dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, we believe there is a need for a suite of high performing CNN instances (and possibly other types of deep learning and design principles) that will cover relevant scenarios in SCA. Besides considering different CNN architectures/instances, we also explore how to make such designs more robust, i. e. , how to enable them to better generalize to previously unseen data. There, we see that adding the Gaussian noise can help significantly in the classification process. In this context, we especially mention two scenarios, the Random Delay dataset with the RD neural network where after the addition of noise we require only 2 traces to break the cryptographic implementation. The second case is the ASCAD neural network and the ASCAD dataset. There, despite the fact that the ASCAD network was designed for the ASCAD dataset, we still see a significant boost in the performance obtained if we add noise.
This paper presents an overview of the foreseen ImageCLEF 2019 lab that will be organized as part of the Conference and Labs of the Evaluation Forum -CLEF Labs 2019. ImageCLEF is an ongoing evaluation initiative (started in 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2019, the 17th edition of Image-CLEF will run four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activities understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with newer data, (iii) a new Coral task about segmenting and labeling collections of coral images for 3D modeling, and (iv) a new Security task addressing the problems of automatically identifying forged content and retrieve hidden information.
Introduction The ImageCLEF evaluation campaign was started as part of the CLEF (Cross Language Evaluation Forum) in 2003  [4, 5]. It has been held every year since then and delivered many results in the analysis and retrieval of images  [15, 17]. Medical tasks started in 2004 and have in some years been the majority of the tasks in ImageCLEF  [14]. The objectives of ImageCLEF have always been the multilingual or languageindependent analysis of visual content. A focus has often been on multimodal data sets, so combining images with structure information, free text or other information that helps in the decision making. Since 2018 ImageCLEF uses the crowdAI  1  platform to distribute the data and received the submitted results. The system allows having an online leader board and gives the possibility to keep data sets accessible beyond competition, including a continuous submission to the leader board.
Similar to previous ImageCLEF annotation tasks  [9, 10, [23] [24] [25] , the 2019 ImageCLEFcoral task will require participants to automatically annotate and localize a collection of images with types of benthic substrate, such as hard coral and sponge. The data for this task originates from a growing, large-scale collection of images taken from coral reefs around the world as part of a coral reef monitoring project with the Marine Technology Research Unit at the University of Essex (currently containing over 2TB of image data of benthic reef structure). The annotated data set comprises several sets of overlapping images, each set taken in an area of underwater terrain. Each image will be labelled by experts for training and evaluation. The performance of the algorithms will be evaluated using the PASCAL VOC 3  style metric of intersection over union (IoU) that calculates the area of intersection between the foreground in the proposed output localization and the ground-truth bounding box localization, divided by the area of their union.
Conclusions In this paper we presented an overview of the upcoming ImageCLEF 2019 campaign. ImageCLEF has organized many tasks in a variety of domains over the past 17 years, from general stock photography, medical and biodiversity data to multimodal lifelogging. The focus has always been on language independent approaches and most often on multimodal data analysis. 2019 has a set of interesting tasks that are expected to again draw a large number of participants. A focus for 2019 has been on the diversity of applications and on creating clean data sets to provide a solid basis for the evaluations.
Visualization Techniques The CLEAR visualizer provides support for visualizing the erroneous part of the LTS and emphasizes all the states (a. k. a. neighbourhoods) where a choice makes the specification either head to correct or incorrect behaviour. This visualization is very useful from a debugging perspective to have a global point of view and not only to focus on a specific erroneous trace (that is, a counterexample). More precisely, the CLEAR visualizer supports the visualization of tagged LTSs enriched with neighbourhoods. These techniques have been developed using Javascript, the AngularJS framework, the bootstrap CSS framework, and the 3D force graph library. These 3D visualization techniques make use of different colors to distinguish correct (green), incorrect (red) and neutral (black) transitions on the one hand, and all kinds of neighbourhoods (represented with different shades of yellow) on the other hand.
Anterior segment dysgenesis (ASD) encompasses a spectrum of ocular disorders affecting the structures of the anterior eye chamber. Mutations in several genes, involved in eye development, are implicated in this disorder. ASD is often accompanied by diverse multisystemic symptoms and another genetic cause, such as variants in genes encoding collagen type IV. Thus, a wide spectrum of phenotypes and underlying genetic diversity make fast and proper diagnosis challenging. Here, we used AMELIE, an automatic text mining tool that enriches data with the most up-to-date information from literature, and wANNOVAR, which is based on well-documented databases and incorporates variant filtering strategy to identify genetic variants responsible for severely-manifested ASD in a newborn child. This strategy, applied to trio sequencing data in compliance with ACMG 2015 guidelines, helped us find two compound heterozygous variants of the B3GLCT gene, of which c.
Introduction In recent years, next-generation sequencing technology (NGS) and access to databases containing sequencing data from patients with eye disorders have accelerated research in ophthalmic molecular genetics. However, still the most difficult and time-consuming part of the work is data analysis and clinical interpretation of reported variants, especially in cases with blended phenotype, when the decision on major clinical feature, crucial for diagnostic process is difficult to make. Additionally, due to massive sequencing data generation and relatively restricted curation, available databases, within reliable information contain false variant pathogenicity attributions. Thus, rigorous variant interpretation, as recommended by the American College of Medical Genetics and Genomics (ACMG)  [1] , followed by extensive review of the existing literature could improve diagnostic accuracy.
The first variant, inherited from the mother, was a substitution, NM_194318. 3:c. 660+1G>A (rs80338851), reported as pathogenic allele (B3GALTL, OMIM:610308:0001, RCV000001326. 4) associated with PPS. Moreover, using Varsome, the discovered variant was classified according to ACMG2015 as pathogenic, with identified criteria to be PVS1, PP3, PP5. The second variant, inherited from the father, was a deletion, NM_194318. 3:c. 755delC (p. T252fs), which has not been previously reported (according to LOVD, and is not present in the gnomAD database). In Varsome, the discovered variant was classified according to ACMG2015 as likely pathogenic, with identified criteria to be PVS1 and PM2. Moreover, this mutation had a MutationTaster score of 1 (prediction: Disease causing) and changed the reading frame downstream of the amino acid (AA) at position 252.
Independent of the sequencing scale, NGS creates considerable data burden Thus, careful variant filtering and prioritization, according to the information available from sequencing databases, in compliance with ACMG standards are required. Additionally, differences in NGS data analysis between laboratories could generate discrepancies in the diagnosis, thus filtering schemes should be clearly reported. Moreover, clinical interpretation of sequencing data requires matching information on patients' genotypes and phenotypes and is usually complemented by the integration of information from databases such as OMIM or ClinVar, which are manually constructed and curated. The search for causal variants should be supported by published evidence if it exists, and this requires manual querying of relevant literature databases.
This step is the most time-consuming in data processing and has a major impact on timely diagnosis and minimizing false negative and false positive findings. Web-based literature search engines are employed to assist manual curation of newly published information. AMELIE is a method for ranking candidate causal genes related to the phenotype, extracted from primary literature using supervised machine learning methods. The inclusion of HPO-encoded phenotype information facilitates communication with clinical specialists. In our opinion, it combines the convenience of other HPO-based variant ranking tools such as Exomiser  [20]  with enrichment of the latest literatures. wANNOVAR is a web server which provides functional annotation of genetic variants, reporting their conservation levels, calculating their predicted functional importance scores, retrieving allele frequencies in public databases, and implementing a protocol to identify a subset of potentially deleterious variants/genes.
On that date, there were 22 unique variants of the B3GLCT gene reported in 10 individuals in the LOVD database. Variant c. 660+1G>A (rs80338851), located at the donor splice site (5 ss) of exon 8 observed in our patient, is one of the most common among variants identified in PPS patients  [28]. It has a frequency of 0. 01% in the gnomAD database for European (non-Finnish) population and accounts for 69% of all reported pathogenic alleles  [21]. The results of Oberstein et al. showed that c. 660+1G>A alters the acceptor site of exon 8, leading to the skipping of this exon and the introduction of a premature termination codon (PTC) at position +10 within exon 9. According to the position rule for PTC, c. 660+1G>A results in a nonsense mRNA that elicits nonsense-mediated mRNA decay (NMD)  [29].
NMD is the surveillance pathway protecting cells from the action of truncated proteins which could be translated from transcripts bearing a PTC  [30]. In mammalian cells, it is the process of degradation of the mRNA, depending on the interaction between translation termination, complex multiprotein assemblies, which takes place when PTC is present at least 50 nucleotides upstream of the last exon junction. mRNA degradation due to NMD is involved in many neurological and developmental disorders  [31]. The second of the detected variants, c. 755delC (p. Thr252fs), was not present in any publicly available database and is the first one reported in exon 9. Pathogenicity prediction algorithms assigned this variant as disrupting. Similar to c. 660+1G>A, c. 755delC introduces a PTC within exon 9, 26 nucleotides from the splice site, which also should result in NMD.
Our study demonstrates the important role and possible diagnostic utility of NGS combined with AMELIE and wANNOVAR. Such an approach, on the one hand, brings data on genes and variants with well-described roles in disease development based on curated, authoritative databases such as OMIM (wANNOVAR) and, on the other hand, updates the analysis with current information from published manuscripts. Such a procedure helps reduce the number of false positive findings not related to a phenotype which are over-abundant in the VCF file annotated with pathogenicity tools.
Data Analysis Raw reads were processed with the Illumina software, generating base calls and corresponding base-call quality scores. These data were then processed through our custom pipeline that uses open-source programs (Figure  4 ). Briefly, generated fastq files were fed to the FastQC software (version 0. 11. 5) to provide quality control checks on sequenced data (Andrews et al. , 2010). Reads were aligned to the human reference genome GRCh37 (hg19) using the BWA-MEM algorithm from the Burrows Wheeler Aligner (BWA, version 0. 7. 5)  [32]. Unmapped, low mapping quality score and duplicated reads were filtered out with SAMtools (version 0. 1. 19) [33]. GATK (version 3. 7) base quality recalibration was applied across all samples simultaneously using variant quality score recalibration (VQSR) according to the GATK best practices recommendations  [34, 35].
Filtered variants were concatenated into one record (VCF file) and then, using GEnome MINIng (GEMINI, version 0. 18. 3) [36] , the discovered variants were annotated with SnpEff (version 4. 2)  [37] , and loaded into the SQLite database for parents-child trio analysis. Using the function for trio analysis implemented in the GEMINI software, we identified three groups of variants in the proband patient: (1) De novo, (2) autosomal recessive, and (3) compound heterozygotes. All variants were concatenated into one VCF file and used to predict in silico the effects of amino acid substitutions and indels using SIFT  [38] , Provean  [39] , FATHMM_XF  [40] , and MutationTaster  [41]. Simultaneously, the generated VCF file was uploaded into two web tools, Automatic Mendelian Literature Evaluation (AMELIE)  [12]  and wANNOVAR  [15] , which connect the ANNOVAR  [13]  annotation pipeline with the Phenolyzer  [42]  gene prioritization pipeline.
Sanger Sequencing For final confirmation of the most plausible causal variants, exons 8 and 9 of B3GLCT (RefSeq NM_194318) were analyzed by Sanger sequencing on the ABI3500 sequencer (Applied Biosystems, ThermoFisher Scientific, Waltham, MA, USA). The obtained sequences were aligned to the reference NC_000013 with the SeqScape software (LifeTechnologies, ThermoFisher Scientific). After identification of an exon-shortening event, the overlapping sequence resulting from the heterozygous deletion was analyzed manually using SnapGene (GSL Biotech, Chicago, IL, USA).
In order to meet the challenges of the Run 3 data rates and volumes, the ALICE collaboration is merging the online and offline infrastructures into a common framework: ALICE-O 2. O 2 is based on FairRoot and FairMQ, a message-based, multi-threaded and multi-process control framework. In FairMQ, processes (possibly on different machines) exchange data via message queues either through 0MQ or nanomsg. In turn, this enables developers to write their reconstruction or analysis process in whatever language they choose or deem appropriate for the task at hand, as long as that programming language can send and receive data through these message queues. This paper introduces fer, a Go-based toolkit that interoperates with the C++ toolkit FairMQ, to explore the realm of polyglot distributed frameworks.
Introduction The ALICE collaboration is merging the online and offline infrastructures into a common framework: ALICE-O 2  [1]. This work is needed to meet the challenges of the Run 3 data rates and volumes. O 2 is a message-based, multi-threaded and multi-process control framework based on two libraries: FairRoot  [2]  and FairMQ  [3]. Sophisticated processing pipelines can be modeled with FairMQ: router/dealer, request/reply, publish/subscribe, client/server, etc. The nodes of these pipelines are processes (possibly on different machines) exchanging data via message queues either through ZeroMQ [4] or nanomsg  [5]. From the standpoint of the FairMQ toolkit, the programming language used to implement a given process in the pipeline is irrelevant, as long as data is correctly propagated through the message queues.
This enables developers to write their reconstruction or analysis process in whatever language they choose or deem appropriate for the task at hand. This paper presents fer, a Go-based  [7]  library compatible and interoperable with FairMQ. This paper starts with a brief introduction of the builtin features that make Go a solid choice when dealing with I/O and concurrency. The principal components of fer and how they interact with C++ FairMQ will then be described. Finally, Sec. 4 will report on the performance (CPU, VMem) of fer and conclude with the main figures of merit of fer, in the context of deployment in a distributed computing setup. FairMQ is a distributed processing toolkit, written in C++, with pluggable transports (ZeroMQ, nanomsg, shmem, InfiniBand). In FairMQ, the bulk of the processing is performed by devices: UNIX processes that are connected with other devices through message queues.
Elements of Go Go is an open source general programming language with built-in support for concurrency programming. It has been released in 2009 under the BSD-3 license. Go has a static type system with first-class functions, closures, and interfaces. The general syntax of Go is close to that of C and C++. Even if Go programs are statically typed, code verbosity is limited thanks to Go's type inference system. Go's concurrency primitives derive from Hoare's Communicating Sequential Processes (CSP)  [8] : goroutines and channels. Channels are typed conduits that connect goroutines together. Goroutines are very lightweight green threads: it is possible to schedule thousands of them on regular hardware. This is achieved thanks to the Go runtime multiplexing goroutines on OS threads and the ability to grow and shrink each goroutine's stack as needed.
The last element for easy concurrency programming is the garbage collector that takes care of freeing memory from the correct thread, when it is not needed anymore. Go is available on all major platforms (Linux, Windows, macOS, Android, iOS, etc. ) and for many architectures (amd64, arm64, i386, s390x, mips64, etc. ). The Go toolchain can very easily cross-compile from one OS/Arch pair to any other pair, by just modifying two environment variables. The Go toolchain generates completely statically compiled binaries. This enables a very efficient deployment model where binaries produced on a developer laptop can be quickly provisioned on a bare metal production cluster. Finally, compiling Go code is not only fast thanks to its package and import system, it is also regular and automatically handles dependencies, recursively discovering and installing them.
Indeed, installing Go packages is usually just a matter of entering in the shell: %> go get -v github. com/sbinet-alice/fer github. com/sbinet-alice/fer/mq github. com/pkg/errors golang. org/x/net/context github. com/sbinet-alice/fer/config nanomsg. org/go-mangos [. ] nanomsg. org/go-mangos/transport/tcp github. com/sbinet-alice/fer/mq/nanomsg github. com/sbinet-alice/fer/mq/zeromq A single command that works on all platforms and operating systems supported by the Go toolchain.
Design and components of fer As introduced in Sec. 2, implementing a toolkit compatible with FairMQ requires to be able to configure FairMQ-like devices, using e. g. the JSON configuration file format; to create topologies of devices according to the configuration and connected via a transport library (e. g. ZeroMQ or nanomsg); and finally to execute the main routines of the devices. The requirements above are met by fer  [9] , a toolkit implemented in Go. Similar to its C++ sibling, fer exposes the concept of a device by way of the Device interface, as shown in Fig. 4. A type implements the Device interface as soon as it has a method named Run() that takes a Controler interface and returns an error. The Controler interface is shown in  The input configuration is parsed by the fer/config package and passed to the devices via the DevConfigurer interface, if that device implements it.
Implementing a fer device This section shows how implementing a fer device and integrating it inside a C++ pipeline can be done. Figure  6  shows how one can implement the processor from Fig. 1. The processor retrieves two channels, the "data1" input channel and the "data2" output channel. The Run() method contains an infinite for loop. At each iteration, the device waits for data coming in from either the idatac or the Done() channel. When the input channel is.
Conclusions The ALICE-O 2 framework based on the message passing infrastructure from FairMQ enables one to build applications that resemble microservices architectures. These types of architectures allow systems to scale horizontally quite easily, by just adding new processor devices and fanning out data to these new processors, in e. g. a round-robin fashion. FairMQ, and microservices in general, is language agnostic: developers can thus implement their device in whatever language they deem adequate for the task at hand. This paper introduced fer, a Go-based toolkit that interoperates with the C++ toolkit FairMQ, to explore the realm of polyglot distributed frameworks. The fer toolkit provides a fast, "one-command" installation procedure that works on all platforms and operating systems supported by the Go toolchain.
// Device is a handle to what users get to run via the Fer toolkit. Excerpt of the fer package, with the main interfaces it exposes. Users are only required to implement the Device interface, other interfaces are optional. Fig. 5. It is used to retrieve named channels bound to incoming or outgoing data, through the Chan() method. An additional Done() method exposes transitions in the fer final state machine (RUN, PAUSE, STOP, etc. ) // Msg is a quantum of data being exchanged between devices. type Msg struct { Data []byte // Data is the message payload. Err error // Err indicates whether an error occured. } // Cmd describes commands to be sent to a device, via a channel. type Cmd byte package fer import "github. com/sbinet-alice/fer/config" type Device interface { Run(ctl Controler) error } type DevConfigurer interface { Configure(cfg config.
We exploit the intra-and intertask correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective cell, task pairs by using reinforcement learning. To effectively capture the intra-and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms.
Exploiting the intra-and inter-task correlations can enhance the Sparse MCS, which further helps to reduce the number of sensed areas and improve the accuracy of the inferred results. However, most of existing works in Sparse MCS focus on the single-task scenarios while ignoring the multi-task requirements  [8] ,  [9] ,  [12] ,  [16] -  [18]. Rana et al. [16]  used incomplete and random crowdsourcing data to recover the urban noise map. Zhu et al. [17]  focused on traffic estimation from the periodically collected data by probe vehicles. Wang et al. [8] ,  [9]  presented a Sparse MCS framework which achieved good performances on temperature, air quality and traffic monitoring datasets, respectively. He and Shin  [18]  used Bayesian compressive sensing to construct the urban signal map. With Sparse MCS, these works select a few effective cells to sense (cell selection), and then use the sensed data to infer the full map with high quality (data inference).