Negative Examples Research Articles

A qualitative assessment of psychosocial aspects that play a role in bladder management after spinal cord injury.

Prospective qualitative study using semi-structured, open-ended interviews. To better understand psychosocial aspects of bladder management after spinal cord injury (SCI). People with SCI, recruited through three tertiary care centres in North America. Thirty-four community-dwelling individuals with SCI were selected using purposive sampling to ensure men and women with both tetraplegia and paraplegia were represented. The interviews were audio recorded and transcribed. NVivo software was used to code psychosocial domains, medical complications, and methods of bladder management. A qualitative interpretive approach was used, and four participants participated in a debrief session to review the study findings. The median age was 49 (IQR 40-62) years and 61% (21/34) were male. Most had complete injuries (AIS A, 47%, 16/34), and most had a cervical lesion (56%, 19/34). There was a large range of time living with a SCI (median 20 years, IQR 9-31), and most participants managed their bladder with intermittent catheterisation (67% 23/34). The five most common psychosocial domains were anxiety (often in the context of urinary infections, incontinence, or not being able to catheterise), loss of control (due to irregular bladder behaviour), and embarrassment (from incontinence occurring in public); the domains of confidence and independence/planning included both positive and negative examples of bladder management experiences. This study demonstrated consistent psychosocial topics related to bladder management in people living with SCI. This information will help counsel people regarding bladder management and identify areas for education and optimisation of bladder function from a psychosocial perspective.

Azerbaycanlı Yazar Celil Memmedguluzâde ve Çevresindeki Ermeniler

Jalil Mammadguluzadeh is one of the most famous writers who was standing at the origins of critical realism in the Azerbaijani literature of the beginning of the 20th century. Jalil Mammadguluzadeh’s life spent in Nakhichevan, Yerevan, Tbilisi, Karabagh, Tabriz and Baku was closely intertwined with the Armenians, Georgians and Russians, and this fact did not only influence his literary works but also left traces in his life. Compared to the relationships with Georgians and Russians, the relations of Azerbaijani Turks and Armenians were sometimes based on good friendship while sometimes, on the contrary, were rested upon bloody hostility. We can see that both cases affected Jalil Mammadguluzadeh’s life. In this work, we have studied positive and negative examples related to the Armenians that surrounded the author since his childhood and his views towards the Armenian-Muslim case that started to appear in 1905-1907 and 1918-1920.

Finding semantic patterns in omics data using concept rule learning with an ontology-based refinement operator

BackgroundIdentification of non-trivial and meaningful patterns in omics data is one of the most important biological tasks. The patterns help to better understand biological systems and interpret experimental outcomes. A well-established method serving to explain such biological data is Gene Set Enrichment Analysis. However, this type of analysis is restricted to a specific type of evaluation. ing from details, the analyst provides a sorted list of genes and ontological annotations of the individual genes; the method outputs a subset of ontological terms enriched in the gene list. Here, in contrary to enrichment analysis, we introduce a new tool/framework that allows for the induction of more complex patterns of 2-dimensional binary omics data. This extension allows to discover and describe semantically coherent biclusters.ResultsWe present a new rapid method called sem1R that reveals interpretable hidden rules in omics data. These rules capture semantic differences between two classes: a target class as a collection of positive examples and a non-target class containing negative examples. The method is inspired by the CN2 rule learner and introduces a new refinement operator that exploits prior knowledge in the form of ontologies. In our work this knowledge serves to create accurate and interpretable rules. The novel refinement operator uses two reduction procedures: Redundant Generalization and Redundant Non-potential, both of which help to dramatically prune the rule space and consequently, speed-up the entire process of rule induction in comparison with the traditional refinement operator as is presented in CN2.ConclusionsEfficiency and effectivity of the novel refinement operator were tested on three real different gene expression datasets. Concretely, the Dresden Ovary Dataset, DISC, and m2816 were employed. The experiments show that the ontology-based refinement operator speeds-up the pattern induction drastically. The algorithm is written in C++ and is published as an R package available at http://github.com/fmalinka/sem1r.

SAR Image Ship Object Generation and Classification With Improved Residual Conditional Generative Adversarial Network

Image generation by the conditional generative adversarial network (CGAN) can provide rich samples for supervised classification tasks. However, the unstable gradient updating problem makes it difficult to be used for image generation, especially for the high-resolution SAR image. In this letter, an improved residual condition generation network is proposed to enhance the quality of generated images and classification accuracy for hard negative examples. First, a residual convolutional-based block is built to clear the detail texture of different types of targets. Then, the gradient penalty and Wasserstein loss are used as discriminators to improve the similarity between real samples and the intra diversity of a generated images. We test the proposed method on generation and classification of three kinds of commercial ships using C-band 3-m Gaofen-3 (GF-3) SAR images. Experimental results show that our method can generate high-quality ship samples and achieve good classification accuracy.

A Feature-Enhanced Anchor-Free Network for UAV Vehicle Detection

Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task. One reason is that the objects are small size, low-resolution, and large scale variations, resulting in weak feature representation. Another reason is the imbalance between positive and negative examples. In this paper, we propose a novel architecture for UAV vehicle detection to solve above problems. In detail, we use anchor-free mechanism to eliminate predefined anchors, which can reduce complicated computation and relieve the imbalance between positive and negative samples. Meanwhile, to enhance the features for vehicles, we design a multi-scale semantic enhancement block (MSEB) and an effective 49-layer backbone which is based on the DetNet59. The proposed network offers appropriate receptive fields that match the small-sized vehicles, and involves precise localization information provided by the contexts with high resolution. The MSEB strengthens discriminative feature representation at various scales, without reducing the spatial resolution of prediction layers. Experiments show that the proposed method achieves the state-of-the-art performance. Particularly, the main part of vehicles, much smaller ones, the accuracy is about 2% higher than other existing methods.

Novel feature selection approaches for improving the performance of sentiment classification

Text based social media has become one of important communication tools between customers and enterprises. In social media, users can easily express their opinions and evaluation regarding products or services. These online user experiences, especially negative evaluations indeed affect other consumers’ behaviors. Consequently, to effectively identify customers’ sentiments and avoid these negative comments to bring a great damage to enterprisers has become one of critical issues. In recent years, machine learning algorithms were viewed as one of effective solutions for sentiment classification. But, when the amount of the online reviews arises, the dimensionality of text data rises remarkably. The performances of machine learning methods have been degraded due to the dimensionality problem. But, conventional feature selection methods tend to select attributes from the majority sentiments, which usually cannot improve classification performance. Therefore, this study attempt to present two feature selection methods called modified categorical proportional difference (MCPD) approach that improves conventional CPD method, and balance category feature (BCF) strategy that equally selects attributes from both positive and negative examples, to improve sentiment classification performances. Finally, several real sentiment cases of text reviews will be provided to demonstrate the effectiveness of our proposed methods. Results showed that the combination of proposed BCF strategy and MCPD method can not only remarkably reduce feature space, but also improve the sentiment classification performance.

Special aspects of legal regulation of fixed-term employment contracts of some European countries

The article is devoted to the study of foreign experience in legal regulation of fixed-term employment contracts. Fixed-termemployment contracts should be considered as one of the earliest and, accordingly, the oldest forms of non-standard employment. Tur -ning to the concept of the application of fixed-term employment contracts in Ukraine in the near future, it seems appropriate to consider the possibility of their further development through the prism of studying European experience. Indeed, in many European countriesthe fixed-term contracts are quite common and therefore analysis of both positive and negative examples of their legal regulation willbe useful for the future development of labour legislation in Ukraine.The article features an analysis of the relevant legislation of the United Kingdom, Estonia, Italy, Poland and France. It is concludedthat the membership of these states in the European Union has had a significant impact on the evolution of national labour le -gislation. This also applies to the United Kingdom, which had been part of this economic and political union for a long time.The transposition of EU legislation into national law by these countries predetermines the existence of common features betweenthem in the legal regulation of fixed-term employment contracts. This common features include: clear time limits of the employmentcontract, maximum allowable number of renewals enshrined in law, compliance with the principle of non-discrimination, etc.In turn, the implementation of fixed-term employment relationships in each country differs in its uniqueness, which is due to thedomestic tradition of their implementation. For example, in the United Kingdom, the dismissal of an employee due to the expiration ofthe employment contract is considered through the lens of fairness of the employer’s actions, while in Italy the number of fixed-termemployment contracts with a particular employer cannot exceed 30 %.The above circumstances should be taken into account by Ukraine when reforming labour legislation. Indeed, the need to implementCouncil Directive 1999/70/EC is clearly provided for in clauses 1139 and 1140 of the Action Plan for the implementation of theAssociation Agreement between Ukraine, on the one hand, and the European Union, the European Atomic Energy Community and theirmember states, on the other hand, approved by Resolution of the Cabinet of Ministers of Ukraine No. 1106, of 25.10.2017.

Improved pedestrian detection with peer AdaBoost cascade

Focusing on data imbalance and intraclass variation, an improved pedestrian detection with a cascade of complex peer AdaBoost classifiers is proposed. The series of the AdaBoost classifiers are learned greedily, along with negative example mining. The complexity of classifiers in the cascade is not limited, so more negative examples are used for training. Furthermore, the cascade becomes an ensemble of strong peer classifiers, which treats intraclass variation. To locally train the AdaBoost classifiers with a high detection rate, a refining strategy is used to discard the hardest negative training examples rather than decreasing their thresholds. Using the aggregate channel feature (ACF), the method achieves miss rates of 35% and 14% on the Caltech pedestrian benchmark and Inria pedestrian dataset, respectively, which are lower than that of increasingly complex AdaBoost classifiers, i.e., 44% and 17%, respectively. Using deep features extracted by the region proposal network (RPN), the method achieves a miss rate of 10.06% on the Caltech pedestrian benchmark, which is also lower than 10.53% from the increasingly complex cascade. This study shows that the proposed method can use more negative examples to train the pedestrian detector. It outperforms the existing cascade of increasingly complex classifiers.

Deep Triplet Neural Networks with Cluster-CCA for Audio-Visual Cross-Modal Retrieval

Cross-modal retrieval aims to retrieve data in one modality by a query in another modality, which has been a very interesting research issue in the field of multimedia, information retrieval, and computer vision, and database. Most existing works focus on cross-modal retrieval between text-image, text-video, and lyrics-audio. Little research addresses cross-modal retrieval between audio and video due to limited audio-video paired datasets and semantic information. The main challenge of the audio-visual cross-modal retrieval task focuses on learning joint embeddings from a shared subspace for computing the similarity across different modalities, where generating new representations is to maximize the correlation between audio and visual modalities space. In this work, we propose TNN-C-CCA, a novel deep triplet neural network with cluster canonical correlation analysis, which is an end-to-end supervised learning architecture with an audio branch and a video branch. We not only consider the matching pairs in the common space but also compute the mismatching pairs when maximizing the correlation. In particular, two significant contributions are made. First, a better representation by constructing a deep triplet neural network with triplet loss for optimal projections can be generated to maximize correlation in the shared subspace. Second, positive examples and negative examples are used in the learning stage to improve the capability of embedding learning between audio and video. Our experiment is run over fivefold cross validation, where average performance is applied to demonstrate the performance of audio-video cross-modal retrieval. The experimental results achieved on two different audio-visual datasets show that the proposed learning architecture with two branches outperforms existing six canonical correlation analysis–based methods and four state-of-the-art-based cross-modal retrieval methods.

Image captioning with semantic-enhanced features and extremely hard negative examples

Image captioning is a task to generate natural descriptions of images. In existing image captioning models, the generated captions usually lack semantic discriminability. Semantic discriminability is difficult as it requires the model to capture detailed differences in images. In this paper, we propose an image captioning framework with semantic-enhanced features and extremely hard negative examples. These two components are combined in a Semantic-Enhanced Module. The semantic-enhanced module consists of an image-text matching sub-network and a Feature Fusion layer which provides semantic-enhanced features of rich semantic information. Moreover, in order to improve the semantic discriminability, we propose an extremely hard negative mining method which utilize the extremely hard negative examples to improve the latent alignment between visual and language information. Experimental results on MSCOCO and Flickr30K show that our proposed framework and training method can simultaneously improve the performance of image-text matching and image captioning, achieving competitive performance against state-of-the-art methods.

School as a Mass Revival Tool

The school defines a system that is too complex to be reduced to functions and practices. Humanity saw the school as a ‘multi-dimensional structure’ in its development adventure; ıts necessity was generally considered to be ‘vital’. Until the last century, there was hardly any serious criticism that the school was unnecessary. Especially the differences such as the innovations of the new century, the monist perspective, the possibilities of the digital world gave the opportunity to discuss the role of the school and its current role was frequently brought up. Even though the evaluations made over the school with the works of thinkers such as Gatto and Illich have a fair share, it will be seen that these are criticisms developed on the basis of ‘negative examples’. In the digital age where the vehicle is rich and purpose is impoverished, the school must be reconstructed as a challenging metaphor. There is a need for a vision of a school that prioritizes the human with wisdom without blessing the machine. In this context, our study regards the school of the future as the most critical institutional phenomenon for human rejuvenation ‘despite all’. School as a natural system is considered to be the strongest structure to rebuild the future against actual and popular ‘negativities’.

Two Examples Related to the Twisted Burnside–Frobenius Theory for Infinitely Generated Groups

TheTBFTf conjecture, which is a modification of a conjecture by Fel’shtyn and Hill, says that if the Reidemeister number R(𝜙) of an automorphism 𝜙 of a (countable discrete) group G is finite, then it coincides with the number of fixed points of the corresponding homeomorphism $$ \hat{\phi} $$ of $$ {\hat{G}}_f $$ (the part of the unitary dual formed by finite-dimensional representations). The study of this problem for residually finite groups has been the subject of some recent activity. We prove here that for infinitely generated residually finite groups there are positive and negative examples for this conjecture. It is detected that the finiteness properties of the number of fixed points of 𝜙 itself also differ from the finitely generated case.

Genome annotation across species using deep convolutional neural networks.

Application of deep neural network is a rapidly expanding field now reaching many disciplines including genomics. In particular, convolutional neural networks have been exploited for identifying the functional role of short genomic sequences. These approaches rely on gathering large sets of sequences with known functional role, extracting those sequences from whole-genome-annotations. These sets are then split into learning, test and validation sets in order to train the networks. While the obtained networks perform well on validation sets, they often perform poorly when applied on whole genomes in which the ratio of positive over negative examples can be very different than in the training set. We here address this issue by assessing the genome-wide performance of networks trained with sets exhibiting different ratios of positive to negative examples. As a case study, we use sequences encompassing gene starts from the RefGene database as positive examples and random genomic sequences as negative examples. We then demonstrate that models trained using data from one organism can be used to predict gene-start sites in a related species, when using training sets providing good genome-wide performance. This cross-species application of convolutional neural networks provides a new way to annotate any genome from existing high-quality annotations in a related reference species. It also provides a way to determine whether the sequence motifs recognised by chromatin-associated proteins in different species are conserved or not.

Desde Marcus Welby, M.D. hasta The resident: los cambios en las representaciones de los médicos en las series de televisión

La forma en que las series de televisión representan a los médicos ha cambiado a lo largo de los años. En las primeras, los personajes principales eran buenos, pacíficos, inteligentes, competentes, empáticos y exitosos. Por ejemplo, Marcus Welby M.D. (TV) de David Victor (Estados Unidos), que este año cumple 50 años desde su primera emisión. Esta representación comenzó a cambiar a mediados de la década de 1990. Mientras mantenían una imagen positiva, se comenzó a poner más énfasis en sus características negativas, y sus dificultades en las relaciones interpersonales, como ER (TV) de Michael Crichton (Estados Unidos) y Doctor House (TV) de David Shore (Estados Unidos). Los médicos fueron descritos como arrogantes, codiciosos y adúlteros, y se mostraban sus errores. Las últimas dos series estrenadas son The Good Doctor (TV) de David Shore (Estados Unidos), con un residente de cirugía con autismo y síndrome de Savant, y The Resident (TV) de Amy Holden Jones, Hayley Schore y Roshan Sethi (Estados Unidos), donde aparecen serios problemas institucionales. Estas series pueden ser útiles como un método para discutir problemas de la ética médica mediante ejemplos positivos o negativos, y también ayudan a representar los cambios en la imagen de los médicos en las últimas décadas, discutiendo las razones de estos cambios.

CONHECIMENTO E MEDO: FAUSTO A CAMINHO DA DANAÇÃO

A Historia von D. Johann Fausten, de 1587, primeira versão impressa da famosa narrativa sobre a vida de Fausto, é uma obra alemã de cunho luterano que objetiva construir um exemplo negativo: o bom cristão, leitor do romance, deve entender as consequências da arrogância e da leviandade diante de Deus. O protagonista, Fausto, buscando conhecimento proibido ao homem, faz um pacto com o demônio, o que o condena à eterna danação no inferno. As formas e consequências do saber são questões centrais do romance e objeto de análise deste artigo, que busca responder qual conhecimento é almejado e realmente alcançado por Fausto e quais são as consequências do pacto. O principal resultado da busca por saber é um protagonista dominado pelo medo, incapaz, por isso, de retornar à misericórdia divina, mesmo após arrepender-se.

Attribute-based structural damage identification by few-shot meta learning with inter-class knowledge transfer

Image archives of multi-class structural damages can be collected by manual inspection and then used for structural damage identification. On one hand, conventional image-processing-based approaches rely on optimal designs of hand-crafted feature detectors and lack universal adaptability for various application cases; on the other hand, regular supervised learning techniques require complete damage types and sufficient training examples to establish a robust damage recognition model, which brings up a time-labor-consuming image collection process. To solve these problems, this study proposes a nested attribute-based few-shot meta learning paradigm for structural damage identification. First, an external few-shot meta learning module is established based on different classification tasks named as meta-batches to produce robust classifiers for new damage types, in which support and query subsets including partial damage types and a few examples are randomly sampled from the original image dataset. Second, an embedded internal attribute-based transfer learning model is trained by minimizing the l2-norm and angular losses of attribute representation vectors in an end-to-end manner, where damage attributes act as the common inter-class knowledge and are transferred from the source damage space of support set to the target damage space of query set. Finally, the proposed approach is validated on a real-world structural damage image dataset, which contains 1000 examples of 10 representative damage types in total. Results show the proposed approach produces an overall accuracy of 93.5% and an average area under the ROC curve of 0.96 for 10 damage types. The general equilibrium of average precision and recall indicates that the trained model is balanced to both positive and negative examples for each damage type. Compared with a regular supervised learning model by directly classifying input images with one-hot vector labels, the proposed approach generates higher accuracy and better robustness. Parameter study suggests the proposed paradigm enables to train a stable and reliable meta learning classification model that can perform well across a series of settings about the ratio between support and query subsets. Theoretical analysis is also performed to explain why meta learning surpasses regular supervised learning.

Estimating a one-class naive Bayes text classifier

Nowadays more and more information extraction projects need to classify large amounts of text data. The common way to classify text is to build a supervised classifier trained on human-labeled positive and negative examples. In many cases, however, it is easy to label positive examples, but hard to label negative examples. In this paper, we address the problem of building a one-class classifier when only the positive examples are labeled. Previous works on building one-class classifier mostly use positive examples and unlabeled data. In this paper, we show that a configurable one-class classifier such as one-class naive Bayes can be optimized by examining the clustering quality of the classification on target data. We propose to use existing and new quality scores for determining clustering quality of the classification. Experimental analysis with real-world data show that our approach generally achieves high classification accuracy, and in some cases improves the accuracy by more than 10% compared to state-of-art baselines.

Generating pairs of projective special linear groups that fail to lift

AbstractThe following problem was originally posed by B. H. Neumann and H. Neumann. Suppose that a group G can be generated by n elements and that H is a homomorphic image of G. Does there exist, for every generating n‐tuple of H, a homomorphism and a generating n‐tuple of G such that ?M. J. Dunwoody gave a negative answer to this question, by means of a carefully engineered construction of an explicit pair of soluble groups. Via a new approach we produce, for , infinitely many pairs of groups that are negative examples to Neumanns' problem. These new examples are easily described: G is a free product of two suitable finite cyclic groups, such as , and H is a suitable finite projective special linear group, such as for a prime . A small modification yields the first negative examples with H infinite.

Mining Expressive Rules in Knowledge Graphs

We describe RuDiK, an algorithm and a system for mining declarative rules over RDF knowledge graphs (KGs). RuDiK can discover rules expressing both positive relationships between KG elements, e.g., “if two persons share at least one parent, they are likely to be siblings,” and negative patterns identifying data contradictions, e.g., “if two persons are married, one cannot be the child of the other” or “the birth year for a person cannot be bigger than her graduation year.” While the first kind of rules identify new facts in the KG, the second kind enables the detection of incorrect triples and the generation of (training) negative examples for learning algorithms. High-quality rules are also critical for any reasoning task involving the KGs. Our approach increases the expressive power of the supported rule language w.r.t. the existing systems. RuDiK discovers rules containing (i) comparisons among literal values and (ii) selection conditions with constants. Richer rules increase the accuracy and the coverage over the facts in the KG for the task at hand. This is achieved with aggressive pruning of the search space and with disk-based algorithms, which enable the execution of the system in commodity machines. Also, RuDiK is robust to errors and missing data in the input graph. It discovers approximate rules with a measure of support that is aware of the quality issues. Our experimental evaluation with real-world KGs shows that RuDiK does better than existing solutions in terms of scalability and that it can identify effective rules for different target applications.

ParSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants.

BackgroundSeveral prediction problems in computational biology and genomic medicine are characterized by both big data as well as a high imbalance between examples to be learned, whereby positive examples can represent a tiny minority with respect to negative examples. For instance, deleterious or pathogenic variants are overwhelmed by the sea of neutral variants in the non-coding regions of the genome: thus, the prediction of deleterious variants is a challenging, highly imbalanced classification problem, and classical prediction tools fail to detect the rare pathogenic examples among the huge amount of neutral variants or undergo severe restrictions in managing big genomic data.ResultsTo overcome these limitations we propose parSMURF, a method that adopts a hyper-ensemble approach and oversampling and undersampling techniques to deal with imbalanced data, and parallel computational techniques to both manage big genomic data and substantially speed up the computation. The synergy between Bayesian optimization techniques and the parallel nature of parSMURF enables efficient and user-friendly automatic tuning of the hyper-parameters of the algorithm, and allows specific learning problems in genomic medicine to be easily fit. Moreover, by using MPI parallel and machine learning ensemble techniques, parSMURF can manage big data by partitioning them across the nodes of a high-performance computing cluster. Results with synthetic data and with single-nucleotide variants associated with Mendelian diseases and with genome-wide association study hits in the non-coding regions of the human genome, involhing millions of examples, show that parSMURF achieves state-of-the-art results and an 80-fold speed-up with respect to the sequential version.ConclusionsparSMURF is a parallel machine learning tool that can be trained to learn different genomic problems, and its multiple levels of parallelization and high scalability allow us to efficiently fit problems characterized by big and imbalanced genomic data. The C++ OpenMP multi-core version tailored to a single workstation and the C++ MPI/OpenMP hybrid multi-core and multi-node parSMURF version tailored to a High Performance Computing cluster are both available at https://github.com/AnacletoLAB/parSMURF.