Miracle-dz/UniFAR-FTUs huggingface dataset:
The Calibration Indexand Taxonomy forRobot KinematicCalibration

The major approaches towards kinematic calibration are uniied by considering an endpoint measurement system as forming a joint and closing the kinematic loop. The calibration index is introduced, based on the mobility equation, that considers sensed and unsensed joints, single and multiple loops, and that expresses the surplus of measurements over degrees of freedom at each pose. Past work using open-loop calibration, closed-loop calibration, and screw axis measurement is classiied according to this calibration index. Numerical issues are surveyed, including task variable scaling, parameter variable scaling, rank determination, pose selection, and input noise handling.
External Sovereignty and International Law

This essay addresses the need to redefine current notions of sovereignty. It returns to earlier concepts of subjects joining to receive the benefits of peace and security provided by the sovereign. It diverges from most contemporary commentary by avoiding what has become traditional second-tier social contract analysis. In place of a social contract of states, this redefinition of sovereignty recognizes that international law in the twentieth century has developed direct links between the individual and international law. The trend toward democracy as an international law norm further supports discarding notions of a two-tiered social contract relationship between the individual and international law.
Estimates of covariances between reproduction and growth in Australian beef cattle.

Estimates of covariance components between scrotal circumference, serving capacity, days to calving, and yearling and final weight were obtained for Hereford, Angus, and Zebu cross cattle in temperate and tropical Australia. Analyses were carried out by REML employing a derivative-free algorithm and fitting bivariate animal models. Aspects of modeling and computational requirements related to the use of this method are discussed. Estimates of heritabilities agreed closely with those from univariate analyses, being low for female reproductive performance and moderate to high for male reproduction and growth. Estimates of genetic correlation between male and female fertility traits were low but favorable, being -. 25, -. 28, and -. 41 between scrotal circumference and days to calving for Herefords, Angus, and Zebu crosses, respectively. Genetic correlations between male reproductive traits and weights ranged from . 24 to . 52 for the temperate breeds and were higher (.65 to .69) for Zebu crosses. Phenotypic correlations between scrotal circumference and weights were similar for all breeds, ranging from . 32 to . 47, whereas serving capacity and weights were phenotypically unrelated. Estimates of correlations between days to calving and weights were less consistent. Phenotypically, there was little association between the two traits. Genetic correlations for Zebu crosses were negative and low to moderate (-.36 to -.66) and estimates for Angus were close to zero.
Vladimir Melnik Nonlinear Locally Adaptive Techniques for Image Filtering and Restoration in Mixed Noise Environments

In this thesis, nonlinear locally adaptive techniques of noise removal and restoration are considered for image processing applications in mixed noise environments. These techniques are designed and tested for radar, ultrasound, and gray-level test images. The image observation models take into account the influence of fluctuating (additive and multiplicative) and impulsive noise as well as blurring effects. Hard-switching robust adaptive algorithms are proposed and analyzed for radar image processing. In these algorithms, a proper filter for each pixel is selected from a set of filters using a local activity indicator (LAI). The problems of design and optimization of L-filters, including their application in hard-switching algorithms, are considered for speckle imagery. The analysis of the algorithms for LAI calculation is performed for different noise models. Considerable attention is given to the selection of thresholds in hard-switching schemes. Several locally adaptive techniques of image restoration are considered in the context of mixed noise models. A simple locally adaptive algorithm is described based on switching of the regularization parameter depending upon the LAI value. In the other algorithms, nonlinear locally adaptive filters are used for preprocessing of blurred images. We also propose iterative restoration techniques, where a nonlinear filtering operation is used in place of the constraint operator. The effectiveness of the proposed methods is demonstrated by means of numerical simulations. Nonlinear spatially adaptive multiscale methods are proposed for noise removal (denoising) based on the so-called block-median pyramidal transform (BMPT). The BMPT is based on non-overlapping block decompositions using the median operation and polynomial interpolation. In addition to the softand hard-thresholding schemes, a locally adaptive thresholding technique that uses a scale dependent local variance is employed. The probability distribution of the BMPT coefficients is analytically derived under the assumption of independent and identically distributed (i.i.d.) input samples. The results of this statistical derivation are used for selecting the thresholds for denoising. Numerical simulations demonstrate that the proposed multiscale methods are effective for noise removal in images. The results of the thesis are presented in the publications in the appendix.
Earlybird : Catching the Internet Worm

Internet worms, or malicious programs that spread autonomously from computer to computer, have become a problem at the forefront of computer security concerns in recent years. Several different approaches to the problem provide partial solutions, but the general problem of detecting when a worm has entered a network has not been solved. In this paper, we apply machine learning techniques to the problem of worm detection. We apply the tools of graphical models and statistical inference to the problem of detecting internet worms. Specifically, we model the problem as an HMM, in which the hidden state is either packet infected or packet clean, and the learner make observations on internet traffic. We survey current problems and solutions in the field of internet worm defense. We discuss approaches to modeling the problems, and we touch on several issues in representation and simulation. We describe models for normal network behavior and worm behavior that enable large-scale simulation of normal networks and worm infection, and we discuss simplifications and tradeoffs that aid in performing such simulation. Finally, we discuss our experience with implementing a simulator and our frustrations due to persistent bugs. We analyze the (incomplete) results we have obtained, and we show that the simulator and learner do produce interesting behavior that shows evidence of learning potential once the bugs are eliminated.
Local disaggregation of demand and excess demand functions: a new question

The literature on the characterization of aggregate excess and market demand has generated three types of results: global, local, or 'at a point'. In this note, we study the relationship between the last two approaches. We prove that within the class of functions satisfying standard conditions and whose Jacobian matrix is negative semi-definite, only n/2 + 1 agents are needed for the 'at' decomposition. We ask whether, within the same class, the 'around' decomposition also requires only n/2 + 1 agents.
Fusion in Fingerprint Authentication: Two Finger Scenarios

This paper presents our initial results of fingerprint fusion by analysing all pair combinations of ten fingers (i.e. 45 pairs). As a fingerprint verification algorithm we use a commercially available software package from Neurotechnology. As a test data set we use GUC100 multi scanner fingerprint database which contains fingerprint images of all ten fingers from 100 subjects collected over several months. We conduct score level fusion and apply two fusion methods, namely max and sum rules. Our analysis indicates that fusion using both rules provide improvement in all scenarios, and max rule showing slightly better performance compared to max rule's performance. The obtained performance improvements (in terms of EER) for sum and max rules are in the range of 60.2\%-91.1\% and 67.4\%-93.5\%, respectively.
The development of an autonomous overhead traveling crane with real time path planning based on the potential method

This paper presents a method to establish real time path planning for three-dimensional (3-D) autonomous mobile overhead crane system. Proposed crane system consists of a visual system and a planning system. In the visual system, CCD image sensors, laser line markers and ultrasonic sensor (USS) are used to scan the transfer space. First, environmental map is generated by CCD image sensors and laser line markers. USS is used at on-line path planning that to renovate environmental map during the transferring in terms of the quick scanning time. This environmental map describes the transfer environment. Real time path planning is also established by expanding the off-line path planning and included in the planning system. Potential method is used as it's algorithm to develop updated Potential Field that describes the latest environment of transfer space. Transfer path is then constructed from the updated potential field and used for crane control references. The usefulness of proposed system is confirmed by the preliminary experimental results.
Speech emotion recognition using SVM with thresholding fusion

This paper presents a methodology for emotion recognition from speech signals and textual information together to improve the confidence level of emotion classification by using the threshold fusion. Some of acoustic features are extracted from the speech signal to analyze the characteristics and behavior of speech. Support Vector Machines (SVMs) are used for recognition of the emotional states. In this approach textual analysis of all emotions and emotional contents are manually defined and labeled. Emotion intensity levels of all emotional content and emotional words are calculated. The absolute emotional state is predicted from the acoustic features and textual contents using threshold based fusion. Results obtained from proposed approach show that the accuracy of the combined system has been improved as compared to the two individual methodologies.
Investigating the Sources of Agricultural Growth in Africa: Factor Accumulation, Total Factor Productivity, and Technology Absorption

This paper investigates sources and determinants of agricultural growth in Africa, concentrating on the growth path during the last three decades. The analysis employs the broader framework provided by empirical growth literature and recent developments in Total Factor Productivity (TFP) measurement to search for fundamental determinants of growth in African agriculture. One main contribution and new findings in this analysis is the quantification of the contribution of the productivity growth and the contribution of different inputs such as land, labor, tractor and fertilizer in the agricultural growth. Growth accounting computation highlights the fact that factor accumulation rather than TFP accounts for a large share of agricultural output growth and fertilizer has been the most statistically important physical input contributor to agricultural growth. The study also highlights the extent to which agricultural growth contributors vary in relation with different country conditions, institutions and politico-historical factors.
Anisotropic Nuclear Spin Interactions for the Morphology Analysis of Proteins in Solution by NMR Spectroscopy

Determining the relative orientation of domains within a protein is an important problem in structural biology, which has been difficult to address by either X-ray crystallography or NMR. The structure of a multidomain protein in a crystal lattice can be altered by crystal packing forces, resulting in different domain arrangements from those in solution. On the other hand, conventional NMR primarily provides short-range structural information, including proton-proton distances derived from nuclear Overhauser effects (NOEs) and torsion angles through vicinal spin couplings. Thus, NMR cannot always determine the precise interdomain arrangements due to the sparsely observed spin interactions between domains. However, the weak alignment of proteins in solution has enabled a new NMR technique to determine the domain arrangement based on the different structural information, which defines the orientation of a structural unit in protein against the magnetic field. This technique relies on the anisotropic nuclear spin interactions that only occur for a molecule in a weakly aligned state. In this review, the basics of the new NMR approach are described with focusing on its application to domain orientation analysis. We also describe our recently established NMR approach using the same spin interactions, which expands the domain arrangement analysis to higher-molecular weight proteins over 100 kDa.
Transport in Transitory Dynamical Systems

We introduce the concept of a “transitory” dynamical system—one whose time-dependence is confined to a compact interval—and show how to quantify transport between two-dimensional Lagrangian coherent structures for the Hamiltonian case. This requires knowing only the “action” of relevant heteroclinic orbits at the intersection of invariant manifolds of “forward” and “backward” hyperbolic orbits. These manifolds can be easily computed by leveraging the autonomous nature of the vector fields on either side of the time-dependent transition. As illustrative examples we consider a two-dimensional fluid flow in a rotating double-gyre configuration and a simple one-and-a-half degree of freedom model of a resonant particle accelerator. We compare our results to those obtained using finite-time Lyapunov exponents and to adiabatic theory, discussing the benefits and limitations of each method.
Weak soundness and completeness preservation by parallel composition

Recent developments of the graph-theoretic account of logics and their combinations led to the proposal of a novel way of combining logics | parallel composition | subsuming bring, fusion and other combinations. Together with a brief survey of parallel composition, some technical lemmas are established that are used to prove the preservation, under some provisos, of weak soundness and weak completeness by parallel composition.
Potent Effects of Flavonoid-Rich Extract from Rosa laevigata Michx Fruit against Hydrogen Peroxide-Induced Damage in PC12 Cells via Attenuation of Oxidative Stress, Inflammation and Apoptosis

Oxidative stress-induced neuronal death has an important role in the pathogenesis of neurodegenerative disorders. The effects and mechanisms of action of the total flavonoids (TFs) from Rosa laevigata Michx fruit against hydrogen peroxide (H2O2)-induced oxidative injury in PC12 cells were investigated in this study. The results demonstrated that the TFs protected against cell apoptosis, DNA and mitochondrial damage caused by H2O2 based on single cell gel electrophoresis, in situ terminal deoxynucleotidyltransferase dUTP nick end labeling (TUNEL), flow cytometry and transmission electron microscope (TEM) assays. In addition, the TFs notably decreased cytochrome C release from mitochondria into the cytosol and intracellular Ca2+ levels, and diminished intracellular generation of reactive oxygen species (ROS). Furthermore, the TFs inhibited the phosphorylation levels of JNK, ERK and p38 MAPK as well as down-regulated the expressions of IL-1, IL-6, TNF-α, Fas, FasL, CYP2E1, Bak, caspase-3, caspase-9, p53, COX-2, NF-κB, AP-1, and up-regulated the expressions of Bcl-2 and Bcl-xl. In conclusion, these results suggest that the TFs from R. laevigata Michx fruit show good effects against H2O2-induced oxidative injury in PC12 cells by adjusting oxidative stress, and suppression of apoptosis and inflammation, and could be developed as a potential candidate to prevent oxidative stress in the future.
Monte Carlo Noise Reduction Using Bayesian Method in Wavelet domain

A novel post-processing approach for removing Monte Carlo noises in synthetic images is presented in this paper. This paper first presents our findings on the statistical characteristics of the Monte Carlo noise, and then proposes a Bayesian method to remove this noise. The aim of this approach is to efficiently produce high quality synthetic images using Monte Carlo based rendering at low sampling rates . Categories and Subject Descriptors (according to ACM CCS): I.4. 3 [Image Processing and Computer Vision]
A Comparative Study of Relational Learning Capacity in Honeybees (Apis mellifera) and Stingless Bees (Melipona rufiventris)

Background Learning of arbitrary relations is the capacity to acquire knowledge about associations between events or stimuli that do not share any similarities, and use this knowledge to make behavioural choices. This capacity is well documented in humans and vertebrates, and there is some evidence it exists in the honeybee (Apis mellifera). However, little is known about whether the ability for relational learning extends to other invertebrates, although many insects have been shown to possess excellent learning capacities in spite of their small brains. Methodology/Principal Findings Using a symbolic matching-to-sample procedure, we show that the honeybee Apis mellifera rapidly learns arbitrary relations between colours and patterns, reaching 68.2% correct choice for pattern-colour relations and 73.3% for colour-pattern relations. However, Apis mellifera does not transfer this knowledge to the symmetrical relations when the stimulus order is reversed. A second bee species, the stingless bee Melipona rufiventris from Brazil, seems unable to learn the same arbitrary relations between colours and patterns, although it exhibits excellent discrimination learning. Conclusions/Significance Our results confirm that the capacity for learning arbitrary relations is not limited to vertebrates, but even insects with small brains can perform this learning task. Interestingly, it seems to be a species-specific ability. The disparity in relational learning performance between the two bee species we tested may be linked to their specific foraging and recruitment strategies, which evolved in adaptation to different environments.
TeamSpace: an environment for team articulation work and virtual meetings

We present TeamSpace, a collaborative workspace system to support the articulation work of high performance development teams. TeamSpace distinguishes between individual, social, and meeting work modes, and facilitates transitions between modes. The system provides a shared workspace that supports understanding and reporting the team's past progress and activities. Awareness of current team activities is supported by a place-based representation of team members' work modes and by a multimedia awareness and communication tool facilitating intentional and serendipitous interactions.
Tracking pitch period using particle filters

Pitch tracking has been used in many speech processing applications. Most present time domain techniques in pitch estimation mainly use autocorrelation methods and the average magnitude difference functions. This paper aims to track pitch period of speech using the particle filter approach. A simple model has been proposed to capture the pitch period variations of noisy speech during voiced periods. Performance of the proposed method is compared with standard pitch detection algorithms. Simulation results show that the proposed method can track the pitch period even if strong noise exists. It suggests that the particle filter approach could be an alternative way to address the pitch tracking problem.
Machine Learning Methods For Estimating Operator Equations

Abstract We consider the problem of fitting a linear operator induced equation to point sampled data. In order to do so we systematically exploit the duality between minimizing a regularization functional derived from an operator and kernel regression methods. Standard machine learning model selection algorithms can then be interpreted as a search of the equation best fitting given data points. For many kernels this operator induced equation is a linear differential equation. Thus, we link a continuous-time system identification task with common machine learning methods. The presented link opens up a wide variety of methods to be applied to this system identification problem. In a series of experiments we demonstrate an example algorithm working on non-uniformly spaced data, giving special focus to the problem of identifying one system from multiple data recordings.
Two average weighted measurement fusion Kalman filtering algorithms in sensor networks

For Kalman filter-based data fusion in sensor networks, based on the weighted least squares (WLS) method, two distributed measurement fusion Kalman filtering algorithms are presented in terms of the average weighted measurements and the average inverse-covariance matrices, where the second algorithm is equivalent to the micro-Kalman filter (or mu-Kalman filter) derived from the centralized Kalman filter in sensor networks. Using the information filter, it is proved that they are functionally equivalent to the centralized fusion Kalman filtering algorithm, i.e. they give the Kalman estimators which are numerically identical to the centralized Kalman estimators. They not only have the global optimality, and but also can reduce the computational burden. Two numerical simulation examples verify their functional equivalence.
Pengaruh Penerapan Good Corporate Governance (GCG) Pada Variabel Ukuran, Debt Ratio, Dan Sektor Industri Terhadap Nilai Perusahaan

GCG berpengaruh terhadap nilai perusahaan karena tingginya kesadaran perusahaan untuk menerapkan GCG sebagai sebuah kebutuhan, bukan sekedar kepatuhan terhadap regulasi yang ada. Ukuran perusahaan berpengaruh terhadap nilai perusahaan didasarkan pada kenyataan bahwa semakin besar ukuran perusahaan, maka ada kecenderungan untuk menggunakan jumlah pinjaman yang lebih besar pula. Debt ratio dipahami sebagai penaksir dari resiko yang melekat pada suatu perusahaan, artinya debt ratio yang semakin besar menunjukkan resiko investasi yang besar pula. Penerapan GCG terhadap nilai perusahaan karena disebabkan oleh sektor industri yang berbeda-beda dan hanya fokus pada satu sektor industri saja, sehingga setiap sektor mempunyai keunikan masing-masing dari profil berbeda. Penelitian ini dilakukan pada semua sektor industri yang tercatat di Bursa Efek Indonesia. Jumlah sampel yang digunakan adalah sebanyak 30 perusahaan selama 5 tahun dari 2007-2011. GCG dihitung menggunakan GCG score dengan variabel tertentu, nilai perusahaan dihitung menggunakan tobins’Q dan terdapat variabel kontrol yaitu ukuran, debt ratio, dan sektor industri. Penelitian ini dilakukan untuk mengetahui pengaruh penerapan GCG pada variabel ukuran, debt ratio, dan sektor industri terhadap nilai perusahaan.