pinecone/core-2020-05-10-deduplication huggingface dataset:
Why Does Synthesized Data Improve Multi-sequence Classification?

The classification and registration of incomplete multi-modal medical images, such as multi-sequence MRI with missing sequences, can sometimes be improved by replacing the missing modalities with synthetic data. This may seem counter-intuitive: synthetic data is derived from data that is already available, so it does not add new information. Why can it still improve performance? In this paper we discuss possible explanations. If the synthesis model is more flexible than the classifier, the synthesis model can provide features that the classifier could not have extracted from the original data. In addition, using synthetic information to complete incomplete samples increases the size of the training set.\ud
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We present experiments with two classifiers, linear support vector machines (SVMs) and random forests, together with two synthesis methods that can replace missing data in an image classification problem: neural networks and restricted Boltzmann machines (RBMs). We used data from the BRATS 2013 brain tumor segmentation challenge, which includes multi-modal MRI scans with T1, T1 post-contrast, T2 and FLAIR sequences. The linear SVMs appear to benefit from the complex transformations offered by the synthesis models, whereas the random forests mostly benefit from having more training data. Training on the hidden representation from the RBM brought the accuracy of the linear SVMs close to that of random forests
Label Stability in Multiple Instance Learning

We address the problem of \emph{instance label stability} in multiple
instance learning (MIL) classifiers. These classifiers are trained only on
globally annotated images (bags), but often can provide fine-grained
annotations for image pixels or patches (instances). This is interesting for
computer aided diagnosis (CAD) and other medical image analysis tasks for which
only a coarse labeling is provided. Unfortunately, the instance labels may be
unstable. This means that a slight change in training data could potentially
lead to abnormalities being detected in different parts of the image, which is
undesirable from a CAD point of view. Despite MIL gaining popularity in the CAD
literature, this issue has not yet been addressed. We investigate the stability
of instance labels provided by several MIL classifiers on 5 different datasets,
of which 3 are medical image datasets (breast histopathology, diabetic
retinopathy and computed tomography lung images). We propose an unsupervised
measure to evaluate instance stability, and demonstrate that a
performance-stability trade-off can be made when comparing MIL classifiers.Comment: Published at MICCAI 201
Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton\ud
in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions.\ud
On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images
Identifying Connectome Module Patterns via New Balanced Multi-Graph Normalized Cut.

Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks
Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003
Interpolation and Averaging of Multi-Compartment Model Images

International audienceMulti-compartment diffusion models (MCM) are increasingly used to characterize the brain white matter microstructure from diffusion MRI. We address the problem of interpolation and averaging of MCM images as a simplification problem based on spectral clustering. As a core part of the framework, we propose novel solutions for the averaging of MCM compartments. Evaluation is performed both on synthetic and clinical data, demonstrating better performance for the “covariance analytic” averaging method. We then present an MCM template of normal controls constructed using the proposed interpolation
Disjunctive normal shape and appearance priors with applications to image segmentation

The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants. We learn shape and appearance statistics at varying spatial scales using nonparametric density estimation. Our method can generate a rich set of shape variations by locally combining training shapes. Additionally, by studying the intensity and texture statistics around each discriminant of our shape model, we construct a local appearance probability map. Experiments carried out on both medical and natural image datasets show the potential of the proposed method
Argument-Based Case Revision in CBR for Story Generation

This paper presents a new approach to case revision in casebased reasoning based on the idea of argumentation. Previous work on case reuse has proposed the use of operations such as case amalgamation (or merging), which generate solutions by combining information coming from different cases. Such approaches are often based on exploring the search space of possible combinations looking for a solution that maximizes a certain criteria. We show how Revise can be performed by arguments attacking specific parts of a case produced by Reuse, and how they can guide and prevent repeating pitfalls in future cases. The proposed approach is evaluated in the task of automatic story generation. © Springer International Publishing Switzerland 2015.This  research  was  partially  supported  by  projects CoInvent (FET-Open grant 611553) and NASAID (CSIC Intramural 201550E02Peer Reviewe
Aspect selection for social recommender systems

In this paper, we extend our previous work on social recommender systems to harness knowledge from product reviews. By mining product reviews, we can exploit sentiment-rich content to ascertain user opinion expressed over product aspects. Aspect aware sentiment analysis provides a more structured approach to product comparison. However, aspects extracted using NLP-based techniques remain too large and lead to poor quality product comparison metrics. To overcome this problem, we explore the utility of feature selection heuristics based on frequency counts and Information Gain (IG) to rank and select the most useful aspects. Here an interesting contribution is the use of top ranked products from Amazon to formulate a binary classification over products to form the basis for the supervised IG metric. Experimental results on three related product families (Compact Cameras, DSLR Cameras and Point & Shoot Cameras) extracted from Amazon.com demonstrate the effectiveness of incorporating feature selection techniques for aspect selection in recommendation task. © Springer International Publishing Switzerland 2015.This  research  has  been  partially  supported  by  AGAUR Scholarship (2013FI-B 00034) and NASAID (CSIC Intramural 201550E022).Peer Reviewe
Music recommendation: audio neighbourhoods to discover music in the long tail.

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the ?long tail? of on-line music. Tag-based recommenders are not effective in this ?long tail? because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track?s tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using Last.fm user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation
Tyranny of Distance: Understanding Academic Library Browsing by Refining the Neighbour Effect

Browsing is a part of book seeking that is important to readers, poorly understood, and ill supported in digital libraries. In earlier work, we attempted to understand the impact of browsing on book borrowing by examining whether books near other loaned books were more likely to be loaned themselves, a phenomenon we termed the neighbour effect. In this paper we further examine the neighbour effect, looking specifically at size, interaction with search and topic boundaries, increasing our understanding of browsing behaviour
Query Expansion for Survey Question Retrieval in the Social Sciences

In recent years, the importance of research data and the need to archive and
to share it in the scientific community have increased enormously. This
introduces a whole new set of challenges for digital libraries. In the social
sciences typical research data sets consist of surveys and questionnaires. In
this paper we focus on the use case of social science survey question reuse and
on mechanisms to support users in the query formulation for data sets. We
describe and evaluate thesaurus- and co-occurrence-based approaches for query
expansion to improve retrieval quality in digital libraries and research data
archives. The challenge here is to translate the information need and the
underlying sociological phenomena into proper queries. As we can show retrieval
quality can be improved by adding related terms to the queries. In a direct
comparison automatically expanded queries using extracted co-occurring terms
can provide better results than queries manually reformulated by a domain
expert and better results than a keyword-based BM25 baseline.Comment: to appear in Proceedings of 19th International Conference on Theory
  and Practice of Digital Libraries 2015 (TPDL 2015
Evaluating the impact of OpenMP 4.0 extensions on relevant parallel workloads

OpenMP has been for many years the most widely used programming model for shared memory architectures. Periodically, new features are proposed and some of them are finally selected for inclusion in the OpenMP standard. The OmpSs programming model developed at the Barcelona Supercomputing Center (BSC) aims to be an OpenMP forerunner that handles the main OpenMP constructs plus some extra features not included in the OpenMP standard. In this paper we show the usefulness of three OmpSs features not currently handled by OpenMP 4.0 by deploying them over three applications of the PARSEC benchmark suite and showing the performance benefits. This paper also shows performance trade-offs between the OmpSs/OpenMP tasking and loop parallelism constructs and shows how a hybrid implementation that combines both approaches is sometimes the best option.This work has been partially supported by the European Research Council under the European Union's 7th FP, ERC Grant Agreement number 321253, by the Spanish Ministry of Science and Innovation under grant TIN2012-34557 and by the HiPEAC Network of Excellence. It has been also supported by the Severo Ochoa Program awarded by the Spanish Government (grant SEV-2011-00067)\ud
M. Moreto has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI- 2012-15047. M. Casas is supported by the Secretary for Universities and Research of the Ministry of Economy and Knowledge of the Government of Catalonia and\ud
the Co- fund programme of the Marie Curie Actions of the 7th R&D Framework Programme of the European Union (Contract 2013 BP_B 00243).Peer ReviewedPostprint (author's final draft
Learning Levels of Mario AI Using Genetic Algorithms

This procceding at: 16th Conference of the Spanish Association for Artiﬁcial Intelligence, CAEPIA 2015, took place at: November 9-12, 2015, in Albacete, Spain.This paper introduces an approach based on Genetic Algorithms to learn levels from the Mario AI simulator, based on the Infinite Mario Bros. game (which is, at the same time, based on the Super Mario World game from Nintendo). In this approach, an autonomous agent playing Mario is able to learn a sequence of actions in order to maximize the score, not looking at the current state of the game at each time. Different parameters for the Genetic Algorithm are explored, and two different stages are executed: in the first, domain independent genetic operators are used; while in the second knowledge about the domain is incorporated to these operators in order to improve the results. Results are encouraging, as Mario is able to complete very difficult levels full of enemies, resembling the behavior of an expert human player.This research work is co-funded by the Spanish Ministry of Industry, Tourism and Commerce under grant agreement no. TSI-090302-2011-11.Publicad
Developments in the Photonic Theory of Fluorescence

Conventional fluorescence commonly arises when excited molecules relax to their ground electronic state, and most of the surplus energy dissipates in the form of photon emission. The consolidation and full development of theory based on this concept has paved the way for the discovery of several mechanistic variants that can come into play with the involvement of laser input – most notably the phenomenon of multiphoton-induced fluorescence. However, other effects can become apparent when off-resonant laser input is applied during the lifetime of the initial excited state. Examples include a recently identified scheme for laser-controlled fluorescence. Other systems of interest are those in which fluorescence is emitted from a set of two or more coupled nanoemitters. This chapter develops a quantum theoretical outlook to identify and describe these processes, leading to a discussion of potential applications ranging from all-optical switching to the generation of optical vortices
Relating ICT Competencies with Personality Types

International audienceICT competency frameworks establish the definition of competences required and deployed by ICT professionals. Job profiles articulate competen-cies together with an organization needs, objectives and constraints. The evolution of the software industry impacts personality trends in the profession. This work-in-progress studies the relationship between competencies, profiles and personality types. 1 Introduction Competency frameworks, such as the e-Competences Framework [1] provides a reference of competences as required and applied at the Information and Communication Technology (ICT) workplace. The application of the European e-Competence Framework is centered upon workplace competence articulation, profiling, assessment and measurement [2]. A person's inclination towards a specific way of acquiring information or making decisions influences their preference for certain tasks and jobs [3]. Because certain jobs require certain competences, we may think that personality types and traits of software engineers are related to engineers' competences and performance. Thus we define a first research question: How can we relate personality types with employees' competencies proficiency? Competences are sufficiently comprehensive to represent complexity and to fit variable organization structures. Customization is generally performed through the definition of various job profiles reflecting organization needs and objectives. The European ICT Professional Profiles [4] was created to define a number of representative ICT Profiles covering, at their level of granularity, the full ICT Business process. The European ICT Profiles build a consistent bridge between existing competence and profile approaches because profiles are worded in terms of capabilities needed to successfully perform a role and related to the required e-competences. Our interest is focused on four major roles involved in the development of software products: project manager, system architect, system analyst and developer. Through the study of a small set of software engineers graduated 10 years ago, we aim to empirically verify that the set of e-competences related to a role is suitable to their job occupation. In section 2, we overview the background and related work. In section 3, we present the selected job profiles and the sample set. In section 4, we discuss the questionnaire results, and then we conclude the paper
Changing situational contexts present a constant challenge to software developers.

A software process can take many forms and its optimality demands that it should be harmonised with the needs of the given software development situational context. This theoretical proposition is reasonably clear. However, the finer details of the interaction between the software process and the factors of the situational context are much less obvious. In previously published research, the authors have elaborated a reference framework that identifies the factors of a situational context that affect the software process [1]. In this paper, we report on the application of our reference framework in an examination of the changing nature of software development situational contexts. Our corresponding study of fifteen software development companies indicates that certain factors appear more subject to change than others. This finding is a potentially important insight that can help us with the recurring challenge of adapting the software process to changing circumstances
Metaphorisms in programming

This paper introduces the metaphorism pattern of relational specification and addresses how specification following this pattern can be refined into recursive programs. Metaphorisms express input-output relationships which preserve relevant information while at the same time some intended optimization takes place. Text processing, sorting, representation changers, etc., are examples of metaphorisms. The kind of metaphorism refinement proposed in this paper is a strategy known as change of virtual data structure. It gives sufficient conditions for such implementations to be calculated using relation algebra and illustrates the strategy with the derivation of quicksort as example.The author wishes to thank the anonymous referees for their comments and suggestions. This work is funded by ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness) and by National Funds through the FCT - Funda¸c˜ao para a Ciˆencia e a Tecnologia (Portuguese Foundation for Science and Technology) within project FCOMP-01-0124-FEDER-02053
Inheritance in Object-Oriented Knowledge Representation

This paper contains the consideration of inheritance mechanism in such
knowledge representation models as object-oriented programming, frames and
object-oriented dynamic networks. In addition, inheritance within
representation of vague and imprecise knowledge are also discussed. New types
of inheritance, general classification of all known inheritance types and
approach, which allows avoiding in many cases problems with exceptions,
redundancy and ambiguity within object-oriented dynamic networks and their
fuzzy extension, are introduced in the paper. The proposed approach bases on
conception of homogeneous and inhomogeneous or heterogeneous class of objects,
which allow building of inheritance hierarchy more flexibly and efficiently.Comment: in Information and Software Technologies, Communications in Computer
  and Information Science, Springer, 201