dataesr/scientific-paragraphs-categorization huggingface dataset:
A large range of PET-AS methods was used in this study to represent different segmentation approaches. These have been described in detail in previous publications  (Berthon et al 2013 (Berthon et al , 2014b)) , and are summarised in table 1, along with references of recent publications using similar segmentation approaches. All methods were implemented in the Matlab programming language (The Mathworks, Natick USA) using the same procedure, so that the only difference between the methods was the underlying segmentation approach. Methods AT, RG and WT are binary classification methods, which previously showed high accuracy in delineating homogeneous lesions  (Berthon et al 2013 (Berthon et al , 2014b)). The clustering methods KM, FCM and GCM were each applied to the detection of 2, 3 and 4 clusters, with the resulting methods named KM2-KM4, FCM2-FCM4 and GCM2-GCM4 respectively.
Building of the statistical model A training dataset was used to build DTs for each PET-AS method described in table 1. We used the PETSTEP simulator  (Berthon et al 2014a (Berthon et al , 2015a) )  for the rapid generation of a large training set. PETSTEP operates in the open source framework of the Matlab-based computational environment for radiotherapy research (CERR)  (Deasy et al 2003). It uses a CT image and a map of the background FDG uptake to generate a PET image from tumour any contours drawn by the user. The training dataset was generated based on existing PET/CT data of a fillable phantom. Target tumours objects covering a range of different characteristics relevant to clinical situations were added to the background of this phantom. A total of 100 spherical tumours objects were modelled for volume and maximum uptake values in the range 0.
5 ml-50 ml and 4000 Bq ml -1 -40000 Bq ml -1 respectively. For all the scans in the training dataset the target outline was known since it was one of the input parameters in the synthetic image generation process. We refer to this outline as 'reference true contour' or 'ground truth'. The workflow to build the statistical model was fully automated and computer scripts were used to (1) determine evenly spaced values of the volume and FDG uptake spanning the ranges specified, (2) generate the synthetic training scans, (3) calculate the value of corresponding tumour parameters in the target object, (4) segment the synthetic scans using the PET-AS methods in table 1, (5) calculate the segmentation accuracy for each method by comparing the result of the segmentation with the ground truth (i. e.
actual target outline and location) and (  6 ) build the corresponding DTs. Only the PET-AS methods that performed differently across the dataset were kept in the study provided non-correlated DSC values across the dataset were kept in the study. Correlated methods were identified using SPSS 20 (IBM, Chicago, USA), as pairs of PET-AS for which the Pearson's r correlation coefficient was larger than 0. 95 (r > 0. 95) and the associated p-value smaller than 0. 05 (p < 0. 05). The best performing (highest average DSC) of each correlated pair was kept. Only the following uncorrelated methods were considered for further analysis: AT, RG, WT, KM2, KM3, KM4, FCM2, GCM3 and GCM4. The segmentation accuracy was assessed by quantifying the conformity of the contours obtained to the reference true contour provided by the simulation template in PETSTEP.
We used the DSC (Dice 1945), defined as: = * ∩ + X Y X Y DSC 2 where |X | and |Y | are the number of voxels in the reference and test contour, respectively, and X Y ∩ the number of voxels in their intersection. DSC above 0. 7 is considered as an indicator of good overlap (Dice 1945). The DTs predicting, for each PET-AS, the DSC score obtained according to the values of the different tumour parameters were built automatically with the Matlab statistics toolbox, based on the Classification And Regression Tree growing method (IBM Corporation 1989 2011). The Matlab method RegressionTree. fit was used with its default settings, including the impurity measure 'Gini', ensuring the data is classified into homogeneous groups of values. For each tree, the cross-validation error was calculated as the relative difference between the true actual DSC and the DSC predicted by the tree, averaged on a randomly chosen subset (10%) of the training dataset.
Validation of the ATLAAS predictive segmentation model The accuracy of ATLAAS was evaluated for the segmentation of a wide range of phantom data generated for previous studies, within the range of volume and TBR peak used in the training of ATLAAS. The data available included: 37 fillable phantom test images obtained from using thin-wall spherical  (Berthon et al 2013)  and non-spherical  (Berthon et al 2014b)  plastic inserts including ellipses, tori, tubes, pear-and drop-shaped objects, 17 cases of heterogeneous spheres in homogeneous background (1, 2 or 4 concentric spherical regions of different uptakes) obtained with the printed subresolution sandwich (SS) phantom  (Berthon et al 2015b) , 31 H&N homogeneous and heterogeneous (random and Gaussian smoothed uptake) irregular H&N cases generated with a the printed SS phantom, using a printout template representing realistic H&N background uptake derived from clinical PET/CT scans.
Example images taken from each one of these datasets are shown on figure 3. The ATLAAS predictive segmentation model was applied to each test case of this validation dataset. The PET-AS methods in were also individually applied to each test case. The mean and minimum (min) DSC across the validation dataset were calculated for ATLAAS and for each of the different PET-AS methods. In addition, we determined for each method the percentage of cases where it returned the best DSC (P best ) and a DSC value within 10% of the best PET-AS algorithm (P 10 ). The Wilcoxon signed-rank test was used to determine if ATLAAS and the PET-AS generated DSC distributions were significantly different, with a significance value set to p = 0. 05.
The ATLAAS predictive segmentation mode The simulated data used for training ATLAAS covered TBRpeak, V and NI ranges of 1. 1-52, 0. 44-52 ml and 8-38 respectively. The clinical data observed at our centre had TBRpeak, V and NI in the range 1. 1-8. 3, 0. 44-59 ml and 13-63 respectively as shown in table  2. A total of 9 trees were generated using all tumour parameters described previously as classifiers. Table  3  shows the performance of the different PET-AS methods on the training dataset (mean DSC and P best ) and the cross-validation error of the associated DTs. All trees achieved a cross-validation error smaller than 1%, except for RG. The relative importance relative to the model of each predictor is given at the bottom of table 3. TBRpeak was by far the parameter with highest importance to the model overall, followed by Volume.
Evaluation of the ATLAAS predictive segmentation model The phantom dataset built for the evaluation of ATLAAS included target objects with volumes and TBR peak values in the range 0. 58 ml-102 ml and 1. 3-17 respectively. The results of the validation of ATLAAS with phantom data are given in table  4 , showing the mean and lowest DSC obtained on the dataset as well as P best and P 10 for ATLAAS and all PET-AS methods considered in the model. ATLAAS returned the best DSC in 56% of cases, and a DSC within 10% of the best in 93% cases, compared to respectively 34% and 88% obtained when using the best single PET-AS methods on this dataset (AT). It is worthwhile noting that on average ATLAAS outperformed all the individual PET-AS methods on all the metrics used. On the phantom dataset, ATLAAS reached a minimum DSC of 0.
650, which is more than twice the minimum DSC obtained using the best single PET-AS methods on this dataset (AT). Results of the Wilcoxon signed-rank test, given at the bottom of table 4, showed statistically significantly different DSC values for ATLAAS compared to WT, KM3, KM4, GCM3 and GCM4. In comparison, ATLAAS built using only TBRpeak, and only TBRpeak and Volume as classifiers achieved mean DSC values across the validation data of 0. 816, 0. 820 respectively. A full comparison of the segmentation accuracy (DSC) obtained for ATLAAS (black) and for the single PET-AS methods (colours), for all the cases of the validation dataset, is depicted in figure 5. It can be noted that the curve representing ATLAAS is higher than individual PET-AS curves in most cases. When ATLAAS did not reach the highest DSC, its DSC was still higher than 0.
The added value of ATLAAS is the capability of predicting the best or near-best segmentation methods in a welldefined and reproducible way, thus providing optimal and robust image segmentation. Our results also showed that ATLAAS largely outperformed all single PET-AS values on fillable phantom data but did not outperform the best PET-AS in the case of printed H&N phantom data (see figure 5 and section 3. 2). This can be explained by the fact that the training dataset for ATLAAS was built using images simulated from fillable phantom templates. Although the training dataset included non-spherical target lesions, ATLAAS was not trained on highly irregular lesions, or on images with heterogeneous background, which were found in the H&N printed data. This shows precisely the influence of the training dataset on the accuracy of the predictive model.
It is expected that a training dataset containing irregular ground truth data for highly irregular lesions and heterogeneous background images, more representative of clinical data may will further improve the performance of ATLAAS. This will be the aim of future work, evaluating how the complexity of the training dataset will affect the performance of ATLAAS, and taking part in collaborations using ATLAAS on clinical data from different centres. Classification parameter NI was chosen from the set of regional grey level co-occurrence matrix which were shown by  Tixier et al (2011)  to be most promising for the prediction of response to therapy. Although such a prediction is not the aim of this study, we hypothesized that regional texture features are therefore more clinically relevant for describing tumour heterogeneity.
Among all regional grey level co-occurrence texture features, we selected only one to avoid biasing the DTs towards texture, and chose NI as it was the one providing the best performance for ATLAAS. However, other texture metrics based on different calculation approaches or matrices may further increase the accuracy of ATLAAS, especially if their value can be controlled in making the training dataset. This was not the case for NI, which explains the discrepancy between clinical and training dataset NI values (see table  2 ). Further work at our centre will be focused on investigating the best metric or combination of independent texture metrics to use. ATLAAS could be additionally improved by adding to the classification parameters shape metrics, which were shown by  Tixier et al (2013)  to be potentially useful for the classification of segmentation methods.
The training dataset was generated using PETSTEP, a fast and flexible PET simulation tool. PETSTEP has been previously calibrated and validated for the simulation of 18F-complex FDG uptake in the H&N  (Berthon et al 2015a). However, the current version of PETSTEP does not include axial filtering, and therefore handles all PET image slices individually, which does not allow accurate modelling of the acquisition process. In addition, Time-of-Flight correction, which was indeed used in for all scans acquired in our centre, is not currently modelled in PETSTEP. Since the quality of the ATLAAS predictive model depends largely on the quality of the training dataset we expect that any improvement in the quality of the images simulated with PETSTEP will result in an improved performance of ATLAAS.
It is worth noting that as part of this work, we have also developed an automated procedure for building the ATLAAS training dataset. This allows building and using the model with data from any centre or any PET imaging system. Indeed ATLAAS could be built for any tracer or imaging modality (e. g. SPECT), as long as corresponding data with reference true contours are available. Furthermore, there is no limit to the number or type of PET-AS algorithms that can be included in the ATLAAS predictive segmentation model.
Alias analysis is one of the most fundamental techniques that compilers use to optimize languages with pointers. However, in spite of all the attention that this topic has received, the current state-of-the-art approaches inside compilers still face challenges regarding precision and speed. In particular, pointer arithmetic, a key feature in C and C++, is yet to be handled satisfactorily. This paper presents a new alias analysis algorithm to solve this problem. The key insight of our approach is to combine alias analysis with symbolic range analysis. This combination lets us disambiguate fields within arrays and structs, effectively achieving more precision than traditional algorithms. To validate our technique, we have implemented it on top of the LLVM compiler. Tests on a vast suite of benchmarks show that we can disambiguate several kinds of C idioms that current state-of-the-art analyses cannot deal with.
The key insight of this paper is the combination of pointer analysis with range analysis on the symbolic interval lattice. In a symbolic range analysis, ranges are defined as expressions of the program symbols, a symbol being either a constant or the name of a variable. There exist many approaches to symbolic range analyses in the literature  [4, 15, 19]. The algorithms that we present in this paper do not depend on any particular implementation. Nevertheless, the more precise the range analysis that we use, the more precise the analysis facts that we produce. In this work we have adopted the symbolic range analysis proposed in 1994 by William Blume and Rudolf Eigenmann  [4]. To validate our ideas, we have implemented them in the LLVM compilation infra-structure  [14]. We have tested our pointer analysis onto three different benchmarks used in previous work related to pointer disambiguation: Prolangs  [20] , PtrDist  [29]  and MallocBench  [9].
The partial ordering is given by - ∞ <. < -2 < -1 < 0 < 1 < 2 <. + ∞. There exists no ordering between two distinct elements of the symbolic kernel of a program. For instance, N < N +1 but there is no relationship between an expression containing N and another expression containing M. A symbolic interval is a pair R = [l, u], where l and u are symbolic expressions. We denote by R ↓ the lower bound l and R ↑ the upper bound u. We define the partially ordered set of (symbolic) intervals S 2 = (S × S, ), where the ordering operator is defined as: [l 0 , u 0 ] [l 1 , u 1 ], if l 1 ≤ l 0 ∧ u 1 ≥ u 0 From the previous definitions, we define the semi-lattice SymbRanges of symbolic intervals as (S 2 , , , ∅, [-∞, +∞]), where the join operator " " is defined as: [a 1 , a 2 ] [b 1 , b 2 ] = [min(a 1 , b 1 ), max(a 2 , b 2 )] Our lattice has a least element ∅, such that: ∅ [l, u] = [l, u] ∅ = [l, u] Author Version of « Symbolic Range Analysis of Pointers » Published in Code Generation and Optimization,  Barcelona, 2016  and a greatest element [-∞, +∞], such that: [-∞, +∞] [l, u] = [l, u] [-∞, +∞] = [-∞, +∞] For sake of clarity, we also define the intersection operator " ": [a 1 , a 2 ] [b 1 , b 2 ] = ∅, if a 2 < b 1 or b 2 < a 1 [max(a 1 , b 1 ), min(a 2 , b 2 )], otherwise [-∞, +∞] is absorbant and ∅ is neutral for.
The result of range analysis is a function R : V → S 2 , that maps each integer variable i in a program to an interval [l, u], l ≤ u, e. g. , R(i) = [l, u]. The more precise the technique we use to produce this result, the more precise our results will be. Nevertheless, the exact implementation of the range analysis is immaterial for the formalization that follows. In this paper, we are using the following widening operator on SymbRanges : [l, u]∇[l , u ] =          [l, u] if l = l and u = u [l, +∞] if l = l and u > u [-∞, u] if l < l and u = u [-∞, +∞] if l < l and u > u The only requirement that we impose on the implementation of range analysis is that it exists over SymbRanges , our lattice of symbolic intervals. We denote by (α SymbRanges , γ SymbRanges ) the underlying galois connection.
Global Range Analysis of Pointers As we have mentioned in Section 2 we use two different strategies to disambiguate pointers: the global and the local test. Our global pointer analysis goes over the entire code of the program, associating variables that have the pointer type with elements of an abstract domain that we will define soon. The local analysis, on the other hand, works only for small regions of the program text. We shall discuss the local test in Section 3. 6. In this section, we focus on the global test, which is an abstract-interpretation based algorithm. An Abstract Domain of Pointer Locations. We associate pointers with tuples of size n: (SymbRanges • ∪⊥) n ; n being the number of program sites where memory is allocated (the cardinal of Loc) and • ∪ is the disjoint union. Let @(loc i ) denotes the actual address value returned by the i th malloc of the program.
By construction, all actual addresses are supposed to be offsets of a given @(loc i ). The abstract value GR(p) = (p 0 ,. , p n-1 ) represents (an abstract version) of the set of memory locations that pointer variable p can address throughout the execution of a program: • p i = ⊥ if there is no adress in S which is an offset of @(loc i ) • p i = α SymbRanges ({d ∈ Z | s = @(loc i ) + d ∈ S}), otherwise. The offsets from a given pointer are abstracted alltogether in the SymbRanges lattice. The goal of our GR analysis is to compute such an abstract value for each pointer of the program. Some elements in a tuple GR(p) are bound to the undefined location, e. g. , ⊥. These elements are not interesting to us, as they do not encode any useful information. Thus, to avoid keeping track of them, we rely on the concept of support, which we state in Definition 2.
p 2 n-1 ), the union GR(p 1 ) GR(p 2 ) is the tuple (q 0 ,. , q n-1 ) where: q i = ⊥ if p 1 i = p 2 i = ⊥ p 1 p 2 else and GR(p 1 ) GR(p 2 ) if an only if all involved (symbolic) intervals of p 1 are included in the ones of p 2 : ∀i ∈ [0. (n -1)], p 1 i p 2 i (considering ⊥ R and ⊥ R = R for all non-empty intervals R). We call this structure, formed by (SymbRanges • ∪ ⊥) n plus its partial ordering the lattice MemLocs. Author Version of « Symbolic Range Analysis of Pointers » Published in Code Generation and Optimization, Barcelona, 2016 EXAMPLE 3. For the example depicted in Figure  7  where we only have two malloc sites denoted by loc 0 and loc 1 , we will obtain the following results: GR (p) = GR(b) = {loc 0 + [0, 0]}, GR(m 0 ) = GR(s) = {loc 1 + [0, 0]}, GR(e) = loc 0 + [N, N ], GR(m 1 ) = loc 1 + [1, +∞], GR(i 7 ) = loc 0 + [N + strlen(m 0 ), N + strlen(m 0 ) + 1].
Experiments We have implemented our range analysis in the LLVM compiler, version 3. 5. In this section, we show a few numbers that we have obtained with this implementation. All our experiments have been performed on an Intel i7-4770K, with 8GB of memory, running Ubuntu 14. 04. 2. Our goal with these experiments is to show: (i) that our alias analysis is more precise than other alternatives of practical runtime; and (ii) that it scales up to large programs. On the Precision of our Analysis. In this section, we compare our analysis against the other pointer analyses that are available in LLVM 3. 5, namely basic and SCEV. The first of them, although called "basic", is currently the most effective alias analysis in LLVM, and is the default choice at the -O3 optimization level. It relies on a number of heuristics to disambiguate pointers 2  : Author Version of « Symbolic Range Analysis of Pointers » Published in Code Generation and Optimization, Barcelona, 2016 • Distinct globals, stack allocations, and heap allocations can never alias.
• Globals, stack allocations, and heap allocations never alias the null pointer. • Different fields of a structure do not alias. • Indexes into arrays with statically differing subscripts cannot alias. • Many common standard C library functions never access memory or only read memory. • Function calls cannot reference stack allocations which never escape from the function that allocates them As we see from the above list, the basic alias analysis has some of the capabilities of the technique that we present in this paper, namely the ability to distinguish fields and indices within aggregate types. In this case, such disambiguation is only possible when the aggregates are indexed with constants known at compilation time. For situations when these indices are symbols, LLVM relies on a second kind of analysis to perform the disambiguation: the "scalar-evolutionbased" (SCEV) alias analysis.
The hints for a new resonance at 750 GeV from ATLAS and CMS have triggered a significant amount of attention. Since the simplest extensions of the standard model cannot accommodate the observation, many alternatives have been considered to explain the excess. Here we focus on several proposed renormalisable weaklycoupled models and revisit results given in the literature. We point out that physically important subtleties are often missed or neglected. To facilitate the study of the excess we have created a collection of 40 model files, selected from recent literature, for the Mathematica package SARAH. With SARAH one can generate files to perform numerical studies using the tailor-made spectrum generators FlexibleSUSY and SPheno. These have been extended to automatically include crucial higher order corrections to the diphoton and digluon decay rates for both CP-even and CP-odd scalars.
These powerful packages provide a way to get a thorough understanding of the new models. The main goal of this work is to support the model builders and encourage them to use these tools. We provide several details about the features of the packages in the spirit of making this paper self-contained and bringing the reader unfamiliar with the tools to the level of knowledge necessary to use them. More information can be found in the manuals of each package. We have created a database of diphoton models in SARAH, by implementing 40 among those proposed in recent literature, which is now available to all interested researchers. For each model we have written model files to interface SARAH with SPheno and FlexibleSUSY. Although in each case we have tried to check very carefully that we implement the model which has been proposed in the literature, it is of course possible that some details have been missed.
This provides the main motivation of this paper and we hope that other model builders will also see the necessity of using these packages. • In Section 3, we discuss the basic features of SARAH, describing how one can use it to extract all analytical properties of the model and how to generate model files or source code for the numerical tools. • In Section 4, we give an introduction to SPheno and FlexibleSUSY and explain how these codes can be easily interfaced with MC tools. We also discuss at some length the implementation of the diphoton and digluon effective vertices. • In Section 5, we give an overview of the models which we have implemented in SARAH and briefly discuss their main features. • In Section 6, we provide an explicit example of how to quickly work out the details of a model, analytically with SARAH and numerically with the other tools.