) bus channel, in which it receives the commands and sends the sensor data based on the CAN protocol.
For this setup to work, both the computer and the sensor were needed to be programmed properly. The software package we have created for this sensor is available for the public use 14. There we provide description and the source codes for: • setting up your computer (for both Intel or ARM processors) for using the CAN-USB converter, • getting the calibration data from the sensor and letting it send the raw data to the computer. The details on the communication protocol of the sensor are available in the wiki-page of the ICub 15. Using this driver it is possible to receive the raw data from the FTSens F/T sensor. For processing this raw data, we implemented a ROS (C++) based software within the TeleKyb framework 16. This software is tested with ROS-Indigo in Ubuntu 14. 04 OS. It receives the raw data from the serial channel the sensor is connected to (through a CAN-USB converter) and as output returns the force and torque measurements in meaningful units as a ROS message.
Let g be the current environment. If g is not terminal, then a step of the distributed algorithm is performed as follows: every node n that is enabled in (g n) is candidate to be executed; some candidates (at least one) are nondeterministically activated, meaning that they atomically update their local state using their local algorithm, leading the system to a new environment g'. This nondeterminism actually materializes the asynchronism of the system. Notice that two environments linked by a step are necessarily different. This point is fundamental in asynchronous deterministic algorithms: the system progress can only be observed when the environment changes. In PADEC, we use the relation Step: Env → Env → Prop to encode all possible steps. A maximal run in the network is defined as a stream of environments, using type Exec: Type := Stream (A := Env), where every pair of consecutive environments in the stream is a step, and if the stream is finite then its last environment is necessarily terminal.
This notion is captured by the predicate is_max_run ( e : Exec ): Prop := Always P_run e. where (P_run e) checks that the stream e matches one of the two following patterns. Either e is (O g) and the environment g is terminal, i. e. , (terminal g) holds; or e is (C g e') (with g an environment and e' a stream) and there is a step from g to (H e'), i. e. , (Step (H e) g') holds. We model the nondeterminism of the system using an artifact called the daemon. In this paper, we focus on the so-called weakly fair daemon  [24] : a maximal run is executed under the weakly fair daemon if and only if every node that is continuously enabled is eventually activated by the daemon. To encode the weakly fair daemon, we define the following predicate: Namely, all along a run e, whenever some node n is enabled (predicate (EN n)), it is eventually either activated or neutralized (predicate (AN n)), i.
The Composite Algorithm in Coq We model the composite algorithm A 1 ⊕ A 2 in Coq as follows. We define the local states S3 of A 1 ⊕ A 2 assuming that the local states of A 1 , noted S1, can be handled using the following getter and setter: -read1: S3 → S1 is a projection from S3 to S1, -write1: S1 → S3 → S3 modifies the S1-part of a composite state. Functions read2 and write2 are defined similarly for the local states S2 of A 2. 4 hose functions follow the properties given by the commutative diagram of Additionally, we encode the fact that any writing in the S2-part (by A 2 ), that respects the read-only condition, actually does not modify the S1-part of an S3 state. Indeed, the common part between S1 and S2 is necessarily read-only for S2 (see x in Figure  2 ). x is the common part between S1 and S2, read-only for S2.
Results and discussion 78  To obtain more information about the flux pinning properties, Kramer plots, i. e. , j 0. 5 c × B 0. 25 versus the applied field, B = µ 0 H a , are employed  [34]. The advantage here is that the information obtained is free of uncertainties such as the determination of H irr. This is especially important when comparing data of different origins, i. e. , unknown measurement parameters such as the field sweep rate. In Figure  2b , the Kramer plot for the same dataset is presented. At applied fields above 0. 6 T, all curves are found to be quasi linear. An extrapolation of these curves yields information about the upper critical field, H c2. It is obvious that H c2 obtained in this way is always larger than the irreversibility fields, as shown below. For comparison of our data with those of a fully-reacted pnictide sample of the same composition, Figure  3a  shows a double-log plot of the j c -data by Weiss et al.
To obtain this information, the 122 grains in the orientation map (b) are encircled by ellipses by the EBSD software. The grain shape aspect ratio, γ ar , is defined as the length of the minor axis divided by the length of the major axis. The value of γ ar ranges between 0 and 1, with higher values indicating similar lengths of the axes  [26]. The color code for this map is indicated on top of it. The plot (g) gives the statistics of the EBSD-determined misorientation angles (number fraction vs. misorientation angle). Here, a large number of small-angle misorientations is obtained, and also a maximum at ∼37 •. Finally, the results of the grain shape aspect ratio analysis are presented in plot (h). The majority of the 122 grains are only slightly elliptic; grains with an elongated major axis γ ar between 0.
Acknowledgments: We thank  J. Wiesenmayer  and  D. Johrendt  ( LMU, München, Germany ) for providing us with the polycrystalline  Ba 0. 6 K 0. 4 Fe 2 As 2  sample,  C. Chang  ( IJL Nancy, France ) for help with some of the magnetic measurements and  K. Berger ,  Q. Nouailhetas ,  B. Douine  ( GREEN, Nancy, France ) for valuable discussions.
Until now, the strategy has privileged to use sufficiently refined meshes (h -refinement), but this has implyied expensive computations. The other classical technique to reach more exact solutions rather than dividing elements into smaller ones is to increase the polynomial degree of the finite element approximation (p -refinement), as proposed in  [22, 23]  for problems using direct solvers (without parallel computations). Therefore, this work studies the influence of the p -refinement not only on the accuracy of the results, but also on the iterative solver (LaTIn-DDM). The numerical implementation is made using the C++ research code MULTI developed at the Laboratoire de Mécanique et Technologies de Cachan 1 using MPI and METIS libraries for the parallel assignments. This work proposes to use second order finite elements in the LaTIn-DDM and to study their effects on the convergence rate and on the calculation time with respect to the LaTIn-DDM previously defined in  [18].
Classically, the code MULTI has considered linear elements P 1 with local h -refinement along the subdomain's boundary, while the spaces W S 0 S′ 0 ,h and F S 0 S′ 0 ,h are piecewise constant functions P 0. In this work, the p -refinement is also explored. Indeed, it is here proposed to use the second order approximation for u_ S 0 not only in the boundary but in the whole subdomain.
Acknowledgements K. Saavedra  acknowledges the financial support from  CONICYT ,  FONDECYT Initiation into research project No 11130623. J. Fernández  acknowledges the financial support from  CONICYT-PFCHA , National Doctorat 2017, No  21171988. The authors also wish to thanks  LMT-Cachan  ( ENS Paris-Saclay/CNRS/Université Paris-Saclay ) for enabling to use the research code MULTI.
INTRODUCTION Context. Memory related errors can provoke serious defects in software  [5]. A study based on IBM MVS software  [26]  reports that about 50% of detected software errors were related to pointers and array accesses. Memory errors account for about 50% of reported security vulnerabilities  [27]. This is particularly an issue for a programming language like C that is both the most commonly used for development of critical system software and one of the most poorly equipped with adequate protection mechanisms. The developer remains responsible for correct allocation and deallocation of memory, initialization of variables, pointer dereferencing and manipulation (using casts, offsets, etc. ), as well as for the validity of indices in array accesses. Among the most useful techniques for detecting and locating software errors, runtime assertion checking has become a widely-used programming practice  [6].
MOTIVATING EXAMPLE Consider the toy C function at the left of Fig. 1. It contains a formal assertion guaranteeing that dereferencing pointer p is safe. This kind of assertions may be automatically generated by Frama-C  [14]. The E-ACSL tool  [23]  is able to take this annotated function as input in order to generate the instrumented version at the right of Fig. 1  (where we assume a 32-bit architecture). If executed, this instrumentation nicely stops the execution whenever the assertion is violated, preventing an incorrect memory access. To be able to check at runtime this kind of memory-related annotations, the code generated by E-ACSL records memory operations in the E-ACSL dynamic memory model linked to the instrumented program  [28]. For instance, every allocation (resp. deallocation) is recorded through a call to function store_block (resp.
Operational semantics This section introduces an operational semantics for our language. The evaluation of expressions draws inspiration from Clight  [3] , the source language of CompCert, while statements have a small-step semantics akin to that of a While language, as in  [19]  for instance. A noticeable trait of our semantics is that it is blocking: any execution ends immediately (cannot continue further) if an assertion is violated. This property is indeed the fundamental property of Frama-C and E-ACSL  [7]. The semantics is expressed by means of six forms of evaluation judgments: • M ⊨ lv l ⇐ (b, δ ) evaluates a left-value l to an address (b, δ ) in a memory M; • M ⊨ e e ⇒ v evaluates an expression e to a value v in a memory M; • M ⊨ t t ⇒ v evaluates a term t to a value v in a memory M; • M ⊨ p p ⇒ P evaluates a predicate p to a truth value P in a memory M; • ⟨s, M 1 , M 1 ⟩ → (M 2 , M 2 ) evaluates a statement s in an execution memory M 1 and an observation memory M 1 , then ends the execution in an execution memory M 2 and an observation memory M 2.
The proof assumes that there is no dynamic memory allocation in the program. Our ultimate goal is to be able to extract a correct E-ACSL-like plugin from a Coq development. This goal necessitates an implementation and verification in Coq of the presented static analysis extended to CompCert C. Our formalization follows the CompCert memory model  [16]. There are several works on the verification of static analyses with a proof assistant. We only briefly present some papers related to the Coq proof assistant. The CompCert compiler itself contains static analysis for the sake of optimization  [2] , including a generic implementation of the Kildall algorithm  [13]. This is however related to the RTL language of CompCert. Like Verasco  [12]  the presented memory analysis will rather be conducted at the level of the Cminor language.
The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases.
In addition, since the first MM whole genome sequencing study  6  , the landscape of nonrecurrent SVs and complex events has not been systematically explored. In this study, we combine a large cohort of serial MM samples analyzed by whole-genome sequencing (WGS) with a publicly available dataset to define driver events and how they group across patients, with implications for disease classification. Furthermore, we describe the temporal evolution of the disease in preclinical phases, highlighting the unexpected dynamism of genomic changes, often in the form of private, complex structural events.
Results Landscape of driver mutations in MM. We performed whole WGS of 67 tumor samples collected at different time points from 30 MM patients, together with matched germline controls (Supplementary Data 1 and 2, "Methods"). We also included in our analyses published whole exome data from 804 patients within the CoMMpass trial (NCT01454297)  14. To discover MM driver genes, we analyzed the ratio of nonsynonymous to synonymous mutations, correcting for mutational spectrum and covariates of mutation density across the genome with the dNdScv algorithm  [15] [16] [17]. Overall, 55 genes were significantly mutated with a false discovery rate of 1% (Fig. 1a  and Supplementary Data 3). Our shortlist of genes showed a 65% overlap with a recently published study from the Myeloma Genome Project (MGP) (Supplementary Data 3)  13  , with less frequently mutated genes accounting for the discordant calls.
This is expected given the random sampling and differences in statistical approaches and power between studies (Supplementary Data 3). To confirm this, we restricted the multiple hypotheses testing pvalue correction to the set of unique driver genes from the MGP (n = 26), thus identifying six additional shared drivers (ABCF1, ZFP36L1, TET2, ARID2, KDM6A, and EP300) (Supplementary Fig. 1  and Supplementary Data 3) and increasing the concordance of the two datasets. Overall, 87% of all MMs had at least one of these 61 driver genes mutated, with an average of 2. 04 (0. 98-3. 08) drivers per sample estimated calculating the global ratio of nonsynonymous to synonymous mutations  17  ("Methods"). A significant fraction of these driver point mutations was present in subclones of the myeloma rather than the ancestral clone, suggesting that they often play a role in later phases of cancer development (Supplementary Fig.
2j ), as previously described  9, 13. However, a pairwise approach does not have the power to detect tertiary, quaternary and higher-level interactions which are expected in a heterogeneous disease as MM  9. Therefore, higherlevel statistics must be used to deconvolute the complex landscape of MM and to identify subgroups of cases with a similar genomic landscape. To this end, we developed a composite analysis employing both Bayesian networks and the hierarchical Dirichlet process (hdp)  23  to define the conditional dependencies of all driver events in MM and attempt a genomics-based classification of the disease (Fig. 1b, c , Supplementary Fig. 3  and Supplementary Software 1)  23, 24. Bayesian network (BN) analysis identified both known and novel patterns of higher-level co-occurrence and mutually exclusivity between driver events.
6 ). These events were acquired with a nonrandom order. Overall, chromothripsis, cycles of templated insertions, chromosomal gains and other SVs accounted for most of the earliest events, emerging as key drivers of disease initiation, seeding the soil for driver events that would later arise and confer further selective advantage to the clone. Focal deletions on distinct oncogenes, whole-genome duplication and chromoplexy were generally acquired during progression and/or relapse, potentially representing new mechanisms of subclonal selection and treatment resistance  5, 10. WGS thus offered a more detailed compendium of driver events, mostly structural, and of their chronological order of acquisition. We next explored how this enriched view of the genome offered by WGS fitted within the groups identified by the combined hdp and BN clustering, based on whole-exome sequencing of the CoMMpass dataset (Fig.
Methods Sample selection. The study involved the use of human samples, which were collected after written informed consent was obtained (Wellcome Sanger Institute protocol number 15/046). Samples and data were obtained and managed in accordance with the Declaration of Helsinki. DNA was extracted from CD138+ cells purified from bone marrow from 30 patients, of which 26 (86%) with multiple sampling, for a total of 67 tumor samples and 30 matched normal samples (Supplementary Data 1). Samples were collected at different clinical time points: smoldering (n = 11), symptomatic (n = 15), and relapsed MM (n = 41) (Supplementary Data 2). Constitutional control DNA originated from peripheral blood mononuclear cells. Purity of the CD138+ fraction was assessed by anti-CD138 immunocytochemistry post sorting, and only samples with >90% plasma cells were sequenced.
Massively parallel sequencing and alignment. Short insert 500 bp genomic libraries were constructed, flowcells prepared and sequencing clusters generated according to Illumina protocols. We performed 108 base/100 base (genomic) paired-end sequencing on HiSeq X10 genome analyzers. The average sequence coverage was 38. 7-fold. Short insert paired-end reads were aligned to the reference human genome (GRCh37) using the Burrows-Wheeler Aligner (BWA) (v0. 5. 9)  41. Processing of genomic data. CaVEMan (Cancer Variants Through Expectation Maximization: http://cancerit. github. io/CaVEMan/) was used to call somatic substitutions  42, 43. Indels were called using a modified Pindel version 2. 0 (http:// cancerit. github. io/cgpPindel/) on the NCBI37 genome build  44. SVs were discovered using a bespoke algorithm, BRASS (Breakpoint Analysis) (https://github.
themmrf. org and www. themmrf. org). We run dNdScv on the entire gene set and, to increase statistical power, on a restricted set including recently reported potential MM driver genes  13. In addition to identifying positively selected genes, dNdScv estimates the excess in nonsynonymous mutations across a set of genes, making it possible to calculate the average number of driver mutations per sample, as recently reported  17. BN analysis. The BN was constructed by first creating a binary matrix of the presence/absence of genomic aberrations and mutations in each patient. The Gobnilp  48  software was then used with default parameters. These include a maximum of three parents. The algorithm finds the globally optimal BN within the constraints set, by posing the learning of the BN structure as a linear optimization problem that is solved using integer linear programming via the SCIP Optimization suite  49.
The BN allows the identification of both complex and simple pattern correlations of drivers. The output is composed by two different figures: (i) the global BN figure (Fig. 1b ), where all the gates/correlations are listed and connected; (ii) the heatmap of driver events for each family in the BN, which allow easy interpretation by scientist through visual inspection (Supplementary Figs. 3  and 13 ). Hierarchical Dirichlet process. The hdp was used to investigate the main MM genomic subgroups (https://github. com/nicolaroberts/hdp)  23. All dN/dS-extracted driver mutations and the most clinically relevant and recurrent CNAs and SVs were included, for a total of 69 variables available for 724 (87%) cases. The final exome-based clusters were extracted combining the hdp and BN data (Supplementary Figs.
17a ) To assign each SNV to either a pregain or postgain status, we have created a script that uses mclust R function for this analysis. Our R function is called "mol_time" and is available on github. com/nicos-angelopoulos/mol_time. The input to the function is the corrected c-VAF profiles as shown in Supplementary Fig. 17. In panels (b) and (c) mclust was run on 2 chromosome gains dividing the SNV catalog in two main groups: one where mutations were detected on two alleles and one where mutations were observed only of one allele out of three. In panel (d) mclust was run on a CN-LOH, and this explains why duplicated and nonduplicated mutations had 100% and 50% c-VAF, respectively. Finally, in the last plot (e) differently from the recently published molecular time analysis  35  , mclust was also run in the presence of two extra copies of the same chromosome.