New quaternary sequences of even length with optimal auto-correlation

Sequences with low auto-correlation property have been applied in code-division multiple access communication systems, radar and cryptography. Using the inverse Gray mapping, a quaternary sequence of even length $N$ can be obtained from two binary sequences of the same length, which are called component sequences. In this paper, using interleaving method, we present several classes of component sequences from twin-prime sequences pairs or GMW sequences pairs given by Tang and Ding in 2010; two, three or four binary sequences defined by cyclotomic classes of order $4$. Hence we can obtain new classes of quaternary sequences, which are different from known ones, since known component sequences are constructed from a pair of binary sequences with optimal auto-correlation or Sidel'nikov sequences.Comment: This paper was submitted to Science China: Information Sciences at Oct 16, 2016, and accpted for publication at Apr 27, 201

Raising the Standard of Living Through Educating People in District 502

Many adults living in District 502, unfortunately, lacked the opportunities to receive a quality education growing up. For this reason, they seek to get a GED diploma in hopes of increasing the standard of living for them and their families. The adult education and English Language Education classes at the College of DuPage provide the necessary resources for this. They offer five classes that differ based on the subjects tested on the GED exam, these being social studies, math, science, writing, and interpreting literature and art. The classes require no fees, are available at multiple locations including online, and can even be taken in Spanish. Although these classes are very thorough and possess high-quality curricula, many adults struggle with passing the classes, preventing them from living a better life. The People Educating People program is a volunteering component of the adult education and English Language Education classes at the College of DuPage. Volunteers attend classes and tutor students either one-on-one or in groups. The purpose of this project is to share my observations on how tutoring benefits adult learners in their continuing education. I volunteered in the program for 41 hours during the fall 2019 semester, attending a second-grade level math class twice a week

Ontologies for the study of neurological disease

We have begun work on two separate but related ontologies for the study of neurological diseases. The first, the Neurological Disease Ontology (ND), is intended to provide a set of controlled, logically connected classes to describe the range of neurological diseases and their associated signs and symptoms, assessments, diagnoses, and interventions that are encountered in the course of clinical practice. ND is built as an extension of the Ontology for General Medical Sciences — a high-level candidate OBO Foundry ontology that provides a set of general classes that can be used to describe general aspects of medical science. ND is being built with classes utilizing both textual and axiomatized definitions that describe and formalize the relations between instances of other classes within the ontology itself as well as to external ontologies such as the Gene Ontology, Cell Ontology, Protein Ontology, and Chemical Entities of Biological Interest. In addition, references to similar or associated terms in external ontologies, vocabularies and terminologies are included when possible. Initial work on ND is focused on the areas of Alzheimer’s and other diseases associated with dementia, multiple sclerosis, and stroke and cerebrovascular disease. Extensions to additional groups of neurological diseases are planned. The second ontology, the Neuro-Psychological Testing Ontology (NPT), is intended to provide a set of classes for the annotation of neuropsychological testing data. The intention of this ontology is to allow for the integration of results from a variety of neuropsychological tests that assay similar measures of cognitive functioning. Neuro-psychological testing is an important component in developing the clinical picture used in the diagnosis of patients with a range of neurological diseases, such as Alzheimer’s disease and multiple sclerosis, and following stroke or traumatic brain injury. NPT is being developed as an extension to the Ontology for Biomedical Investigations

Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Scattering statistics of rock outcrops: Model-data comparisons and Bayesian inference using mixture distributions

The probability density function of the acoustic field amplitude scattered by the seafloor was measured in a rocky environment off the coast of Norway using a synthetic aperture sonar system, and is reported here in terms of the probability of false alarm. Interpretation of the measurements focused on finding appropriate class of statistical models (single versus two-component mixture models), and on appropriate models within these two classes. It was found that two-component mixture models performed better than single models. The two mixture models that performed the best (and had a basis in the physics of scattering) were a mixture between two K distributions, and a mixture between a Rayleigh and generalized Pareto distribution. Bayes' theorem was used to estimate the probability density function of the mixture model parameters. It was found that the K-K mixture exhibits significant correlation between its parameters. The mixture between the Rayleigh and generalized Pareto distributions also had significant parameter correlation, but also contained multiple modes. We conclude that the mixture between two K distributions is the most applicable to this dataset.Comment: 15 pages, 7 figures, Accepted to the Journal of the Acoustical Society of Americ

Discerning the Impact of Powder Feedstock Variability on Structure, Property, and Performance of Selective Laser Melted Alloy 718: A Principal Component Analysis (PCA) of Feedstock Variability

Extensive mechanical, chemical and microstructural analyses were conducted on additively manufactured Alloy 718 to characterize powders from multiple vendors to determine the effects of variations observed in the powders had on the consolidated material. With over 190 variables examined, it was necessary to reduce the number of variables and identify the variables and classes of variables that had the greatest effect. Principle Component Analysis (PCA) was used to reduce the number of variable to effectively 12 while identifying several classes of variables as most important

A Mid-Infrared Imaging Survey of Embedded Young Stellar Objects in the Rho Ophiuchi Cloud Core

Results of a comprehensive, new, ground-based mid-infrared imaging survey of the young stellar population of the Rho Ophiuchi cloud are presented. Data were acquired at the Palomar 5-m and at the Keck 10-m telescopes with the MIRLIN and LWS instruments, at 0.25 arcsec and 0.25 arcsec resolutions, respectively. Of 172 survey objects, 85 were detected. Among the 22 multiple systems observed, 15 were resolved and their individual component fluxes determined. A plot of the frequency distribution of the detected objects with SED spectral slope shows that YSOs spend ~400,000 yr in the Flat Spectrum phase, clearing out their remnant infall envelopes. Mid-infrared variability is found among a significant fraction of the surveyed objects, and is found to occur for all SED classes with optically thick disks. Large-amplitude near-infrared variability, also found for all SED classes with optically thick disks, seems to occur with somewhat higher frequency at the earlier evolutionary stages. Although a general trend of mid-infrared excess and NIR veiling exists proceeding through SED classes, with Class I objects generally exhibiting K-veilings > 1, Flat Spectrum objects with K-veilings > 0.58, and Class III objects with K-veilings =0, Class II objects exhibit the widest range of K-band veiling values, 0-4.5. However, the highly variable value of veiling that a single source can exhibit in any of the SED classes in which active disk accretion can take place is striking, and is direct observational evidence for highly time-variable accretion activity in disks. Finally, by comparing mid-infrared vs. near-infrared excesses in a subsample with well-determined effective temperatures and extinction values, disk clearing mechanisms are explored. The results are consistent with disk clearing proceeding from the inside-out.Comment: 18 pages + 5 tables + 7 figure

Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?

To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy min-max neural network with its basic learning procedure is used within six different algorithm independent learning schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions examined is whether and when to use a single classifier or a combination of a number of component classifiers within a multiple classifier system