Your dog will trust you less when you're angry: Canines lose confidence in people who show a negative attitude

Daily Mail - Science & tech

When humans have a bad attitude, their canine companions might not be so quick to follow their lead. In a recent study, researchers conducted a series of experiments to see the effects of human emotions on a dog's response to pointing cues. While adding a positive expression to the gesture wasn't found to increase a dog's ability to locate a treat, the dogs hesitated before exploring when responding to a person with a negative disposition. When humans have a bad attitude, their canine companions might not be so quick to follow their lead. In a recent study, researchers found that adding a positive expression to pointing didn't increase a dog's ability to locate a treat, but the dogs hesitated before exploring when responding to a person with a negative disposition Researchers studied the response of dogs to an unfamiliar adult gesturing toward two covered bowls.


Can YOU tell which if these women are ill just by looking at them?

Daily Mail - Science & tech

It may be possible to spot if your relative, friend or colleague is ill just by looking at them, research suggests. Scientists injected volunteers with either E.coli or a placebo before asking others how sick they looked two hours later. The infected patients were judged to look'significantly worse', with people noticing their drooping eyelids and mouths. They also showed more negative facial expressions, which may be brought on by inflammation as the immune system fights off the infection. Researchers believe humans may have evolved the ability to pick up on subtle cues that suggest someone is contagious to avoid getting ill.


Identifying Opinion Holders for Question Answering in Opinion Texts

AAAI Conferences

Question answering in opinion texts has so far mostly concentrated on the identification of opinions and on analyzing the sentiment expressed in opinions. In this paper, we address another important part of Question Answering (QA) in opinion texts: finding opinion holders. Holder identification is a central part of full opinion identification and can be used independently to answer several opinion questions such as "Is China supporting Bush's war on Iraq?" and "Do Iraqi people want U.S. troops in their soil?". Our system automatically learns the syntactic features signaling opinion holders using a Maximum Entropy ranking algorithm trained on human annotated data. Using syntactic parsing features, our system achieved 64% accuracy on identifying the holder of opinions in the MPQA dataset.


Norfolk Students Uncover Century-Old Glass Negatives

U.S. News

Most of the children wear somber expressions as if to say they would rather be running and playing than posing. It's doubtful that any of them considered the fact that 100 years later, a couple of curious high school students would discover clues as to the children's lifestyles and habits by closely examining their expressions, clothing and other details of the photo.


KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis

arXiv.org Artificial Intelligence

Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.