Miracle-dz/UniFAR-FTUs huggingface dataset:
An optimal algorithm for the (≤ k)-levels, with applications to separation and transversal problems

This paper gives an optimal <inline-equation><f>O<fen lp="par">n<rf>log</rf>n+nk<rp post="par"></fen></f></inline-equation> time algorithm for constructing the levels1,…,<? Pub Fmt italic>k<? Pub Fmt /italic> in an arrangement of<? Pub Fmt italic>n<? Pub Fmt /italic> lines in the plane. This algorithmis extended to compute these levels in an arrangement of<? Pub Fmt italic>n<? Pub Fmt /italic> unbounded<? Pub Fmt italic>x<? Pub Fmt /italic>-monotone polygonal convex chains,of which each pair intersects at most a constant number of times. These algorithms can be used to solve the following separation andtransversal problems. For a set <? Pub Fmt italic>n<? Pub Fmt /italic>blue points and an set of <? Pub Fmt italic>n<? Pub Fmt /italic> redpoints, find a line that separates the two sets in such a way that thesum, <? Pub Fmt italic>m<? Pub Fmt /italic>, of the number of red pointsabove the line and the number of blue points below the line isminimized. Such an optimal line can be found in <inline-equation><f>O<fen lp="par">nm<rf>log</rf>m+n<rf>log</rf>n<rp post="par"></fen></f></inline-equation> time. For a set of<? Pub Fmt italic>n<? Pub Fmt /italic>line segments in the plane, find aline that intersects the maximum number of the line segments. Such anoptimal line can be found in <inline-equation><f>O<fen lp="par">nm<rf>log</rf>m+n<rf>log</rf>n<rp post="par"></fen></f></inline-equation> time for vertical segments and in <inline-equation><f>O<fen lp="par"><fen lp="par">nm<rf>log</rf>m+n<lim align="r"><op><rf>log</rf></op><ul>2</ul></lim>n<rp post="par"></fen><g>a</g><fen lp="par">n<rp post="par"></fen><rp post="par"></fen></f></inline-equation> expected time for arbitrary line segments, where<? Pub Fmt italic>m<? Pub Fmt /italic> denotes the <? Pub Caret>number ofline segments not intersected by the optimal line.
Regulation of beta‐adrenergic receptor expression in rat liver

To begin defining the factors regulating neurotransmitter receptor expression, we examined beta‐adrenergic receptors in rat liver in vivo and in primary liver cultures under defined hormonal conditions. β‐receptors described a remarkable developmental profile in vivo, increasing fivefold between embryonic days 16 and 20, and decreasing tenfold by early adulthood. The developmental decrease reflected reduced receptor number without a change in receptor properties. The ontogenetic decrease appeared to be specific for β‐receptors α‐receptors developed in a hyperbolic fashion, reaching high plateau values by the third postnatal week.
Triazidotris[μ-2-(2-pyridyl)ethanolato]dicobalt(II) acetonitrile monosolvate

In the title compound, [Co2(C7H8NO)3(N3)3]·CH3CN, the two CoII ions in the dinuclear complex have different coordination environments, both in a distorted octahedral geometry. One CoII atom is coordinated by three O atoms from the three 2-hydroxyethylpyridine (HEP) bridging ligands, two N atoms from two HEP ligands and one azido ligand, while the other CoII atom is coordinated by the same three O atoms, one N atom from an HEP ligand and two azido ligands. Weak intermolecular C—H⋯N hydrogen bonds link the dinuclear complexes into corrugated layers parallel to the bc plane. These layers are further packed with the formation of channels propagating in [010] and filled with the disordered [in a ratio 0.691 (13):0.309 (13)] acetonitrile solvate molecules.
Differential Responses of Children with Varying Degrees of Reactive and Proactive Aggression to Two Forms of Psychosocial Treatment

Based on the unique clinical correlates of two subtypes of childhood aggression (reactive and proactive), this study examined possible differential treatment effects for children with varying degrees of reactive or proactive aggression receiving one of two types of psychosocial treatment aimed at addressing these unique characteristics. Fortyseven affectively dysregulated children with oppositional defiant disorder (ODD) participated in the study. Results suggest a main effect for type of treatment, modest support for the moderating role of proactive aggression, and no support for reactive aggression as a moderator of treatment outcome. Implications for assessment and treatment of aggression are discussed. Acknowledgements I would like to thank a number of key individuals for their help and support throughout this project and within my life. First, I would like to thank Ross Greene for investigating this topic and generously allowing me to extend the research. Moreover, I thank him for his continued guidance, support, and friendship. Second, I wish to thank my supervisors and colleagues for their support and assistance. In particular, I would like to thank Thomas Ollendick for his wisdom, direction, patience, reassurance, and for reading countless rough drafts. I would also like to extend my appreciation to Heather Alvarez, for her boundless energy and invaluable editing. Third, I would like to thank my friends and family who have provided a firm foundation for learning and growing. Most of all, I would like to thank my parents and grandparents for their confidence in me, love, and support. I am also grateful to Jill and Julie for their friendship both near and far. Finally, I wish to thank Andy for helping me to smile whether circumstances warranted it or not and for his encouragement and love. Table of
Vitamin D-binding Protein Gene Transcription Is Regulated by the Relative Abundance of Hepatocyte Nuclear Factors 1α and 1β*

Vitamin D-binding protein (DBP)/Gc-globulin, the major carrier of vitamin D and its metabolites in blood, is synthesized predominantly in the liver in a developmentally regulated fashion. By transient transfection analysis, we identified three regions in the 5′-flanking region of the rat DBP gene, segments F-2, B, and A, that contain tissue-specific transcriptional determinants. Gel mobility shift and DNase I footprinting analyses showed that all three regions contained binding sites for the hepatocyte nuclear factor 1 (HNF1), a transcriptional regulator composed of HNF1α and HNF1β hetero- and homodimers. The activity of the most proximal segment A (coordinates −141 to −43) was DBP promoter-specific, position-dependent, and positively controlled by HNF1α. In contrast, the two more distal determinants (segments F-2 and B; coordinates −1844 to −1621 and −254 to −140, respectively) acted as classical enhancers in transfected hepatocyte-derived HepG2 cells; their activities were promoter- and orientation-independent, and disruption of their respective HNF1-binding sites resulted in marked loss of DBP gene expression. Remarkably, the activities of these two distal elements depended upon the relative levels of HNF1α and HNF1β; HNF1α had a major stimulatory effect, whereas HNF1β acted as a trans-dominant inhibitor of HNF1α-mediated enhancer activity. These results suggested that the net expression of the DBP gene reflected a balance between the two major HNF1 species; the relative abundance of HNF1α and HNF1β proteins in a cell may thus play a critical role in determining the pattern of gene expression.
E-commerce alarming security symptoms review and discussion of attacks indicators in e-commerce

The research in the field of e-commerce security and cyber frauds is continually arising. Many measures were taken to facilitate dealing with security attacks to protect the e-commerce system as a whole. However, most of these measures work only after the attack took place. In this research, we take a new approach in identifying some symptoms relating to the e-commerce website, web-server or network that might be an alarming indicator that the e-commerce system is at risk. The correlation of the existence of these symptoms with security attacks is relatively high as we show in this study and is highly crucial for e-commerce websites operators to protect their e-commerce system as a whole. The preliminary contribution of this paper is being one of the first to investigate such alarming symptoms in the field of e-commerce to ultimately detect vulnerabilities causing security breaches to the e-commerce system before their occurrence.
Applying machine learning techniques for email reply prediction

For several years now, email has grown rapidly as the most-used communications tool on the internet. One advantage of the Internet is the ease with which people can communicate online. The popularity of online communication has created an explosion of users who regularly access the internet to connect with others. Many people use email to stay in touch with relatives and friends who live far away geographically. We propose a new framework to help prioritised email better using machine learning techniques; an intelligent email reply prediction system. Our goal is to provide concise, highly structured and prioritised emails, thus saving the user from browsing through thousands of emails and help to reduce time spent on checking and reading email messages.
Feature selection for text categorization on imbalanced data

A number of feature selection metrics have been explored in text categorization, among which information gain (IG), chi-square (CHI), correlation coefficient (CC) and odds ratios (OR) are considered most effective. CC and OR are one-sided metrics while IG and CHI are two-sided. Feature selection using one-sided metrics selects the features most indicative of membership only, while feature selection using two-sided metrics implicitly combines the features most indicative of membership (e.g. positive features) and non-membership (e.g. negative features) by ignoring the signs of features. The former never consider the negative features, which are quite valuable, while the latter cannot ensure the optimal combination of the two kinds of features especially on imbalanced data. In this work, we investigate the usefulness of explicit control of that combination within a proposed feature selection framework. Using multinomial naïve Bayes and regularized logistic regression as classifiers, our experiments show both great potential and actual merits of explicitly combining positive and negative features in a nearly optimal fashion according to the imbalanced data.
Hyperspectral data classification using an ensemble of class-dependent neural networks

Hyperspectral data are characterized by a huge size due to hundreds of narrow frequency bands. However, the classes of interest are often characterized by only a few features from the available (ormodified) feature space. Using a few samples of the classes of interest it is possible to identify the features characterizing the classes by calculating the Bhattacharya distance, B or the Jeffries-Matusita distance, J for every feature and for every class combination. However, the classification using these features is not trivial. We use a new architecture based on class-dependent neural networks for this purpose. Class-dependent neural network is a feed forward neural network for every class with features characterizing only that class as inputs. In the combined architecture, all the classes are first, individually separated from other classes using first level class-dependent neural networks to map the characteristic features to a fuzzy value for each of the classes. Then, a final classification decision is made using a second level neural network with inputs from the outputs of the first level neural networks.
Use of within-array replicate spots for assessing differential expression in microarray experiments

MOTIVATION Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability. RESULTS A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org
TRAC-IK: An open-source library for improved solving of generic inverse kinematics

The Inverse Kinematics (IK) algorithms implemented in the open-source Orocos Kinematics and Dynamics Library (KDL) are arguably the most widely-used generic IK solvers worldwide. However, KDL's only joint-limit-constrained IK implementation, a pseudoinverse Jacobian IK solver, repeatedly exhibits false-negative failures on various humanoid platforms. In order to find a better IK solver for generic manipulator chains, a variety of open-source, drop-in alternatives have been implemented and evaluated for this paper. This article provides quantitative comparisons, using multiple humanoid platforms, between an improved implementation of the KDL inverse Jacobian algorithm, a set of sequential quadratic programming (SQP) IK algorithms that use a variety of quadratic error metrics, and a combined algorithm that concurrently runs the best performing SQP algorithm and the improved inverse Jacobian implementation. The best alternative IK implementation finds solutions much more often than KDL, is faster on average than KDL for typical manipulation chains, and (when desired) allows tolerances on each Cartesian dimension, further improving speed and convergence when an exact Cartesian pose is not possible and/or necessary.
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation

We show how to approximate a data matrix A with a much smaller sketch ~A that can be used to solve a general class of constrained k-rank approximation problems to within (1+ε) error. Importantly, this class includes k-means clustering and unconstrained low rank approximation (i.e. principal component analysis). By reducing data points to just O(k) dimensions, we generically accelerate any exact, approximate, or heuristic algorithm for these ubiquitous problems. For k-means dimensionality reduction, we provide (1+ε) relative error results for many common sketching techniques, including random row projection, column selection, and approximate SVD. For approximate principal component analysis, we give a simple alternative to known algorithms that has applications in the streaming setting. Additionally, we extend recent work on column-based matrix reconstruction, giving column subsets that not only 'cover' a good subspace for A}, but can be used directly to compute this subspace. Finally, for k-means clustering, we show how to achieve a (9+ε) approximation by Johnson-Lindenstrauss projecting data to just O(log k/ε2) dimensions. This is the first result that leverages the specific structure of k-means to achieve dimension independent of input size and sublinear in k.
Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries

In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion LiFePO4 cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.
High performance full attitude control of a quadrotor on SO(3)

This paper presents a novel quadrotor UAV attitude control algorithm to realize complex acrobatic UAV maneuvers. A nonlinear dynamic model based on the exponential coordinates parametrization of rotation is proposed. By analysing the model using Lie Group and Lie Algebra theory, cascaded linear PID controllers are designed. To further improve the controller performance, PID controllers are augmented with smith predictor and rotational trajectory planner. The experiments conducted on a real quadrotor show that our control algorithm surpasses most known quadrotor controllers.
Targeting Mitochondrial Function to Treat Quiescent Tumor Cells in Solid Tumors

The disorganized nature of tumor vasculature results in the generation of microenvironments characterized by nutrient starvation, hypoxia and accumulation of acidic metabolites. Tumor cell populations in such areas are often slowly proliferating and thus refractory to chemotherapeutical drugs that are dependent on an active cell cycle. There is an urgent need for alternative therapeutic interventions that circumvent growth dependency. The screening of drug libraries using multicellular tumor spheroids (MCTS) or glucose-starved tumor cells has led to the identification of several compounds with promising therapeutic potential and that display activity on quiescent tumor cells. Interestingly, a common theme of these drug screens is the recurrent identification of agents that affect mitochondrial function. Such data suggest that, contrary to the classical Warburg view, tumor cells in nutritionally-compromised microenvironments are dependent on mitochondrial function for energy metabolism and survival. These findings suggest that mitochondria may represent an “Achilles heel” for the survival of slowly-proliferating tumor cells and suggest strategies for the development of therapy to target these cell populations.
Inferring Network Dynamics and Neuron Properties from Population Recordings

Understanding the computational capabilities of the nervous system means to “identify” its emergent multiscale dynamics. For this purpose, we propose a novel model-driven identification procedure and apply it to sparsely connected populations of excitatory integrate-and-fire neurons with spike frequency adaptation (SFA). Our method does not characterize the system from its microscopic elements in a bottom-up fashion, and does not resort to any linearization. We investigate networks as a whole, inferring their properties from the response dynamics of the instantaneous discharge rate to brief and aspecific supra-threshold stimulations. While several available methods assume generic expressions for the system as a black box, we adopt a mean-field theory for the evolution of the network transparently parameterized by identified elements (such as dynamic timescales), which are in turn non-trivially related to single-neuron properties. In particular, from the elicited transient responses, the input–output gain function of the neurons in the network is extracted and direct links to the microscopic level are made available: indeed, we show how to extract the decay time constant of the SFA, the absolute refractory period and the average synaptic efficacy. In addition and contrary to previous attempts, our method captures the system dynamics across bifurcations separating qualitatively different dynamical regimes. The robustness and the generality of the methodology is tested on controlled simulations, reporting a good agreement between theoretically expected and identified values. The assumptions behind the underlying theoretical framework make the method readily applicable to biological preparations like cultured neuron networks and in vitro brain slices.
Early experience on using glideinWMS in the cloud

Cloud computing is steadily gaining traction both in commercial and research worlds, and there seems to be significant potential to the HEP community as well. However, most of the tools used in the HEP community are tailored to the current computing model, which is based on grid computing. One such tool is glideinWMS, a pilot-based workload management system. In this paper we present both what code changes were needed to make it work in the cloud world, as well as what architectural problems we encountered and how we solved them. Benchmarks comparing grid, Magellan, and Amazon EC2 resources are also included.
On the Capacity of Relay-Selection Cooperative-Diversity Networks under Adaptive Transmission

In this paper, the use of adaptive source transmission with amplify-and-forward best relaying selection scheme is proposed. Three different adaptive techniques are considered: (i) optimal simultaneous power and rate adaptation; (ii) constant power with optimal rate adaptation; (iii) channel inversion with fixed rate. The capacity upper bounds of these adaptive protocols are derived for the relay-selection amplify-and-forward cooperative network over independent and identical (i.i.d) and independent and non-identical (i.n.d) Rayleigh fading channels. The capacity analysis is based on an upper bound on the effective received signal-to-noise ratio (SNR). It is shown that at high SNR the optimal simultaneous power and rate adaptation and the optimal rate adaptation with constant power provide roughly the same capacity. Channel inversion is shown to suffer from a deterioration in capacity relative to the other adaptive techniques.
Self-aware sensing and attention-based data collection in Multi-Processor System-on-Chips

Self-awareness is the foundation for many of the nowadays desired system characteristics, such as self-optimization and self-adaption. This awareness is rooted in observation and sensory data obtained by the system regarding itself and its environment. Given the important role which data collection plays in creating this awareness, we believe that it merits more attention than it has so far received. For example, increasing the amount of collected data can overload the system with increased computational cost, communication load, and power consumption. Self-awareness can help the system by making data collection smarter and better oriented. In this paper, we propose an attention-based data collection method, inspired by self-awareness, and exploit its potential in the context of Multi-Processor System-on-Chips (MPSoCs). Our case study shows that this method can reduce the computation and communication load related to processing sensory data up to 95%, at the cost of a negligible overhead at the sensor node.