The Delusional Hedge Algorithm as a Model of Human Learning From Diverse Opinions.

Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground-truth outcome. We consider how people can learn which opinions to trust in such scenarios by extending the hedge algorithm: a classic solution for learning from diverse information sources. We first introduce a semi-supervised variant we call the delusional hedge capable of learning from both supervised and unsupervised experiences.

Diverging Effects of Violence Exposure and Psychiatric Symptoms on Amygdala-Prefrontal Maturation During Childhood and Adolescence.

Background: Violence exposure during childhood and adolescence is associated with increased prevalence and severity of psychopathology. Neurobiological correlates suggest that abnormal maturation of emotion-related brain circuitry, such as the amygdala-prefrontal cortex (PFC) circuit, may underlie the development of psychiatric symptoms after exposure. However, it remains unclear how amygdala-PFC circuit maturation is related to psychiatric risk in the context of violence.

BAP31 Regulates Wnt Signaling to Modulate Cell Migration in Lung Cancer.

B-cell receptor-associated protein 31 (BAP31) has been shown to overexpress in a wide range type of cancers. The present study aims to investigate the role of BAP31 on migration in lung cancer. Results showed that the migration of BAP31 knockdown cells was weaken than the control cells.

Magnesium-Assisted Cisplatin Inhibits Bladder Cancer Cell Survival by Modulating Wnt/β-Catenin Signaling Pathway.

Magnesium, an essential mineral micronutrient, plays a role in the activation of various transporters and enzymes. The present study aimed to investigate the possibility of applying magnesium to enhance the efficacy of cisplatin which is still ranked as one of the major chemotherapeutic drugs for bladder cancer patients. Results showed that the survival rate and colony formation of bladder cancer cells were reduced by combinatorial treatment with cisplatin and magnesium chloride (MgCl).

Differential Patterns of Delayed Emotion Circuit Maturation in Abused Girls With and Without Internalizing Psychopathology.

Objective: Childhood abuse represents one of the most potent risk factors for developing psychopathology, especially in females. Evidence suggests that exposure to early-life adversity may be related to advanced maturation of emotion processing neural circuits. However, it remains unknown whether abuse is related to early circuit maturation and whether maturation patterns depend on the presence of psychopathology.

High-density lipoprotein cholesterol concentration and acute kidney injury after noncardiac surgery.

Background: Abnormal High-density Lipoprotein Cholesterol Concentration is closely related to postoperative acute kidney injury (AKI) after cardiac surgeries. The purpose of this study was to analyze the relationship between High-density Lipoprotein Cholesterol Concentration and acute kidney injury after non-cardiac surgeries.

Method: This was a single-center cohort study for elective non-cardiac non-kidney surgery from January 1, 2012, to December 31, 2017.

Model-guided search for optimal natural-science-category training exemplars: A work in progress.

Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category.

Are Female Applicants Disadvantaged in National Institutes of Health Peer Review? Combining Algorithmic Text Mining and Qualitative Methods to Detect Evaluative Differences in R01 Reviewers' Critiques.

Background: Women are less successful than men in renewing R01 grants from the National Institutes of Health. Continuing to probe text mining as a tool to identify gender bias in peer review, we used algorithmic text mining and qualitative analysis to examine a sample of critiques from men's and women's R01 renewal applications previously analyzed by counting and comparing word categories.

Methods: We analyzed 241 critiques from 79 Summary Statements for 51 R01 renewals awarded to 45 investigators (64% male, 89% white, 80% PhD) at the University of Wisconsin-Madison between 2010 and 2014.

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

Background: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a semi-supervised learning strategy known as the self-training model, and applied this method to genomic prediction of residual feed intake (RFI) in dairy cattle.

Methods: We describe a self-training model that is wrapped around a support vector machine (SVM) algorithm, which enables it to use data from animals with and without measured phenotypes.

Analysis of National Institutes of Health R01 Application Critiques, Impact, and Criteria Scores: Does the Sex of the Principal Investigator Make a Difference?

Purpose: Prior text analysis of R01 critiques suggested that female applicants may be disadvantaged in National Institutes of Health (NIH) peer review, particularly for renewals. NIH altered its review format in 2009. The authors examined R01 critiques and scoring in the new format for differences due to principal investigator (PI) sex.

Drift in children's categories: when experienced distributions conflict with prior learning.

Psychological intuitions about natural category structure do not always correspond to the true structure of the world. The current study explores young children's responses to conflict between intuitive structure and authoritative feedback using a semi-supervised learning (Zhu et al., 2007) paradigm.

An improved filter-u least mean square vibration control algorithm for aircraft framework.

Active vibration control of aerospace vehicle structures is very a hot spot and in which filter-u least mean square (FULMS) algorithm is one of the key methods. But for practical reasons and technical limitations, vibration reference signal extraction is always a difficult problem for FULMS algorithm. To solve the vibration reference signal extraction problem, an improved FULMS vibration control algorithm is proposed in this paper.

Human semi-supervised learning.

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner.

Can semi-supervised learning explain incorrect beliefs about categories?

Three experiments with 88 college-aged participants explored how unlabeled experiences-learning episodes in which people encounter objects without information about their category membership-influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants' implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory for the items encountered during the supervised learning phase.

Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors.

Users of topic modeling methods often have knowledge about the composition of words that should have high or low probability in various topics. We incorporate such domain knowledge using a novel Dirichlet Forest prior in a Latent Dirichlet Allocation framework. The prior is a mixture of Dirichlet tree distributions with special structures.