Negative Examples Research Articles

Reconstructing the Temple: The Royal Rhetoric of Temple Renovation in the Ancient Near East and Israel

Reconstructing the Temple: The Royal Rhetoric of Temple Renovation in the Ancient Near East and Israel

APSE: Attention-Aware Polarity-Sensitive Embedding for Emotion-Based Image Retrieval

With the popularity of social media, an increasing number of people are accustomed to expressing their feelings and emotions online using images and videos. An emotion-based image retrieval (EBIR) system is useful for obtaining visual contents with desired emotions from a massive repository. Existing EBIR methods mainly focus on modeling the global characteristics of visual content without considering the crucial role of informative regions of interest in conveying emotions. Further, they ignore the hierarchical relationships between coarse polarities and fine categories of emotions. In this paper, we design an attention-aware polarity-sensitive embedding (APSE) network to address these issues. First, we develop a hierarchical attention mechanism to automatically discover and model the informative regions of interest. Specifically, both polarity- and emotion-specific attended representations are aggregated for discriminative feature embedding. Second, we propose a generated emotion-pair (GEP) loss to simultaneously consider the inter- and intra-polarity relationships of the emotion labels. Moreover, we adaptively generate negative examples of different hard levels in the feature space guided by the attention module to further improve the performance of feature embedding. Extensive experiments on four popular benchmark datasets demonstrate that the proposed APSE method outperforms the state-of-the-art EBIR approaches by a large margin.

Issues of stigmatization to mental patients from medical students and medical staff of psychiatric institutions and measures for its minimization

Annotation. The aim of the study was to identify the presence of stigmatization among students of medical educational institutions and medical stuff of psychiatric institutions toward mental patients, to establish the causes and develop measures to minimize it. Methods were used in the work: bibliosemantic, historical, content analysis, as well as a questionnaire method using a specially designed questionnaire. Using Telegram, Microsoft Excel 2003 software, 1062 students were interviewed anonymously of 4–5 courses of medical institutions of higher education of Ukraine. The average age of students ranged from 20 to 22 years – 913 (85.9 %); from 23 to 24 years old – 119 (11.3 %) and from 25 to 26 years old – 30 (2.8 %). It was found that 93.8% of respondents from the total number of students surveyed are convinced that people with mental illness need special treatment; 87.9 % – were convinced that people who have mental illness pose a threat to society; 17.3 % respondents witnessed the humiliation of mental patients by working medical personnel in the presence of medical students. It is a fact, but in 2.1 % of cases, future doctors allowed themselves to offend patients who were hospitalized in psychiatric healthcare facilities during training. Thus, the inheritance of the Soviet system of medical education in the field of training young specialists for the mental health sector in Ukraine, which, unfortunately, has not experienced significant structural changes, is reflected in the behavior of the working medical personnel of psychiatric hospitals and the views of future doctors. Stigmatization of mental patients was carried out by every fourth medical worker (24.5 %) in psychiatric institutions in Odessa and every fifth medical worker in psychiatric institutions in Poltava (21.4 %) and Chernivtsi (20.9 %), which is violation of the moral and ethical principles of the doctor's behavior and unacceptable. The stigmatization of the mentally ill by medical students of medical schools is 2.1 %, which indicates that students use the negative example of the medical personnel of psychiatric institutions for their future medical activities.

New Image Classification Framework for Improving Image Retrieval Based on Relevance Feedback (Dept.E)

Content-based image retrieval (CBIR) is a promising technology to assist image finding, CBIR retrieves images by visual features inherent in images. Relevance feedback allows the user to reflect his preference to the system, then the system can reformulate the query according to the positive and/or negative examples responded by the user. This paper presents two efficient frameworks for image classification through the analysis of the visual features (such as color, shape, texture) of an example image. The first framework is based on comparing the norm and the direction of vectors ia multi-dimensional space for both the target and query image vectors, which allows classification according to their probabilities oli existence. The second framework depends on acquiring classification knowledge from a large empirical image database in a specific domain and utilizes that knowledge for image classification. The initial classification process is used as a training phase to feed the system with a classification tree for images in the retrieval domain. This tree is the best reduction of dimensionality that would result from all possible combinations of feature divisions.

Examining the Thoughts of 60-72 Month-Old Children's Fathers on the Concept of Value and the Processes of Having Values Acquired to their Children

This research was conducted to examine the thoughts of the fathers of 60-72 month-old children about the concept of the value and the processes of having values acquired to their children. The research was carried out by adopting the phenomenological method, which is one of the qualitative research methods. In the study, the data were collected through a semi-structured interview form. The study group of the study consisted of 14 fathers whose children were educated in kindergarten in the 2019-2020 academic year. Content analysis was applied to analyse the data. As a result of the research, it was concluded that fathers define the value as the principles which can guide life and see the purpose of values education as continuing culture and ensure social reconciliation. It was concluded that fathers attach importance to the concept of the value and that they attach more importance to moral, national cultural and religious values within values. The fathers stated that they tried to convey the values as role models and with plain expression. In the process of having values acquired to their children, they stated that they mostly used books, games and cartoons. They stated that the problems they experienced in values education arose from the fact that they could not allocate enough time for their children and lack of knowledge, and negative examples and mass media in the society negatively affected the value education process. It was concluded that fathers considered the school and teacher as important in the education of values and that they cared to cooperate with their spouses. In line with the results of the research, in order for fathers to realize their role and responsibilities in bringing value to their children and to have the knowledge, it was suggested that studies should be conducted and seminars should be given by the field experts and social environments where fathers can share their values with their children should be supported.

Remembrance Light At Memorial Sites

Everyone in Russia has been preparing to the 75th anniversary of the victory in World War Two despite the fact that the state-level commemoration events are being impeded by the global disaster which may be compared to a world war: the COVID‑19 pandemic. Like a war, it will eventually end but the memory about the anniversary must and will live on. Therefore, the subject of the article is topical: commemoration light and memory in light of an eternal flame and artistic and sacral illumination (Tribute in Light, like it was in New York in 2001). Numerous issues of architectural lighting of memorial sites and monuments in different Russian cities are under consideration. Positive and negative examples of light design solutions are described. It is also noted that information on contemporary state of this area is extremely insufficient.

Multi-task learning for the simultaneous reconstruction of the human and mouse gene regulatory networks

The reconstruction of Gene Regulatory Networks (GRNs) from gene expression data, supported by machine learning approaches, has received increasing attention in recent years. The task at hand is to identify regulatory links between genes in a network. However, existing methods often suffer when the number of labeled examples is low or when no negative examples are available. In this paper we propose a multi-task method that is able to simultaneously reconstruct the human and the mouse GRNs using the similarities between the two. This is done by exploiting, in a transfer learning approach, possible dependencies that may exist among them. Simultaneously, we solve the issues arising from the limited availability of examples of links by relying on a novel clustering-based approach, able to estimate the degree of certainty of unlabeled examples of links, so that they can be exploited during the training together with the labeled examples. Our experiments show that the proposed method can reconstruct both the human and the mouse GRNs more effectively compared to reconstructing each network separately. Moreover, it significantly outperforms three state-of-the-art transfer learning approaches that, analogously to our method, can exploit the knowledge coming from both organisms. Finally, a specific robustness analysis reveals that, even when the number of labeled examples is very low with respect to the number of unlabeled examples, the proposed method is almost always able to outperform its single-task counterpart.

A Face Recognition Algorithm Based on Dual-Channel Images and VGG-cut Model

In the embedded system environment, both large amount of face image data and the slow recognition process speed are the main problem facing face recognition of end devices. This paper proposes a face recognition algorithm based on dual-channel images and adopts a cropped VGG-like model referred as VGG-cut model for predicting. The training set uses the same single-layer images of the same person combined into dual channels as a positive example, and single-layer images of different people are combined into dual channels as a negative example. After model training, the trained face verification model is used as the basis of face recognition, and the final recognition result can be obtained through loop matching and similarity ranking. The experimental results on a RISC-V embedded FPGA platform show that compared with the ResNet model called in Dlib, the VGG-like model and MobileFaceNet trained by Keras, our algorithm is increased by 240×, 88×, and 19× in recognition speed, respectively without significant accuracy reduction.

HOLISMOKES

We present a systematic search for wide-separation (with Einstein radiusθE ≳ 1.5″), galaxy-scale strong lenses in the 30 000 deg2of the Pan-STARRS 3πsurvey on the Northern sky. With long time delays of a few days to weeks, these types of systems are particularly well-suited for catching strongly lensed supernovae with spatially-resolved multiple images and offer new insights on early-phase supernova spectroscopy and cosmography. We produced a set of realistic simulations by painting lensed COSMOS sources on Pan-STARRS image cutouts of lens luminous red galaxies (LRGs) with redshift and velocity dispersion known from the sloan digital sky survey (SDSS). First, we computed the photometry of mock lenses ingribands and applied a simple catalog-level neural network to identify a sample of 1 050 207 galaxies with similar colors and magnitudes as the mocks. Second, we trained a convolutional neural network (CNN) on Pan-STARRSgriimage cutouts to classify this sample and obtain sets of 105 760 and 12 382 lens candidates with scores ofpCNN > 0.5 and > 0.9, respectively. Extensive tests showed that CNN performances rely heavily on the design of lens simulations and the choice of negative examples for training, but little on the network architecture. The CNN correctly classified 14 out of 16 test lenses, which are previously confirmed lens systems above the detection limit of Pan-STARRS. Finally, we visually inspected all galaxies withpCNN > 0.9 to assemble a final set of 330 high-quality newly-discovered lens candidates while recovering 23 published systems. For a subset, SDSS spectroscopy on the lens central regions proves that our method correctly identifies lens LRGs atz ∼ 0.1–0.7. Five spectra also show robust signatures of high-redshift background sources, and Pan-STARRS imaging confirms one of them as a quadruply-imaged red source atzs = 1.185, which is likely a recently quenched galaxy strongly lensed by a foreground LRG atzd = 0.3155. In the future, high-resolution imaging and spectroscopic follow-up will be required to validate Pan-STARRS lens candidates and derive strong lensing models. We also expect that the efficient and automated two-step classification method presented in this paper will be applicable to the ∼4 mag deepergristacks from theRubinObservatory Legacy Survey of Space and Time (LSST) with minor adjustments.

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Multitask deep neural networks learn to predict ligand-target binding by example, yet public pharmacological data sets are sparse, imbalanced, and approximate. We constructed two hold-out benchmarks to approximate temporal and drug-screening test scenarios, whose characteristics differ from a random split of conventional training data sets. We developed a pharmacological data set augmentation procedure, Stochastic Negative Addition (SNA), which randomly assigns untested molecule-target pairs as transient negative examples during training. Under the SNA procedure, drug-screening benchmark performance increases from R2 = 0.1926 ± 0.0186 to 0.4269 ± 0.0272 (122%). This gain was accompanied by a modest decrease in the temporal benchmark (13%). SNA increases in drug-screening performance were consistent for classification and regression tasks and outperformed y-randomized controls. Our results highlight where data and feature uncertainty may be problematic and how leveraging uncertainty into training improves predictions of drug-target relationships.

Az egészséges környezethez való jog, mint alapvető emberi jog – Lehetséges megközelítések az egyes emberi jogvédelmi mechanizmusok gyakorlata alapján, különös tekintettel az őslakos népeket érintő kérdésekre

The aim of the author is to examine the nexus between the development of the indigenous peoples’ rights – which came like a blast – and the prevalence of the right to a healthy environment. As another goal, the author aims to reveal how the protection of indigenous peoples’ rights can facilitate the realisation of environmental protection and sustainable development goals. In order to achieve his goals, the author – after clarifying the definitions in the first chapter – introduces the indigenous peoples and healthy environment related practice of the three regional human rights protection mechanisms – namely the European, the Inter-American and the African – in the second chapter. In the third chapter, the author briefly introduces those rights of the indigenous peoples, which could serve the protection of indigenous peoples’ rights and the positive and negative examples. The author draws his conclusions in the last chapter.

In what Sense Can the Scientifically Driven Theology Be Considered as a Continuation of the Doctrinal Tradition?

The central goal of the presented article is to show that the type of rationality proper to the method of the contemporary sciences yields a unique conceptual environment in which the spirit of rationality instilled to the theological thought by its encounter with the Greek philosophy finds its natural expansion. At the outset, the origins of the Greek rationality in the Ionian school of philosophy are briefly discussed in order to illuminate their adequacy for the exposition and defense of doctrine in the times of the early Church Fathers. Next, the specificity of the scholastic method of St. Thomas Aquinas briefly surveyed to indicate the nature of the unique harmony between faith and reason achieved by the Angelic Doctor. In the following step, the role of rationality in theology is gleaned through negative examples of its elimination as evident in nominalism and the subsequent Lutheran intervention. This is followed by a short discourse into of the origins and the specificity of the contemporary scientific method with particular emphasis on the method’s unique potential to expand and deepen the Greek rationality. Finally, a case study of the evolutionary theology is offered in which the implementation of the dynamic (evolutionary) picture of the Universe as the conceptual foundation opens up new and profound ways of understanding the Universe’s meaning and the meaning of man in particular. Thus the spirit of the hellenistic rationality in theology is not only preserved but also significantly enhanced.

A systematic method to building Dirac quantum walks coupled to electromagnetic fields

A quantum walk whose continuous limit coincides with Dirac equation is usually called a Dirac quantum walk (DQW). A new systematic method to build DQWs coupled to electromagnetic (EM) fields is introduced and put to test on several examples of increasing difficulty. It is first used to derive the EM coupling of a 3D walk on the cubic lattice. Recently introduced DQWs on the triangular lattice are then re-derived, showing for the first time that these are the only DQWs that can be defined with spinors living on the vertices of these lattices. As a third example of the method’s effectiveness, a new 3D walk on a parallelepiped lattice is derived. As a fourth, negative example, it is shown that certain lattices like the rhombohedral lattice cannot be used to build DQWs. The effect of changing representation in the Dirac equation is also discussed. Furthermore, we show the simulation of the established DQWs can be efficiently implemented on a quantum computer.

Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning.

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It is challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high-dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Notably, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function datasets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.

On the Construction of Web NER Model Training Tool based on Distant Supervision

Named entity recognition (NER) is an important task in natural language understanding, as it extracts the key entities (person, organization, location, date, number, etc.) and objects (product, song, movie, activity name, etc.) mentioned in texts. However, existing natural language processing (NLP) tools (such as Stanford NER) recognize only general named entities or require annotated training examples and feature engineering for supervised model construction. Since not all languages or entities have public NER support, constructing a tool for NER model training is essential for low-resource language or entity information extraction. In this article, we study the problem of developing a tool to prepare training corpus from the Web with known seed entities for custom NER model training via distant supervision. The major challenge of automatic labeling lies in the long labeling time due to large corpus and seed entities as well as the concern to avoid false positive and false negative examples due to short and long seeds. To solve this problem, we adopt locality-sensitive hashing (LSH) for various length of seed entities. We conduct experiments on five types of entity recognition tasks, including Chinese person names, food names, locations, points of interest (POIs), and activity names to demonstrate the improvements with the proposed Web NER model construction tool. Because the training corpus is obtained by automatic labeling of the seed entity–related sentences, one could use either the entire corpus or the positive only sentences for model training. Based on the experimental results, we found the decision should depend on whether traditional linear chained conditional random fields (CRF) or deep neural network–based CRF is used for model training as well as the completeness of the provided seed list.

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors

BackgroundBrain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are detected by radiologists based on their clinical experience. However, this process is inefficient. This paper proposes a machine learning-based method to 1) determine the presence of a tumor, 2) automatically segment the tumor, and 3) classify it as benign or malignant. MethodsWe implemented an Extended Kalman Filter with Support Vector Machine (EKF-SVM), an image analysis platform based on an SVM for automated brain tumor detection. A development dataset of 120 patients which supported by Tiantan Hospital was used for algorithm training. Our machine learning algorithm has 5 components as follows. Firstly, image standardization is applied to all the images. This is followed by noise removal with a non-local means filter, and contrast enhancement with improved dynamic histogram equalization. Secondly, a gray-level co-occurrence matrix is utilized for feature extraction to get the image features. Thirdly, the extracted features are fed into a SVM for classify the MRI initially, and an EKF is used to classify brain tumors in the brain MRIs. Fourthly, cross-validation is used to verify the accuracy of the classifier. Finally, an automatic segmentation method based on the combination of k-means clustering and region growth is used for detecting brain tumors. ResultsWith regard to the diagnostic performance, the EKF-SVM had a 96.05% accuracy for automatically classifying brain tumors. Segmentation based on k-means clustering was capable of identifying the tumor boundaries and extracting the whole tumor. ConclusionThe proposed EKF-SVM based method has better classification performance for positive brain tumor images, which was mainly due to the dearth of negative examples in our dataset. Therefore, future work should obtain more negative examples and investigate the performance of deep learning algorithms such as the convolutional neural networks for automatic diagnosis and segmentation of brain tumors.

What Can We Learn from How Socrates Dominates Thrasymachus in Plato’s Republic?

In this paper I will show how Socrates dominated Thrasymachus by failing to create a relationship with Thrasymachus in which meaningful learning could occur. Although this is a negative example of how a teacher did not help a student learn, from it I believe that we can learn the importance of creating relationships with our students to help them learn and thereby modeling for them how to create such relationships with their students. Listening to, respecting, and responding to our students as they are, to their questions, and to their motivations to learn or not to learn are essential ways for us to help them learn what they and we believe is important for living fulfilling lives. This is justice in teaching.

Pairwise Acquisition Prediction with SHAP Value Interpretation

Unexpected acquisitions can occur in the marketplace, which can significantly impact the market environment. Thus, predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate various non-financial factors and to address the lack of negative samples. Concerning the former, we proposed including a network feature that represented the importance of an acquirer and an acquiree in the investment and category networks, as well as a company relation feature associated with their similarity and closeness. Considering the latter requirement, as negative examples, we set the pairs of acquirers and acquirees with the features that were similar to those of positive examples. This allowed learning minor differences between the companies selected for acquisition and the candidate ones. We evaluated our proposed prediction model using 2000-2018 acquisition logs collected from CrunchBase. Based on the analysis of the high SHapley additive explanation (SHAP) value features, we found that the newly considered network and company relation features had high significance (10 out of 22 top key features). We also clarified how these novel features contributed to the prediction of acquisition occurrence by interpreting the SHAP value.

Efficient multiple constraint acquisition

Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. In this paper we propose algorithmic and heuristic methods to deal with both these issues. We first describe an algorithm, called MQuAcq, that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. A detailed theoretical analysis of the proposed algorithm is also presented. %We also present a technique that boosts the performance of constraint acquisition by reducing the number of queries significantly. Then we turn our attention to query generation which is a significant but rather overlooked part of the acquisition process. We describe %in detail how query generation in a typical constraint acquisition system operates, and we propose heuristics for improving its efficiency. Experiments from various domains demonstrate that our resulting algorithm that integrates all the new techniques does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, in average query generation time as well as in total run time, and also largely alleviates the premature convergence problem.

Regulating Principles of Disclosure of Information to Shareholders under G20 / ОECD Principles

The present research analyses Principles of Corporate Governance G20 OECD in the context of disclosure of information to the specified group of stakeholders – shareholders of a corporation. The analysis of court practice and both positive and negative examples of disclosure of information to shareholders is provided. The core part of the research is dedicated to the problem of interaction between majority shareholder & minority shareholders or the corporation itself while getting the information with application of British legislation to the matter, systems of “hard” and “soft” law. The author resumes on the necessity of practical application and control of particular recommendations of the Principles of Corporate Governance G20/OECD, i.e. cumulative voting for the board of directors.