Miracle-dz/UniFAR-FTUs huggingface dataset:
Accessible information about quantum states: An open optimization problem

We give a brief summary of the current status of the problem of extracting the accessible information when a quantum system is received in one of a finite number of pre-known quantum states. We review analytical methods as well as a numerical strategy. In particular, the group-covariant positive-operator-valued measures are discussed, and several explicit examples are worked out in detail. These examples include some that occur in the security analysis of schemes for quantum cryptography. 11.
PyOncoPrint: a python package for plotting OncoPrints

OncoPrint, the plot to visualize an overview of genetic variants in sequencing data, has been widely used in the field of cancer genomics. However, still, there have been no Python libraries capable to generate OncoPrint yet, a big hassle to plot OncoPrints within Python-based genetic variants analysis pipelines. This paper introduces a new Python package PyOncoPrint, which can be easily used to plot OncoPrints in Python. The package is based on the existing widely used scientific plotting library Matplotlib, the resulting plots are easy to be adjusted for various needs.
Assessing the Reliability of Linear Composites

The past several years have marked a considerable upsurge of interest in the conceptualization and measurement of reliability in the social sciences, particularly in regard to measures that are formed as linear composites (weighted or unweighted sums) of individual items (Novick and Lewis, 1967; Bohrnstedt, 1969; Werts and Linn, 1970; Heise and Bohrnstedt, 1970; Armor, 1974; Smith, 1974a). One result of this interest has been a proliferation in the literature of a variety of "reliability coefficients" that measure
An Attention-Based Architecture for Hierarchical Classification With CNNs

Branch Convolutional Neural Nets have become a popular approach for hierarchical classification in computer vision and other areas. Unfortunately, these models often led to hierarchical inconsistency: predictions for the different hierarchy levels do not necessarily respect the class-subclass constraints imposed by the hierarchy. Several architectures to connect the branches have arisen to overcome this limitation. In this paper, we propose a more straightforward and flexible method: let the neural net decide how these branches must be connected. We achieve this by formulating an attention mechanism that dynamically determines how branches influence each other during training and inference. Experiments on image classification benchmarks show that the proposed method can outperform state-of-the-art models in terms of hierarchical performance metrics and consistency. Furthermore, although sometimes we found a slightly lower performance at the deeper level of the hierarchy, the model predicts much more accurately the ground-truth path between a concept and its ancestors in the hierarchy. This result suggests that the model does learn not only local class memberships but also hierarchical dependencies between concepts.
Research on Corner Matching Method Based on Harris

This paper proposes a method to extract corners from the image by the Harris algorithm,and gets rough matched corner set by similar measures.And then compares the simple clustering method and the disparity gradient constraint method for pre-purring matching corners.The experimental results show that the clustering method is superior to the parallax Gradient constraint method.It also needs to further improve the efficiency of RANSAC algorithm in order to make it suitable for real-time processing of the occasion.
Unternehmensbewertung der Mittel- und Kleinbetriebe

Erbschaft , Verkauf oder Gesellschafterwechsel – stehen in kleinen und mittleren Unternehmen existenzielle Entscheidungen an, ist meist eine Unternehmensbewertung notwendig. Dieses Buch behandelt betriebswirtschaftliche Verfahren der Unternehmensbewertung, bewertungsrelevante Besonderheiten von KMU und praktisch anwendbare Bewertungsmethoden. Mit Informationen zum IDW Standard 1. Besonderes Augenmerk schenkt die Neuauflage dem Praxisproblem der Bemessung des Risikozuschlags. Bereits berücksichtigt ist die jüngste Erbschaft- und Schenkungsteuerreform. Fundiert, verständlich und mit vielen Checklisten!
On identification disclosure and prediction disclosure for microdata

Two definitions of statistical disclosure ‐ identification disclosure and prediction disclosure ‐ are compared. Identification disclosure implies prediction disclosure but not vice versa. It is argued, however, that if sampling takes place then cases where prediction disclosure occurs and identification disclosure does not either have very small probability or do not present disclosure problems different from those normally met in the release of aggregate statistics. Finally the estimation of population uniqueness using the Poisson‐Gamma model is considered.
A Paradox Concerning Shrinkage Estimators

When estimating, under quadratic loss, the location parameter? of a spherically symmetric distribution with known scale parameter, we show that it may be that the common practice of utilizing the residual vector as an estimate of the variance is preferable to using the known value of the variance. In the context of Stein-like shrinkage estimators, we exhibit sufficient conditions on the spherical distributions for which this paradox occurs. In particular, we show that it occurs fort-distributions when the dimension of the residual vector is sufficiently large. The main tools in the development are upper and lower bounds on the risks of the James? Stein estimators which are exact at? =0.
Modulation of Dietary Fatty Acids in an Open-Label Study Improves Psoriasis and Dampens the Inflammatory Activation Status

Obesity and high abdominal fat mass are risk factors for developing the chronic inflammatory skin disease psoriasis. They are associated with increased incidence, prevalence and severity of the disease. A positive effect of weight loss on psoriasis activity has been shown in several studies. Obesity-related factors such as the dysregulation of glucose and lipid metabolism, the activation of adipose tissue and resultant persistent low-grade inflammation have been discussed as links of obesity and inflammatory diseases. Recently, we demonstrated a critical role of free fatty acids (FFAs) in obesity-mediated exacerbation of psoriatic skin inflammation in both mice and humans. In the present study, we translated these findings into a therapeutic intervention. An open-label study focusing on the dietary reduction of FFAs was conducted in patients with mild-to-moderate plaque psoriasis, and disease severity and serum markers of inflammation were analyzed. Here, we show that such a dietary intervention improves psoriatic disease activity independently of weight loss. Diet-related metabolic changes, such as a reduction in saturated free fatty acids (SFAs), may thus be more important than weight loss itself. Moreover, dietary intervention inhibited the overall pro-inflammatory activation status in patients, as shown by analysis of serum inflammatory parameters using the Olink platform. From our pilot study, we conclude that dietary intervention focusing on SFA reduction has the capacity to reduce disease activity and general inflammatory status in psoriasis patients.
Shape Optimization for Conforming Airfoils

Interest in the design and development of unmanned aerial vehicles (UAVs) has increased dramatically in the last two and a half decades. The Buckle-Wing UAV concept being developed in this research is designed to “morph” in a way which facilitates variations in wing loading, aspect ratio and wing section shapes. The Buckle-Wing consists of two highly elastic beam-like lifting surfaces joined at the outboard wing tips in either a pinned or clamped configuration. The Buckle-Wing UAV is capable of morphing between a separated wing configuration designed for maneuverability to a single fixed wing configuration designed for long range/high endurance. The design of the Buckle-Wing’s aerodynamic shapes is critical to the functioning of this adaptive UAV airframe. The airfoils must be capable of functioning both as independent lifting surfaces and as a fused single wing. The adaptive airframe of the BuckleWing requires that the two airfoils/wings conform as one single wing for the extended range and/or endurance configuration. This paper is focused on the use of shape optimization technologies to optimally tailor the aerodynamic performance of the UAV airfoils in both the separated and single fixed wing configuration. A conforming multi-objective and multilevel airfoil shape optimization problem is formulated and solved. Given an exterior airfoil, optimized for long endurance, shape optimization can be used to decomposed the exterior airfoil into two conforming airfoils in such a way that when separated the airfoils produce a 85% increase in lift providing improved maneuverability. ∗Graduate Research Assistant, Student Member AIAA †Professor, Associate Fellow AIAA. Nomenclature α Angle of attack ρ∞ Free stream density ω Turning rate c Thrust-specific fuel consumption cd Drag coefficient cl Lift coefficient D Total drag E Endurance g Acceleration of gravity L Total lift n Wing load factor r Turning radius R Range S Planform Area t Time V∞ Free stream velocity W Weight of aircraft at any given time W1 Weight of aircraft without fuel and with full payload Wo Weight of aircraft with full fuel and payload
A Relationship between Arbitrary Positive Matrices and Stochastic Matrices

The author (2) has shown that corresponding to each positive square matrix A (i.e. every aij > 0) is a unique doubly stochastic matrix of the form D1AD2, where the Di are diagonal matrices with positive diagonals. This doubly stochastic matrix can be obtained as the limit of the iteration defined by alternately normalizing the rows and columns of A. In this paper, it is shown that with a sacrifice of one diagonal D it is still possible to obtain a stochastic matrix. Of course, it is necessary to modify the iteration somewhat. More precisely, it is shown that corresponding to each positive square matrix A is a unique stochastic matrix of the form DAD where D is a diagonal matrix with a positive diagonal. It is shown further how this stochastic matrix can be obtained as a limit to an iteration on A.
Annotation and Identification of Phytochemicals from Eleusine indica Using High-Performance Liquid Chromatography Tandem Mass Spectrometry: Databases-Driven Approach

Eleusine indica (L.) Gaertn is a perennial herb belonging to the Poaceae family. As the only species of Eleusine found abundantly in Malaysia, it is locally known as “rumput sambau” and has been traditionally used to treat various ailments including pain relief from vaginal bleeding, hastening the placenta delivery after childbirth, asthma, hemorrhoids, urinary infection, fever, and as a tonic for flu-related symptoms. A diverse array of biological activities have been reported for the plant, such as antimicrobial, cytotoxic, anticonvulsant, anti-inflammatory, analgesic, antipyretic, and hepatoprotective action. Despite many reports on its traditional uses and biological activities, limited chemical databases are available for the plant. Thus, the aims of this study were to annotate and identify the phytochemical constituents in the methanolic extract of E. indica through tandem LCMS-based analysis techniques using MZmine, GNPS, Compound Discoverer, and SIRIUS platforms. This technique managed to identify a total of 65 phytochemicals in the extract, comprising primary and secondary metabolites, and was verified by the isolation of one of the identified phytochemicals. The structural elucidation mainly using 1D and 2D NMR as well as comparison with values in the literature confirms the isolated phytochemical to be a 3-OH anomer of loliolide, a benzofuran-type of compound, which consequently increases the level of confidence in the applied technique. The research describes a useful method for the fast and simultaneous identification of phytochemicals in E. indica, contributing to the study of the chemical properties of the genus and family.
Hydrodynamic behavior of non-Newtonian fluids in a square duct

The fully‐established friction factors of non‐Newtonian fluids in laminar and turbulent flow through a square duct were experimentally determined. The laminar friction factors of both purely viscous and viscoelastic fluids show good agreement with the well‐known laminar correlation for circular pipe flow, 16/Re*, where Re* is the Reynolds number proposed by Kozicki etal. for non‐Newtonian fluids in non‐circular duct flow. The established friction factors of purely viscous fluids in turbulent flow through rectangular channels agree well with the Dodge and Metzner equation for power law fluids in circular pipe flow if their generalized Reynolds number, Re′, is replaced by Re*. In the case of viscoelastic aqueous polymer solutions, it was found that the experimental asymptotic friction factors for fully established turbulent flow through a square duct are much higher than the corresponding circular tube results when compared at a fixed value of the Kozicki Reynolds number, Re*. In contrast, the turbulent rec.
Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

The traditional grey wolf algorithm is widely used for feature selection. However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence. In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed. Firstly, the multi-population topology is adopted to enhance the population’s diversity for improving search space utilization. Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population. At the same time, the command of $\beta $ and $\delta $ wolves (second and third best positions) for population position updates is removed. Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem. Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness. Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison. The Wilcoxon’s rank-sum non-parametric statistical test is carried out at 5% significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms. In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7%, while the highest of the other algorithms is 92.8% and the selected feature subset is 25% of the total dataset. The MTBGWO algorithm selects much smaller subset of features than other algorithms. In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds. The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem.
Type I Supernovae

Several projects involving Type I supernovae and their impact on nucleosynthesis are described. 1) A major effort is underway to compute the spectrum at epochs near maximum light. 2) Dynamical calculations have been done exploring the range of possible outcomes of the evolution of binary white dwarfs. 3) Consideration is given to the need to eject iron from the Galaxy to avoid overproduction by Type I events. 4) The nature of the class of peculiar Type I supernovae is explored.
The influence of health information attention and app usage frequency of older adults on persuasive strategies in mHealth education apps

With the development of mobile communication technology, persuasive technology is widely used in mobile health. Using personalized persuasive strategies in mobile health education (MHE) apps can effectively improve users’ health literacy and health behaviors. The transtheoretical model explains the process of user behavior change. Different usage frequency of the app reflects changes in user behavior. However, few studies have examined how the perceived importance of persuasive strategies among older adults changes with increasing use frequency. In this study, we analyzed the sensitivity of 111 older adults in China to persuasive strategies in MHE apps. Thirteen persuasive strategies were selected for this study. A repeated measure analysis of variance (RM-ANOVA) was used to demonstrate the influence of gender, health information attention and frequency of use on the sensitivity of perceived persuasive strategies among older adults. The results revealed older adults with a high usage frequency of health apps were more receptive to persuasive strategies, especially in social comparison strategy. This result may help developers consider factors such as the frequency of use by older users when designing personalized persuasive strategies for MHE apps.
Formation tracking for interconnected networks with interaction silence and minimum total energy restriction

The minimum‐energy formation tracking design and analysis problems for interconnected networks with distributed tracking protocols are addressed, where the effects of the total energy restriction and the interaction silence on the formation tracking are discussed, respectively. The key characteristic of this article is that the value of the energy restriction is required to be minimum, while the guaranteed‐cost tracking strategy and the energy‐constraint tracking strategy cannot ensure the minimum property of the total energy restriction. For interconnected networks without the interaction silence, sufficient conditions for minimum‐energy formation tracking are presented, whose effectiveness can be checked by the optimization procedure associated with the generalized eigenvalue method. Then, for interconnected networks with the interaction silence, by constructing the specific constraint associated with the maximum silent rate and two inhibition parameters, sufficient conditions for minimum‐energy formation tracking are proposed. Finally, numerical examples are shown to illustrate the efficacy of main results.
A Review of Monte Carlo Simulation and Resampling Methods for Social Science

Simulation and resampling methods play an important role in quantitative research. In the literature of social science (e.g., sociology, political science, economics, education, and psychology, to name a few), quantitative scholars are increasingly using this class of computer-intensive methods for a variety purposes such as evaluating different models/estimators/substantive theories, making inferences for techniques that do not directly provide such relevant information, and of course illustrating statistical ideas in classroom teaching. In psychological and educational research, many studies have been published addressing a wide variety of statistical modeling issues (e.g., violation of data distribution assumption, model misspecification, and missing data) that are pertinent to Jöreskog’s covariance structure model, item response theory (IRT) models, mixture models, and some completely newly developed models/methods. Motivated by these real-world purposes, Carsey and Harden (2013) bring together the key steps (i.e., generating random data, analyzing the data, and organizing and accumulating the results) for carrying out simulation studies. In this review, some specific comments are provided on the chapters, followed by a short summary at the end.
Translation, Cultural Adaptation, and Psychometric Evaluation of the Brazilian Portuguese Version of the Quality in Psychiatric Care—Outpatients Instrument

Measuring the quality of care received by patients of mental health services is necessary to determine the effectiveness of prevention programs and mental health treatment. This study translated the original Swedish Quality in Psychiatric Care—Outpatient (QPC-OP) instrument to Brazilian Portuguese, adapted it to the context of Brazilian psychosocial care centers (CAPS), and evaluated its psychometric properties. The instrument was translated and back-translated by two independent professional translators. A seven-person expert group of professionals and 31 psychiatric outpatients verified the content validity of the Brazilian Portuguese QPC-OP, which then was completed by 253 outpatients from 16 CAPS in São Paulo, Brazil. Confirmatory factor analysis revealed adequate goodness of fit for the factor structure corresponding to the original Swedish version, except for the discharge dimension. Three additional items added in the Brazilian Portuguese QPC-OP formed a separate factor. The internal consistency of the entire scale was excellent but low in some dimensions. In conclusion, the translation and cultural adaptation of the Brazilian Portuguese QPC-OP was satisfactory, and the psychometric evaluation demonstrated that the concept of quality of mental health care is similarly understood in the Brazilian and Swedish cultural context. Thus, the Brazilian Portuguese QPC-OP is a useful instrument for assessing the quality of care in the Brazilian CAPS context and will be useful in quality assurance and in cross-cultural research addressing quality of mental health care from the patient’s perspective.
Estimation for small areas in household surveys

In many socio-economic surveys the objective is estimation of total or proportion of persons with a particular attribute. Multi-stage area samples are drawn from geographic strata and population within areal units is used as an auxiliary variable in ratio estimation. For large administrative areas, the auxiliary variable totals are available as population projections based on the last census. However, for small areas population changes are significantly affected by non-demographic factors and hence projections with high enough reliability are not available for small areas. In such situations the efficiency of design-based estimators for small areas can be improved by a ratio adjustment based on the auxiliary variable total for a large area. An inequality on the efficiency of the ratio adjusted estimator is established and its bias and variance is investigated
Heat treatment effect on tocopherols, total phenolics and fatty acids in table olives (Olea europaea L.)

The olive fruits are rich source of oil, vitamins, minerals, organic acids and pigments. The fruits contain high level of bioactive compounds. The aim of this study was to examine the effect of heat treatment on tocopherols, total phenolics and antioxidant activity in green and black olives, as well as their fatty acid composition. The instrumental methods used in this experiment were high performance liquid chromatography (HPLC), gas chromatography with flame ionization detection (GC/FID) and spectrophotometric methods. The results revealed that the β+γ-tocopherols content after the heat treatment had the biggest reduction, which was 68.4% for green and 80.2% for black olives. Also, a significant loss of total phenolic content was observed after heat treatment in green and black olives by 18.6% and 18.4%, respectively, as well as antioxidant activity (decrease up to 28.1%). The most abundant fatty acids in green and black olives were oleic (C18:1), palmitic (C16:0) and linoleic acid (C18:2). The changes in fatty acids composition during the heat treatment occurred mostly at the level of polyunsaturated fatty acids, especially linolenic acid (C18:3) in black olives had the significant reduction (by 57.4%) in relation to the initial quantity.