Temporomandibular joint loading patterns related to joint morphology: a theoretical study

It is unclear which aspects of the temporomandibular joint (TMJ) anatomy and/or kinematics determine shape and location of disk-compressive areas (stress field). The aim of this study was a quantitative analysis of TMJ anatomy to predict stress field path direction. Twenty-five asymptomatic TMJs (12 females and 13 males, aged 20-38 years) were tracked during unloaded opening/closing cycles. All TMJs were magnetic resonance (MR) imaged, reconstructed and animated with the recorded kinematics. Quantitative morphological parameters were calculated and entered into cross-validated multivariate discriminant analysis. Stress field paths during jaw opening were classified as mediolateral (ML) in 14 (9 females and 5 males) and lateromedial (LM) in 11 joints (3 females and 8 males). Curvature and incongruence as well as the dorsoventral position of the condyle in the fossa showed statistically significant differences (Mann-Whitney U test, p < 0.05). A combination of the lateral incongruence, the distance from the posterior slope of the eminence as well as the maximum posterior sagittal curvature enabled to correctly predict the direction of stress field paths in 92% of cases. In particular, ML type joints had laterally more congruent condyles/fossae and condyles more distant from the posterior slope of the eminence than LM type joints. Within the limits of this study, TMJ morphology seems to determine stress field path patterns

Combining content and sentiment analysis on lyrics for a lightweight emotion-aware Chinese song recommendation system

Traditional music recommendation systems (RS) rely on collaborative filtering technique (CF) to recommend songs or artists in which recommendations are made based on the neighboring analysis of items/users. It is computationally efficient and performs well when the data is ideally full, when there are limited user inputs or few user/item inputs, it immediately lost its competitive advantage. Additionally, traditional RS techniques including content-based one heavily rely on explicit user feedback (e.g. user rating) to generate recommendations. In music/song recommendation, however, implicit feedbacks such as play frequency, play list prevail. Making recommendation on such implicit feedbacks requires efficient and accurate latent factor learning techniques to construct user or item feature space, which is inherently computationally costly. This paper presents a new and lightweight classification model for Chinese song RS based on computational analysis of the lingual part of song lyrics. Through extracting and combining the term frequency and inverse document frequency (tf∗idf) from song lyrics, we construct a composite emotion point matrix for each song which can then be used to further classify songs based on its inherent emotion and make recommendation accordingly