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Problem based learning

Anh Pham, Raviv Raich, Xiaoli Fern
Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach...
January 5, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Reem M Ghandour, Katherine Flaherty, Ashley Hirai, Vanessa Lee, Deborah Klein Walker, Michael C Lu
OBJECTIVES: Infant mortality remains a significant public health problem in the U.S. The Collaborative Improvement & Innovation Network (CoIIN) model is an innovative approach, using the science of quality improvement and collaborative learning, which was applied across 13 Southern states in Public Health Regions IV and VI to reduce infant mortality and improve birth outcomes. We provide an in-depth discussion of the history, development, implementation, and adaptation of the model based on the experience of the original CoIIN organizers and participants...
January 18, 2017: Maternal and Child Health Journal
Catharina Nordin, Peter Michaelson, Margareta K Eriksson, Gunvor Gard
BACKGROUND: Patients' participation in their health care is recognized as a key component in high-quality health care. Persons with persistent pain are recommended treatments with a cognitive approach from a biopsychosocial explanation of pain, in which a patient's active participation in their rehabilitation is in focus. Web-based interventions for pain management have the potential to increase patient participation by enabling persons to play a more active role in rehabilitation. However, little is known about patients' experiences of patient participation in Web-based interventions in clinical practice...
January 18, 2017: Journal of Medical Internet Research
Juan M J Ramos
It has recently been suggested that the different cortices of the medial temporal lobe support a mixture of object and spatial processing functions, challenging the anterior model that emphasized a strict functional differentiation between regions. However, for some structures, the perirhinal cortex (Prh) for example, a number of studies using lesion methods have shown a profound deficit exclusively in tasks involving object learning but not allocentric spatial learning. It may be that the learning paradigms used in previous studies have not been sensitive enough to detect a possible allocentric deficit in Prh-lesioned animals...
January 18, 2017: Hippocampus
Jenna Jacob, Davide De Francesco, Jessica Deighton, Duncan Law, Miranda Wolpert, Julian Edbrooke-Childs
Goal formulation and tracking may support preference-based care. Little is known about the likelihood of goal formulation and tracking and associations with care satisfaction. Logistic and Poisson stepwise regressions were performed on clinical data for N = 3757 children from 32 services in the UK (M age = 11; SDage = 3.75; most common clinician-reported presenting problem was emotional problems = 55.6%). Regarding the likelihood of goal formulation, it was more likely for pre-schoolers, those with learning difficulties or those with both hyperactivity disorder and conduct disorder...
January 18, 2017: European Child & Adolescent Psychiatry
Xujun Liang, Pengfei Zhang, Lu Yan, Ying Fu, Fang Peng, Lingzhi Qu, Meiying Shao, Yongheng Chen, Zhuchu Chen
MOTIVATION: Exploring the potential curative effects of drugs is crucial for effective drug development. Previous studies have indicated that integration of multiple types of information could be conducive to discovering novel indications of drugs. However, how to efficiently identify the mechanism behind drug-disease associations while integrating data from different sources remains a challenging problem. RESULTS: In this research, we present a novel method for indication prediction of both new drugs and approved drugs...
January 17, 2017: Bioinformatics
Yiding Lu, Yufan Guo, Anna Korhonen
BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc...
January 17, 2017: BMC Bioinformatics
Jodie R Fox
HOW TO OBTAIN CONTACT HOURS BY READING THIS ARTICLE INSTRUCTIONS XX contact hours will be awarded by Villanova University College of Nursing upon successful completion of this activity. A contact hour is a unit of measurement that denotes 60 minutes of an organized learning activity. This is a learner-based activity. Villanova University College of Nursing does not require submission of your answers to the quiz. A contact hour certificate will be awarded once you register, pay the registration fee, and complete the evaluation form online at http://goo...
January 17, 2017: Journal of Gerontological Nursing
Minnan Luo, Feiping Nie, Xiaojun Chang, Yi Yang, Alexander G Hauptmann, Qinghua Zhang
Robust principal component analysis (PCA) is one of the most important dimension-reduction techniques for handling high-dimensional data with outliers. However, most of the existing robust PCA presupposes that the mean of the data is zero and incorrectly utilizes the average of data as the optimal mean of robust PCA. In fact, this assumption holds only for the squared [Formula: see text]-norm-based traditional PCA. In this letter, we equivalently reformulate the objective of conventional PCA and learn the optimal projection directions by maximizing the sum of projected difference between each pair of instances based on [Formula: see text]-norm...
January 17, 2017: Neural Computation
Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference...
January 17, 2017: Neural Computation
E Emary, Hossam M Zawbaa, Crina Grosan
In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis...
January 10, 2017: IEEE Transactions on Neural Networks and Learning Systems
Gwenole Quellec, Guy Cazuguel, Beatrice Cochener, Mathieu Lamard
Multiple-Instance Learning (MIL) is a recent machine learning paradigm that is particularly well suited to Medical Image and Video Analysis (MIVA) tasks. Based solely on class labels assigned globally to images or videos, MIL algorithms learn to detect relevant patterns locally in images or videos. These patterns are then used for classification at a global level. Because supervision relies on global labels, manual segmentations are not needed to train MIL algorithms, unlike traditional Single-Instance Learning (SIL) algorithms...
January 10, 2017: IEEE Reviews in Biomedical Engineering
Yueying Kao, Ran He, Kaiqi Huang
Human beings often assess the aesthetic quality of an image coupled with the identification of the image's semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multitask deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Dihong Gong, Zhifeng Li, Weilin Huang, Xuelong Li, Dacheng Tao
Heterogeneous face recognition is an important yet challenging problem in face recognition community. It refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The major challenge of heterogeneous face recognition lies in the great discrepancies between different image modalities. Conventional face feature descriptors, e.g. LBP, HOG and SIFT, are mostly designed in a handcrafted way and thus generally fail to extract the common discriminant information from the heterogeneous face images...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Dacheng Tao
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, Lei Zhang
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets...
January 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Kousik Kundu, Rolf Backofen
Src homology 2 (SH2) domain is an important subclass of modular protein domains that plays an indispensable role in several biological processes in eukaryotes. SH2 domains specifically bind to the phosphotyrosine residue of their binding peptides to facilitate various molecular functions. For determining the subtle binding specificities of SH2 domains, it is very important to understand the intriguing mechanisms by which these domains recognize their target peptides in a complex cellular environment. There are several attempts have been made to predict SH2-peptide interactions using high-throughput data...
2017: Methods in Molecular Biology
Roy J Adams, Nazir Saleheen, Edison Thomaz, Abhinav Parate, Santosh Kumar, Benjamin M Marlin
The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space...
June 2016: Proceedings of the ... International Conference on Machine Learning
Annamalai Natarajan, Gustavo Angarita, Edward Gaiser, Robert Malison, Deepak Ganesan, Benjamin M Marlin
Mobile health research on illicit drug use detection typically involves a two-stage study design where data to learn detectors is first collected in lab-based trials, followed by a deployment to subjects in a free-living environment to assess detector performance. While recent work has demonstrated the feasibility of wearable sensors for illicit drug use detection in the lab setting, several key problems can limit lab-to-field generalization performance. For example, lab-based data collection often has low ecological validity, the ground-truth event labels collected in the lab may not be available at the same level of temporal granularity in the field, and there can be significant variability between subjects...
September 2016: Proceedings of the ACM International Conference on Ubiquitous Computing
Pietro Giusti, Stephen D Skaper, Laura Facci, Morena Zusso
Neuroplasticity is not only shaped by learning and memory but is also a mediator of responses to neuron attrition and injury (compensatory plasticity). As an ongoing process it reacts to neuronal cell activity and injury, death, and genesis, which encompasses the modulation of structural and functional processes of axons, dendrites, and synapses. The range of structural elements that comprise plasticity includes long-term potentiation (a cellular correlate of learning and memory), synaptic efficacy and remodelling, synaptogenesis, axonal sprouting and dendritic remodelling, and neurogenesis and recruitment...
January 13, 2017: CNS & Neurological Disorders Drug Targets
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