Reviewing the current state of machine learning for artificial intelligence with regards to the use of contextual information

This paper will consider the current state of Machine Learning for Artificial Intelligence, more specifically for applications, such as: Speech Recognition, Game Playing and Image Processing. The artificial world tends to make limited use of context in comparison to what currently happens in human life, while it would benefit from improvements in this area. Additionally, the process of transferring knowledge between application domains is another important area where artificial system can improve. Using context and transferability would have several potential benefits, such as: better ability to function in multiple problem domains, improved understanding of human interaction and stronger grasping of current and potential future situations. While these items are all quite usual to us humans, it is particularly challenging to integrate them into artificial systems, as will be shown within this review. The limitations of our current systems with regards to these topics and the achievable improvements, if these items would be addressed, will also be covered. It is expected that by utilising transferability and/or context, many algorithms in the artificial intelligence field will be able to expand their functionality considerably and should provide for more general purpose learning algorithms

The benefits of contextual information for speech recognition systems

This paper demonstrates the significance of using contextual information in machine learning and speech recognition. While the benefits of contextual information in human communication are widely known, their significance is rarely explored or discussed with a view to their potential for improving speech recognition accuracy. The presented research primarily focuses on an undertaken empirical study that looks at how context affects human communication and understanding. During the study, comparisons between human communication with and without context, have shown overall recognition improvements of over 30% when contextual information is provided. The study has also investigated the importance of the former/middle/latter part of a word towards recognition. These results show that the first two-thirds of a spoken word are key for humans to correctly infer a word. The conclusions from the performed study are then drawn upon to identify useful types of context that can help a machine’s understanding, and how such contextual information can be gathered in speech recognition and machine learning systems. This paper shows that context is not only highly important for human communication, but can easily provide a wealth of information to enhance computational systems

LMDA Canada Newsletter, May 2004

Contents include: Letter from LMDA Canada Chair, Creative Dramaturgy and New Play Development: A Preview of Canadian Theatre Review 119 Summer 2004, LMDA Canada Meeting Friday March 5 2004, LMDA Canada Membership Listhttps://soundideas.pugetsound.edu/lmdanewsletter/1031/thumbnail.jp

Adhesive loose packings of small dry particles

5 pages, 4 figure

Rapid detection of similarity in protein structure and function through contact metric distances

The characterization of biological function among newly determined protein structures is a central challenge in structural genomics. One class of computational solutions to this problem is based on the similarity of protein structure. Here, we implement a simple yet efficient measure of protein structure similarity, the contact metric. Even though its computation avoids structural alignments and is therefore nearly instantaneous, we find that small values correlate with geometrical root mean square deviations obtained from structural alignments. To test whether the contact metric detects functional similarity, as defined by Gene Ontology (GO) terms, it was compared in large-scale computational experiments to four other measures of structural similarity, including alignment algorithms as well as alignment independent approaches. The contact metric was the fastest method and its sensitivity, at any given specificity level, was a close second only to Fast Alignment and Search Tool—a structural alignment method that is slower by three orders of magnitude. Critically, nearly 40% of correct functional inferences by the contact metric were not identified by any other approach, which shows that the contact metric is complementary and computationally efficient in detecting functional relationships between proteins. A public ‘Contact Metric Internet Server’ is provided

Prognostic Breast Cancer Signature Identified from 3D Culture Model Accurately Predicts Clinical Outcome across Independent Datasets

One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds prognostic value for both ER-positive and ER-negative breast cancer. The signature was selected using a novel biological approach and hence holds promise to represent the key biological processes of breast cancer

Atomic-accuracy prediction of protein loop structures through an RNA-inspired ansatz

Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative modeling and protein design, can become intractable as loop lengths exceed 10 residues and if surrounding side-chain conformations are erased. This article introduces a modeling strategy based on a 'stepwise ansatz', recently developed for RNA modeling, which posits that any realistic all-atom molecular conformation can be built up by residue-by-residue stepwise enumeration. When harnessed to a dynamic-programming-like recursion in the Rosetta framework, the resulting stepwise assembly (SWA) protocol enables enumerative sampling of a 12 residue loop at a significant but achievable cost of thousands of CPU-hours. In a previously established benchmark, SWA recovers crystallographic conformations with sub-Angstrom accuracy for 19 of 20 loops, compared to 14 of 20 by KIC modeling with a comparable expenditure of computational power. Furthermore, SWA gives high accuracy results on an additional set of 15 loops highlighted in the biological literature for their irregularity or unusual length. Successes include cis-Pro touch turns, loops that pass through tunnels of other side-chains, and loops of lengths up to 24 residues. Remaining problem cases are traced to inaccuracies in the Rosetta all-atom energy function. In five additional blind tests, SWA achieves sub-Angstrom accuracy models, including the first such success in a protein/RNA binding interface, the YbxF/kink-turn interaction in the fourth RNA-puzzle competition. These results establish all-atom enumeration as a systematic approach to protein structure that can leverage high performance computing and physically realistic energy functions to more consistently achieve atomic resolution.Comment: Identity of four-loop blind test protein and parts of figures 5 have been omitted in this preprint to ensure confidentiality of the protein structure prior to its public releas

Eph receptors in breast cancer: roles in tumor promotion and tumor suppression

Eph receptor tyrosine kinase signaling regulates cancer initiation and metastatic progression through multiple mechanisms. Studies of tumor-cell-autonomous effects of Eph receptors demonstrate their dual roles in tumor suppression and tumor promotion. In addition, Eph molecules function in the tumor microenvironment, such as in vascular endothelial cells, influencing the ability of these molecules to promote carcinoma progression and metastasis. The complex nature of Eph receptor signaling and crosstalk with other receptor tyrosine kinases presents a unique challenge and an opportunity to develop therapeutic intervention strategies for targeting breast cancer

Roflumilast in moderate-to-severe chronic obstructive pulmonary disease treated with longacting bronchodilators: two randomised clinical trials

Background Patients with chronic obstructive pulmonary disease (COPD) have few options for treatment. The efficacy and safety of the phosphodiesterase-4 inhibitor roflumilast have been investigated in studies of patients with moderate-to-severe COPD, but not in those concomitantly treated with longacting inhaled bronchodilators. The effect of roflumilast on lung function in patients with COPD that is moderate to severe who are already being treated with salmeterol or tiotropium was investigated. Methods In two double-blind, multicentre studies done in an outpatient setting, after a 4-week run-in, patients older than 40 years with moderate-to-severe COPD were randomly assigned to oral roflumilast 500 mu g or placebo once a day for 24 weeks, in addition to salmeterol (M2-127 study) or tiotropium (M2-128 study). The primary endpoint was change in prebronchodilator forced expiratory volume in 1s (FEV(1)). Analysis was by intention to treat. The studies are registered with ClinicalTrials.gov, number NCT00313209 for M2-127, and NCT00424268 for M2-128. Findings In the salmeterol plus roflumilast trial, 466 patients were assigned to and treated with roflumilast and 467 with placebo; in the tiotropium plus roflumilast trial, 371 patients were assigned to and treated with roflumilast and 372 with placebo. Compared with placebo, roflumilast consistently improved mean prebronchodilator FEV(1) by 49 mL (p<0.0001) in patients treated with salmeterol, and 80 mL (p<0.0001) in those treated with tiotropium. Similar improvement in postbronchodilator FEV(1) was noted in both groups. Furthermore, roflumilast had beneficial effects on other lung function measurements and on selected patient-reported outcomes in both groups. Nausea, diarrhoea, weight loss, and, to a lesser extent, headache were more frequent in patients in the roflumilast groups. These adverse events were associated with increased patient withdrawal. Interpretation Roflumilast improves lung function in patients with COPD treated with salmeterol or tiotropium, and could become an important treatment for these patients

Modelling of the effect of ELMs on fuel retention at the bulk W divertor of JET

Effect of ELMs on fuel retention at the bulk W target of JET ITER-Like Wall was studied with multi-scale calculations. Plasma input parameters were taken from ELMy H-mode plasma experiment. The energetic intra-ELM fuel particles get implanted and create near-surface defects up to depths of few tens of nm, which act as the main fuel trapping sites during ELMs. Clustering of implantation-induced vacancies were found to take place. The incoming flux of inter-ELM plasma particles increases the different filling levels of trapped fuel in defects. The temperature increase of the W target during the pulse increases the fuel detrapping rate. The inter-ELM fuel particle flux refills the partially emptied trapping sites and fills new sites. This leads to a competing effect on the retention and release rates of the implanted particles. At high temperatures the main retention appeared in larger vacancy clusters due to increased clustering rate