Nurodymai autoriams

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CHARACTERISTICS OF LATGALIAN POPULAR MUSIC (2005–2016)

Looking at the events of the last decade in the life of Latgalian popular music, there may be twofold feelings; one will feel that in the field of popular music life is in full swing; another will feel that everything is stunted and hopeless. The purpose of the research is to describe the situation of Latgalian popular music (success, problems) between 2005 and 2016. The object of the research is Latgalian groups and performers. In the given study, the concept of the popular Latgalian music is presented, which includes pop music, rock music, jazz, rap, and other styles of popular music that may involve folk elements, as well as shlager music performed in the Latgalian language, as well as its authors (preferably) have a kinship with Latgale and who (preferably) have released at least one album where at least one song (preferably not a folk song) is in Latgalian, and even if it is a folk song, then in a less traditional arrangement. The following resources are used: correspondence with musicians, Internet resources, author’s own observations as a listener and as a musician. In Latgale musical tendencies keep up with the times and, this is also confirmed by one of the few music reviewers who mentions Latgalian music, Sandris Vanzovičs, here (in Latgale) virtually all the music styles of the world are represented, all niches are filled (Gusāns 2015: 1). Latgalian popular music has high quality ethno-rock artists – „Laimas muzykanti”, poprock group „Bez PVN”, „Dabasu Durovys”, specific rap group „Borowa MC”, strong rock and metal performers „Green Novice” and Sovvaļnīks, and a representative of ethno jazz Biruta Ozoliņa. Also in the last few years an interesting alternative stage has been created represented by the group „Kapļi” and „Jezups i Muosys”. Between 2005 and 2016 at least 39 albums of Latgalian popular music have been released. The most successful style for Latgalian performers („Galaktika”, „Ginc un Es”, Inga un Normunds, „Baltie Lāči”, „Patrioti. Ig”, „Dricānu Dominante”, etc.) was and still is the shlager music style, where several performers are still active and gaining success in the main criterion of Latvian music evaluation - in different song polls. The list of successes for pop and rock musicians is not so long, also taking into account the differences in the rating system, usually only the winner is emphasized; therefore, getting on the list of the five nominees for the given prize is highly appreciated. The biggest problem in Latgale and also in Latvian music is the decline of the music market. The greatest potential for loss and success is the introduction of new technologies, which make the majority of listeners choose to play music on their phone or computer, resulting in the loss of significance of music recorded in CDs. Artists are now trying to distribute music through the Internet, where much is determined by chance for the group to be noticed among other amounts of information and thus begins to symbolically earn on the sale of recordings on the Internet, but very often there are situations where high quality performances and lovely songs are left unnoticed. Thus, it is also musicians' own responsibility for the originality of the material being placed on the Internet, both in musical, textual and visual form, to promote visibility. The second biggest problem is the decrease in the audience that affects musicians in several ways: a) the decrease of the number of people, including the Latgalian audience (emigration), makes the sale of CDs meaningless; the lack of purchasers; b) the decrease of population also has an effect on the concerts and festivals; c) not only a part of the public, but also talented musicians emigrate in relation to the economic situation. Also, the third problem of Latgalian popular music is very topical; it is the place of Latgalian culture in the Latvian media, here it is worth noting the intolerance of the Latvian media, especially the strongest broadcasting stations (with a few exceptions over a decade) against songs performed in Latgalian. Therefore, Latgalian groups can rarely present their musical compositions elsewhere in Latvia, as a result of which many performers write songs in Latvian, not in Latgalian. It is necessary to emphasize that in recent years musicians („Dabasu Durovys”, „Green Novice”, etc.) pay more attention to the written language and its consistent use in published texts and song titles unless it is presented as a stylistic, specific feature of the group. Thus group texts can be mentioned as worth considering for people who want to learn or get in touch with Latgalian texts. Acquiring a place on the Latvian music market depends on many factors – recognizable, high quality song, successful management, solid concert performance, and elaborate group image and, above all, the idea of why it is being done

Gait Recognition from Motion Capture Data

Gait recognition from motion capture data, as a pattern classification discipline, can be improved by the use of machine learning. This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion. Experiments on the CMU MoCap database show that the suggested method outperforms thirteen relevant methods based on geometric features and a method to learn the features by a combination of Principal Component Analysis and Linear Discriminant Analysis. The methods are evaluated in terms of the distribution of biometric templates in respective feature spaces expressed in a number of class separability coefficients and classification metrics. Results also indicate a high portability of learned features, that means, we can learn what aspects of walk people generally differ in and extract those as general gait features. Recognizing people without needing group-specific features is convenient as particular people might not always provide annotated learning data. As a contribution to reproducible research, our evaluation framework and database have been made publicly available. This research makes motion capture technology directly applicable for human recognition.Comment: Preprint. Full paper accepted at the ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), special issue on Representation, Analysis and Recognition of 3D Humans. 18 pages. arXiv admin note: substantial text overlap with arXiv:1701.00995, arXiv:1609.04392, arXiv:1609.0693

Graph learning and its applications : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science, Massey University, Albany, Auckland, New Zealand

Since graph features consider the correlations between two data points to provide high-order information, i.e., more complex correlations than the low-order information which considers the correlations in the individual data, they have attracted much attention in real applications. The key of graph feature extraction is the graph construction. Previous study has demonstrated that the quality of the graph usually determines the effectiveness of the graph feature. However, the graph is usually constructed from the original data which often contain noise and redundancy. To address the above issue, graph learning is designed to iteratively adjust the graph and model parameters so that improving the quality of the graph and outputting optimal model parameters. As a result, graph learning has become a very popular research topic in traditional machine learning and deep learning. Although previous graph learning methods have been applied in many fields by adding a graph regularization to the objective function, they still have some issues to be addressed. This thesis focuses on the study of graph learning aiming to overcome the drawbacks in previous methods for different applications. We list the proposed methods as follows. • We propose a traditional graph learning method under supervised learning to consider the robustness and the interpretability of graph learning. Specifically, we propose utilizing self-paced learning to assign important samples with large weights, conducting feature selection to remove redundant features, and learning a graph matrix from the low dimensional data of the original data to preserve the local structure of the data. As a consequence, both important samples and useful features are used to select support vectors in the SVM framework. • We propose a traditional graph learning method under semi-supervised learning to explore parameter-free fusion of graph learning. Specifically, we first employ the discrete wavelet transform and Pearson correlation coefficient to obtain multiple fully connected Functional Connectivity brain Networks (FCNs) for every subject, and then learn a sparsely connected FCN for every subject. Finally, the ℓ1-SVM is employed to learn the important features and conduct disease diagnosis. • We propose a deep graph learning method to consider graph fusion of graph learning. Specifically, we first employ the Simple Linear Iterative Clustering (SLIC) method to obtain multi-scale features for every image, and then design a new graph fusion method to fine-tune features of every scale. As a result, the multi-scale feature fine-tuning, graph learning, and feature learning are embedded into a unified framework. All proposed methods are evaluated on real-world data sets, by comparing to state-of-the-art methods. Experimental results demonstrate that our methods outperformed all comparison methods

Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

We introduce a novel method for multilingual transfer that utilizes deep contextual embeddings, pretrained in an unsupervised fashion. While contextual embeddings have been shown to yield richer representations of meaning compared to their static counterparts, aligning them poses a challenge due to their dynamic nature. To this end, we construct context-independent variants of the original monolingual spaces and utilize their mapping to derive an alignment for the context-dependent spaces. This mapping readily supports processing of a target language, improving transfer by context-aware embeddings. Our experimental results demonstrate the effectiveness of this approach for zero-shot and few-shot learning of dependency parsing. Specifically, our method consistently outperforms the previous state-of-the-art on 6 tested languages, yielding an improvement of 6.8 LAS points on average.Comment: NAACL 201

Universal Spatiotemporal Sampling Sets for Discrete Spatially Invariant Evolution Systems

Let $(I,+)$ be a finite abelian group and $\mathbf{A}$ be a circular convolution operator on $\ell^2(I)$. The problem under consideration is how to construct minimal $\Omega \subset I$ and $l_i$ such that $Y=\{\mathbf{e}_i, \mathbf{A}\mathbf{e}_i, \cdots, \mathbf{A}^{l_i}\mathbf{e}_i: i\in \Omega\}$ is a frame for $\ell^2(I)$, where $\{\mathbf{e}_i: i\in I\}$ is the canonical basis of $\ell^2(I)$. This problem is motivated by the spatiotemporal sampling problem in discrete spatially invariant evolution systems. We will show that the cardinality of $\Omega$ should be at least equal to the largest geometric multiplicity of eigenvalues of $\mathbf{A}$, and we consider the universal spatiotemporal sampling sets $(\Omega, l_i)$ for convolution operators $\mathbf{A}$ with eigenvalues subject to the same largest geometric multiplicity. We will give an algebraic characterization for such sampling sets and show how this problem is linked with sparse signal processing theory and polynomial interpolation theory

Implementation of Best-Evidence Osteoarthritis Care: Perspectives on Challenges for, and Opportunities From, Low and Middle-Income Countries

The “Joint Effort Initiative” (JEI) is an international consortium of clinicians, researchers, and consumers under the auspices of the Osteoarthritis Research Society International (OARSI). The JEI was formed with a vision to improve the implementation of coordinated programs of best evidence osteoarthritis care globally. To better understand some of the issues around osteoarthritis care in low- and middle-income countries (LMICs), the JEI invited clinician researcher representatives from South Africa, Brazil, and Nepal to discuss their perspectives on challenges and opportunities to implementing best-evidence osteoarthritis care at the OARSI World Pre-Congress Workshop. We summarize and discuss the main themes of the presentations in this paper. The challenges to implementing evidence-based osteoarthritis care identified in LMICs include health inequities, unaffordability of osteoarthritis management and the failure to recognize osteoarthritis as an important disease. Fragmented healthcare services and a lack of health professional knowledge and skills are also important factors affecting osteoarthritis care in LMICs. We discuss considerations for developing strategies to improve osteoarthritis care in LMICs. Existing opportunities may be leveraged to facilitate the implementation of best-evidence osteoarthritis care. We also discuss strategies to support the implementation, such as the provision of high-quality healthcare professional and consumer education, and systemic healthcare reforms