Miracle-dz/UniFAR-FTUs huggingface dataset:
A hierarchical hidden semi-Markov model for modeling mobility data

Ubiquity of portable location-aware devices and popularity of online location-based services, have recently given rise to the collection of datasets with high spatial and temporal resolution. The subject of analyzing such data has consequently gained popularity due to numerous opportunities enabled by understanding objects' (people and animals, among others) mobility patterns. In this paper, we propose a hidden semi-Markov-based model to understand the behavior of mobile entities. The hierarchical state structure in our model allows capturing spatio-temporal associations in the locational history both at stay-points and on the paths connecting them. We compare the accuracy of our model with a number of other spatio-temporal models using two real datasets. Furthermore, we perform sensitivity analysis on our model to evaluate its robustness in presence of common issues in mobility datasets such as existence of noise and missing values. Results of our experiments show superiority of the proposed scheme compared with the other models.
An Empirical Study of Security Issues Posted in Open Source Projects

When developers gain thorough understanding and knowledge of software security, they can produce more secure software. This study aims at empirically identifying and understanding the security issues posted on a random sample of GitHub repositories. We tried to understand the presence of security issues and their key themes and topics. We applied a mixed-methods approach, combining modeling techniques and qualitative analysis. Our findings have revealed that a) the rate of security-related issues was rather small ( approx. 3% of all issues ), b) the majority of the security issues were related to identity management and cryptography topics. We present 7 high-level themes of problems that developers face in implementing security features.
Emergence of consensus and shared vocabularies in collaborative tagging systems

This article uses data from the social bookmarking site del.icio. us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio. us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging.
Feature selection for automatic classification of musical instrument sounds

In this paper, we carry out a study on classification of musical instr uments using a small set of features selected from a broad range of extracted ones by sequential forward feature selection method. Firstly, we extract 58 features for each record in the music database of 351 sound files. Then, the sequential forward selection method is adopted to choose the best feature set to achieve high classification accuracy. Three different classification techniques have been tested out and an accuracy of up to 93% can be achieved by using 19 features.
F ASTER N EURAL N ETWORKS S TRAIGHT FROM JPEG

Training CNNs directly from RGB pixels has enjoyed overwhelming empirical success. But can more performance be squeezed out of networks by using different input representations? In this paper we propose and explore a simple idea: train CNNs directly on the blockwise discrete cosine transform (DCT) coefficients computed and available in the middle of the JPEG codec. We modify libjpeg to produce DCT coefficients directly, modify a ResNet-50 network to accommodate the differently sized and strided input, and evaluate performance on ImageNet. We find networks that are both faster and more accurate, as well as networks with about the same accuracy but 1.77x faster than ResNet-50.
Spatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization

WIFI-based received signal strength indicator (RSSI) fingerprinting is widely used for indoor localization due to desirable features such as universal availability, privacy protection, and low deployment cost. The key of RSSI fingerprinting is to construct a trustworthy RSSI map, which contains the measurements of received access point (AP) signal strengths at different calibration points. Location can be estimated by matching live RSSIs with the RSSI map. However, a fine-grained map requires much labor and time. This calls for developing efficient interpolation and approximation methods. Besides, due to environmental changes, the RSSI map requires periodical updates to guarantee localization accuracy. In this paper, we propose a spatio-temporal (S-T) similarity model which uses the S-T correlation to construct a fine-grained and up-to-date RSSI map. Five S-T correlation metrics are proposed, i.e., the spatial distance, signal similarity, similarity likelihood, RSSI vector distance, and the S-T reliability. This model is evaluated based on experiments in our indoor WIFI positioning system test bed. Results show improvements in both the interpolation accuracy (up to 7%) and localization accuracy (up to 32%), compared to four commonly used RSSI map construction methods, namely, linear interpolation, cubic interpolation, nearest neighbor interpolation, and compressive sensing.
Analysis of Free Text with Omaha System Targets in Community-Based Care to Inform Practice and Terminology Development

Summary The Omaha system is one of the most widely used interface terminologies for documentation of community-based care. It is influential in disseminating evidence-based practice and generating data for health care quality research. Thus, it is imperative to ensure that the Omaha system reflects current health care knowledge and practice. The purpose of this study was to evaluate free text associated with Omaha system terms to inform issues with electronic health record system use and future Omaha system standard development. Two years of client records from two diverse sites (a skilled homecare, hospice, and palliative care program and a maternal child health home visiting program) were analyzed for the use of free text as a component of the intervention when structured targets for interventions were not identified. Intervention text entries very commonly contained duplicate “carry forward entries”, multiple concepts, mismatched problem focus, or failure to identify an existing appropriate target. Our findings support the need to better address education gaps for clinicians. We identified additional suggested targets for Omaha system problems, and propose new targets for consideration in future Omaha system revisions.
Genetic Programming for data classification: partitioning the search space

When Genetic Programming is used to evolve decision trees for data classification, search spaces tend to become extremely large. We present several methods using techniques from the field of machine learning to refine and thereby reduce the search space sizes for decision tree evolvers. We will show that these refinement methods improve the classification performance of our algorithms.
Mycosporine-Like Amino Acids from Coral Dinoflagellates

ABSTRACT Coral reefs are one of the most important marine ecosystems, providing habitat for approximately a quarter of all marine organisms. Within the foundation of this ecosystem, reef-building corals form mutualistic symbioses with unicellular photosynthetic dinoflagellates of the genus Symbiodinium. Exposure to UV radiation (UVR) (280 to 400 nm) especially when combined with thermal stress has been recognized as an important abiotic factor leading to the loss of algal symbionts from coral tissue and/or a reduction in their pigment concentration and coral bleaching. UVR may damage biological macromolecules, increase the level of mutagenesis in cells, and destabilize the symbiosis between the coral host and their dinoflagellate symbionts. In nature, corals and other marine organisms are protected from harmful UVR through several important photoprotective mechanisms that include the synthesis of UV-absorbing compounds such as mycosporine-like amino acids (MAAs). MAAs are small (<400-Da), colorless, water-soluble compounds made of a cyclohexenone or cyclohexenimine chromophore that is bound to an amino acid residue or its imino alcohol. These secondary metabolites are natural biological sunscreens characterized by a maximum absorbance in the UVA and UVB ranges of 310 to 362 nm. In addition to their photoprotective role, MAAs act as antioxidants scavenging reactive oxygen species (ROS) and suppressing singlet oxygen-induced damage. It has been proposed that MAAs are synthesized during the first part of the shikimate pathway, and recently, it has been suggested that they are synthesized in the pentose phosphate pathway. The shikimate pathway is not found in animals, but in plants and microbes, it connects the metabolism of carbohydrates to the biosynthesis of aromatic compounds. However, both the complete enzymatic pathway of MAA synthesis and the extent of their regulation by environmental conditions are not known. This minireview discusses the current knowledge of MAA synthesis, illustrates the diversity of MAA functions, and opens new perspectives for future applications of MAAs in biotechnology.
Microstructure and properties of friction stir welded aluminium alloys

Aluminium alloy 7136 belongs to the Al–Zn–Mg–Cu group of aluminium alloys strengthened by precipitation. These alloys offer very good properties, i.e. high strength combined with good corrosion resistance, which makes them suitable for aerospace applications. The limited range of applications of these alloys is due to problems associated with their welding. The Al–Zn–Mg–Cu alloys are classified as non-weldable. The aim of this study was to determine the quality and properties of friction stir welded (FSW) joints of alloy 7136-T76. This article presents the results of a detailed study into the microstructure and mechanical properties of FSW welds. The paper demonstrates that the FSW method is suitable for joining Al–Zn–Mg–Cu alloys. The FSW joints are of good quality and high mechanical properties. Tests of joints created at various tool rotation speeds have shown that joints of suitable quality, in terms of microstructure and properties, can be obtained for a relatively wide range of process parameters. The tool rotation speeds applied during the welding process did not have a significant influence on the quality of the welds.
Self-oscillating series-resonant led driver applied to reduce low-frequency current ripple transmission

This paper presents the analysis and design of an LED driver based on the series-resonant converter (SRC) and on the self-oscillating command circuit (SOCC). The use of SRC provides a reduction of the low frequency (LF) ripple in the LEDs' current, originated by the voltage ripple in the output of the power factor correction (PFC) stage (bus voltage ripple). The attenuation of the ripple transmission from the bus voltage to the LEDs' current achieves some desired goals: (1) reduction of the bulk capacitance used at the output of the PFC stage, enabling the employment of long-life film capacitors, and (2) increasing of the efficiency of the LED driver because of the low switching losses of the series-resonant converter. The SOCC stamps on the LED driver the low-cost, simplicity, and robustness features. In order to avoid any mismatch between the SRC LF current ripple reduction design guidelines and the SOCC design, a design methodology is presented. Experimental results from a self-oscillating series resonant converter to supply a 10 Watts LED lamp are presented, validating the feasibility of the developed methodology and the proposed analysis.
Assessing the Effects of Local Taxation Using Microgeographic Data

We study the impact of local taxation on the location and growth of firms. Our empirical methodology pairs establishments across jurisdictional boundaries to estimate the impact of taxation. Our approach improves on existing work as it corrects for unobserved establishment heterogeneity, for unobserved time-varying site specific effects, and for the endogeneity of local taxation. Applied to data for English manufacturing establishments we find that local taxation has a negative impact on employment growth, but no effect on entry.
Simulation of Reflectance of Porous Thin-Layer Systems

behavior of reflectance under the variation of the usual parameters. In this paper we provide a guideline for the behavior of the reflectance signal in thin porous layer systems. We work with a simulated substrate using the model by Forouhi and Bloomer [6]. In this model the complex dielectric function of the material is derived from a one-electron model using a finite lifetime for electron states, producing good agreement for a variety of materials. We work with the parameters they reported for silicon. To simulate different configurations we must be able to vary sample parameters such as layer thickness or introduce multiple layers the optical functions fur such configurations may be obtained from different summation techniques. For our simulation we use two different methods: the LTR method and the Generalized Matrix Method [7, 8]. The porosity is simulated and compared using two different effective medium theories: the Bruggeman and the Looyenga [9, 10, 11, 12]. These models are known to be fundamentally different and their best performance is achieved on different porosity regimes. In section 2, we simulate a system composed of a thin porous layer on top of semi-infinite substrate. We analyze how the reflectance is affected by variations of the porous layer thickness at different porosity regime, we emphasize a trend that is independent of the porosity model. Next, we study the response of the reflectance as the porosity increases. We show how the reflectance changes its trend after a critical value of the porosity. Once more we show that the main trend is independent of the porosity model.
On Near Optimality of One-sample Update for Joint Detection and Estimation

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple one-sample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio(GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential analysis and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical examples validate our theory.
Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator

Neural networks are an increasingly attractive algorithm for natural language processing and pattern recognition. Deep networks with >50 M parameters are made possible by modern graphics processing unit clusters operating at <50 pJ per op and more recently, production accelerators are capable of <5 pJ per operation at the board level. However, with the slowing of CMOS scaling, new paradigms will be required to achieve the next several orders of magnitude in performance per watt gains. Using an analog resistive memory (ReRAM) crossbar to perform key matrix operations in an accelerator is an attractive option. This paper presents a detailed design using the state-of-the-art 14/16 nm process development kit for of an analog crossbar circuit block designed to process three key kernels required in training and inference of neural networks. A detailed circuit and device-level analysis of energy, latency, area, and accuracy are given and compared with relevant designs using standard digital ReRAM and static random access memory (SRAM) operations. It is shown that the analog accelerator has <inline-formula> <tex-math notation="LaTeX">$270\times $ </tex-math></inline-formula> energy and <inline-formula> <tex-math notation="LaTeX">$540\times $ </tex-math></inline-formula> latency advantage over a similar block utilizing only digital ReRAM and takes only 11 fJ per multiply and accumulate. Compared with an SRAM-based accelerator, the energy is <inline-formula> <tex-math notation="LaTeX">$430\times $ </tex-math></inline-formula> better and latency is <inline-formula> <tex-math notation="LaTeX">$34\times $ </tex-math></inline-formula> better. Although training accuracy is degraded in the analog accelerator, several options to improve this are presented. The possible gains over a similar digital-only version of this accelerator block suggest that continued optimization of analog resistive memories is valuable. This detailed circuit and device analysis of a training accelerator may serve as a foundation for further architecture-level studies.
Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications

MOTIVATION The identification of active transcriptional regulatory elements is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases. It has recently been shown that chromatin structure information, such as DNase I hypersensitivity (DHS) or histone modifications, significantly improves cell-specific predictions of transcription factor binding sites. However, no method has so far successfully combined both DHS and histone modification data to perform active binding site prediction. RESULTS We propose here a method based on hidden Markov models to integrate DHS and histone modifications occupancy for the detection of open chromatin regions and active binding sites. We have created a framework that includes treatment of genomic signals, model training and genome-wide application. In a comparative analysis, our method obtained a good trade-off between sensitivity versus specificity and superior area under the curve statistics than competing methods. Moreover, our technique does not require further training or sequence information to generate binding location predictions. Therefore, the method can be easily applied on new cell types and allow flexible downstream analysis such as de novo motif finding. AVAILABILITY AND IMPLEMENTATION Our framework is available as part of the Regulatory Genomics Toolbox. The software information and all benchmarking data are available at http://costalab.org/wp/dh-hmm. CONTACT ivan.costa@rwth-aachen.de or eduardo.gusmao@rwth-aachen.de SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
econstor Make Your Publications Visible . A Service of zbw

There is a growing interest in cross-border medical care and its comparative advantages. In addition, medical care can be defined as a local assurance good. Little research is being carried out in this field. This paper discusses the individual considerations for medical treatment offered at home and abroad within a micro-economical framework. Specific assumptions as mistrust, monetary and non-monetary transaction-costs, a price and cost gradient, illness severity as well as a lump-sum insurance are discussed. We show that a demand abroad can be utility maximizing, however, only second best. There are inefficiencies in the dimensions of ex-post demand and income risk either on the side of gross-income or of costs. Furthermore, the foreign demand is restricted for low health stages driven by mistrust and restrictions in quality. Higher stages are more capable if fixed costs are low. To demand abroad the marginal treatment costs abroad must fall short of a threshold level. Finally, an out-of-pocket payment can reduce the moral hazard when treatment takes place abroad. JEL-Classification: I19 , F1, D01, D80, R23
Heparan Sulfate Regulates Fibrillin-1 N- and C-terminal Interactions*

Fibrillin-1 N- and C-terminal heparin binding sites have been characterized. An unprocessed monomeric N-terminal fragment (PF1) induced a very high heparin binding response, indicating heparin-mediated multimerization. Using PF1 deletion and short fragments, a heparin binding site was localized within the domain encoded by exon 7 after the first hybrid domain. Rodent embryonic fibroblasts adhered to PF1 and deletion fragments, and, when cells were plated on fibrillin-1 or fibronectin Arg-Gly-Asp cell-binding fragments, cells showed heparin-dependent spreading and focal contact formation in response to soluble PF1. Within domains encoded by exons 59–62 near the fibrillin-1 C terminus are novel conformation-dependent high affinity heparin and tropoelastin binding sites. Heparin disrupted tropoelastin binding but did not disrupt N- and C-terminal fibrillin-1 interactions. Thus, fibrillin-1 N-terminal interactions with heparin/heparan sulfate directly influence cell behavior, whereas C-terminal interactions with heparin/heparan sulfate regulate elastin deposition. These data highlight how heparin/heparan sulfate controls fibrillin-1 interactions.
Ontology Matching Using BabelNet Dictionary and Word Sense Disambiguation Algorithms

Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu & Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu & Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).