Miracle-dz/UniFAR-FTUs huggingface dataset:
SOME VECTOR VALUED MULTIPLIER DIFFERENCE SEQUENCE SPACES DEFINED BY A SEQUENCE OF ORLICZ FUNCTIONS

In this article we introduce some new dierence sequence spaces with a real 2-normed linear space as base space and which are dened using a sequence of Orlicz functions, a bounded sequence of positive real numbers and a sequence of non-zero reals as multiplier sequence. We show that these spaces are complete paranormed spaces when the base space is a 2-Banach space and investigate these spaces for solidity, symmetricity, convergence free, monotonicity and sequence algebra. Further we obtain some relation between these spaces as well as prove some inclusion results.
Microwave filters with improved stopband based on sub-wavelength resonators

The main aim of this paper is to demonstrate the potentiality of sub-wavelength resonators, namely, split-ring resonators, complementary split-ring resonators, and related structures to the suppression of undesired spurious bands in microwave filters, a key aspect to improve their rejection bandwidths. The main relevant characteristics of the cited resonators are their dimensions (which can be much smaller than signal wavelength at resonance) and their high-Q factor. This allows us to design stopband structures with significant rejection levels, few stages, and small dimensions, which can be integrated within the filter active region. By this means, no extra area is added to the device, while the passband of interest is virtually unaltered. A wide variety of bandpass filters, implemented in both coplanar-waveguide and microstrip technologies, have been designed and fabricated by the authors. The characterization of these devices points out the efficiency of the proposed approach to improve filter responses with harmonic rejection levels near 40 dB in some cases. It is also important to highlight that the conventional design methodology for the filters holds. For certain configurations, the presence of the resonators slightly lowers the phase velocity at the frequencies of interest with the added advantage of some level of reduction in device dimensions.
The Development of a Preference for Cocaine over Food Identifies Individual Rats with Addiction-Like Behaviors

Rationale Cocaine dependence is characterized by compulsive drug taking that supercedes other recreational, occupational or social pursuits. We hypothesized that rats vulnerable to addiction could be identified within the larger population based on their preference for cocaine over palatable food rewards. Objectives To validate the choice self-administration paradigm as a preclinical model of addiction, we examined changes in motivation for cocaine and recidivism to drug seeking in cocaine-preferring and pellet-preferring rats. We also examined behavior in males and females to identify sex differences in this “addicted” phenotype. Methods Preferences were identified during self-administration on a fixed-ratio schedule with cocaine-only, pellet-only and choice sessions. Motivation for each reward was probed early and late during self-administration using a progressive-ratio schedule. Reinstatement of cocaine- and pellet-seeking was examined following exposure to their cues and non-contingent delivery of each reward. Results Cocaine preferring rats increased their drug intake at the expense of pellets, displayed increased motivation for cocaine, attenuated motivation for pellets and greater cocaine and cue-induced reinstatement of drug seeking. Females were more likely to develop cocaine preferences and recidivism of cocaine- and pellet-seeking was sexually dimorphic. Conclusions The choice self-administration paradigm is a valid preclinical model of addiction. The unbiased selection criteria also revealed sex-specific vulnerability factors that could be differentiated from generalized sex differences in behavior, which has implications for the neurobiology of addiction and effective treatments in each sex.
Stresses and Disability in Depression across Gender

Depression, though generally episodic, results in lasting disability, distress, and burden. Rising prevalence of depression and suicide in the context of epidemiological transition demands more attention to social dimensions like gender related stresses, dysfunction, and their role in outcome of depression. Cross-sectional and follow-up assessment of men and women with depression at a psychiatric tertiary centre was undertaken to compare their illness characteristics including suicidal ideation, stresses, and functioning on GAF, SOFAS, and GARF scales (N = 107). We reassessed the patients on HDRS-17 after 6 weeks of treatment. Paired t-test and chi-square test of significance were used to compare the two groups, both before and after treatment. Interpersonal and marital stresses were reported more commonly by women (P < 0.001) and financial stresses by men (P < 0.001) though relational functioning was equally impaired in both. Women had suffered stresses for significantly longer duration (P = 0.0038). Men had more impairment in social and occupational functioning compared to females (P = 0.0062). History of suicide attempts was significantly associated with more severe depression and lower levels of functioning in case of females with untreated depression. Significant cross-gender differences in stresses, their duration, and types of dysfunction mandate focusing on these aspects over and above the criterion-based diagnosis.
Mutual Fund Surviorship

Survivorship induces a variety of biases in mutual fund research. I show analytically that biases in performance estimates depend on sample length and whether funds disappear after one or many poor returns. Using a sample free of survivor bias, I document higher risk and predominantly multipleyear underperformance in nonsurviving funds. This causes the bias in mean return estimates to increase in the time-length of the sample. In my data set, the bias is 0.43 percent per year in fiveyear samples and approximately one percent for samples longer than fifteen years. I also find downward bias in persistence tests and both upward and downward bias in the relations between performance and fund attributes depending on the type of selection bias. The results cast doubt on the conclusions of many published mutual fund studies. Survivorship affects almost every mutual fund study. Commercially available mutual fund data sets include only funds currently in operation, and many commonly used research methodologies impose additional selection biases. With the exception of a few recent papers, however, researchers frequently ignore selection biases altogether or argue that their effect is insignificant. This attitude is unfortunate, as selection-bias issues pervade most empirical studies of panel data sets. This paper offers a comprehensive study of survivorship issues in mutual fund research. I examine how survivorship affects mutual fund studies both theoretically and empirically, measuring the effects of survivorship in a new mutual fund data set carefully created to mitigate selection-bias problems. I study the effect of survivorship on three types of mutual fund studies: (1) estimates of average performance, (2) tests of performance persistence, and (3) cross-section estimates of the relation between performance and fund attributes. The analysis divides survivorship into the separate but related issues of survivor bias and look-ahead bias, an important distinction rarely acknowledged in the literature. My results indicate that survivorship substantially alters the inferences from mutual fund studies, but that the effects vary across test type, form of survivorship, and sample time length. A number of recent papers also address issues in mutual fund survivorship. Brown, Goetzmann, Ibbotson and Ross (1992) and Carpenter and Lynch (1997) study the effects of survivorship on tests of performance persistence. In simulated samples, they show that the direction of test bias depends on variation in fund risk and the performance selection criteria. Grinblatt and Titman (1989) and Wermers (1997) study the effect of survivorship on a database of underlying stock holdings. From their estimates of gross returns, both Grinblatt and Titman and Wermers find
Thesis: Multiple Kernel Learning for Object Categorization

Object Categorization is a challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. In the past, many descriptors have been proposed which aid object categorization even in such adverse conditions. Each descriptor has its own merits and de-merits. Some descriptors are invariant to transformations while the others are more discriminative. Past research has shown that, employing multiple descriptors rather than any single descriptor leads to better recognition. The problem of learning the optimal combination of the available descriptors for a particular classification task is studied. Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of descriptors for object categorization. Existing MKL formulations often employ block l-1 norm regularization which is equivalent to selecting a single kernel from a library of kernels. Since essentially a single descriptor is selected, the existing formulations maybe sub- optimal for object categorization. A MKL formulation based on block l-infinity norm regularization has been developed, which chooses an optimal combination of kernels as opposed to selecting a single kernel. A Composite Multiple Kernel Learning(CKL) formulation based on mixed l-infinity and l-1 norm regularization has been developed. These formulations end in Second Order Cone Programs(SOCP). Other efficient alter- native algorithms for these formulation have been implemented. Empirical results on benchmark datasets show significant improvement using these new MKL formulations.
Mafia infiltration, public administration and local institutions: A comparative study in Northern Italy

Nuanced explanations of the factors underpinning the mafia’s movements across territories have recently been proposed. However, more light must be shed on the mechanisms through which mafiosi try to infiltrate the legal economy in non-traditional territories. Accordingly, this study aims to micro-found interactions and exchanges that mainly involve mafiosi, politicians and economic actors in expansion areas. Focusing on the local level, we will show how the misuse of several administrative tools generates a profitable opportunity structure for mafiosi. To this end, we present an in-depth comparative case study of three events involving the construction industry that took place in Northern Italy. The main findings show that: (i) mafiosi are skilled at smoothing social relations, enlarging and consolidating opaque networks predating their arrival; (ii) they give rise to different types of mutual exchanges and network structures.
Nonlinear estimation using central difference information filter

In this contribution, we introduce a new state estimation filter for nonlinear estimation and sensor fusion, which we call central difference information filter (CDIF). As we know, the extended information filter (EIF) has two shortcomings: one is the limited accuracy of the Taylor series linearization method, the other is the calculation of the Jacobians. These shortcomings can be compensated by utilizing sigma point information filters (SPIFs), e.g., the unscented information filter (UIF), which uses deterministic sigma points to approximate the distribution of Gaussian random variables and does not require the calculation of Jacobians. As an alternative to the UIF, the CDIF is derived by using Stirling's interpolation to generate sigma points in the SPIFs architecture, which uses less parameters, has lower computational cost and achieves the same accuracy as UIF. To demonstrate the performance of our algorithm, a classic space vehicle reentry tracking simulation is used.
A Possible Effect of the Period of Galaxy Formation on the Angular Correlation Function

During the epoch of galaxy formation, the formation of the first galaxies in regions of high overdensity may lead to an initial amplitude of the spatial correlation function of galaxies, ξ0, much higher than that expected in linear perturbation theory. This initially high “bias” would consequently decrease to the near-unity values expected from local observations. Such a decreasing correlation period (DCP), detected in N-body simulations under certain conditions by several authors, is parameterized in a simple way, ending at a “transition” redshift zt. Consequences for w0, the amplitude of the angular correlation function of faint galaxies (for a fixed apparent magnitude range), have been estimated. The sensitivity of w0 to zt, and how this is affected by the redshift distribution of the galaxies in the sample, by low-redshift spatial correlation function behavior, by cosmology, and by the rate at which ξ0 decreases during the DCP, are shown and discussed. This simple model is intended as a first exploration of the effects of the DCP for a likely range of parameter space. Comparison with the Hubble Deep Field (HDF) estimate of w0 indicates that the DCP is compatible with observation for values of zt in the theoretically expected range zt ≳ 1. However, tighter observational constraints on the HDF redshift distribution and on low-redshift correlation function growth will be needed either to detect the DCP unambiguously or to constrain zt to redshifts greater than those of galaxies in existing surveys.
Meanings and Implications of NDEr Transformations: Some Preliminary Findings and Implications

This paper discusses the positive implications of experiencing NDEs. "The available evidence shows, in decreasing order of saliency: greatly increased concern for others; lessened fear of death and increased belief in an afterlife; increased religious interest and feeling, both non-institutionalized and institutionalized; and lessened desires for material success and approval of others" (abstract). "NDErs might be harbingers of a more humane future" (abstract).
SwingPong: analysis and suggestion based on motion data from mobile sensors for table tennis strokes using decision tree

Table tennis is a sport that everyone can play, regardless of age or gender. It becomes a popular sport since it does not require much space and its equipment is affordable. For beginners, they may not have a chance to get some helpful advice to learn how to control a table tennis ball. Thus, we decided to analyze user's motion data and build a model to develop an application called SwingPong detecting basic table-tennis strokes in order to provide helpful suggestion to players. SwingPong is an android application on a smartphone analyzing user's forehand and backhand strokes and providing suggestion back to the user in order to improve his or her swing. There are two main modules in the application: Module #1: storing angular acceleration and angular velocity of strokes. When a table tennis ball is hit on the racket, the angular acceleration and angular velocity of user's swing are collected using accelerometer and gyroscope sensors in smartphones. We collected the training data set from table tennis national players, Thai youth national team table tennis athletes and coaches. J48 Decision Tree was used in order to build a model of hitting classification. Module #2: providing suggestions. We applied the model from Module#1 to evaluate user's basic strokes and provide alert sound when a ball is hit. After a user hits the ball in a given time, the application analyzes the user's strokes and shows suggestions. The experimental results show that SwingPong is able to recognize strokes whether the ball is good (or in), out, or hits the net. The accuracy of classification of forehand strokes is 77.21% and the accuracy of classification of backhand strokes is 69.63%. In addition, SwingPong can provide helpful suggestions so that users can use the feedbacks to improve their swings.
Convectively Generated Potential Vorticity in Rainbands and Formation of the Secondary Eyewall in Hurricane Rita of 2005

AbstractEyewall replacements in mature tropical cyclones usually cause intensity fluctuations. One reason for eyewall replacements remaining a forecasting challenge is the lack of understanding of how secondary eyewalls form. This study uses high-resolution, full-physics-model forecast fields of Hurricanes Katrina and Rita (2005) to better understand potential vorticity (PV) generation in the rainbands and the role that convectively generated PV played in the formation of a secondary eyewall in Hurricane Rita. Previous studies have focused on dynamic processes in the inner core and/or the effects of certain specified PV distributions. However, the initial development of a concentric PV ring in the rainband region has not been fully addressed. Katrina and Rita were extensively observed by three research aircraft during the Hurricane Rainband and Intensity Change Experiment (RAINEX), which was designed to study the interaction of the rainbands and the inner core. Rita developed a secondary eyewall and went .
A Linguistic Approach to Categorical Color Assignment for Data Visualization

When data categories have strong color associations, it is useful to use these semantically meaningful concept-color associations in data visualizations. In this paper, we explore how linguistic information about the terms defining the data can be used to generate semantically meaningful colors. To do this effectively, we need first to establish that a term has a strong semantic color association, then discover which color or colors express it. Using co-occurrence measures of color name frequencies from Google n-grams, we define a measure for colorability that describes how strongly associated a given term is to any of a set of basic color terms. We then show how this colorability score can be used with additional semantic analysis to rank and retrieve a representative color from Google Images. Alternatively, we use symbolic relationships defined by WordNet to select identity colors for categories such as countries or brands. To create visually distinct color palettes, we use k-means clustering to create visually distinct sets, iteratively reassigning terms with multiple basic color associations as needed. This can be additionally constrained to use colors only in a predefined palette.
Estimating the effects of seawater intrusion on an estuarine nitrogen cycle by comparative network analysis

Nitrogen (N) removal from estuaries is driven in part by sedimentary microbial processes. The processes of denitrification and anaerobic ammonium oxidation (anammox) remove N from estuaries by producing di-nitrogen gas, and each can be coupled to N recycling pathways such as nitrification and dissimilatory nitrate reduction to ammonium (DNRA). Environmental conditions in estuaries influence sedimentary N cycling processes; therefore, seawater intrusion may affect the coupling of N cycling processes in the freshwater portions of estuaries. This study investigated the potential effects of seawater intrusion on these process couplings through a comparative modeling approach. We applied environ analysis, a form of ecological network analysis, to two N cycling mass-balance network models constructed at freshwater (oligohaline) and saltwater (polyhaline) sites in the Cape Fear River Estuary, North Carolina. We used a space-for-time substitution to predict the effects of seawater intrusion on the sedimentary N cycle. Further, we conducted an uncertainty analysis using linear inverse modeling to evaluate the effects of parameterization uncertainty on model results. Nitrification coupled to both denitrification and anammox was 2.5 times greater in the oligohaline model, while DNRA coupled to anammox was 2.7 times greater in the polyhaline model. However, the total amount of N2 gas produced relative to the nitrogen inputs to each network was 4.7% and 4.6% at the oligohaline and polyhaline sites, respectively. These findings suggest that changes in water chemistry from seawater intrusion may favor direct over coupled nitrogen removal, but may not substantially change the N removal capacity of the sedimentary microbial processes.
The Derivation of a New Model of Equity Duration

This paper sets out to address the issue of equity duration, one of several risk measures available for asset and liability management. Equity duration, as derived from the use of traditional dividend discount models, results in extremely long duration estimated for equities - often in excess of 50 years for growth stocks. Leibowitz, in his seminal paper (1986), identified an alternative framework for assessing equity duration empirically. This methodology yields equity duration measures more consistent with the experience of practitioners, implying that equities behave as if they are much shorter duration instruments. In our paper, based on an application to UK data, we develop the intuition behind the Leibowitz approach to generate equity duration as a by-product of asset pricing, Our analysis suggests that the equity premium puzzle may comprise an important element in reconciling the Leibowitz approach to equity duration, with the more traditional dividend discount model alternative.
The 844ins68 cystathionine beta-synthase and C677T MTHFR gene polymorphism and the vaso-occlusive event risk in sickle cell disease

Introduction Sickle cell disease (SCD) is an inflammatory condition with an increase in the adhesion of sickled erythrocytes, and it is a potential cause of vaso-occlusive episodes, an event related to clinical manifestations, morbidity and mortality. The cystathionine beta-synthase enzyme gene (CBS) and the methylenetetrahydrofolate reductase enzyme gene (MTHFR) are risk factors for thromboembolic disorders. This study evaluated the frequency of the 844ins68 CBS and C677T MTHFR gene polymorphisms and their possibility to be risk factors for vaso-occlusive crises. Material and methods In total 91 blood samples from SCD patients were studied by PCR-RFLP and PCR-allele-specific, for the SCD genotype confirmation and polymorphism identification. Results The presence of clinical manifestations related to vaso-occlusive crises were more frequent among patients with the Hb SS genotype (p = 0.007). The CBS enzyme gene was three times more frequent (p = 0.011) among patients with vaso-occlusive complications. The MTHFR gene mutation frequency showed no increased risk for vaso-occlusive crises in SCD patients (p = 0.193). The interaction between the two polymorphisms was evaluated in 12.08% of the SCD patients and doubled the vaso-occlusive disease risk (relative risk: 2.16). Conclusions We conclude that the presence of 844ins68 CBS and C677T MTHFR gene polymorphism was a risk factor for vaso-occlusive episodes in the SCD patients evaluated.
Prodigies of the Prairie: The Talent Development Stories of Four Elite Nebraska Youth Performers

This qualitative study addressed this primary question: How does talent develop among four Nebraska youth? Four highly talented Nebraska youth in the domains of baton twirling, swimming, rodeo, and both softball and music were interviewed along with their parents and coaches. Results revealed unique talent stories and commonalities among those stories. The stories described a twirling talent trilogy that involved coordinated efforts among student, parent, and coach; a swimmer with all hands on deck that involved supportive family, strong coaching, and center of excellence; a one-horse talent hotbed with a talent-equipped ranch and rodeo coaching parents; and a father-influenced music and softball double play where a father led the way to dual talents. The commonalities among stories included hard work, focus on fundamentals, prior family links to the talent domain, extraordinary parent involvement, and a parent-engineered environment for talent development. The limited roles schools play in fostering talent and parenting implications were also addressed.