Multilevel metric rank match for person re-identification

Metric learning is one of the important ways to improve the person re-identification (ReID) accurate, of which triplet loss is the most effect metric learning method. However, triplet loss only ranks the extracted feature at the end of the network, in this paper, we propose a multilevel metric rank match (MMRM) method, which ranks the extracted feature on multilevel of the network. At each rank level, the extracted features are ranked to find the hard sample pairs and the backward dissemination triplet loss. Each rank level has different penalize value to adjust the network, in which the value is bigger with the deeper level of the whole network. Experiment results on CUHK03, Market1501 and DukeMTMC datasets indicate that The MMRM algorithm can outperform the previous state-of-the-arts.     

Application of deep transfer learning for automated brain abnormality classification using MR images

Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.     

Latent Growth Models of Individual Change: The Case of Newcomer Adjustment

One commonality to various theoretical frameworks concerning employee work adjustment is change in employee-organization linkages over time, yet few empirical tests of employee work adjustment theories have adequately operationalized change. We develop and test a model of longitudinal change on one particular form of employee adjustment, employee attachment, operationalized in terms of O'Reilly and Chatman's (1986) triparite structure of commitment, using second-order factor (SOF) latent growth modeling (LGM). Results were generally supportive of the theoretical model and illustrate several advantages to using SOF LGM to measure longitudinal change. Copyright 2000 Academic Press.     

A computational account of deep dysphasia: evidence from a single case study.

We present a case study of a patient, NC, who demonstrates the defining characteristics of deep dysphasia including semantic errors in repetition and an inability to repeat nonwords. In addition, NC's single word repetition and lexical decision performances are influenced by the imageability of the word input. NC also demonstrates a severely restricted phonological short-term memory (one digit, one word). Although his phonological discrimination is good in a minimal pairs judgment task, it becomes impaired when a delay is imposed or rehearsal is prevented between presentation of each member of a pair. NC's output is fluent but contains many formal paraphasias and neologisms. NC's total language profile is evaluated within the framework of Dell's (1986) interactive spreading activation model of language production. Adapting this output model to input processes, we account for all of NC's deep dysphasic symptoms as well as his pattern of production in a way that is more parsimonious than other attempts to model this disorder. In particular, we suggest that the semantic and formal paraphasias in naming and repetition result from a pathological increase in the rate of decay of primed nodes in the semantic-lexical-phonological network. This rapid decay increases the probability that phonologically and/or semantically related lexical nodes primed by top-down and bottom-up feedback during the operation of lexical activation and retrieval will be activated and selected instead of the lexical target. The advantages of using this model to account for aphasic symptoms and the implications for other lexical theories are discussed.     

Amalgamation of future time orientation,epistemological beliefs,achievement goals and study strategies: Empirical evidence established

Background Recently research evidence emphasizes two main lines of inquiry, namely the relations between future time perspective (FTP), achievement goals (mastery, performance‐approach, and performance‐avoidance) and study processing strategies, and the relations between epistemological beliefs, achievement goals and study processing strategies. To date, however, there have been very few attempts made to amalgamate these two strands of inquiry within one study and how they in totality determine the success of academic learning. Aims This study proposed and tested a conceptual model of relationships among FTP, epistemological beliefs, achievement goals (mastery, performance‐approach, and performance‐avoidance), study processing strategies and academic performance. Sample Two hundred and seventy‐five tertiary second‐year students (167 females, 108 males) enrolled in a university in the Pacific participated in this study. Method Likert‐scale inventories were used to elicit relevant data from students; for example, the epistemological questionnaire (EQ; Schommer, 1990 ) and the Zimbardo time perspective inventory ( Zimbardo & Boyd, 1999 ). Academic performance was collated from students' course and final exam marks in the course educational psychology. LISREL 8.72 and SPSS 15 was used to test and evaluate the conceptual model proposed. Results Latent variables procedures supported the conceptual model in general, although not all hypothesized paths were significant. MANOVA indicated no gender differences in the five theoretical frameworks or academic performance. Discussion The determinants of academic performance from our findings are deep and surface processing strategies. Furthermore, the established supports the mediating roles of deep processing strategies, mastery goals, and performance‐approach and performance‐avoidance goals.     

Rethinking the Role of Positive Affect in Self-Regulation

This introduction to the two-part special issue reviews recent evidence that suggests that positive mood may play a beneficial, multifaceted, and flexible role in self-regulatory processes that cannot be explained by most current theories. First, under some conditions positive mood seems to facilitate careful processing of goal-relevant information, even negative information. Second, the relation of positive mood to cognition and behavior seems to be strongly moderated by goal-relevant features of the task context. Three frameworks (mood as input, processing advantages conferred by positive mood, and mood as resource) that may account for these facilitating effects of positive mood on self-regulation are discussed.     

The effects of contrastive encoding of semantic information on children's memory for words

The effect of context-sensitive contrastive encoding of semantic item information at input on children's memory for words is examined. In two experiments, second and fifth graders and college adults were presented with word triplets varying in categorical relatedness. Each triplet contained a target item (eg., canary) that was highly related (hawk, eagle, canary), moderately related (goose, swan, canary), or unrelated (river, lake, canary) to the other triplet members. Subjects were required to either isolate and remember the odd target word (oddity encoding) or simply read and remember the word identified by the experimenter (read encoding). Both recall and recognition were tested. The results showed that recall and recognition varied as a function of decision difficulty in isolating the target members. Developmental differences in both absolute retention levels and the patterns associated with decision difficulty were maximized in the read and minimized in the oddity encoding condition. This suggests that children differ from adults in the degree to which they perform distinctive contrastive encoding of item specific information at input, and that retention varies as a result.     

How Do Therapists Understand Their Client’s Problem and Its Resolution: Objectification in Theories of Change

Images and metaphors help to structure the therapist’s belief system in two ways. First, images represent strategies used by therapists in order to simplify the most abstract theoretical concepts. Second, images provide a system according for organizing the information about a client and anticipate the patterns of client change. Within the theoretical frameworks of social representations and of goal directed action theory, the present study explores the metaphorical aspects linked with therapeutic knowledge. This study aims to reconstruct the social representations that therapists use to objectify their theoretical model. The present research relies on a qualitative methodology. The results show that therapists from different orientations rely on different metaphors although, in certain conditions, they tend to share a more general representational system. Implications for psychotherapy training and clinical practice are considered.     

Integrating Divine Attachment Theory and the Enneagram to Help Clients of Abuse Heal in Their Images of Self,Others, and God

For victims of childhood abuse, healing their unconscious images of themselves, others, and God is of utmost importance in therapeutic work. Addressing the healing from both a scientific and a spiritual-contemplative approach is ideal in the context of spiritually integrated psychotherapy. Combining the frameworks of divine attachment theory and the Enneagram can help abused clients on their journey towards holistic healing in a way that addresses both the scientific and soulful dimensions of their deep unconscious wounds. This integration has the potential to heal multiple aspects of wounding, including unconscious images of clients, their offenders, and God. It also has the potential to dignify clients by showing how their unique God-given personality has a finite correspondence to God’s very own infinite personality and by outlining potential points of existential connection between aspects of their personality traits and the personality traits of the eternal God. Finally, clients can also grow in empathy and compassion for their abusers, which will help in the process of forgiveness.     

Modeling recognition memory using the similarity structure of natural input

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed natural input to stored representations. By complementing the recognition memory process with a perceptual front end, the NIM model is able to make predictions about memorability based directly on individual natural stimuli. We demonstrate that the NIM model is able to simulate experimentally obtained similarity ratings and recognition memory for individual stimuli (i.e., face images).     

Detection of subtype blood cells using deep learning

Deep Learning has already shown power in many application fields, and is accepted by more and more people as a better approach than the traditional machine learning models. In particular, the implementation of deep learning algorithms, especially Convolutional Neural Networks (CNN), brings huge benefits to the medical field, where a huge number of images are to be processed and analyzed. This paper aims to develop a deep learning model to address the blood cell classification problem, which is one of the most challenging problems in blood diagnosis. A CNN-based framework is built to automatically classify the blood cell images into subtypes of the cells. Experiments are conducted on a dataset of 13k images of blood cells with their subtypes, and the results show that our proposed model provide better results in terms of evaluation parameters.     

A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems.In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes.In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models.Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.     

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method.MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET).ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process.ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.     

When Parents Refuse: Resolving Entrenched Disagreements Between Parents and Clinicians in Situations of Uncertainty and Complexity

When shared decision making breaks down and parents and medical providers have developed entrenched and conflicting views, ethical frameworks are needed to find a way forward. This article reviews the evolution of thought about the best interest standard and then discusses the advantages of the harm principle (HP) and the zone of parental discretion (ZPD). Applying these frameworks to parental refusals in situations of complexity and uncertainty presents challenges that necessitate concrete substeps to analyze the big picture and identify key questions. I outline and defend a new decision-making tool that includes three parts: identifying the nature of the disagreement, checklists for key elements of the HP and ZPD, and a “think list” of specific questions designed to enhance use of the HP and ZPD in clinical decision making. These tools together will assist those embroiled in complex disagreements to disentangle the issues to find a path to resolution.     

Contrasting graded effects of semantic similarity and association across the concreteness spectrum

Evidence from healthy individuals and neuropsychological patients with deep dyslexia or semantic refractory access dysphasia suggests that abstract and concrete concepts have different dependencies upon associative and similarity-based representational frameworks. However, the importance of information about semantic similarity for concepts that lie across the full concreteness spectrum has not been investigated. Here we report the performance of healthy individuals on an odd-one-out task involving semantically related word triplets that showed continuous variation for the key variables of concreteness, similarity strength, and association strength. In addition, data from a stroke aphasic patient tested on a matching-to-sample task based on the same abstract, middle-concreteness, and concrete stimuli are also presented. The effects of similarity and association strength upon performance were both shown to interact significantly with concreteness, but in opposite directions. The effect of semantic similarity increased with concreteness but the effect of association decreased with concreteness. This research provides further evidence for the proposal that abstract and concrete words have different dependencies upon associative and similarity-based information. It also develops the proposal by providing data that are consistent with a graded and not binary or ungraded model of the relationship between concreteness and these two forms of semantic relationship.     

Contrasting graded effects of semantic similarity and association across the concreteness spectrum

Evidence from healthy individuals and neuropsychological patients with deep dyslexia or semantic refractory access dysphasia suggests that abstract and concrete concepts have different dependencies upon associative and similarity-based representational frameworks. However, the importance of information about semantic similarity for concepts that lie across the full concreteness spectrum has not been investigated. Here we report the performance of healthy individuals on an odd-one-out task involving semantically related word triplets that showed continuous variation for the key variables of concreteness, similarity strength, and association strength. In addition, data from a stroke aphasic patient tested on a matching-to-sample task based on the same abstract, middle-concreteness, and concrete stimuli are also presented. The effects of similarity and association strength upon performance were both shown to interact significantly with concreteness, but in opposite directions. The effect of semantic similarity increased with concreteness but the effect of association decreased with concreteness. This research provides further evidence for the proposal that abstract and concrete words have different dependencies upon associative and similarity-based information. It also develops the proposal by providing data that are consistent with a graded and not binary or ungraded model of the relationship between concreteness and these two forms of semantic relationship.     

What is the link between language and spatial images? Behavioral and neural findings in blind and sighted individuals

In order to find objects or places in the world, multiple sources of information, such as visual input, auditory input and asking for directions, can help you. These different sources of information can be converged into a spatial image, which represents configurational characteristics of the world. This paper discusses the findings on the nature of spatial images and the role of spatial language in generating these spatial images in both blind and sighted individuals. Congenitally blind individuals have never experienced visual input, yet they are able to perform several tasks traditionally associated with spatial imagery, such as mental scanning, mental pathway completions and mental clock time comparison, though perhaps not always in a similar manner as sighted. Therefore, they offer invaluable insights into the exact nature of spatial images. We will argue that spatial imagery exceeds the input from different input modalities to form an abstract mental representation while maintaining connections with the input modalities. This suggests that the nature of spatial images is supramodal, which can explain functional equivalent results from verbal and perceptual inputs for spatial situations and subtle to moderate behavioral differences between the blind and sighted.     

婚姻冲突与儿童问题行为关系研究的理论进展

大量研究指出婚姻冲突与儿童问题行为紧密相关,但在二者关系的解释上有不同观点。其中代表性的理论有社会学习理论、间接作用观点、认知-背景理论和情绪安全假设等。文中详细介绍了4个理论的基本观点并分析了其优缺点。综合这些理论,作者在理解婚姻冲突和儿童问题行为时应该坚持如下3个观点:第一,系统和生态的观点;第二,主客体相互作用的观点;第三,情感和认知并重的观点。