Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis, Structure, and Antiproliferative Activity of Three Gallium(III) Azole Complexes

As part of our interest into the bioinorganic chemistry of gallium, gallium(III) complexes of the azole ligands 2,1,3-benzothiadiazole (btd), 1,2,3-benzotriazole (btaH), and 1-methyl-4,5-diphenylimidazole (L) have been isolated. Reaction of btaH or btd with GaBr3 or GaCl3 resulted in the mononuclear complexes [GaBr3(btaH)2] (1) and [GaCl3(btd)2] (2), respectively, while treatment of GaCl3 with L resulted in the anionic complex (LH)2[GaCl4] (3). All three complexes were characterized by single-crystal X-ray crystallography and IR spectroscopy, while their antiproliferative activities were investigated against a series of human and mouse cancer cell lines.
Political Monopolies in American Cities: The Rise and Fall of Bosses and Reformers

Around the same time that Richard J. Daley governed Chicago, greasing the wheels of his notorious political machine during a tenure that lasted from 1955 to his death in 1976, Anthony 'Dutch' Hamann's 'reform' government centralized authority to similar effect in San Jose. In light of their equally exclusive governing arrangements - a similarity that seems to defy their reputations - Jessica Trounstine asks whether so-called bosses and reformers are more alike than we might have realized.Situating her in-depth studies of Chicago and San Jose in the broad context of data drawn from more than 240 cities over the course of a century, she finds that the answer - a resounding yes - illuminates the nature of political power. Both political machines and reform governments, she reveals, bias the system in favor of incumbents, effectively establishing monopolies that free governing coalitions from dependence on the support of their broader communities. Ironically, Trounstine goes on to show, the resulting loss of democratic responsiveness eventually mobilizes residents to vote monopolistic regimes out of office. Envisioning an alternative future for American cities, Trounstine concludes by suggesting solutions designed to free urban politics from this damaging cycle.
Aplication of spectral subtraction for reducing industrial noises

In this paper, we use spectral subtraction to remove non-stationary industrial noise from speech signal. Motor generated industrial noises often contain a wide-band part and some limited number of dominant sinusoidal signals that they carry most of the noise energy, especially, at low frequencies. The latter components highly drop the speech signal quality. To fulfil the requirements of spectral subtraction, we present a novel approach based on multi-band minimum statistics to estimate the noise. To evaluate the performance of the proposed algorithm, tests are conducted that carry reduction of real angle grinder noise, in both engaged and non engaged phases, from speech samples. Objective and subjective measures reveals that our proposed algorithm can eliminate wide-band angle grinder noise plus the non-stationary dominant frequency components well enough, with minimum speech distortion and musical noise in the processed speech signal.
PressureFish: a method to improve control of discrete pressure-based input

Studies investigating user control of pressure input have reported time-accuracy trade-offs of, on average, over 30%, when interacting with a large number of pressure levels. To increase the level of control with pressure input, we designed and evaluated four different discretization functions: linear, fisheye, visual fisheye, and clustered. The fisheye discretization dynamically modifies the range of pressure values based on the position of the pressure cursor. Our results show that a fisheye function results in significantly lower error rates and a lower number of crossings than have been reported in the literature. Furthermore, the fisheye function improves control without compromising speed. We discuss the findings of our study and identify several design recommendations for integrating pressure control into common interface tasks.
Incremental Concept Formation made More Efficient by the Use of Associative Concepts

Formal Concept Analysis (FCA) is interested in the formation of concept lattices from binary relations between objects and attributes, a.k. a. contexts. Many algorithms have been proposed to generate the set of all concepts, and also the edges of the lattice between these concepts. We develop the principle and the code of a new algorithm combining two existing ones, Godin's and Bordat's algorithms. Then, we show by both a theoretical and practical study that it is the most efficient algorithm for sparse contexts, which are usually found in real applications.
Reliable measurements of threat detection.

Airport security technology has evolved remarkably over the last decades, which is especially evident when state-of-the art detection systems are concerned. In many cases however, such systems will be only as effective as the personnel who operates them. Indeed, the importance of human factors has gained much attention recently and it has become clear that effective selection, evaluation and training of airport security personnel are crucial factors for increasing airport security and efficiency. Since June 2000 researchers from the University of Zurich have conducted several studies at Zurich Airport, which revealed important insights for the following issues: (1) reliable measurements of threat detection, (2) screener evaluation and selection, (3) training of screeners, and (4) pre-employment assessment. The scientific studies were conducted in close collaboration with Zurich State Police, Airport Division and were funded by Zurich Airport. In AIRPORT 3/2002 an overview of these studies was presented (page 20-21). In this article the first topic is discussed in more detail, i.e. how threat detection can be measured reliably. Measuring threat detection: an example Evaluation and certification are important topics in this year's airport security agendas, in Europe as well as in the US. Reliable measures of threat detection are essential in order to determine the performance of individuals, companies and airports. But measuring threat detection is not so simple. Consider the following example: two screeners take a threat detection test in which 200 x-ray images are shown and half of them contain a threat item (e.g. a gun, knife, dangerous good or bomb). Both screeners achieve the same hit rate, i.e. they both detect threat items in about 90 % of the cases. Intuitively, one is tempted to conclude that both screeners are comparable in terms of their threat detection performance. But unfortunately , the hit rate alone does not tell you much. The reason is easy to understand: at test, a participant could simply judge each x-ray image as being not ok and thereby achieve a hit rate of 90 % or more. In order to find out whether a high hit rate just reflects such a " liberal " response bias and not a good detection performance we have to consider the false alarm rate, too. In the current example 200 images were used at test and only half of them contained a threat item. The hit rate is the percentage of times a bag was judged to be …
Dispersability of Carbon Nanotubes in Biopolymer-Based Fluids

In this review the dispersability of carbon nanotubes in aqueous solutions containing proteins, or nucleic acids, is discussed. Data reported previously are complemented by unpublished ones. In the mentioned nanotube-based systems several different phases are observed, depending on the type and concentration of biopolymer, as well as the amount of dispersed nanotubes. The phase behavior depends on how much biopolymers are adsorbing, and, naturally, on the molecular details of the adsorbents. Proper modulation of nanotube/biopolymer interactions helps switching between repulsive and attractive regimes. Dispersion or phase separation take place, respectively, and the formation of liquid crystalline phases or gels may prevail with respect to dispersions. We report on systems containing ss-DNA- and lysozyme-stabilized nanotubes, representative of different organization modes. In the former case, ss-DNA rolls around CNTs and ensures complete coverage. Conversely, proteins randomly and non-cooperatively adsorb onto nanotubes. The two functionalization mechanisms are significantly different. A fine-tuning of temperature, added polymer, pH, and/or ionic strength conditions induces the formation of a given supra-molecular organization mode. The biopolymer physico-chemical properties are relevant to induce the formation of different phases made of carbon nanotubes.
Pattern Match Query in a Large Uncertain Graph

Many studies have been conducted on seeking an efficient solution for pattern matching over graphs. This interest is largely due to large number of applications in many fields, which require efficient solutions for pattern matching, including protein complex prediction, social network analysis and structural pattern recognition. However, in many real applications, the graph data are often noisy, incomplete, and inaccurate. In other words, there exist many uncertain graphs. Therefore, in this paper, we study pattern matching in a large uncertain graph. Specifically, we want to retrieve all qualified matches of a query pattern in the uncertain graph. Though pattern matching over an uncertain graph is NP-hard, we employ a filtering-and verification framework to speed up the search. In the filtering phase, we propose a probabilistic matching tree, PM-tree, based on match cuts obtained by a cut selection process. Based on PM-tree, we devise a collective pruning strategy to prune a large number of unqualified matches. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed algorithms.
Robust Biometric Person Identification Using Automatic Classifier Fusion of Speech, Mouth, and Face Experts

Information about person identity is multimodal. Yet, most person-recognition systems limit themselves to only a single modality, such as facial appearance. With a view to exploiting the complementary nature of different modes of information and increasing pattern recognition robustness to test signal degradation, we developed a multiple expert biometric person identification system that combines information from three experts: audio, visual speech, and face. The system uses multimodal fusion in an automatic unsupervised manner, adapting to the local performance (at the transaction level) and output reliability of each of the three experts. The expert weightings are chosen automatically such that the reliability measure of the combined scores is maximized. To test system robustness to train/test mismatch, we used a broad range of acoustic babble noise and JPEG compression to degrade the audio and visual signals, respectively. Identification experiments were carried out on a 248-subject subset of the XM2VTS database. The multimodal expert system outperformed each of the single experts in all comparisons. At severe audio and visual mismatch levels tested, the audio, mouth, face, and tri-expert fusion accuracies were 16.1%, 48%, 75%, and 89.9%, respectively, representing a relative improvement of 19.9% over the best performing expert
Conserved Functional Connectivity but Impaired Effective Connectivity of Thalamocortical Circuitry in Schizophrenia

Schizophrenia is a severe mental illness with neurobiological bases that remain elusive. One hypothesis emphasizes disordered thalamic function. We previously used concurrent single pulse transcranial magnetic stimulation (spTMS) and functional magnetic resonance imaging (fMRI) to show that individuals with schizophrenia have a decreased spTMS-evoked response in the thalamus, and decreased effective connectivity between thalamus and insula and thalamus and superior frontal gyrus. To better understand the factors that may accompany or account for these findings, we investigated, in the same participants, resting state functional connectivity, white matter structural connectivity, and grey matter integrity. Patients with schizophrenia did not differ from healthy control subjects in resting state functional- or white matter structural connectivity, although they did show decreased measures of grey matter integrity in the insula. However, in this region, the spTMS-evoked response did not differ between groups. In a region of the thalamus that also had grey matter intensity abnormalities, although not at a level that survived correction for multiple comparisons, the spTMS-evoked response in patients was deficient. These results suggest that measures of structure and function are not necessarily complementary. Further, given its sensitivity for identifying deficits not evident with traditional imaging methods, these results highlight the utility of spTMS-fMRI, a method that directly and causally probes effective connectivity, as a tool for studying brain-based disorders.
Word Level Language Identification and Back-Transliteration

In this paper, I describe a Rule based and List-Searching system for Word-Level Language Identification and Named Entity recognition in bilingual text. My method uses dictionary search, rules for LI and CRF++, character n-gram for NER. The model does not use any Language specific rules, therefore can easily be replicated on most languages having mixed pair with English. The model also does back-transliteration of words into native language script and recognizes named entity. The model performance is carried on the test sets provided by the shared task on language Identification for English Hindi (En-Hi) Pair, Microsoft Research India. The experimental results show a consistent performance with high precision.
IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments

IsoGene is an R package for the analysis of dose-response microarray experiments to identify gene or subsets of genes with a monotone relationship between the gene expression and the doses. Several testing procedures (i.e., the likelihood ratio test, Williams, Marcus, the M , and Modiﬁed M ), that take into account the order restriction of the means with respect to the increasing doses are implemented in the package. The inference is based on resampling methods, both permutations and the Signiﬁ-cance Analysis of Microarrays (SAM).
Determinants of Worldwide Software Piracy

Software piracy or ‘softlifting’, the unauthorized use or illegal copying of computer software seems to be a persistent and truly global phenomenon, in spite of international efforts to reduce it. Could the battle against it be won by just merely accepting international standards for the protection of intellectual property rights? The answer to this question is the main focus of this research. This paper explores the determinants of cross-national variation in software piracy rates by performing a quantitative analysis using economic, cultural and legal variables. Based on regression analysis results, we identified factors which have a significant impact on software piracy. Among them, economic and legal factors make the most important contribution to the variability of worldwide software piracy rates. The implications of these results both for practice and theory are discussed.
Energy Modeling and HMST-Based Wireless Sensor Networks Routing Protocol

With the advances of MEMS, low-power analog and digital electronics, and low-power RF design, the design of wireless sensors has been developed rapidly. The advances of wireless sensors have enabled wireless sensor networks which are deployed to collect useful information from an area of interest. Because of the energy restriction of wireless sensors, the problem to extend the lifetime of wireless sensor networks becomes the most important problem in the field of wireless sensor networks research. In this paper, we analysis the Energy Modeling of wireless sensor networks and propose a routing algorithm based on hierarchical minimum spanning tree. Our simulation results show that our algorithm performs well than LEACH algorithm.
DForC: A real-time method for reaching, tracking and obstacle avoidance in humanoid robots

We present the Dynamic Force Field Controller (DForC), a reliable and effective framework in the context of humanoid robotics for real-time reaching and tracking in presence of obstacles. It is inspired by well established works based on artificial potential fields, providing a robust basis for sidestepping a number of issues related to inverse kinematics of complex manipulators. DForC is composed of two layers organized in descending order of abstraction: (1) at the highest level potential fields are employed to outline on the fly collision-free trajectories that serve to drive the robot end-effector toward fixed or moving targets while accounting for obstacles; (2) at the bottom level an optimization algorithm is exploited in place of traditional techniques that resort to the Transposed or Pseudo-Inverse Jacobian, in order to deal with constraints specified in the joints space and additional conditions related to the robot structure. As demonstrated by experiments conducted on the iCub robot, our method reveals to be particularly flexible with respect to environmental changes allowing for a safe tracking procedure, and generating reliable paths in practically every situation.
Research of PageRank Algorithm Based on Transition Probability

In order to improve the veracity of the web search, this paper studies the PageRank algorithm, proposes a new method PBTP Algorithm (PageRank Based on Transition Probability), that is an improvement for the classical PageRank method. As forwarding links in a web page are different, the transition probability of a link to be clicked is different too. For the classical PageRank value, should assign more authority value to the page according to its clicking probability with high authority value, effectively to focus the authority value on more meaningful web page, finally extracts meaningful page with high authority value. This paper takes advantage of Web link structure, proposes an unequal way to treat the different pages when distributing authorities. And the experiment shows that PBTP can improve PageRank effectively.
Dividend Policy: Its Impact on Firm Value

The purpose of this book is to review the theory and empirical evidence regarding the impact of dividend policy on shareholder wealth. It cuts to the quick of such long-standing questions as Why do corporations pay dividends? and Why should investors care? With relevant anecdotes, surveys, examples, and research from the financial press, company documents, and academic literature, the book focuses less on mathematics and more on the intuition of share valuation as a function of dividend policy. While the authors acknowledge the irrelevance of dividend policy in a world with perfect capital markets, they stress how market imperfections such as taxes, imperfect information, and agency issues can alter the dividend irrelevance conclusion. It concludes with the authors'recommendations on how managers should incorporate market imperfections most relevant to their firm in setting dividend policy. Not mathematically rigorous.
Segmentation of textured images using a multiresolution Gaussian autoregressive model

We present a new algorithm for segmentation of textured images using a multiresolution Bayesian approach. The new algorithm uses a multiresolution Gaussian autoregressive (MGAR) model for the pyramid representation of the observed image, and assumes a multiscale Markov random field model for the class label pyramid. The models used in this paper incorporate correlations between different levels of both the observed image pyramid and the class label pyramid. The criterion used for segmentation is the minimization of the expected value of the number of misclassified nodes in the multiresolution lattice. The estimate which satisfies this criterion is referred to as the "multiresolution maximization of the posterior marginals" (MMPM) estimate, and is a natural extension of the single-resolution "maximization of the posterior marginals" (MPM) estimate. Previous multiresolution segmentation techniques have been based on the maximum a posterior (MAP) estimation criterion, which has been shown to be less appropriate for segmentation than the MPM criterion. It is assumed that the number of distinct textures in the observed image is known. The parameters of the MGAR model-the means, prediction coefficients, and prediction error variances of the different textures-are unknown. A modified version of the expectation-maximization (EM) algorithm is used to estimate these parameters. The parameters of the Gibbs distribution for the label pyramid are assumed to be known. Experimental results demonstrating the performance of the algorithm are presented.
Link Mass Optimization Using Genetic Algorithms for Industrial Robot Manipulators

Obtaining optimum energy performance is the primary design concern of any mechanical system, such as ground vehicles, gear trains, high speed electromechanical devices and especially industrial robot manipulators. The optimum energy performance of an industrial robot manipulator based on the minimum energy consumption in its joints is required for developing of optimum control algorithms (Delingette et al., 1992; Garg & Ruengcharungpong, 1992; Hirakawa & Kawamura, 1996; Lui & Wang, 2004). The minimization of individual joint torques produces the optimum energy performance of the robot manipulators. Optimum energy performance can be obtained to optimize link masses of the industrial robot manipulator. Having optimum mass and minimum joint torques are the ways of improving the energy efficiency in robot manipulators. The inverse of inertia matrix can be used locally minimizing the joint torques (Nedungadi & Kazerouinian, 1989). This approach is similar to the global kinetic energy minimization. Several optimization techniques such as genetic algorithms (Painton & Campbell, 1995; Chen & Zalzasa, 1997; Choi et al., 1999; Pires & Machado, 1999; Garg & Kumar, 2002; Kucuk & Bingul, 2006; Qudeiri et al., 2007), neural network (Sexton & Gupta, 2000; Tang & Wang, 2002) and minimax algorithms (Pin & Culioli, 1992; Stocco et al., 1998) have been studied in robotics literature. Genetic algorithms (GAs) are superior to other optimization techniques such that genetic algorithms search over the entire population instead of a single point, use objective function instead of derivatives, deals with parameter coding instead of parameters themselves. GA has recently found increasing use in several engineering applications such as machine learning, pattern recognition and robot motion planning. It is an adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. This provides a robust search procedure for solving difficult problems. In this work, GA is applied to optimize the link masses of a three link robot manipulator to obtain minimum energy. Rest of the Chapter is composed of the following sections. In Section II, genetic algorithms are explained in a detailed manner. Dynamic equations and the trajectory generation of robot manipulators are presented in Section III and Section IV, respectively. Problem definition and formulation is described in Section V. In the following Section, the rigid body dynamics of a cylindrical robot manipulator is given as example. Finally, the contribution of this study is presented in Section VII.
On Information Ratio

In this short technical paper, we obtain an equation for calculating the Information Ratio of a portfolio in the context of the single-index model. The equation is obtained by manipulating algebraically the fitted single-index model. Mathematically speaking, the equation can yield a maximum value for the Information Ratio for a very specific value of the beta of portfolio P.