Learning Active Basis Models by EM-Type Algorithms

EM algorithm is a convenient tool for maximum likelihood model fitting when the data are incomplete or when there are latent variables or hidden states. In this review article we explain that EM algorithm is a natural computational scheme for learning image templates of object categories where the learning is not fully supervised. We represent an image template by an active basis model, which is a linear composition of a selected set of localized, elongated and oriented wavelet elements that are allowed to slightly perturb their locations and orientations to account for the deformations of object shapes. The model can be easily learned when the objects in the training images are of the same pose, and appear at the same location and scale. This is often called supervised learning. In the situation where the objects may appear at different unknown locations, orientations and scales in the training images, we have to incorporate the unknown locations, orientations and scales as latent variables into the image generation process, and learn the template by EM-type algorithms. The E-step imputes the unknown locations, orientations and scales based on the currently learned template. This step can be considered self-supervision, which involves using the current template to recognize the objects in the training images. The M-step then relearns the template based on the imputed locations, orientations and scales, and this is essentially the same as supervised learning. So the EM learning process iterates between recognition and supervised learning. We illustrate this scheme by several experiments.Comment: Published in at http://dx.doi.org/10.1214/09-STS281 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Serum level of A-kinase anchoring protein 1, negatively correlated with insulin resistance and body mass index, decreases slightly in patients with newly diagnosed T2DM

Introduction: At present, the number of people suffering from diabetes and obesity is increasing in China, and also all over the world. Researchers found that decreased expression of A-kinase anchoring protein 1 (AKAP1), which was thought to regulate the function and structure of mitochondria, might be related to these two diseases. However, as far as we know, there is no study about the changes of serum AKAP1 protein in these two diseases. Hence we conducted this experiment to study the relationship between serum levels of AKAP1 with T2DM and obesity. Material and methods: There were 261 subjects involved in the experiment, including 130 patients with newly diagnosed T2DM and 131 individuals with normal glucose tolerance (NGT). They were further divided into four groups as follows. Subjects with NGT and normal weight (NW) were assigned to the NGT+NW group, those with NGT but with overweight (OW) or obesity (OB) were assigned to the NGT+OW/OB group, and so on; the rest were divided into the T2DM+NW group and the T2DM+OW/OB group. Serum AKAP1 levels were tested by ELISA method and compared by T-test. Linear regression was applied to discuss independent factors of AKAP1. Multiple logistic regression was used to analyse the relationship between AKAP1 and the prevalence of T2DM. Results: Serum AKAP1 in the NGT+NW group was 1.74 ± 0.42 ng/mL, higher than that in the NGT+OW/OB group, at 1.59 ± 0.41 ng/mL (t = 2.114, p = 0.036), and the T2DM+OW/OB group, at 1.52 ± 0.36 ng/ml (t = 3.219, p = 0.002). A-kinase anchoring protein 1 in 130 subjects with T2DM was lower than that in subjects with NGT, 1.57 ± 0.35 ng/mL vs. 1.67 ± 0.42 ng/mL, t = 2.036, p = 0.043. Liner regression showed that insulin resistance (IR) and body mass index (BMI) were independent factors negatively related to AKAP1: b = –0.019 and –0.032, respectively. Compared to the highest tertile of AKAP1, the prevalence of T2DM was higher in the other two tertiles; OR was 2.207 (1.203, 4.050) and 2.051 (1.121, 3.753), respectively. Conclusions: Serum AKAP1 level decreases slightly in patients with T2DM and obesity. Subjects with lower leve1s of serum AKAP1 are susceptible to T2DM.

Reducing Sensitivity on Speaker Names for Text Generation from Dialogues

Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model's sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.Comment: findings of ACL'2