Reliability of B-mode ultrasonography for abdominal muscles in asymptomatic and patients with acute low back pain

The purpose of this methological study was to develop a reliable method for measuring transversus abdominis, rectus abdominis, external oblique and internal oblique muscles in asymptomatic human subjects and patients with acute low back pain (ALBP). This was a single operator reliability design study using ultrasound imaging to measure muscle thickness in 27 subjects on three separate occasions. Intra-class correlation coefficients (ICC) and standard error of measurement (SEM) were used to analyze muscle thickness. The mean, SD, ICC and SEM for external oblique, internal oblique, transversus abdominis and rectus abdominis muscles in asymptomatic subjects were (5.38, 1.64, 0.96, 0.33), (9.35, 3.42, 0.97, 0.073), (4.36, 1.93, 0.81, 0.45), (10.8, 2.18, 0.85, 0.84), respectively. The mean, SD, ICC, SEM for external oblique, internal oblique and transversus abdominis muscles in patients with ALBP were (5.58, 0.97, 0.87, 0.35), (9.72, 1.92, 0.87, 0.31), (4.36, 1, 0.91, 0.3), respectively. Earlier study on ultrasonographic measurement for neck multifidus muscles has suggested that the reliability of muscle thickness is higher in asymptomatic subjects compared with those in the symptomatic subjects. However, the present study showed high reliability for both symptomatic and asymptomatic subjects. This difference may be related to non-atrophic changes in abdominal muscles in acute low back patients. The results of this study indicate that the measurement of abdominal muscle thickness with B-mode ultrasonography can be performed reliably even in patients with ALBP. © 2005 Elsevier Ltd. All rights reserved

Workspace Analysis of a 4 Cable-Driven Spatial Parallel Robot

International audienceThis paper presents the static equilibrium workspace of an under-constrained cable-driven robot with four cables taking into account the forces and the moments due to the forces acting on the moving platform. The problem is formulated as a non-linear optimization problem with maintaining static equilibrium as the objective function. The simulations are done using MATLAB. The maximum force on the cables and tilting angle of the platform are used to define the feasible static equilibrium workspace and the results obtained are used to finalize the design of the collaborative cable-driven robot to be installed in existing production lines for the agile handling of parts in a manufacturing industry

Associations between femininity ideology and body appreciation among British female undergraduates

The current study examined the relationships between gender ideology and body image in a sample of British female undergraduates. A total of 135 women completed the Body Appreciation Scale (BAS), the Femininity Ideology Scale (FIS), and provided their demographic details. Inter-item correlations showed significant negative correlations between body appreciation scores and the total FIS score, FIS subscales of Purity and Stereotypic Images and Activities, and respondent BMI (rs −.17 to −.26). A multiple linear regression showed that Stereotypic Images and Activities and participant BMI alone explained 10.0% of the variance in body appreciation. These results are discussed in relation to previous studies showing mixed associations between gender role orientation and body image and eating disorders

Using qualitative methods to explore non-disclosure: the example of self-injury

Attempts to investigate non-disclosure are hampered by the very aspect being examined, namely an unwillingness to disclose non-disclosure. Although qualitative interviews may be considered to be an appropriate method for in-depth exploration of personal experiences, a lack of anonymity and the desire to conform to what is perceived to be socially acceptable limit its application in sensitive research. The current study, using a qualitative approach, addresses non-disclosure in the context of non-suicidal self-injury. Twenty-five young adults from diverse cultural backgrounds were interviewed in depth about their perceptions of self-injury, without the researchers asking directly whether the participants had ever self-harmed. Two techniques were used to enhance discussion within the qualitative interview: participants were invited to (a) discuss three hypothetical scenarios and (b) explore alternative interpretations of statistical data on patterns of self-harm. Key themes emerged regarding disclosure, gender issues, and culturally shaped concerns about the consequences of disclosure. The contributions of each element of the interview to understanding participants’ perceptions are highlighted and alternative methodological approaches for examining disclosure are discussed

A note on solving the fuzzy Sylvester matrix equation

In this work, we present theoretical analysis of the solution of Fuzzy Sylvester Matrix Equation (FSME) in the form A (X) over tilde + (X) over tildeB = (C) over tilde. The necessary and sufficient conditions for the existence of fuzzy solutions are proposed and some operators to finding the solution of FSME are exploited. Furthermore, an iterative scheme which can solve two n x n system instead one 2n x 2n system is presented to solving extended fuzzy linear system. Several numerical examples are performed to illustrate developed theory.A. Sadeghi, Ahmad I. M. Ismail, A. Ahmad and M. E. Abbasneja

Infinite variational autoencoder for semi-supervised learning

This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us to automatically vary the number of autoencoders in the mixture based on the data. Experiments show the flexibility of our method, particularly for semi-supervised learning, where only a small number of training samples are available.M. Ehsan Abbasnejad, Anthony Dick, Anton van den Henge

Show, price and negotiate: a hierarchical attention recurrent visual negotiator

OnlinePublNegotiation, as a seller or buyer, is an essential and complicated aspect of online shopping. It is challenging for an intelligent agent because it requires (1) extracting and utilising information from multiple sources (e.g. photos, texts, and numerals), (2) predicting a suitable price for the products to reach the best possible agreement, (3) expressing the intention conditioned on the price in a natural language and (4) consistent pricing. Conventional dialog systems do not address these problems well. For example, we believe that the price should be the driving factor for the negotiation and understood by the agent. But conventionally, the price was simply treated as a word token i.e. being part of a sentence and sharing the same word embedding space with other words. To that end, we propose our Visual Negotiator that comprises of an end-to-end deep learning model that anticipates an initial agreement price and updates it while generating compelling supporting dialog. For (1), our visual negotiator utilises attention mechanism to extract relevant information from the images and textual description, and feeds the price (and later refined price) as separate important input to several stages of the system, instead of simply being part of a sentence; For (2), we use the attention to learn a price embedding to estimate an initial value; Subsequently, for (3) we generate the supporting dialog in an encoder-decoder fashion that utilises the price embedding. Further, we use a hierarchical recurrent model that learns to refine the price at one level while generating the supporting dialog in another; For (4), this hierarchical model provides consistent pricing. Empirically, we show that our model significantly improves negotiation on the CraigslistBargain dataset, in terms of the agreement price, price consistency, and the language quality.Amin Parvaneh, Ehsan Abbasnejad, Qi Wu, Javen Sh

Reinforcement learning with attention that works: a self-supervised approach

Attention models have had a significant positive impact on deep learning across a range of tasks. However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. Unlike the selective attention models used in previous attempts, which constrain the attention via preconceived notions of importance, our implementation utilises the Markovian properties inherent in the state input. We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment.Anthony Manchin, Ehsan Abbasnejad, and Anton van den Henge