Miracle-dz/UniFAR-FTUs huggingface dataset:
THE FREQUENCY OF LOW-MASS EXOPLANETS. III. TOWARD η⊕ AT SHORT PERIODS

Determining the occurrence rate of “super-Earth” planets (m sin i < 10 M⊕) is a critically important step on the path toward determining the frequency of Earth-like planets (η⊕), and hence the uniqueness of our solar system. Current radial-velocity surveys, achieving precisions of 1 m s−1, are now able to detect super-Earths and provide meaningful estimates of their occurrence rate. We present an analysis of 67 solar-type stars from the Anglo-Australian Planet Search specifically targeted for very high precision observations. When corrected for incompleteness, we find that the planet occurrence rate increases sharply with decreasing planetary mass. Our results are consistent with those from other surveys: in periods shorter than 50 days, we find that 3.0% of stars host a giant (msin i > 100 M⊕) planet, and that 17.4% of stars host a planet with msin i < 10 M⊕. The preponderance of low-mass planets in short-period orbits is in conflict with formation simulations in which the majority of super-Earths reside at larger orbital distances. This work gives a hint as to the size of η⊕, but to make meaningful predictions on the frequency of terrestrial planets in longer, potentially habitable orbits, low-mass terrestrial planet searches at periods of 100–200 days must be made an urgent priority for ground-based Doppler planet searches in the years ahead.
The Importance of Social Embeddedness: Churn Models at Mobile Providers

This article argues the importance of social embeddedness at mobile providers by examining the effects of customers’ network topological properties on churn probability—the probability of a customer switching from one telecommunication provider to another. This article uses data from regional snowball sampling—the only practically feasible network sampling method—to identify groups with significantly different churn ratios for customers with different network topological properties. Clear evidence indicates that individual network characteristics (node-level metrics) have considerable impact on churn probabilities. The inclusion of network-related measures in the churn model allows a longer-term projection of churners and improves the predictive power of the model. With no possibility to carry out repeated sampling, sample stability was checked through simulation results. On the one hand, this article highlights the importance and effectiveness of the provider's tailored marketing campaigns by showing that customers targeted by direct marketing campaigns are less threatened by churn than nontargeted customers. On the other, this article shows that social embeddedness blocks the impact of the very same marketing efforts. This article forwards the idea that social embeddedness, also prevalent in vendor switching, can be extended to understanding the development of professional societies threatened by membership churn.
Active Learning for Web Search Ranking via Noise Injection

Learning to rank has become increasingly important for many information retrieval applications. To reduce the labeling cost at training data preparation, many active sampling algorithms have been proposed. In this article, we propose a novel active learning-for-ranking strategy called ranking-based sensitivity sampling (RSS), which is tailored for Gradient Boosting Decision Tree (GBDT), a machine-learned ranking method widely used in practice by major commercial search engines for ranking. We leverage the property of GBDT that samples close to the decision boundary tend to be sensitive to perturbations and design the active learning strategy accordingly. We further theoretically analyze the proposed strategy by exploring the connection between the sensitivity used for sample selection and model regularization to provide a potentially theoretical guarantee w.r. t. the generalization capability. Considering that the performance metrics of ranking overweight the top-ranked items, item rank is incorporated into the selection function. In addition, we generalize the proposed technique to several other base learners to show its potential applicability in a wide variety of applications. Substantial experimental results on both the benchmark dataset and a real-world dataset have demonstrated that our proposed active learning strategy is highly effective in selecting the most informative examples.
Age-related Changes in p56lck Protein Levels and Phenotypic Distribution of T Lymphocytes in Young Rats

p56lck is involved in the maturation of T-cells from double negative (DN) into double positive (DP) T-cells. The objective of this experiment was to determine changes in the levels of thymic and splenic T-cell p56lck using Western immunoblotting, along with the proportion and number of T-cell subsets in thymus, spleen and blood using flow cytometry in growing Sprague-Dawley rats. Thymic p56lck levels were negatively correlated with age (r=-0.42, p=0.04) and positively correlated with age in the spleen (r=0.50, p=0.01). Nine-week-old rats had a higher percentage of thymic DN and CD8 cells with fewer DP cells compared to younger rats. There were minor differences in the proportions of T-cell subsets in the spleen and blood. T-cell numbers remained proportional to body weight in the lymphoid organs; however, the lower absolute number of T-cells in the younger rats might indicate that they are less able to respond to antigens.
A Matching Method Based on SVD for Image Retrieval

Feature matching method is the key technology of image retrieval. A matching method based on SVD is proposed in this paper. Firstly, the feature matrix is constructed and the eigenvalue matrix is calculated based on SVD transformation¿Then, the eigenvalue matrix is mapped into vector space¿and similarity is measured by comparing the angle between vectors. Finally, the matching method is applied to image retrieval. Experimental results show that the proposed method is feasible and superior to the traditional distance matching method.
Asymptotic expansions for high-contrast elliptic equations

In this paper, we present a high-order expansion for elliptic equations in high-contrast media. The background conductivity is taken to be one and we assume the medium contains high (or low) conductivity inclusions. We derive an asymptotic expansion with respect to the contrast and provide a procedure to compute the terms in the expansion. The computation of the expansion does not depend on the contrast which is important for simulations. The latter allows avoiding increased mesh resolution around high conductivity features. This work is partly motivated by our earlier work in [Domain decomposition preconditioners for multiscale flows in high-contrast media, Multiscale Model Simul.8 (2010) 1461–1483] where we design efficient numerical procedures for solving high-contrast problems. These multiscale approaches require local solutions and our proposed high-order expansion can be used to approximate these local solutions inexpensively. In the case of a large-number of inclusions, the proposed analysis can help to design localization techniques for computing the terms in the expansion. In the paper, we present a rigorous analysis of the proposed high-order expansion and estimate the remainder of it. We consider both high- and low-conductivity inclusions.
Convolutional Neural Networks for night-time animal orientation estimation

In rural areas, wildlife animal road crossings are a threat to both the driver and the wildlife population. Since most accidents take place at night, recent night vision driver assistance systems are supporting the driver by automatically detecting animals on infrared camera imagery. After detecting an animal on the roadside, the orientation towards the road can give a first cue for an upcoming trajectory prediction. This paper describes an novel classification-based scheme for nighttime animal orientation estimation from single infrared images. Our system classifies already detected animals, in particular deer, as being either oriented left, right or back/front. We propose an approach based on Convolutional Neural Networks which learns multiple stages of invariant features in a supervised end-to-end fashion. Experiments show that our method outperforms baseline methods like HOG/SVM or boosted Haar-stumps on this task.
Central and Peripheral Axon Branches from One Neuron Are Guided Differentially by Semaphorin3D and Transient Axonal Glycoprotein-1

For multiple axons from one neuron to extend in different directions to unique targets, the growth cones of each axon must have distinct responses to guidance cues. However, the mechanisms by which separate axon branches are guided along different pathways are mainly unknown. Zebrafish Rohon-Beard (R-B) sensory neurons extend central axon branches in the spinal cord and peripheral axons to the epidermis. To investigate the differential guidance mechanisms of the central versus peripheral R-B axon branches, we used live-growth cone imaging in vivo combined with manipulation of individual guidance molecules. We show that a semaphorin expressed at the dorsal spinal cord midline, Semaphorin3D (Sema3D), may act to repel the peripheral axons out of the spinal cord. Sema3D knock-down reduces the number of peripheral axons. Remarkably, Sema3D ectopic expression repels and induces branching of peripheral axons in vivo but has no effect on central axons from the same neurons. Conversely, central axons require a growth-promoting molecule, transient axonal glycoprotein-1 (TAG-1), to advance, whereas peripheral axons do not. After TAG-1 knock-down, central growth cones display extensive protrusive activity but make little forward advance. TAG-1 knock-down has no effect on the motility or advance of peripheral growth cones. These experiments show how Sema3D and TAG-1 regulate the motility and behavior of growth cones extending in their natural in vivo environment and demonstrate that two different axon branches from one neuron respond differently to guidance cues in vivo.
An educational distributed Cosmic Ray detector network based on ArduSiPM

The advent of high performance microcontrollers equipped with analog and digital peripherals, makes the design of a complete particle detector and a relative acquisition system on a single microcontroller chip possible. The existence of a world wide data infrastructure such as the internet, allows for the conception of a distributed network of cheap detectors able to elaborate and send data as well as to respond to setting commands. The internet infrastructure enables the distribution of the absolute time, with precision of a few milliseconds, to all devices independently of their physical location, when the sky view is accessible it possible to use a GPS module to reach synchronization of tens of nanoseconds. These devices can be far apart from each other and their relative distance can range from a few meters to thousands of kilometers. This allows for the design of a crowdsourcing experiment of citizen science, based on the use of many small scintillation-based particle detectors to monitor the high energetic cosmic ray and the radiation environment.
Statistical Learning for Relational and Structured Data

Learning with relational and structured data has gained a growing interest within the machine learning community in recent years. The great development of this research area has been mainly due to the large amount of available data, organized in complex relational structures, coming from a variety of fields, like molecular biology, social networks analysis, natural language parsing, and many others. Traditional machine learning algorithms, which typically consider the examples as independent, had to be extended and generalized in order to handle relational and structured data. For this reason, Statistical Relational Learning (SRL) community was born in recent years with the aim of applying statistical learning methodologies to the relational setting. SRL algorithms usually combine a first-order logic representation of the domain of interest, with the probabilistic framework of graphical models. Many different methods have been developed following this idea, often providing very similar formalisms and solving similar problems. This thesis is organized in three distinct parts, each addressing a different aspect of statistical relational learning. In the first part of this dissertation, a semi-generative approach to parameter learning for stochastic grammars is proposed, showing applications to natural language parsing and RNA secondary structure prediction. In the second part, Markov logic networks (MLNs) are applied to bioinformatics tasks, and an extension of standard MLNs is presented, in order to handle continuous features and to embed discriminative classifiers within that framework. Finally, the third part of this thesis considers a classical problem of Inductive Logic Programming (ILP), which consists in learning logical representation of objects directly from data: a new scoring function, called relational information gain, is used to capture the potential informativeness of literals.
Joint source and channel coding using ring convolutional coded CPM

In this paper, a novel trellis coded quantization (TCQ) scheme based on ring convolutional codes is presented. Joint source and channel coding (JSCC) using trellis coded continuous phase modulation (CPM) with convolutional codes over rings is investigated. The channel is assumed to be an additive white Gaussian noise (AWGN) channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the a posteriori probability (APP) algorithm for trellis coded CPM with ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). It has been demonstrated that TCQ based on ring convolutional codes is superior to conventional TCQ of the same complexity. The novelties of this work are the development of a TCQ method based on ring convolutional codes, the use of a soft decoder and the APP algorithm for the combined systems
The Differentiation of Human Th-17 Cells Requires Transforming Growth Factor-b and Induction of the Nuclear Receptor Roryt. Nature Il-6 Programs Th-17 Cell Differentiation by Promoting Sequential Engagement of the Il-21 And

recruitment of antigen-presenting cells by chemerin, a novel processed ligand from human inflammatory fluids. T: HMGB proteins function as universal sentinels for nucleic-acid-mediated innate immune responses. A critical function for transforming growth factor-B, interleukin 23 and proinflammatory cytokines in driving and modulating human Th-17 responses. Purification of a human monocyte-derived neutrophil chemotactic factor that has peptide sequence similarity to other host defense cytokines. DJ: IL-23 drives a pathogenic T cell population that induces autoimmune inflammation. Malefyt R: Development, cytokine profile and function of human interleukin 17-producing helper T cells.
Lithium-ion batteries life estimation for plug-in hybrid electric vehicles

This paper deals with life estimation of lithium batteries for plug-in hybrid electric vehicles (PHEVs). An aging model, based on the concept of accumulated charge throughput, has been developed to estimate battery life under “real world” driving cycles (custom driving cycles based on driving statistics). The objective is to determine the “damage” on the life related to each driving pattern to determine equivalent miles/years. Results indicates that Lithium-ion batteries appear to be 10 year/150,000 mile capable, provided that they are not overcharged, nor consistently operated at high temperatures, nor in charge sustaining mode at a very low state of charge.
A Study on Deception Detection Based on Classification for Chinese Text

Deception detection on Chinese text is vital to the safety of people's life, the survival of enterprises and the stability of the country. The expansion of the Internet has significantly increased the amount of textual communication received and stored by individuals and organizations. Inundated with massive amounts of textual information transmitted through Computer-mediated Communication (CMC), people remain largely unsuccessful and inefficient in detecting those messages that may be deceptive. Proposing an automated deception detection method that could help people flag the possible deceptive messages in CMC is desirable, but first it is necessary to construct the deceptive and non-deceptive Chinese text corpora, which have not been published so far. Further, according to the corpora analysis results we put forward a novel classification-based method on deception detection for Chinese text. Our method, along with feature selection and parameter adjustment, respectively conduct deception detection experiments by using the Bayes classifier and SVM classifier. The experimental results show the precision rate, the recall rate and the F-value may achieve 79.6%, 75% and 0.77 separately by using SVM classifier in open test.
An Efficient Approach for Iris Recognition by Improving Iris Segmentation and Iris Image Compression

The biometric person authentication technique based on the pattern of the human iris is well suited to be applied to any access control system requiring a high level of security. Commercial iris recognition systems are currently employed to allow passengers in some airports to be rapidly processed thru security, to allow access to secure areas, and for secure access to computer networks. With increasing need of the biometric systems the question arises naturally how to store and handle the acquired data. If required storage space is not adequate for these images, compression is an alternative. It allows a reduction in the space needed to store these iris images, although it may be at a cost in some amount of information loss in the process. This paper investigates the effects of image compression on iris recognition based on proposed algorithm and compression is performed using JPEG2000. The proposed technique is computationally effective as well as reliable in terms of recognition rates.
Effect of restarts and pauses on achieving a state of mutual orientation between a human and a robot

In this paper we consider the development of a museum guide robot that has both autonomous and remotely controlled features. We focus on the capabilities such a robot could have to help focus the attention of a visitor on an object or artefact. Inspired by studies of social interaction, which investigate whether the robot could deploy "restarts" and "pauses" at certain moments in its talk to first elicit the visitor's attention/gaze towards the robot. We report an experiment where we deployed such a robot to interact with real visitors to a science museum. These experiments show that such a strategy does seem to have a significant impact on obtaining the visitor's gaze.
Identification and functional characterization of novel transcriptional enhancers involved in regulating human GLI3 expression during early development

The zinc‐finger transcription factor GLI3 acts as a primary transducer of Sonic hedgehog (Shh) signaling in a context‐dependent combinatorial fashion. GLI3 participates in the patterning and growth of many organs, including the central nervous system (CNS) and limbs. Previously, we reported a subset of human intronic cis‐regulators controlling many known aspects of endogenous Gli3 expression in mouse and zebrafish. Here we demonstrate in a transgenic zebrafish assay the potential of two novel tetrapod‐teleost conserved non‐coding elements (CNEs) docking within GLI3 intronic intervals (intron 3 and 4) to induce reporter gene expression at known sites of endogenous Gli3 transcription in embryonic domains such as the central nervous system (CNS) and limbs. Interestingly, the cell culture based assays reveal harmony with the context dependent dual nature of intra‐GLI3 conserved elements. Furthermore, a transgenic zebrafish assay of previously reported limb‐specific GLI3 transcriptional enhancers (previously tested in mice and chicken limb buds) induced reporter gene expression in zebrafish blood precursor cells and notochord instead of fin. These results demonstrate that the appendage‐specific activity of a subset of GLI3‐associated enhancers might be a tetrapod innovation. Taken together with our recent data, these results suggest that during the course of vertebrate evolution Gli3 expression control acquired a complex cis‐regulatory landscape for spatiotemporal patterning of CNS and limbs. Comparative data from fish and mice suggest that the functional aspects of a subset of these cis‐regulators have diverged significantly between these two lineages.
A comparison of Pose Estimation algorithms for Machine Vision based Aerial Refueling for UAVs

This paper focuses on the analysis of the performance of specific 'detection and labeling' and 'pose estimation' algorithms within a machine vision (MV)-based approach for the problem of autonomous aerial refueling (AAR) of UAVs. A robust 'detection and labeling algorithm' for the correct identification and sorting of the optical markers is proposed; a sorted list of marker positions is then provided as input to the 'pose estimation' algorithm. A detailed study of the performance of two specific 'pose estimation' algorithms (GLSDC and LHM) is performed with special emphasis on the required computational effort as well as on the robustness and error propagation characteristics. Extensive simulation studies demonstrate the performance of the LHM and GLSDC algorithms and show the importance of a robust 'detection and labeling' algorithm. The simulation effort is performed using a detailed modeling of the AAR maneuver according to the USAF refueling method
Election by Majority Judgement : Experimental Evidence Michel Balinski

The majority judgement is a method of election. It is the consequence of a new theory of social choice where voters judge candidates instead of ranking them. The theory is explained elsewhere [2, 4]. This article describes and analyzes electoral experiments conducted in parallel with the last two French presidential elections to: (1) show that the majority judgement is a practical method, (2) describe it and its salient properties, (3) establish that it escapes the classical paradoxes, (4) illustrate how in practice the well known electoral mechanisms all fail to meet important criteria. The demonstrations introduce new concepts and methods. Mots clés: choix social, mécanismes de vote, expérience électorale, jugement majoritaire, le paradoxe d'Arrow
Mobile robot guidance using adaptive event-based pose estimation and camera sensor

We present the application of an event-based state estimator to the guidance of a mobile robot. The control feedback loop is closed by means on an estimator based on an Unscented Kalman Filter (UKF) with event-based updates. The estimator requests measurements from a camera sensor only when it needs them. The measurements are triggered by an adaptive condition on the pose estimation error covariance matrix. The main advantage of this method is that it allows for reducing the volume of CPU intensive image processing which is characteristic of these kind of sensors, as well as avoiding the unnecessary overload of the communication channel. The main contribution of the current work is the actual implementation with a P3-DX robotic unit remotely controlled. We discuss practical aspects of the implementation and provide performance results for different parameter settings.