Miracle-dz/UniFAR-FTUs huggingface dataset:
Computation of Kamada-Kawai Algorithm Using Barzilai-Borwein Method

Graph type of data may be found all around us and it can help to simplify the description of many very complicated situations as well as it presents a different description of any complex system with respect to mutual relationships between system parts. There is lot of known methods for graph drawing. In the paper it is suggested to use the algorithm presented by Kamada and Kawai. The main idea of the presented work is to present the comparison of classic Kamada-Kawai algorithm with Newton-Raphson method used for the minimization and the Kamada-Kawai algorithm with Barzilai-Borwein method used instead the Newton-Raphson method. For all cases the results are presented in 2D. As it was proved the computation time was rapidly changed using the suggested Barzilai-Borwein.
Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression

Facial landmark localization plays an important role in face recognition and analysis applications. In this paper, we give a brief introduction to a coarse-to-fine pipeline with neural networks and sequential regression. First, a global convolutional network is applied to the holistic facial image to give an initial landmark prediction. A pyramid of multi-scale local image patches is then cropped to feed to a new network for each landmark to refine the prediction. As the refinement network outputs a more accurate position estimation than the input, such procedure could be repeated several times until the estimation converges. We evaluate our system on the 300-W dataset [11] and it outperforms the recent state-of-the-arts.
IMPROVED AND QUALITY-ASSESSED EMISSION AND ABSORPTION LINE MEASUREMENTS IN SLOAN DIGITAL SKY SURVEY GALAXIES

We present a new database of absorption and emission-line measurements based on the entire spectral atlas from the Sloan Digital Sky Survey (SDSS) 7th data release of galaxies within a redshift of 0.2. Our work makes use of the publicly available penalized pixel-fitting (pPXF) and gas and absorption line fitting (gandalf) IDL codes, aiming to improve the existing measurements for stellar kinematics, the strength of various absorption-line features, and the flux and width of the emissions from different species of ionized gas. Our fit to the stellar continuum uses both standard stellar population models and empirical templates obtained by combining a large number of stellar spectra in order to fit a subsample of high-quality SDSS spectra for quiescent galaxies. Furthermore, our fit to the nebular spectrum includes an exhaustive list of both recombination and forbidden lines. Foreground Galactic extinction is implicitly treated in our models, whereas reddening in the SDSS galaxies is included in the form of a simple dust screen component affecting the entire spectrum that is accompanied by a second reddening component affecting only the ionized gas emission. In order to check for systematic departures from the rather standard set of assumptions that enters our models, we provide a quality assessment for our fit to the SDSS spectra in our sample, for both the stellar continuum and the nebular emissions and across different wavelength regions. This quality assessment also allows the identification of objects with either problematic data or peculiar features. We hope to foster the discovery potential of our database; therefore, our spectral fit is available to the community. For example, based on the quality assessment around the Hα and [N ii] λ6584 lines, approximately 1% of the SDSS spectra classified as “galaxies” by the SDSS pipeline do in fact require additional broad lines to be matched, even though they do not show a strong continuum from an active nucleus, as do the SDSS objects classified as “quasars. ” Finally, we provide new spectral templates for galaxies of different Hubble types, obtained by combining the results of our spectral fit for a subsample of 452 morphologically selected objects.
A SiGe HBT 24 GHz sub-harmonic direct-conversion IQ-demodulator

The paper presents an IQ-demodulator for use in a 24 GHz direct conversion receiver. The circuit has been monolithically implemented in a commercial SiGe HBT process production line using a standard (20 /spl Omega/cm) substrate. Measured results show a conversion gain of 11 dB, a LO/RF isolation of 35 dB and a 1-dB compression point of -10 dBm while consuming 101 mA at 3 V Vcc.
Choice of Regularization Parameter in Constrained Total Variational Image Restoration Model

Image restoration problem is ill-conditioning and is generally formulated to solve a total-variational based minimization problem. Because of the physics of the underlying image formation process, the intensities of the images lie in a box range. Hence, it is reasonable to add the box constraints in the minimization problem. The minimization problem includes an unknown regularization parameter. We propose a numerical scheme to simultaneous solve the box constrained Total Variation (TV) minimization using primal-dual method and variable splitting method and choose the suitable regularization parameter according to the discrepancy principle. Numerical simulations are used to demonstrate the performance of the proposed method.
ENERGY EFFICIENT UNEQUAL CLUSTERING ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORK

Clustering provides an effective method for extending the lifetime of a wireless sensor network. Current clustering methods selecting cluster heads with more residual energy, and rotating cluster heads periodically to distribute the energy consumption among nodes in each cluster. However, they rarely consider the hot spot problem in multi hop sensor networks. When cluster heads forward their data to the base station, the cluster heads closer to the base station are heavily burdened with traffic and tend to die much faster. To mitigate the hot spot problem, we propose a Novel Energy Efficient Unequal Clustering Routing (NEEUC) protocol. It uses residual energy and groups the nodes into clusters of unequal layers.
Undiscounted Value Iteration in Stable Markov Decision Chains with Bounded Rewards

This work considers denumerable state Markov Decision Chains endowed with a long-run expected average reward criterion and bounded rewards. Apart from standard continuity-compactness restrictions, it is supposed that the Lyapunov Function Condition for bounded rewards holds true; this assumption guarantees the existence of a (possibly) unbounded solution of the optimality equation yielding optimal stationary policies. In this context, it is shown that the relative value functions and diier-ential rewards produced by the Value Iteration method converge pointwise to the solution of the optimality equation, and that it is possible to obtain a sequence of stationary policies whose limit points are optimal. These results extend those in 17], where it was assumed that thè`rst error function' is bounded, and in 6], where weaker convergence results were obtained assuming that under the action of an arbitrary stationary policy the state space is a communicating class.
Light field depth from multi-scale particle filtering

Rich information could be extracted from the high dimensional light field (LF) data, and one of the most fundamental output is scene depth. State-of-the-art depth calculation methods produce noisy calculations especially over texture-less regions. Based on Super-pixel segmentation, we propose to incorporate multi-level disparity information into a Bayesian Particle Filtering framework. Each pixels' individual as well as regional information are involved to give Maximum A Posteriori (MAP) predictions based on our proposed statistical model. The method can produce equivalent or better scene depth interpolation results than some of the state-of-the art methods, with possible potential in image processing applications such as scene alignment and stablization.
Productive argument selection: Is lexical semantics enough?

Abstract This paper is concerned with the ability of lexical semantic classes to predict the behavior of argument structures in productively selecting novel lexical material. It will be argued that while lexical semantic classes can explain a great deal about the spectrum of potential arguments and resulting entailments for those arguments, constructions which are synonymous from a formal semantic perspective exhibit significant differences in their empirically observed propensity for argument realization. Using corpus data and methods from Baayen's morphological productivity paradigm, I will show that such differences are not restricted to lexicalized, collocational preferences, but also extend to the readiness with which different constructions admit novel arguments.
GiantSteps: Semi-Structured Conversations with Musicians

This paper describes an initial interview session involving users in the imagination and exploration of working creatively with electronic music. The underlying concern for this process is to not just improve existing user interfaces, but rather to aim for interface structures developed in direct peer collaborations with expert users, with an aim to provide increased flow and unbroken periods of concentration and creativity. This work-in-progress paper describes the theoretical concerns informing the process and the results from the first round of interviews.
Design and Implementation of a Simulation Framework for the ARM Embedded System

This paper proposes a basic ARM full system simulation framework, a novel approach to understanding the behavior of modern embedded systems. Our simulation framework implements the kernel architecture of the ARM ISA, on which one can run and debug system or user-level programs. It has flexible software architecture, and good encapsulation of the communication between virtual peripherals and host peripherals. On the other hand, we implement a remote debugging system based on our simulation kernel, which extends remote serial protocol (RSP) of GDB and using Eclipse as the front-UI to build a platform-independent system. Since the simulation kernel has the full control of the target programs, the debugging system provides a flexible mechanism to collect the simulation information. These allowed people use or extend it to build a new ARM development environment, in order to study the embedded systems or help investigating the embedded operating system and low-level software.
Investment, Uncertainty and Irreversibility: Evidence from Belgian Accounting Data

This paper investigates the effects of uncertainty on the investment behaviour using firm-level data for a sample of Belgian manufacturing firms. In general, the results confirm former analysis at the aggregate level, stating that uncertainty does matter but that the sign of the effect and its magnitude largely depend on which proxies are used and how they are defined. It is shown that uncertainty has mainly an impact on the decision to invest and to a much lesser extent on the amount invested. Furthermore, the difference between reversible and irreversible investment is crucial. The impact of volatility on irreversible investment is far more larger than on reversible investment. In some cases, the amount of reversible investment will increase with higher volatility.
Investigating the Divergence in Wheat Prices

This study investigates whether importers of U.S. wheat form an integrated market or a series of segmented markets. Two market integration tests are applied: one based on equilibrium price relationships and one based on disequilibrium price relationships. With the exception of a few importers, both tests tend to agree. This study also seeks to explain the reasons for the wide divergence in prices paid by importers of U.S. wheat. We find that factors such as the class of wheat which is purchased and the size of annual purchases influence pricing. We also find that there continues to be large persistence difference in the prices paid by importers.
Creation of Audiovisual Presentations as a Tool to Develop Key Competences in Secondary-School Students. A Case Study in Science Class.

New curricular plans based on key competences create the need for new educational proposals that allow their development. This article describes a proposal to develop key competences through project-based learning. The project's objective is the creation of a digital video. The following study was carried out with students in their final two years of non-mandatory secondaryschool, in the subject of “Physics and Chemistry”. The students created didactic documentary videos describing different aspects of kinematics. The planning of the project focused on to involve the students in all steps of the process, in order to be able to evaluate the competences developed during each part of the project. The results showed an important improvement in both the digital and science competences. It was also shown that the necessary stages to create an audiovisual presentation involve the use of the competences related to communication, personal initiative or learning to learn; this provides an opportunity for the student to develop said capacities.
Autophagy linked FYVE (Alfy/WDFY3) is required for establishing neuronal connectivity in the mammalian brain

The regulation of protein degradation is essential for maintaining the appropriate environment to coordinate complex cell signaling events and to promote cellular remodeling. The Autophagy linked FYVE protein (Alfy), previously identified as a molecular scaffold between the ubiquitinated cargo and the autophagic machinery, is highly expressed in the developing central nervous system, indicating that this pathway may have yet unexplored roles in neurodevelopment. To examine this possibility, we used mouse genetics to eliminate Alfy expression. We report that this evolutionarily conserved protein is required for the formation of axonal tracts throughout the brain and spinal cord, including the formation of the major forebrain commissures. Consistent with a phenotype reflecting a failure in axon guidance, the loss of Alfy in mice disrupts localization of glial guidepost cells, and attenuates axon outgrowth in response to Netrin-1. These findings further support the growing indication that macroautophagy plays a key role in the developing CNS. DOI: http://dx.doi. org/10.7554/eLife.14810. 001
Influence of Firm and Partner Resources on Firm Performance in the Alliance Portfolio

Abstract. Although a firm benefits from the resource endowment of the partners in its alliance portfolio, research has so far concentrated on partners. This study proposes that mutual conditions of network resources between a firm and its partners – the compatibility of underlying resources, including physical and R & D resources, strategy, status, and the complementarity of technology – have a positive relationship to the firm’s economic performance in its alliance portfolio. By analyzing alliances within the global semiconductor industry, this study shows that a firm’s economic performance increases when the compatibility and complementarity of network resources are high.
Photo2Trip: an interactive trip planning system based on geo-tagged photos

In this technical demonstration, we present a novel interactive trip planning system, i.e. Photo2Trip, by leveraging existing travel clues recovered from 20 million geo-tagged photos. Compared with the most common ways of trip planning, such as surveying travelogues and resorting to travel forums, Photo2Trip enables users to plan their trips in a more effective way. To meet users' diverse travel requirements, the system considers the following preferences: travel location (e.g. Beijing, Paris, or New York), travel duration (e.g. a two-day trip or a five-day trip), visiting time (e.g. summer, winter, March, or October), and travel style preference (e.g. prefer historic or prefer scenery sites). According to user requirements, Photo2Trip can automatically recommend popular travel routes among multiple destinations (attractions/ landmarks), and suggest typical internal paths within each destination. Moreover, users are allowed to interactively adjust the suggested plans by adding or removing destinations to get more customized travel routes from the system. Owning to 20 million geo-tagged photos and 200,000 travelogues, Photo2Trip is capable of supporting users plan travel routes for over 30,000 attractions/landmarks in more than 100 countries and territories.
Corporate Social Capital and Business Model Innovation: The Mediating Role of Organizational Learning

Existing studies demonstrate that corporate social capital can not only enable a firm to gain access to heterogeneous information and resources, but may also facilitate the establishment of behavioral norms, communication models and mutual-trust platforms with stakeholders in its social network. As a result, corporate social capital drives organizational changes and has the potential to boost business model innovation. This paper recognizes organizational learning as a mediating mechanism that transforms corporate social capital into business model innovation. Using survey data collected from 164 Chinese firms, we tested the relationship between corporate social capital and business model innovation, as well as the mediating effect of organizational learning. Our findings clearly show how corporate social capital flows through organizational learning efforts to business model innovation.
The Macroeconomic Forces Behind Stock Market Return

This paper deals with the forces behind stock market return in the U.S. In order to test the relationship, 10 variables are chosen and then applied to the APT model. The data is divided into three periods and then a linear regression is performed on each period. Throughout the three periods and thus through the period 1980-2011, Current Account, Money Supply and Price of Petroleum were not significant at any of the three periods at any level of significance. While the Consumer Confidence Indicator, Consumer Price Index, Federal Funds Rate, Treasury Security, Industrial Production, New Orders Index and Unemployment were significant on at least one occasion.
The Evolution of Artificial Neurogenesis

Evolutionary development as a strategy for the design of artificial neural networks is an enticing idea, with possible inspiration from both biology and existing indirect representations. A growing neural network can not only optimize towards a specific goal, but can also exhibit plasticity and regeneration. Furthermore, a generative system trained in the optimization of the resultant neural network in a reinforcement learning environment has the capability of on-line learning after evolution in any reward-driven environment. In this abstract, we outline the motivation for and design of a generative system for artificial neural network design.
Influence of Giant CCN on warm rain processes in the ECHAM5 GCM

Increased Cloud Condensation Nuclei (CCN) load due to anthropogenic activity might lead to non-precipitating clouds because the cloud drops become smaller (for a constant liquid water content) and, therefore, less efficient in rain formation (aerosol indirect effect). Adding giant CCN (GCCN) into such a cloud can initiate precipitation (namely, drizzle) and, therefore, might counteract the aerosol indirect effect. The effect of GCCN on global climate on warm clouds and precipitation within the ECHAM5 General Circulation Model (GCM) is investigated. Therefore, the newly introduced prognostic rain scheme (Posselt and Lohmann, 2007) is applied so that GCCN are directly activated into rain drops. The ECHAM5 simulations with incorporated GCCN show that precipitation is affected only locally. On the global scale, the precipitation amount does not change. Cloud properties like total water (liquid + rain water) and cloud drop number show a larger sensitivity to GCCN. Depending on the amount of added GCCN, the reduction of total water and cloud drops account for up to 20% compared to the control run without GCCN. Thus, the incorporation of the GCCN accelerate the hydrological cycle so that clouds precipitate faster (but not more) and less condensed water is accumulated in the atmosphere. An estimate of the anthropogenic aerosol indirect effect on the climate is obtained by comparing simulations for present-day and pre-industrial climate. The introduction of the prognostic rain scheme lowered the anthropogenic aerosol indirect effect significantly compared to the standard ECHAM5 with the diagnostic rain scheme. The incorporation of the GCCN changes the model state, especially the cloud properties like TWP and N l . The precipitation changes only locally but globally the precipitation is unaffected because it has to equal the global mean evaporation rate. Changing the cloud properties leads to a local reduction of the aerosol indirect effect and, hence, partly compensating for the increased anthropogenic CCN concentrations in that regions. Globally, the aerosol indirect effect is nearly the same for all simulations.
Performance-based variable selection for scoring models

The performance of a direct marketing scoring model at a particular mailing depth, d, is usually measured by the total amount of revenue generated by sending an offer to the customers with the 100d% largest scores (predicted values). Commonly used variable selection algorithms optimize some function of model fit (squared difference between training and predicted values). This article (1) discusses issues involved in selecting a mailing depth, d, and (2) proposes a variable selection algorithm that optimizes the performance as the primary objective. The relationship between fit and performance is discussed. The performance-based algorithm is compared with fit-based algorithms using two real direct marketing data sets. These experimental results indicate that performance-based variable selection is 3–4% better than corresponding fit-based models, on average, when the mailing depth is between 20% and 40%.
Acting 2.0: when entertainment technology helps actors to perform

Motion capture shoots involve a wide range of technology and entertainment production systems such as motion capture cameras, tracking software and digital environments to create entertainment applications. However, acting in this high-tech environment is still traditional and brings its own challenges to the actors. Good acting and imagination skills are highly needed for many motion capture shoots to deliver satisfying results. In our research, we are exploring how to support the actors and use a head-mounted projection display to create a mixed reality application helping actors to perform during motion capture shoots. This paper presents the latest enhancements of our head-mounted projection display application and discusses the use of this technology for motion capture acting as well as the potential use for entertainment purposes.