Detailed anatomy of the ventral and lateral part of the elbow joint capsule and its influence on the elbow stability

Introduction： The stability of the elbow joint is maintained by the bones, joints, ligaments, and joint capsule that make up the articulation. As the joint between the shoulder and the wrist, maintaining stability in the elbow joint is important for the accomplishment of upper extremity functions. The role of bones, joints, and ligaments on the stability of the elbow joint is self-evident, but the influence of the joint capsule on the stability of the elbow joint is less well understood. In this thesis, based on the morphological and structural characteristics of the elbow joint capsule in applied anatomy, the role of the various parts of the elbow joint capsule in maintaining the stability of the elbow joint is studied from a biomechanical perspective. the aim is to provide an anatomical basis for the clinical understanding and treatment of acute and chronic elbow instability. Methods： Seven intact elbow cadavers with only capsular ligamentous coverings (no evidence of previous surgery or injury to these specimens) were recorded and biomechanically tested as follows. The changes in the surface of the elbow capsule during motion were recorded for each perspective in a specific angular range using a DIC device: -10 to 30° in the ventral part, 50 to 140° in the medial and lateral part. After we obtained all the displacement field maps, we found that the presence of special v shape is consistent with the hypothesis of previous scholars on the structure of the ventral joint capsule. We accordingly do a comparison of the displacement of the special v structure with that of the surrounding tissue in different axial dimensions. The resulting data were statistically analyzed using Wilson Signed Rank Test. Results and Conclusion： No significant difference was found in the contribution of the dorsal joint capsule to the stability of the elbow joint during flexion and extension. There were differences in the mechanical effects of various components of the ventral joint capsule during elbow flexion and extension, and we further investigated the contribution of a specific V-zone to elbow joint stability. Two bands within the ventral joint capsule comprising the V-zone could provide more stability in the vertical direction. We can perform further experimental verification in fresh frozen cadaveric specimen to demonstrate whether these two bands can be named as ligaments

Public Restrooms and Queueing

In this research, we consider using unisex restrooms to replace the traditional separate restrooms for males and females. In detail, we will consider that people use restrooms at different rates for different types of service (I and II), and the two types of facilities in the male restroom will be differentiated. We perform analysis using queueing theory and specifically matrix analytic methods. We also use computer software for simulations. Through numerical examples, we compare the waiting probabilities of males and females under the two different restroom systems

CrossSinger: A Cross-Lingual Multi-Singer High-Fidelity Singing Voice Synthesizer Trained on Monolingual Singers

It is challenging to build a multi-singer high-fidelity singing voice synthesis system with cross-lingual ability by only using monolingual singers in the training stage. In this paper, we propose CrossSinger, which is a cross-lingual singing voice synthesizer based on Xiaoicesing2. Specifically, we utilize International Phonetic Alphabet to unify the representation for all languages of the training data. Moreover, we leverage conditional layer normalization to incorporate the language information into the model for better pronunciation when singers meet unseen languages. Additionally, gradient reversal layer (GRL) is utilized to remove singer biases included in lyrics since all singers are monolingual, which indicates singer's identity is implicitly associated with the text. The experiment is conducted on a combination of three singing voice datasets containing Japanese Kiritan dataset, English NUS-48E dataset, and one internal Chinese dataset. The result shows CrossSinger can synthesize high-fidelity songs for various singers with cross-lingual ability, including code-switch cases.Comment: Accepted by ASRU202

How Are We Connected? Measuring Audience Galvanic Skin Response of Connected Performances."

Accurately measuring the audience response during a performance is a difficult task. This is particularly the\ud case for connected performances. In this paper, we staged a connected performance in which a remote\ud audience enjoyed the performance in real-time. Both objective (galvanic skin response and behaviours) and\ud subjective (interviews) responses from the live and remote audience members were recorded. To capture\ud galvanic skin response, a group of self-built sensors was used to record the electrical conductance of the skin.\ud The results of the measurements showed that both the live and the remote audience members had a similar\ud response to the connected performance even though more vivid artistic artefacts had a stronger effect on the\ud live audience. Some technical issues also influenced the experience of the remote audience. In conclusion we\ud found that the remoteness had little influence on the connected performance

A3^3T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation. However, all the above tasks are in the direction of speech understanding, but for the inverse direction, speech synthesis, the potential of representation learning is yet to be realized, due to the challenging nature of generating high-quality speech. To address this problem, we propose our framework, Alignment-Aware Acoustic-Text Pretraining (A$^3$T), which reconstructs masked acoustic signals with text input and acoustic-text alignment during training. In this way, the pretrained model can generate high quality of reconstructed spectrogram, which can be applied to the speech editing and unseen speaker TTS directly. Experiments show A$^3$T outperforms SOTA models on speech editing, and improves multi-speaker speech synthesis without the external speaker verification model.Comment: under review, 12 pages, 10 figure

Improved multi-label classifiers for predicting protein subcellular localization

Protein functions are closely related to their subcellular locations. At present, the prediction of protein subcellular locations is one of the most important problems in protein science. The evident defects of traditional methods make it urgent to design methods with high efficiency and low costs. To date, lots of computational methods have been proposed. However, this problem is far from being completely solved. Recently, some multi-label classifiers have been proposed to identify subcellular locations of human, animal, Gram-negative bacterial and eukaryotic proteins. These classifiers adopted the protein features derived from gene ontology information. Although they provided good performance, they can be further improved by adopting more powerful machine learning algorithms. In this study, four improved multi-label classifiers were set up for identification of subcellular locations of the above four protein types. The random k-labelsets (RAKEL) algorithm was used to tackle proteins with multiple locations, and random forest was used as the basic prediction engine. All classifiers were tested by jackknife test, indicating their high performance. Comparisons with previous classifiers further confirmed the superiority of the proposed classifiers