Miracle-dz/UniFAR-FTUs huggingface dataset:
H ∞ State Estimation for Discrete-Time Complex Networks with Linear Fractional Uncertainties

This paper is concerned with the state estimation problem for a class of discrete time-delay nonlinear complex networks with linear fractional uncertainties. The nonlinear functions are described by the sector-like nonlinearities that are more general than the commonly used Lipschitz ones. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that the dynamics of the estimation error is guaranteed to be globally asymptotically stable in the mean square and the effect from the exogenous disturbances to be estimation accuracy is attenuated a given level by means of an H∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semi-definite programming method. Finally, a numerical example is applied to demonstrate the effectiveness of the proposed state estimation approach.
FPT hardness for Clique and Set Cover with super exponential time in k

We give FPT-hardness for setcover and clique with super exponential time in the parameter k. Under the ð and pgc we prove that setcover admits no log k ratio, for c > 0 for any algorithm with running t(k) = exp ( k(log k) f ) ) · poly(n) for constant f > 0. Under the eth alone, we prove that setcover admits no √ log k ratio, in time exp ( k(log f k) ) · poly(n) for constant f > 0. Under the eth we prove clique admits no (c, t(k)), approximation, for any constant c in time exp(exp(kd)) with d a constant that depends on c.
Performance investigation of polar coded IM/DD optical OFDM for short reach interconnection

In this paper, we design a novel polar coded intensity modulation direct detection (IM/DD) optical orthogonal frequency division multiplexing-based (OFDM) system used in short reach interconnection. The experimental results demonstrate the 9.5-dB net coding gain (NCG) at the BER of 1e-3 after 40-km standard single mode fiber (SSMF) transmission.
A Standardized Terminology for Describing Reproductive Development in Fishes

Abstract As the number of fish reproduction studies has proliferated, so has the number of gonadal classification schemes and terms. This has made it difficult for both scientists and resource managers to communicate and for comparisons to be made among studies. We propose the adoption of a simple, universal terminology for the phases in the reproductive cycle, which can be applied to all male and female elasmobranch and teleost fishes. These phases were chosen because they define key milestones in the reproductive cycle; the phases include immature, developing, spawning capable, regressing, and regenerating. Although the temporal sequence of events during gamete development in each phase may vary among species, each phase has specific histological and physiological markers and is conceptually universal. The immature phase can occur only once. The developing phase signals entry into the gonadotropin-dependent stage of oogenesis and spermatogenesis and ultimately results in gonadal growth. The spawning capable phase includes (1) those fish with gamete development that is sufficiently advanced to allow for spawning within the current reproductive cycle and (2) batch-spawning females that show signs of previous spawns (i.e., postovulatory follicle complex) and that are also capable of additional spawns during the current cycle. Within the spawning capable phase, an actively spawning subphase is defined that corresponds to hydration and ovulation in females and spermiation in males. The regressing phase indicates completion of the reproductive cycle and, for many fish, completion of the spawning season. Fish in the regenerating phase are sexually mature but reproductively inactive. Species-specific histological criteria or classes can be incorporated within each of the universal phases, allowing for more specific divisions (subphases) while preserving the overall reproductive terminology for comparative purposes. This terminology can easily be modified for fishes with alternate reproductive strategies, such as hermaphrodites (addition of a transition phase) and livebearers (addition of a gestation phase).
Marginal log-linear parameterization of conditional independence models

Models defined by a set of conditional independence restrictions play an important role in statistical theory and applications, especially, but not only, in graphical modelling. In this paper we identify a subclass of these consisting of hierarchical marginal log-linear models, as defined by Bergsma & Rudas (2002a). Such models are smooth, which implies the applicability of standard asymptotic theory and simplifies interpretation. Furthermore, we give a marginal log-linear parameterization and a minimal specification of the models in the subclass, which implies the applicability of standard methods to compute maximum likelihood estimates and simplifies the calculation of the degrees of freedom of chi-squared statistics to test goodness-of-fit. The utility of the results is illustrated by applying them to block-recursive Markov models associated with chain graphs.
NN-based Poker Hand Classification and Game Playing

Poker is a family of complex card games, each with a different set of rules. In our project, we utilize a simplified version of Texas Hold’em poker to build and train a poker bot that learns to classify hands and devises a playing strategy in order to be competitive player. We developed a system that uses a fully-connected neural network that can be trained to understand the patterns between the cards and the hands that can be formed from those cards. Thus, the trained neural network can be the foundation of a poker bot. Once it can accurately classify hands, we devise a playing strategy based on a number of parameters, and record our results. We name our poker bot, ‘AlphaPo’.
Research on the Segmentation of MRI Image Based on Immune Support Vector Machine

In head MRI image, the boundary of each encephalic tissue is highly complicated and irregular. It is a real challenge to traditional segmentation algorithms. Support vector machine (SVM) has high generalization ability, especially for dataset with small number of samples in high dimensional space. However, selecting parameters for SVM is a complicated problem which directly affects segmentation result. immune algorithm (IA), mainly applied to optimization, has the abilities of learning, memorizing and self- adaptive adjusting. The main idea is to search optimal parameters for SVM using IA. In this paper, an immune support vector machine (ISVM) is proposed to segment MRI image. As our experiment shown, the boundaries of 5 kinds of encephalic tissues are extracted successfully, and ISVM reaches satisfactory generalization accuracy.
Immigrant college students' academic obstacles

Immigrant college student populations continue to grow on college campuses across the nation; yet, little is known about the experiences of immigrant students. This paper examines differences in perceived academic obstacles between immigrant students and non-immigrant students at six large, public research universities (n = 56,000). The researchers found that immigrant students reported greater obstacles to their academic success, including weak math and English skills, inadequate study skills, poor study behaviors, poor study environments, and poor mental health. Using the framework of academic self-efficacy, the researchers offer guidelines to higher education practitioners, including faculty, advisors, learning assistance center staff, and other student affairs professionals, to decrease the effects of academic obstacles on immigrant students and enhance their academic self-efficacy.
Working Paper Series No : 2014 / 14 Cambridge Working Paper Economics : 1463 THE “ MYSTERY OF THE PRINTING PRESS ” MONETARY POLICY AND SELF-FULFILLING DEBT CRISES First Version : December 2012

We study the conditions under which unconventional (balance-sheet) monetary policy can rule out selffulfilling sovereign default in a model with optimizing but discretionary fiscal and monetary policymakers. When purchasing sovereign debt, the central bank effectively swaps risky government paper for monetary liabilities only exposed to inflation risk, thus yielding a lower interest rate. As central bank purchases reduce the (ex ante) costs of debt, we characterize a critical threshold beyond which, absent fundamental fiscal stress, the government strictly prefers primary surplus adjustment to default. Because default may still occur for fundamental reasons, however, the central bank faces the risk of losses on sovereign debt holdings, which may generate inefficient inflation. We show that these losses do not necessarily undermine the credibility of a backstop, nor the monetary authorities’ ability to pursue its inflation objectives. Backstops are credible if either the central bank enjoys fiscal backing or fiscal authorities are sufficiently averse to inflation. Giancarlo Corsetti Luca Dedola (University of Cambridge) (European Central Bank) Cambridge-INET Institute Faculty of Economics
A Self-Adaptive Metadata Management Model for Data Grid

Metadata management is a key technology of heterogeneous data sources integration. Metadata management in data grid is based on distributed multi-layer mapping on resources, which has the characteristics of centralization in indexing and decentralization in resources. These characteristics may lead multiple request redirections in the relevant resource search process and eventually increase the cost of resources searching. In order to lower the probability of request redirections and improve querying efficiency, this paper studied into the metadata management model and presented an adaptive metadata management model. This model introduced the neighborhoods between metadata servers and constructed first-fit-first-link algorithm for adjusting the neighborhoods in the model dynamically. The model also replaced request redirection and its subsequent querying with the querying in metadata replication, strived to improve efficiency in resources searching.
Actitudes frente al aprendizaje de los estudiantes de la escuela superior de cómputo del instituto politécnico nacional, como aprendices del siglo XXI

El enfoque del modelo Educativo del IPN se centra en el aprendizaje. (IPN, 2000) por lo que los profesores pretenden desarrollar en sus alumnos, no solo conocimientos, sino tambien competencias. Para lo cual es necesario tomar en cuenta los conocimientos previos e intereses del alumno, su tipo de pensamiento y estilos de aprendizaje, entre otros. El presente trabajo expone como los estudiantes de la carrera de Ingenieria en Sistemas Computacionales de la Escuela Superior de Computo del Instituto Politecnico Nacional, experimentan su vida en las aulas, a partir del analisis de sus actitudes frente a las actividades de aprendizaje propuestas por los profesores asi como a la exigencia academica que se les impone. Con un diseno de investigacion de tipo transeccional con componente predominante cuantitativo, empleando un cuestionario cerrado que fue administrado a traves de formularios de Google, en el periodo de mayo a junio de 2015, se detecta que el 60% de los estudiantes encuestados se siente agotado frente a las exigencias academicas, manifiestan aburrimiento (49%), disgusto (55%), debido al tipo de actividades de aprendizaje propuestas en el aula, asi como por las actividades extraclase que deben realizar en forma cotidiana; se destaca sin embargo, que el 71% concluye sus actividades academicas de forma eficaz.
Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

Abstract. A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1°×1° and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets. A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75% of the MSG-retrieved AOD at 0.6 and 0.8 μm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15 τ and a high correlation coefficient (R>0.86). The AOD datasets derived using the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval and analysis, and the analysis and validation of reflectance will be given in a following paper.
Comparison of capacitor voltages balancing methods for cascaded H-Bridge STATCOM

The object of the research work is the multilevel high-voltage converters with Cascaded H-Bridges (CHB) controlled by two different methods. They are the sorting algorithm and the fundamental harmonic displacement method. The comparison was carried out on the basis of mathematical modeling for the case of cascade connection of 10 cells with the rated voltage on the capacitors equal to 1000 V. Such circuits can be implemented, in particular, in high power STATCOMs with the voltage of 10 kV. Such converters are widely applied to compensate reactive power of electrical facilities at ironworks, for example at arc furnaces or continuous hot or cold rolling mills. In the process of control system design, it is necessary to solve a dual problem, particularly, to provide balancing of capacitor voltages for the minimum time and within a definite range. It is also necessary to avoid the overloading of the slave switching devices caused by the increase in the number of commutations. The modeling results showed that the suggested methods of balancing can provide the acceptable voltage quality at the point of connection, THD of output voltage is 5.23% and 7% for the first and the second method respectively. When the sorting algorithm is used, balancing takes less time as compared with the displacement method. However, this is achieved at the cost of higher load imbalance of semiconductor switches, which is undesirable in terms of thermal conditions. When balancing was carried out using the displacement method, the load of semiconductor switches was more uniform. This method can be recommended for practical application as the main one.
Long-Term Earnings Forecast Models for Nonseasonal Firms

This paper examines the long-term predictive ability of earnings forecast models for a sample of 167 firms whose quarterly earnings numbers exhibit nonseasonal behavior. Empirical evidence is provided showing that: 1) a large number of firms (n=167, i.e., 28.2% of the sample) exhibit nonseasonal patterns in their quarterly earnings series; 2) the use of quarterly ARIMA forecast models does not result in enhanced annual earnings predictions versus annual ARIMA models for nonseasonal firms; 3) the size effect documented by Bathke et al. (1989) for short-term quarterly earnings forecasts also pertains to long-term, annual earnings forecasts: and 4) larger firms’ earnings series display enhanced levels of earnings persistence versus those of smaller firms.
Convolutional and recurrent neural networks for real-time data classification

In this paper, methods of classification of real-time data are provided by using convolutional and recurrent neural networks. During the last several years, artificial intelligence researchers and specialists achieved notable results in visual, voice and natural language processing tasks by using new methods and approaches of deep learning, such as convolutional and recurrent neural networks. However, not much research is going on of usage such networks for elaboration of real-time data. The goal of this work is exploring, experimenting and providing new approaches of classification nonstationary data using above mentioned neural networks.
Inhibitory effect of active extract of Polygonum Cuspidatum on humancolon cancer SW480 Cells.

This study was conducted to enrich and purify the active constituent in Polygonum cuspidatum, and to study its anticancer activity. Extraction conditions of Polygonum cuspidatum were optimized by investigating the macroporous resin model, flow rate and ethanol concentration. MTT assay and electron microscopy were used to analyze the inhibitory effect of Polygonum cuspidatum extract on human SW480 cells. During the investigation of enrichment and purification process, the optimal conditions for enrichment of Polygonum cuspidatum extract with D101 macroporous resin were identified to be a 3-fold amount of 75% ethanol, and a flow rate of 2 BV/h. Under electron microscope, nuclear morphological changes are seen in SW480 cells treated with Polygonum cuspidatum extract; apoptotic characteristics are significant, and apoptotic bodies are formed eventually. MTT assay results showed that the inhibitory effect of Polygonum cuspidatum extract on SW480 cells increases with the increase of concentration and prolongation of action time within the experimental concentration range, which proves a dose- and time-dependence of action. Polygonum cuspidatum extract has a marked inhibitory effect on human colon cancer SW480 cells.
Unraveling the thread of nature’s tapestry: the genetics of diversity and convergence in animal pigmentation

Animals display incredibly diverse color patterns yet little is known about the underlying genetic basis of these phenotypes. However, emerging results are reshaping our view of how the process of phenotypic evolution occurs. Here, we outline recent research from three particularly active areas of investigation: melanin pigmentation in Drosophila, wing patterning in butterflies, and pigment variation in lizards. For each system, we highlight (i) the function and evolution of color variation, (ii) various approaches that have been used to explore the genetic basis of pigment variation, and (iii) conclusions regarding the genetic basis of convergent evolution which have emerged from comparative analyses. Results from these studies indicate that natural variation in pigmentation is a particularly powerful tool to examine the molecular basis of evolution, especially with regard to convergent or parallel evolution. Comparison of these systems also reveals that the molecular basis of convergent evolution is heterogeneous, sometimes involving conserved mechanisms and sometimes not. In the near future, additional work in other emerging systems will substantially expand the scope of available comparisons.
Droop Control as an Alternative Inertial Response Strategy for the Synthetic Inertia on Wind Turbines

In several countries, the wind power penetration increased tremendously in recent years. To ensure the proper functioning of the power system, some grid operators already require the capability to provide inertial response or primary control with wind turbines. This paper discusses the emulated inertial response with wind turbines by means of the synthetic inertia and the droop control strategy. The behavior of the synthetic inertia strategy is determined by the inertial and the droop constant, whereas droop control only has a droop constant. When these strategies are used, it is important to tune the control parameters depending on the power system to which the wind turbines are connected. Simulations show that it is possible to enhance but also to deteriorate the frequency response of the system, dependent on these parameters. For different power system compositions, the optimal inertial constant is always close to zero. This way, the synthetic inertia strategy reduces to a fast droop control strategy. This is an important outcome, as it means that no differentiation of the frequency is needed to obtain an optimal inertial response from the wind turbines, which is beneficial in terms of robustness. Consequently, droop control is a viable alternative for the synthetic inertia.
Optimal Adiposity Measurement and Risk Stratification in Established Ischaemic Stroke

Background: Prevention strategies post-stroke should target risk factor reduction which includes consideration of weight, diet and lipoprotein profiles. Limited data informs the optimal adiposity measurement post-stroke to target those at highest recurrent risk. This study aims to identify adiposity measurement/s post-stroke that best predict cardiovascular and co-morbid risk. Subjects and Methods: 142 stroke patients (100 males, 42 females; mean age 63 years) participated. Adiposity and metabolic profiles included BMI, waist circumference, waist to height ratio (WHR), triglyceride levels and hypertriglyceridemic waist. The predictive ability of these measures with indices of cardiovascular risk (Cardiovascular Risk Score) and co-morbidity (Charlson’s co-morbidity index) were examined. Results: In hierarchical multiple regression models, age and gender controlled, waist (p = 0.002), triglyceride levels (p = 0.006), BMI and WHR (p = 0.014), uniquely and significantly contributed to the variance in cardiovascular risk, in their models. Only one combination of measures (waist and triglyceride levels) improved the predictive ability of waist in cardiovascular risk stratification (p = 0.001). In men, waist (p = 0.013) and in women triglyceride levels (p = 0.012) performed as the best predictors of cardiovascular risk respectively. No combination of measures was superior to triglyceride levels in women or waist circumference measures in men in predicting cardiovascular risk. With Charlson’s co-morbidity index as the dependent variable, triglyceride levels significantly contributed to variance of the model with age and gender influences controlled (p = 0.047). No combination of measures improved the predictive ability of triglyceride levels for co-morbidity. Conclusion: Waist circumference and triglyceride levels should form a minimum dataset for adiposity when considering cardiovascular and comorbid risk post-stroke.