Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency

Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or string of symbols, which can be seen as an input for various subsequence matching approaches. The variety of data types, specific tasks and their partial or full solutions is so wide that the choice, implementation and parametrization of a suitable solution for a given task might be complicated and time-consuming; a possibly fruitful combination of fragments from different research areas may not be obvious nor easy to realize. The leading authors of this field also mention the implementation bias that makes difficult a proper comparison of competing approaches. Therefore we present a new generic Subsequence Matching Framework (SMF) that tries to overcome the aforementioned problems by a uniform frame that simplifies and speeds up the design, development and evaluation of subsequence matching related systems. We identify several relatively separate subtasks solved differently over the literature and SMF enables to combine them in straightforward manner achieving new quality and efficiency. This framework can be used in many application domains and its components can be reused effectively. Its strictly modular architecture and openness enables also involvement of efficient solutions from different fields, for instance efficient metric-based indexes. This is an extended version of a paper published on DEXA 2012.Comment: This is an extended version of a paper published on DEXA 201

Liquid-Drop Model and Quantum Resistance Against Noncompact Nuclear Geometries

The importance of quantum effects for exotic nuclear shapes is demonstrated. Based on the example of a sheet of nuclear matter of infinite lateral dimensions but finite thickness, it is shown that the quantization of states in momentum space, resulting from the confinement of the nucleonic motion in the conjugate geometrical space, generates a strong resistance against such a confinement and generates restoring forces driving the system towards compact geometries. In the liquid-drop model, these quantum effects are implicitly included in the surface energy term, via a choice of interaction parameters, an approximation that has been found valid for compact shapes, but has not yet been scrutinized for exotic shapes.Comment: 9 pages with 3 figure

Techniques for Complex Analysis of Contemporary Data

Contemporary data objects are typically complex, semi-structured, or unstructured at all. Besides, objects are also related to form a network. In such a situation, data analysis requires not only the traditional attribute-based access but also access based on similarity as well as data mining operations. Though tools for such operations do exist, they usually specialise in operation and are available for specialized data structures supported by specific computer system environments. In contrary, advance analyses are obtained by application of several elementary access operations which in turn requires expert knowledge in multiple areas. In this paper, we propose a unification platform for various data analytical operators specified as a general-purpose analytical system ADAMiSS. An extensible data-mining and similarity-based set of operators over a common versatile data structure allow the recursive application of heterogeneous operations, thus allowing the definition of complex analytical processes, necessary to solve the contemporary analytical tasks. As a proof-of-concept, we present results that were obtained by our prototype implementation on two real-world data collections: the Twitter Higg's boson and the Kosarak datasets

Mass independence and asymmetry of the reaction: Multi-fragmentation as an example

We present our recent results on the fragmentation by varying the mass asymmetry of the reaction between 0.2 and 0.7 at an incident energy of 250 MeV/nucleon. For the present study, the total mass of the system is kept constant (ATOT = 152) and mass asymmetry of the reaction is defined by the asymmetry parameter (? = | (AT - AP)/(AT + AP) |). The measured distributions are shown as a function of the total charge of all projectile fragments, Zbound. We see an interesting outcome for rise and fall in the production of intermediate mass fragments (IMFs) for large asymmetric colliding nuclei. This trend, however, is completely missing for large asymmetric nuclei. Therefore, experiments are needed to verify this prediction

Knowledge is at the Edge! How to Search in Distributed Machine Learning Models

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.Comment: Published in CoopIS 201

Visual Image Search: Feature Signatures or/and Global Descriptors

The success of content-based retrieval systems stands or falls with the quality of the utilized similarity model. In the case of having no additional keywords or annotations provided with the multimedia data, the hard task is to guarantee the highest possible retrieval precision using only content-based retrieval techniques. In this paper we push the visual image search a step further by testing effective combination of two orthogonal approaches – the MPEG-7 global visual descriptors and the feature signatures equipped by the Signature Quadratic Form Distance. We investigate various ways of descriptor combinations and evaluate the overall effectiveness of the search on three different image collections. Moreover, we introduce a new image collection, TWIC, designed as a larger realistic image collection providing ground truth. In all the experiments, the combination of descriptors proved its superior performance on all tested collections. Furthermore, we propose a re-ranking variant guaranteeing efficient yet effective image retrieval

Investigating the Contraction Pattern of the Zygomaticus Major Muscle and its Clinical Relevance:A Functional MRI Study

Background: Our understanding of facial anatomy has significantly evolved, yet the detailed contraction patterns of facial muscles and their presentation during clinical imaging remain largely unexplored. Understanding the contraction patterns and visual presentation of these muscles, particularly the zygomaticus major could enhance pre-surgical facial assessments and the development of new treatment strategies. Methods: A total of 34 healthy young individuals (17 female, 17 male) with a mean age of 23.6 (2.4) years [range: 20–30] were investigated regarding the length, thickness, width, and angle of the zygomaticus major muscle in five different facial expressions (i.e., repose, anger, joy, surprise, and sadness) utilizing MR imaging. Results: Joyful expressions caused a reduction in muscle length to 85.6% of its original length and an increase in width (103.4%), thickness (108.4%), and facial angle (2.72°) when compared to that in repose, suggesting isotonic contraction. Conversely, expressions of anger, surprise, and sadness generally led to muscle stretching, seen through changes in length (98.9%, 104.3%, and 102.7%, respectively), width (98.8%, 96.5%, and 99.4%, respectively), and thickness (91.2%, 91.0%, and 102.7%, respectively), with variable alterations in facial angle (0.55°, 1.85°, and 1.00°, respectively) depending on the specific expression. Conclusion: This MRI-based study indicates that the zygomaticus major muscle experiences isotonic contraction, characterized by decreased length and increased width and thickness. The findings underline the importance of muscle thickness as a reliable parameter in assessing facial muscle function and offer valuable guidance for practitioners in accurately evaluating muscle performance during different facial expressions. No Level Assigned: This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.</p

Consequences of a covariant Description of Heavy Ion Reactions at intermediate Energies

Heavy ion collisions at intermediate energies are studied by using a new RQMD code, which is a covariant generalization of the QMD approach. We show that this new implementation is able to produce the same results in the nonrelativistic limit (i.e. 50MeV/nucl.) as the non-covariant QMD. Such a comparison is not available in the literature. At higher energies (i.e. 1.5 GeV/nucl. and 2 GeV/nucl.) RQMD and QMD give different results in respect to the time evolution of the phase space, for example for the directed transverse flow. These differences show that consequences of a covariant description of heavy ion reactions within the framework of RQMD are existing even at intermediate energies.Comment: LaTex-file, 28 pages, 8 figures (available upon request), accepted for publication in Physical Review