dataesr/scientific-paragraphs-categorization huggingface dataset:
In particular, this paper presents a case study where evolved image operators are ported from Matlab to OpenCV, and then the source code is optimized an improved using Genetic Improvement of Software for Multiple Objectives (GISMOE). In the example we show that functional behavior is maintained (output image) while improving non-functional properties (computation time). Despite the fact that this first example is a simple case, it clearly illustrates the possibilities of using GP principles in two distinct stages of the software development process, from design to improved implementation.
INTRODUCTION Over the past twenty years or so, Genetic Programming (GP) has established itself as one of the most successful evolutionary computation paradigms  [5, 10]. In one sense, GP is similar to other supervised machine learning algorithms, such as neural networks, support vector machines or decision trees, where given a dataset of known inputs and expected outputs these algorithms derive models that best fit, or help explain, the data. The similarities are evident when we consider that the most common application domains for all of these algorithms, including GP, are quite similar, namely regression and classification, probably the most common supervised learning tasks in machine learning. Indeed, the success of standard GP 1  has lead researchers to develop even more powerful GP-based approaches for these type of tasks, including the Fast Function Extraction algorithm (FFX)  [11]  and Geometric Semantic Genetic Programming (GSGP)  [24].
Luckily, GP has been recently expanded to address this kind of task, in what is known as Genetic Improvement (GI)  [9, 2, 8, 15, 25] , allowing us to use the same basic principles of GP in this new task. In GI the idea is to start with a complete and functional implementation of a specific algorithm or software system, which is written in a particular programming language and which is expected to run on a specific computing device. It is assumed that this implementation is not optimal in some way, so the goal is to use an evolutionary search process to modify the code in such a way that the main functional properties of the software are maintained but that one or several non functional properties are improved. Previous works in GI have shown great promise, greatly improving non functional properties such as computational cost or energy consumption while at the same time maintaining the code completely functional and able to solve the original task for which it was designed.
PROBLEM STATEMENT: IMPROV-ING A HUMAN WRITTEN PROGRAM WITH GP The interest point operator described in the preceding section was evolved using Matlab and the GPLab toolbox  [18] , a popular open source GP library for Matlab. Afterward, the operator was ported to C++ based on the OpenCV computer vision library, probably the most widely used vision library. The C++ source code is show in Figure  3. This operator and implementation is used as the initial case study of the proposal put forth in this paper: optimizing the new implementation of a previously evolved design produced by a GP search, using the principles of GI. As a brief summary, GI  [9, 2, 8, 15, 25]  is the process of automatically improving the functional and non-functional properties of a system using GP. Langdon presents a very complete overview of GI in  [7].
In the present work we have chosen to use the Genetic Improvement of Software for Multiple Objective Exploration (GISMOE) approach and the G a u s s i a n B l u r ( im dst , S i z e ( 9 , 9 ) , 1. 0 , 1. 0 ) ; a b s d i f f ( dst , im , d s t ) ; G a u s s i a n B l u r ( dst , dst , S i z e ( 9 , 9 ) , 2. 0 , 2. 0 ) ; im2=d s t * W; pow ( im , 2 , d s t ) ; G a u s s i a n B l u r ( dst , dst , S i z e ( 9 , 9 ) , 1. 0 , 1. 0 ) ; l o g ( dst , d s t ) ; G a u s s i a n B l u r ( dst , dst , S i z e ( 9 , 9 ) , 1. 0 , 1. 0 ) ; im2=d s t+im2 ; G a u s s i a n B l u r ( im , dst , S i z e ( 9 , 9 ) , 1. 0 , 1. 0 ) ; d i v i d e ( dst , im , d s t ) ; d s t=d s t+im2 ; d s t=abs ( d s t ) ; pow ( dst , 2 , im2 ) ; G a u s s i a n B l u r ( im2 , im2 , S i z e ( 9 , 9 ) , 2. 0 , 2. 0 ) ; Figure  3 : The MOP operator in C++ using the OpenCV library and written by a human programmer public open-source libraries  [9, 16]  which can be downloaded at http://www0.
EXPERIMENTS AND RESULT The experiment consists of applying the GISMOE approach to the MOP program to improve the functional and non-functional properties of our implementation. In this case, the functional property is measured by calculating the normalized cross correlation (NCC) between the output (interest) image of the modified MOP operator and the output (interest) image of the original MOP operator. The NCC is a common measure used in template matching, which is used to measure the similarity between the interest image and the expected result, the NCC is given in the range [0, 1], where 1 indicates that the two images are almost identical (highly correlated) and 0 indicates that they are very different (not correlated). The non-functional property we measure is the related computational effort, measured by calculating the processing time of applying each operator, which is measured using the functions getTickCount and getTickFrequency provided by the OpenCV library.
The MOP program was implemented in C++ using OpenCV v2. 4. 2 in Linux Ubuntu 12. 04, and it is available in the GP for Computer Vision (GPCV) library published under the GPL license at http://www. tree-lab. org/index. php/gpcv. The GISMOE and MOP program were executed on a PC with a AMD Turion(tm) II Dual-Core Mobile M520 × 2 processor. It is important to note that the MOP operator function consists of 13 lines of code that the GISMOE approach attempts to improve. The original human implementation of the MOP operator is given in Figure  3. The MOP program was executed with h = 0. 10. We applied the GISMOE approach without the sensitivity analysis and the output bins due to the short amount of lines of code of the program and since only one test case was used. While using a single image as a test case seems low, it is actually quite appropriate for the interest point detection problem if we consider that a single image can have many different types of points with different local neighborhoods which are sufficiently varied.
Indeed, that is the approach we took when evolving the original MOP operator as reported in  [13, 14]. The test case used in GISMOE consists of a reference image I1 shown in Figure  4 (a), a grayscale image of size 384 × 430. I1 is the input for the modified MOP program, which is then compared with the expected output interest image OMOP (which is the same same size as I1) of the MOP program, shown in Figure  4(b). The GISMOE parameters used in the experiment are: • Representation: List of replacements, deletions and insertions into BNF grammar. • Fitness: Based on compiling modified code and testing it for the NCC and processing time. • Selection: An evolved version cannot be selected to be a parent unless it has an NCC more than a threshold p = 0. 5 and the processing time is less than t ref = 0.
In both cases we can see that the majority of the interest points match, approximately 90% of the total of interest points between both images. The improvement of the GI version is reflected in the processing time with a reduction in total computation time of 21%. This reduction is verified by computing the average processing time of 1000 executions of both the MOP program and its improved GI version. The reduction can be seen in the source code of the improved version presented in Figure  6 , or in its translated mathematical expression, which is given by.
CONCLUSIONS It is now becoming evident that the principles of artificial evolution could have a deep impact in the way software solutions are constructed or designed. Going beyond the automatic program induction or problem solving of traditional GP, this paper explores the possibility of using GP principles at two distinct stages of software development. This paper provide a first, and admittedly rather simple, example of the role that GP can have in the software development process. First, GP is used to evolve a program design, a specialized operator that performs a specific task for higher-level systems. Then, the evolved design is ported to a different software system, using different programming languages and libraries, a scenario that could be quite com-  mon in real-world domains. This task is performed by a human programmer.
The operator was evolved using a Matlab GP toolbox, relying heavily on Matlab to perform the image processing tasks. It was then ported to C++ and OpenCV, a widely used library that has become an unofficial standard in the field. We then use GISMOE to optimize our C++ implementation, achieving a 21% reduction in computation time, a substantial improvement, while maintaining the main functional behavior as evidenced by the almost perfect correlation between the expected output and the output generated by the improved version. While the example is rather simple, it is sufficient to illustrate how two important parts of software development can be solved by using artificial evolution. The case study shows how GP can be used to evolve novel designs, and then optimize new implementations using the same general principles, from automatic design to improved implementation.
Identifying Significant Preferentially Expressed Alleles Our approach to identify causative mutations using both RNA and exome sequencing data was motivated by an observation made about the variants present in TP53 in three patients. In our dataset, each patient had a different high or moderate impact variant in TP53. High and moderate impact variants are those changing the protein sequence or that affect a splice site. When comparing the RNA and exome sequencing data of those variants, we observed that while the variant alleles were present in both the RNA and exome data, the TP53 variant allele was overrepresented in the RNA sequencing data when compared to the exome sequencing data (Fig  1 ). Given the known centrality of TP53 variants in cancers (TP53 somatic mutations have been observed in 96% of ovarian cancer samples  [3] ), this suggested to us that there could be a selection process occurring in these patients where variant TP53 is overexpressed or normal TP53 is suppressed or both.
These results reveal that an analysis of somatic mutation is better complemented with allelic expression studies which show whether a mutated allele is expressed and the degree and direction of that expression. Mutated alleles that are not expressed likely have no impact on tumor progression. In addition to analyzing somatic mutations, we analyzed all variant alleles that consist of germline and somatic variants. For most variants, there is no significant difference in the exome and RNA allele fractions (Fig  4A ). However, for a relatively small number of variants, there are significant allele fraction differences (Fig 4A  and 4B ). These variants are both germline and as previously stated somatic with the vast majority (~96%) being germline. Furthermore, using the normal data for two patients we estimated how many reference/alternate alleles would have been incorrectly assigned to be able to estimate the error for the third patient Clustering of the allele fraction differences for all somatic genes shows somatic mutations with preferential allele expression for the mutant allele (orange), no preferential expression (white), reference allele preferential expression (blue) and no expression of any allele (gray).
Shared Preferentially Expressed Alleles To analyze the significance of the observed preferentially expressed variants, we compared the cell lines and patient shared genes. We observe a striking difference between cell lines and patient shared genes as the variant genes identified are very different between the two. The patient shared variants are substantial with 28 shared genes while only few genes are shared among the four different cell lines (Fig  4C ). No gene is shared between cell lines and patients. However, the number of significant variants is larger overall in patients which would lead to higher number of shared variants. To determine if the relatively high number of shared variants in patient data is due to chance, we performed a significance analysis. We selected variants randomly from the total pool of variants in the data and counted how many times these variants are shared between patients and cells.
Fourth, many of the genes we identify have very well known or plausible effects on tumor biology. Fifth, as discussed previously and below, many of the genes we identify with significant allele fraction differences are known drug targets. Sixth, comparing cell line data to patient data reveals large differences in the type of variants observed. There is also overall large similarity within patients than within cell lines suggesting that the preferentially expressed variant alleles in patients are specific. We therefore conclude that while there could be some biases due to low coverage or a higher error rate in one dataset, the majority of genes we identify are relevant to tumor biology and merit further investigation. There are multiple mechanisms that can give rise to preferential allele expression.
These results, while puzzling, are quite interesting. Indeed, the previous two studies on multiple site sampling  [4, 5]  demonstrated a high degree of mutational heterogeneity but most patients have a good initial response at all sites to chemotherapy potentially indicating shared biological features despite mutational heterogeneity. Understanding tumor biology to impact clinical care requires the integration of multiple datasets in clinically meaningful ways. This work adds an additional novel dimension to the large body of work in tumor biology by analyzing preferential allele expression using a combined genome and transcriptome sequencing approach. The striking result of finding a set of shared biologically relevant genes with preferentially expressed variant alleles suggests that these genes may be driver genes.
Allele Fraction Differences Allele fractions were calculated for single nucleotide variants identified in exome and RNA sequencing data. We limited our analysis to reads with base and mapping quality greater than or equal to 20 and non-duplicated reads for both exome and RNA sequencing data. We further focused only on protein coding variants. Allele fractions are defined as the ratio of the number of reads of the reference allele to the total number of reads at a site. The Fisher Exact Test with the mid-P correction was used to obtain p-values between the RNA and Exome data (significance was set at a p-value less than 0. 1). In addition, we imposed a filter that there should be at least eight reads for both the exome and RNA sequencing data to account for errors in sequencing and alignment  [56].
Variant Annotation SNPeff  [57]  was used to annotate the variants to identify protein coding variants. We use the same 'IMPACT' categorization as SNPEFF (High, Moderate, Low and Modifier). We used Ingenuity Pathway Analysis software (Ingenuity Systems) to annotate and functionally characterize gene lists and to identify drug targets. To identify SNPs that were in COSMIC  [58] , we downloaded the COSMIC complete export list (v72) and cross-referenced it with our variants based on genomic coordinates.
Network Toplogy and Reactome Functional Interactions Shared genes in both the patient positive and negative RAD sets were input into Jepetto  [38] , a Cytoscape interface for TopoGA  [37]  and topology calculated using the large dataset settings. Functional interactions were determined by using the Reactome Functional Interactions  [39, 40]  Cytoscape plugin using the 2014 dataset and using linker genes.
Gene Expression RNA sequencing data was used to estimate relative gene expression. Alignments (see sequencing) were processed with FeatureCounts of Rsubread (version 1. 18)  [59]  in paired-end mode, excluding overlapping reads and overlapping genes to map reads to genes (gencode v19 gene models). Filtration (remove all genes which have less than 0. 1 counts per million) and normalization was done with edgeR (version 3. 4. 2)  [60]  to obtain reads per kilobase per million (rpkm) values for each gene. Patient and cell line data was analyzed identically.
Visualization GenomeBrowse (Golden Helix) was used to visualize the TP53 gene region. Cytoscape (version 3. 2. 1) [61]  was used to visualize networks in Fig 5. Network diagrams in Fig  7  were made using Ingenuity software (Qiagen). Proportional Venn diagrams were made using BioVenn  [62]  and adjusted with Inkscape (version 0. 91) Hierarchical clustering was done with built-in Matlab (version R2015a) (Mathworks) commands. Pileups, gene subplots, gene expression plots and parallel plots were made with custom Matlab scripts.
Therefore, while a MapReduce job   can be profiled for a best-e↵ort MARP configuration in a specific Hadoop cluster, any unpredictable changes in the workload dynamics will lead to a performance degradation. Finally, we consider heterogeneous workloads. Concretely, we use SWIM (Statistical Workload Injector for Mapreduce)  [3]  to generate 4 realistic MapReduce workload. SWIM contains several large workloads (thousands of jobs), with complex data, arrival, and computation patterns that were synthesized from historical traces from Facebook 600-nodes Hadoop cluster. The proportion of job sizes in each input workloads has been scaled down to fit our cluster size using a Zipfian distribution (see http://xlinux. nist. gov/dads/HTML/zipfian.
Handling Drops of Memory Utilization Drops of memory utilization are caused by the completion of map/reduce tasks that release large blocks of memory. Such memory fluctuation can result in MARP oscillations when the Algorithms 1 2 3 will be constantly scaling up and down the MARP value. To prevent this, we use a Kalman filter to smooth the input-i. e. , the memory utilization. It helps to stabilize the value and eliminate the noise induced by the memory fluctuation  [18]. Concretely, we apply   Fig. 6. Performance comparisons from the HiBench benchmark suite, each with two datasets (30MB and 3GB). Figure  5  shows the mean job completion time of 100 jobs using, (1) the vanilla Hadoop 2. 6. 0 configuration (MARP = 10%), (2) the best-e↵ort statically profiled configuration where the values were obtained from our initial experiments (cf.
Conclusion Optimizing the performance of Hadoop clusters has become a key concern for big data processing. In YARN, inappropriate memory usage may lead to significant performance degradation. In this paper, we propose a self-adaptation approach based on a closed feedback control loop that automatically balances the memory utilization between YARN MapReduce processes. We have shown that it outperforms the default Hadoop configuration as well as the best-e↵ort statically profiled ones. For the further work, we will focus on HDFS I/O throughput and plan to complement our approach with a support for I/O intensive jobs.
Whole transcriptome sequencing (RNA-seq) has become a standard for cataloguing and monitoring RNA populations. Among the plethora of reconstructed transcripts, one of the main bottlenecks consists in correctly identifying the different classes of RNAs, particularly those that will be translated (mRNAs) from the class of long non-coding RNAs (lncRNAs). Here, we present FEELnc (FlExible Extraction of LncRNAs), an alignment-free program which accurately annotates lncRNAs based on a Random Forest model trained with general features such as multi k-mer frequencies and relaxed open reading frames. Benchmarking versus five state-of-art tools shows that FEELnc achieves similar or better classification performance on GENCODE and NONCODE datasets. The program also provides several specific modules that enable to fine-tune classification accuracy, to formalize the annotation of lncRNA classes and to annotate lncRNAs even in the absence of training set of noncoding RNAs.
We used FEELnc on a real dataset comprising 20 new canine RNA-seq samples produced in the frame of the European LUPA consortium to expand the canine genome annotation and classified 10,374 novel lncRNAs and 58,640 new mRNA transcripts. FEELnc represents a standardized protocol for identifying and annotating lncRNAs and is freely accessible at https://github. com/tderrien/FEELnc.
Following RNA sequencing, the computational reconstruction of transcripts models either by genome-guided  (11, 12)  or de novo assembly  (13)  usually produces tens of thousands of known and novel transcript models. Among this wealth of assembled transcripts, it remains crucial to annotate the different classes of RNAs and especially to distinguish protein coding from non-coding RNAs. To this aim, several bioinformatics tools have been developed in order to compute a coding potential score (thereafter termed CPS) used to discriminate the coding status of the RNA gene models. Broadly, they can be divided into programs using sequence alignments either between species  (14)  or alignments to protein databases  (15)  and alignment-free software  (16) (17) (18). The alignment-dependent methods, although very specific in term of performance, are often very time and resource consuming.
Here we present FEELnc, for FlExible Extraction of LncRNAs, a new tool to annotate lncRNAs from RNA-seq assembled transcripts. FEELnc is an all-in-one solution from the filtering of non-lncRNA-like transcript models, to the computation of a coding potential score and the formalization of the definition of the lncRNA classes. As an alignment-free method, the program employs Random Forest  (26)  to classify lncRNA and mRNAs based on a relaxed definition of ORFs and a very fast analysis of small and large k-mer frequencies (from k = 1 to 12). We benchmarked FEELnc versus five existing programs (CNCI, CPC, CPAT, PhyloCSF, PLEK) using known set of lncRNAs annotated in multiple organisms (GENCODE for human and mouse  (27)  and NONCODE for other species (28)), and showed that FEELnc performance metrics outperformed or are similar to state-of-the-art programs.
We developed FEELnc to be used on "non-model" organisms for which no set of lncRNAs is available by deriving species-specific lncRNA models which are shuffled from mRNA sequences and automatically computing the CPS cutoff that maximizes classification performances. FEELnc also allows user to provide its own specificity thresholds in order to annotate high-confidence sets of lncRNAs and mRNAs and to define a class of ambiguous transcript status. Finally, we produced 20 RNA sequencing dataset as part of the LUPA consortium  (29)  sampled from 16 different canine tissues and applied FEELnc to annotate 10,374 new lncRNAs and 58,640 novel mRNA isoforms. We also classified the new lncRNAs into 5,033 intergenic (lincRNAs) and 5,341 genic sense or antisense lncRNAs based on the FEELnc classifier module.
Datasets For the sake of reproducibility, all datasets and scripts made to generate the benchmark files are available in supplemental materials. Human long non-coding and protein-coding genes were downloaded from the manually curated GENCODE version 24 (EnsEMBL v83 on the GRCh38 human genome assembly) with the following biotypes "lincRNA" or "antisense" and "protein_coding", respectively. From this initial dataset, we extracted 10,000 mRNAs and 10,000 lncRNAs that were divided into two files for the learning and the testing steps (denoted HL and HT, respectively), each of these files contains 5,000 mRNAs and 5,000 lncRNAs. Importantly, only one transcript per locus was extracted for all biotypes in order not to bias by introducing two isoforms of the same gene in both the HL and HT sets. For mouse, we used the GENCODE version M4 (EnsEMBL v79) and derived the learning and testing files (denoted ML and MT) as for human.