Miracle-dz/UniFAR-FTUs huggingface dataset:
A machine learning approach to QoE-based video admission control and resource allocation in wireless systems

The rapid growth of video traffic in cellular networks is a crucial issue to be addressed by mobile operators. An emerging and promising trend in this regard is the development of solutions that aim at maximizing the Quality of Experience (QoE) of the end users. However, predicting the QoE perceived by the users in different conditions remains a major challenge. In this paper, we propose a machine learning approach to support QoE-based Video Admission Control (VAC) and Resource Management (RM) algorithms. More specifically, we develop a learning system that can automatically extract the quality-rate characteristics of unknown video sequences from the size of H.264-encoded video frames. Our approach combines unsupervised feature learning with supervised classification techniques, thereby providing an efficient and scalable way to estimate the QoE parameters that characterize each video. This QoE characterization is then used to manage simultaneous video transmissions through a shared channel in order to guarantee a minimum quality level to the final users. Simulation results show that the proposed learning-based QoE classification of video sequences outperforms commonly deployed off-line video analysis techniques and that the QoE-based VAC and RM algorithms outperform standard content-agnostic strategies.
A New Ankylosaurid Dinosaur from the Upper Cretaceous (Kirtlandian) of New Mexico with Implications for Ankylosaurid Diversity in the Upper Cretaceous of Western North America

A new ankylosaurid (Ankylosauria: Dinosauria), Ziapelta sanjuanensis, gen. et sp. nov., is based on a complete skull, an incomplete first cervical half ring, a possible fragment of the second cervical half ring, and additional fragmentary osteoderms. The holotype specimen is from the Upper Cretaceous (Upper Campanian, Kirtlandian Land-Vertebrate Age) Kirtland Formation (De-na-zin Member) at Hunter Wash, San Juan Basin, in northwestern New Mexico, USA. Diagnostic characters of Ziapelta include: a large, prominent triangular median nasal caputegulum; a mixture of flat and bulbous frontonasal caputegulae; ventrolaterally oriented squamosal horns with a sharp, prominent dorsal keel; and the ventral surface of basicranium with three prominent anteroposteriorly oriented fossae. A phylogenetic analysis suggests that Ziapelta is not closely related to the other ankylosaurid from the De-na-zin Member, Nodocephalosaurus, but allies it to the northern North American ankylosaurids Ankylosaurus, Anodontosaurus, Euoplocephalus, Dyoplosaurus, and Scolosaurus.
Learning from Neighbors

When payoffs from different actions are unknown, agents use their own past experience as well as the experience of their neighbors to guide their current decision making. This paper develops a general framework to study the relationship between the structure of information flows and the process of social learning. We show that in a connected society, local learning ensures that all agents obtain the same utility, in the long run. We develop conditions under which this utility is the maximal attainable, i.e. optimal actions are adopted. This analysis identifies a structural property of information structures -- local independence -- which greatly facilitates social learning. Our analysis also suggests that there exists a negative relationship between the degree of social integration and the likelihood of diversity. Simulations of the model generate spatial and temporal patterns of adoption that are consistent with empirical work.
Complementary approaches to the study of decision making across the adult life span

Learning to choose adaptively between different behavioral options in order to reach goals is a pervasive task in life for people of all ages. Individuals are often confronted with complex, uncertain situations that nonetheless require decisive actions that would facilitate the pursuit of short-term or long-term goals. Adaptive decision making as such entails interactions between processes that monitor the choice-outcome relations as well as processes that evaluate these relations with respect to goal relevance. These dynamics implicate close interplays between attention, learning, memory, motivation, and emotion, which are subserved by cortical-subcortical networks and are neurochemically regulated by transmitters, such as norepinephrine, dopamine, and serotonin. Across the life span, these functional brain circuits as well as neurotransmitter systems undergo basic biological maturation and senescence as well as plasticity due to the accumulation of experience or changes in motivational goals (Braver and Barch, 2002; Li and Sikstrom, 2002; Duzel et al., 2010; Li et al., 2010; Mohr et al., 2010; Li, 2013). Studying decision making across different adult life periods may shed light on how the very processes of decision making adapt to constraints on brain resources due to aging, how these processes benefit from experience, or how decision making is influenced by shifting goals.
Do Mutual Funds Perform When It Matters Most to Investors ? US Mutual Fund Performance and Risk in Recessions and Booms 1962-1994

In this paper I employ a novel conditional (latent state) performance measurement methodology to examine whether US mutual funds perform, when it matters to investors the most, i.e. in recessions when investors’ marginal utility of wealth is high. I find that the average US mutual fund performs better in recession than in boom periods and that there are asymmetries in the time-variation. The results are robust to the choice of different stock-based performance benchmarks. Growth/income and balanced/income funds exhibit the largest time-variation and outperform aggressive growth and growth funds in recessions. Evidence of correlation in performance time-variation across funds questions the ability of traditional factor models to fully explain crosssectional fund returns. I motivate the observed time-varying performance in the context of a model of time-varying information asymmetries and equilibrium asset prices. The empirical results imply that traditional unconditional performance measures understate the value added by active mutual fund managers in recessions.
Protean career orientation, vocational identity, and self-efficacy: an empirical clarification of their relationship

ABSTRACT There is a large interest in how people can be more protean in their career development, exhibiting a self-directed striving for personally valued career outcomes. However, existing research on the protean career needs to better address issues of antecedents and outcomes as well as unique effects of a protean career orientation (PCO). We present two studies investigating how PCO is related to vocational identity clarity and occupational self-efficacy. Study 1 reports a 1-year, three-wave cross-lagged study among 563 university students and established that PCO preceded changes in identity and self-efficacy—but not the other way around. A 6-month longitudinal study of 202 employees, Study 2 showed that identity clarity and self-efficacy mediated the effects of PCO on career satisfaction and proactive career behaviours. PCO only possessed incremental predictive validity regarding proactive career behaviours. However, we could not confirm specific direct or mediated effects of PCO on job satisfaction. These results imply that PCO is closely related to vocational identity clarity and self-efficacy because it enhances these career attitudes. Moreover, identity and self-efficacy mediate some, but not all of the effects of PCO on important career outcomes.
‘We achieve the impossible’: Discourses of freedom and escape at music festivals and free parties

In this article, we explore the notion of freedom as a form of governance within contemporary consumer culture in a sphere where ‘freedom’ appears as a key component: outdoor music-based leisure events, notably music festivals and free parties. ‘Freedom’ is commodified as central to the marketing of many music festivals, which now form a highly commercialised sector of the UK leisure industry, subject to various regulatory restrictions. Free parties, in contrast, are unlicensed, mostly illegal and far less commercialised leisure spaces. We present data from two related studies to investigate how participants at three major British outdoor music festivals and a small rural free party scene draw on discourses of freedom, escape and regulation. We argue that major music festivals operate as temporary bounded spheres of ‘licensed transgression’, in which an apparent lack of regulation operates as a form of governance. In contrast, free parties appear to ‘achieve the impossible’ by creating alternative (and illegal) spaces in which both freedom and regulation are constituted in different ways compared to music festival settings.
Do Exam Wrappers Increase Metacognition and Performance? A Single Course Intervention

Previous research has indicated that an intervention called “exam wrappers” can improve students’ metacognition when they are using wrappers in more than one course per academic term. In this study, we tested if exam wrappers would improve students’ metacognition and academic performance when used in only one course per academic term. A total of 86 students used either exam wrappers (an exercise with metacognitive instruction), sham wrappers (an exercise with no metacognitive instruction), or neither (control). We found no improvements on any of three exams, final grades, or metacognitive ability (measured with the Metacognitive Awareness Inventory, MAI) across conditions. All students showed an increase in MAI over the course of the semester, regardless of condition. We discuss the challenges of improving metacognitive skills and suggest ideas for additional metacognitive interventions.
Individual and Households Determinants of Women Empowerment: Application to the Case of Egypt

This research deals with women’s empowerment as an outcome of interest by defining the different individual and socio-demographic determinants that affect women’s empowerment in the Egyptian society. The paper analyzed two dimensions of women’s empowerment; the decision-making and the mobility aspects of Egyptian women. Using the Egypt Labor Market Panel Survey (ELMPS) 2012, we estimated a decision-making index and a mobility index. Our results came in line with the literature; age, education, employment, poverty status, number of children, having an adult son in addition to a woman’s husband, and her father’s characteristics appeared as significant determinants of empowerment. Further, most of these determinants, showed varying impact depending on the dimension of empowerment studied. The regional context was found to be very important in explaining Egyptian women’s empowerment. Context was not only found to be an important determinant of women’s empowerment as measured by our two indices, but it was also found to affect the impact of the other individual and socio demographic determinants on women’s empowerment.
Iowaite, a re-investigation

Abstract Iowaite has been re-examined using new, well crystallized material from the Palabora open pit mine, Transvaal. Microprobe, carbon analyser and thermal analyses, powder and single-crystal X-ray diffraction, infrared, UV-visible and mass spectroscopic and optical studies were made. The ideal formula is (OH)16Cl2·4H2O. It is trigonal, R3̄m, a = 3.1183(9), c = 24.113(8) Å, V = 203.1(2) Å3, Z = 3/8; D obs 2.09 g/cm3. ; D calc 2.04 g/cm3; hardness (Mohs)=2 1/2. The interlayer Cl− ions are displaced from the threefold axis. It is uniaxial negative, with ω = 1.561 ± 0.002, ε = 1.543 ± 0.002; coloured crystals are intensely pleochroic, due to intervalence charge transfer between the Fe3+ and Fe2+ substituting for Mg2+, with O pale yellow, E deep blue-green.
Advance Reservation and Dynamic Scheduling of Point to Multipoint Lightpaths

Advance reservation is a technique employed so that a request is guaranteed 100% availability of the resource it applied for sometimes earlier. In this paper we introduce the concept of advance reservation of lightpaths for a certain request characterized by some source and multiple destinations and also the time when network resources are demanded. We first present a point to multipoint adaptive routing algorithm that forms the basis of establishing lightpaths. Then the lightpaths are in fixed on the route through a wavelength assignment technique that works along with an advance reservation scheme to ensure conflict resolution among incoming requests. Based on our algorithm, we run our simulation model to generate results and exemplify the fact that advance reservation outruns on-demand reservation as far as the blocking probability is concerned.
The Ecology of Star Clusters and Intermediate-Mass Black Holes in the Galactic Bulge

We simulate the inner 100 pc of the Milky Way to study the formation and evolution of the population of star clusters and intermediate-mass black holes (IMBHs). For this study we perform extensive direct N-body simulations of the star clusters that reside in the bulge, and of the inner few tenth of parsecs of the supermassive black hole in the Galactic center. In our N-body simulations the dynamical friction of the star cluster in the tidal field of the bulge are taken into account via semianalytic solutions. The N-body calculations are used to calibrate a semianalytic model of the formation and evolution of the bulge. We find that ~10% of the clusters born within ~100 pc of the Galactic center undergo core collapse during their inward migration and form IMBHs via runaway stellar merging. After the clusters dissolve, these IMBHs continue their inward drift, carrying a few of the most massive stars with them. We predict that a region within ~10 pc of the supermassive black hole (SMBH) is populated by ~50 IMBHs of ~1000 M☉. Several of these are still expected to be accompanied by some of the most massive stars from the star cluster. We also find that within a few milliparsecs of the SMBH there is a steady population of several IMBHs. This population drives the merger rate between IMBHs and the SMBH at a rate of about one per 10 Myr, sufficient to build the accumulated majority of mass of the SMBH. Mergers of IMBHs with SMBHs throughout the universe are detectable by LISA at a rate of about two per week.
Efficient Sparse Representation Classification Using Adaptive Clustering

This paper is presenting a method for an efficient face recognition algorithm based on sparse representation classification (SRC) using an adaptive K-means clustering. In the context of face recognition, SRC is implemented based on the assumption that a face image from a particular subject can be represented as a linear combination of other face images from the same subject. SRC uses a set of extracted features from the original face images as columns of a training matrix. This training matrix is used to form an under-determined system of linear equations. An unknown face image can be classified by finding the sparse solution of this linear system using an l-norm optimization which has a quadratic time complexity. In practice using all the training face images for a large population increases computational and memory requirements which may not be feasible to be used in mobile devices. The proposed method reduces the size of the training matrix by using adaptive K-means clustering along with SRC method which lowers computational requirements of the overall recognition system. Experimental results on a face dataset with 14,794 training face images from 100 classes show that the new proposed method reduces the running time of the classification algorithm compared to SRC method while preserving the accuracy.
An Undergraduate Course to Bridge the Gap between Textbooks and Scientific Research

This article reports on a one-semester Advanced Cell Biology course that endeavors to bridge the gap between gaining basic textbook knowledge about cell biology and learning to think and work as a researcher. The key elements of this course are 1) learning to work with primary articles in order to get acquainted with the field of choice, to learn scientific reasoning, and to identify gaps in our current knowledge that represent opportunities for further research; 2) formulating a research project with fellow students; 3) gaining thorough knowledge of relevant methodology and technologies used within the field of cell biology; 4) developing cooperation and leadership skills; and 5) presenting and defending research projects before a jury of experts. The course activities were student centered and focused on designing a genuine research program. Our 5-yr experience with this course demonstrates that 1) undergraduate students are capable of delivering high-quality research designs that meet professional standards, and 2) the authenticity of the learning environment in this course strongly engages students to become self-directed and critical thinkers. We hope to provide colleagues with an example of a course that encourages and stimulates students to develop essential research thinking skills.
Generalized Laminar Population Analysis (gLPA) for Interpretation of Multielectrode Data from Cortex

Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population.
Transmission Strategies in MIMO Ad Hoc Networks

Precoding problem in multiple-input multiple-output (MIMO) ad hoc networks is addressed in this work. Firstly, we consider the problem of maximizing the system mutual information under a power constraint. In this context, we give a brief overview of the nonlinear optimization methods, and systematically we compare their performances. Then, we propose a fast and distributed algorithm based on the quasi-Newton methods to give a lower bound of the system capacity of MIMO ad hoc networks. Our proposed algorithm solves the maximization problem while diminishing the amount of information in the feedback links needed in the cooperative optimization. Secondly, we propose a different problem formulation, which consists in minimizing the total transmit power under a quality of signal constraint. This novel problem design is motivated since the packets are captured in ad hoc networks based on their signal-to-interference-plus-noise ratio (SINR) values. We convert the proposed formulation into semidefinite optimization problem, which can be solved numerically using interior point methods. Finally, an extensive set of simulations validates the proposed algorithms.
Fuzzy automatic contrast enhancement based on fuzzy C-means clustering in CIELAB color space

Some traditional methods for image contrast enhancement are based on histogram equalization, which however has the drawbacks of producing visual artifacts or excessive image strengthening due to improper settings of enhancement parameters or non-smooth color adjustments in different color spaces. A novel method of Fuzzy Automatic Contrast Enhancement (FACE) is presented in this paper. FACE first performs a fuzzy clustering method to segment an image while the pixels with similar colors in the CIELAB color space are classified into smaller image clusters with similar characteristics. The pixels in each group are then spread out away from the center of the belonging cluster in the RGB color space in order to enhance the image contrast but keeping the similarity of pixel colors in the same cluster. A universal contrast enhancement variable (UCEV) was defined and optimized to maximize the image randomness (i.e. entropy of the image) in order to automatically enhance the image contrast. A more uncongested distribution of the image pixels ensures a greater image contrast. The proposed entropy-maximization process is capable of improving the image quality without any human-defined control parameters. The fully automated image enhancement process intelligently clusters the pixels with similar color characteristics and is general for the contrast enhancement of images in various color distributions. Many images with different color distributions were tested and the results showed that FACE is capable of avoiding visual artifacts and excessive strengthening. Compared with the traditional histogram equalization method, the proposed method shows higher effectiveness in contrast enhancement and performs better in retaining the colors of the original images.
A Study on the Diesel DI-HCCI Combustion Characteristics using 2-stage Injection Method

In this study, combustion characteristics and various performances of a Diesel fuel DI-HCCI engine using 2-stage injection method were investigated. From these researches, application ability of 2-stage injection strategy to a DI-HCCI engine was confirmed and improvement methods of performances were considered. As the results, Using 2-stage injection method, without change of engine specifications and loss of IMEP, exhaust of NOx and Smoke emissions could be reduced to about 1/3 (at 1400rpm, IMEP 6bar) compared to conventional Diesel combustion.
Monitoring tropical forests using synthetic aperture radar.

Advanced Synthetic Aperture Radar (SAR) remote sensing systems, their versatility and applications for environmental monitoring and survey are briefly introduced. The results of experiments conducted at sites of the Tropenbos Foundation in Colombia and Indonesia are shown to illustrate their capabilities. These are: (1) A newly developed technique to derive 3D tree maps from airborne high-resolution interferometric radar images. (2) Land cover change and fire damage monitoring by the ERS SAR satellite. (3) Selection of appropriate radar system specifications for very accurate monitoring of deforestation, land and forest degradation, secondary regrowth and land cover change. The information needs for tropical forest areas are very diverse. The use of dedicated airborne radar systems in combination with aerial photography and operational satellite systems would provide a sound basis for efficient data acquisition in support of prudent future forest management.