Miracle-dz/UniFAR-FTUs huggingface dataset:
Women artists in the Spanish African-American music scene : identities , discourses and emotions in the public sphere

Resumen This paper explores the presence and importance of women artists in the Spanish African-American music scene, specifically in the blues, jazz and soul-funk sub-scenes. It presents an ethnography focused on the increasing prominence of women, and it addresses different musical events, texts and artists associated with a diversity of voices, identities, discourses and emotions in the public sphere. Methodologically, we draw on participant observation, interviews, documentation and discourse analysis from a communicative and sociosemiotic perspective. We study the Spanish soul music scene, where African-descendant singers play a leading role, and we examine in depth the case of Aretha Soul Divas, a supergroup tribute to Aretha Franklin. The discourses analysed refer to the emotional dimension of musical experience, where the encounter between artists and audiences facilitates mutual understanding and emotional liberation. Furthermore, we verify the connection between the demands enunciated within the music scene, and the demands of the African descendant collective in Spain, which denounces racism and lack of visibility. The events, texts and discourses that emerge in these confluence spaces contribute to the passionate construction of a symbolic territory of their own, where the individual is combined with the collective. Este artículo explora la presencia e importancia de las mujeres artistas en la escena española de música afroamericana, en concreto en las subescenas de blues, jazz y soul-funk. Se trata de una etnografía centrada en el creciente protagonismo de las mujeres y aborda distintos eventos musicales, textos y artistas asociados a una diversidad de voces, identidades, discursos y emociones en la esfera pública. Metodológicamente, realizamos observación participante, entrevistas, documentación y análisis del discurso desde una perspectiva comunicativa y sociosemiótica. Indagamos en la escena de soul española, donde las cantantes afrodescendientes ocupan un rol protagonista, y examinamos en profundidad el caso de Aretha Soul Divas, un supergrupo de homenaje a Aretha Franklin. Los discursos analizados apuntan a la dimensión emocional de la experiencia musical, donde el encuentro entre artistas y públicos facilita el entendimiento mutuo y la liberación emocional. Además, constatamos la conexión entre las reivindicaciones enunciadas en la escena musical y las demandas del colectivo afrodescendiente en España, que denuncia el racismo y la falta de visibilidad. En esos espacios de confluencia emergen eventos, textos y discursos que contribuyen a la apasionada construcción de un territorio simbólico propio, donde lo individual se conjuga con lo colectivo.
Graphing Crumbling Cookies

Device graphs are datasets that organize and associate the many identifiers produced by PCs, phones, TVs, and tablets as they access media on the internet. Digital cross-media, the delivery and measurement of advertisements across screens, has grown increasingly reliant on device graphs. In response to privacy and tracking concerns, some web browsers limit the persistence of the identifiers used in device graphing. Examples include Safari’s implementation of Intelligent Tracking Prevention and user invoked incognito/private browsing capabilities. Non-persistent identifiers create both a scale and accuracy challenge for device graphing. Motivated by accurate audience measurement, this paper demonstrates how measurement and other entities in the digital advertising ecosystem can overcome the lack of persistence of identifiers without the need for techniques such as browser fingerprinting. The approach is based on first constructing a device graph and applying community detection using persistent identifiers, and then appending non-persistent identifiers to the original communities using a technique termed graph backfilling. The resulting device graphs are of immense scale, organizing more than 4.7 billion identifiers worldwide.
Retinal Image Synthesis

Medical imaging has been aiding diagnosis and treatment of diseases by creating visual representations of the interior of the human body. Experts hand-mark these images for abnormalities and diagnosis. Supplementing experts with these rich visualization has enabled detailed clinical analysis and rapid medical intervention. However, deep learning-based methods rely on abundantly large volumes of data for training. Procuring data for medical imaging applications is especially difficult because abnormal cases by definition are rare and the data, in general, requires experts for labelling. With Deep learning algorithms, data with high class imbalance or of insufficient variability leads to poor classification performance. Thus, alternate approaches like using generative modelling to artificially generate more data have been of interest. Most of these methods are GAN [11] based approaches. While they can be helpful with data imbalance they still require a lot of data to be able to generate realistic images. Additionally, a lot of these methods have been shown to work on natural images where the images are relatively noise-free and smaller artifacts aren’t as damaging. Thus, this thesis aims at providing synthesis methods which overcome the limitations of smaller datasets and noisy profile. We do this for two different modalities, Fundus imaging and Optical Coherence Tomography (OCT). Firstly, we present a fundus image synthesis method aimed at providing paired Optic Cup and Image data for Optic Cup (OC) Segmentation. The synthesis method works well on small datasets by minimising the information to be learnt by leveraging domain-specific knowledge and by providing most of the structural information to the network. We demonstrate this method’s advantages over a more direct synthesis method. We show how leveraging domain-specific knowledge can provide higher quality images and annotations. Inclusion of these generated images and their annotations in training of an OC segmentation model showed a significant improvement in performance, thus showing their reliability. Secondly, we present a novel unpaired image to image translation method which can introduce abnormality (Drusen) to OCT images while avoiding artifacts and preserving the noise profile. Comparison with other state-of-the-art images to image translation methods shows that our method is significantly better at preserving the noise profile and is better at generating morphologically accurate structures.
Binary vapour-liquid equilibria for oxygen-containing compounds.

v TABLE OF CONTENTS vi LIST OF FIGURES x LIST OF TABLES xxi NOMENCLATURE xxv CHAPTER 1: INTRODUCTION 1 CHAPTER 2: BINARY SYSTEMS CHOSEN FOR EXPERIMENTATION . 3 2.1. Carboxylic acids 4 2.2. Alcohols 7 2.3. Ketones 8 CHAPTER 3: THE THEORETICAL TREATMENT OF VLE DATA 9 3.1. Fundamental thermodynamic relationships and equations 9 3.2. Chemical potential 11 3.3. Fugacity and fugacity coefficients 12 3.4. Activity and activity coefficients 14 3.5. Theoretical VLE methods 16 3.5. 1. The combined method (gamma-phi approach, activity coefficient approach) 17
Deep Policy Optimization for E-commerce Sponsored Search Ranking Strategy

In the E-commerce platform, the sponsored search engine not only takes the role of revenue contributor, but also contributes to the long-term growth of the platform by improving user experiences and facilitating advertisers’ commercial goals. The key to the satisfactory of the platform, the user and the advertiser is the decision of the list of advertisements to show and the charging prices for the advertisers. In the sponsored search platform, these decisions are made according to a ranking function. In the E-commerce platform, advertisements showing positions under different queries from different users may be associated with advertisement candidates of different bid price distributions and click probability distributions, which requires the ranking functions to be optimized adaptively to the traffic characteristics. In this work, we proposed a generic framework to optimize the ranking functions by deep reinforcement learning methods. Experimental results on a large-scale sponsored search platform (Alibaba sponsored search engine) confirm the effectiveness of the proposed method
Lifted Logic for Task Networks: TOHTN Planner Lilotane in the IPC 2020

We present our contribution to the International Planning Competition (IPC) 2020. Our planner Lilotane builds upon ideas established by Tree-REX and encodes a Totally Ordered Hierarchical Task Network (TOHTN) planning problem into incremental formulae of propositional logic (SAT). Lilotane , however, instantiates reductions and actions lazily and mini-malistically without the need for full grounding, hence accelerating the planning process signiﬁcantly. We discuss the re-sults of the IPC and conclude that Lilotane , scoring second in the Total Order track, is an overall competitive system, what demonstrates the viability of our approach and its signiﬁcance for future research.
BMP and Notch Signaling Pathways differentially regulate Cardiomyocyte Proliferation during Ventricle Regeneration

Adult mammalian hearts show limited capacity to proliferate after injury, while zebrafish are capable to completely regenerate injured hearts through the proliferation of spared cardiomyocytes. BMP and Notch signaling pathways have been implicated in cardiomyocyte proliferation during zebrafish heart regeneration. However, the molecular mechanism underneath this process as well as the interaction between these two pathways remains to be further explored. In this study we showed BMP signaling was activated after ventricle ablation and acted epistatic downstream of Notch signaling. Inhibition of both signaling pathways differentially influenced ventricle regeneration and cardiomyocyte proliferation, as revealed by time-lapse analysis using a cardiomyocyte-specific FUCCI (fluorescent ubiquitylation-based cell cycle indicator) system. Further experiments revealed that inhibition of BMP and Notch signaling led to cell-cycle arrest at different phases. Overall, our results shed light on the interaction between BMP and Notch signaling pathways and their functions in cardiomyocyte proliferation during cardiac regeneration.
Imaging Analysis of Photoswitching Fluorophores Using Single-Imaging Analysis of Photoswitching Fluorophores Using Single-Molecule Microscopy Molecule Microsco

Single-molecule localization microscopy (SMLM) is a developing field of biological imaging that employs the use of photoswitching fluorophores to image sub-cellular biological structures at a higher resolution than was previously possible. These fluorophores are used for protein labeling, so that the sample can be imaged under fluorescence microscopy. This type of microscopy requires the use of many different types of fluorophores, which are fluorescent organic compounds that blink stochastically on and off. Thus, it is critical for developers in the field to have easy access to statistical models of the behaviors of different fluorophores. Here, we take AlexaFluor 647 and analyze it using a fluorescence microscope, taking data on its blinking behaviors and discerning its properties when immersed in a fluorescence-dampening buffer solution. We find that the compound behaves best in buffer solution, and we forge a new methodology for evaluating new fluorophores in a systematic fashion using readily available computer software.
Rapid And high throughput RT-qPCR assay for identification and differentiation between SARS-CoV-2 variants B.1.1.7 and B.1.351

Emerging SARS-CoV-2 (SC-2) variants with increased infectivity and vaccine resistance are of major concern. Rapid identification of such variants is important for the public health activities and provide valuable data for epidemiological and policy decision making. We developed a multiplex quantitative RT-qPCR (qPCR) assay that can specifically identify and differentiate between the emerging B.1. 1.7 and B.1. 351 SC-2 variants. In a single assay, we combined four reactions: one that detects SC-2 RNA independently of the strain, one that detects the D3L mutation, which is specific to variant B.1. 1.7, and one that detects the 242-244 deletion, which is specific to variant B.1. 351. The fourth reaction identifies human RNAseP gene, serving as an endogenous control for RNA extraction integrity. We show that the strain-specific reactions target mutations that are strongly associated with the target variants, and not with other major known variants. The assay specificity was tested against a panel of respiratory pathogens (n=16), showing high specificity towards SC-2 RNA. The assay sensitivity was assessed using both In-vitro transcribed RNA and clinical samples, and was determined to be between 20 and 40 viral RNA copies per reaction. The assay performance was corroborated with Sanger and whole genome sequencing, showing complete agreement with the sequencing results. The new assay is currently implemented in the routine diagnostic work at the Central Virology Laboratory, and may be used in other laboratories to facilitate the diagnosis of these major worldwide circulating SC-2 variants.
SECONDARY FLOW INDUCED IN COAL-FIRED FARADAY MHD GENERATOR AT IEE ∗

The secondary flow induced in a coal-fired MHD Faraday generator with 25 MW thermal input at Institute of Electrical Engineerging (IEE), China has been studied with time-dependent three-dimensional analyses. It is found that the strong secondary flow is induced in the Faraday MHD channel studied even though the MHD interaction is small. This is because rather large Hall current is generated by the leakage through the slag layers on the walls. The influence of secondary flow is still small on the overall generator performance of the studied generator, even though rather large secondary flow is induced on the y-z plane. The main flow of velocity is decelerated when the load resistance becomes small. The kinetic energy is changed to the thermal energy, and the temperature becomes high. This also influences the pressure and the mass density. The swirls in the secondary flow on the y-z plane shift to the downstream slighly with higher load resistance.
Comment on “Identification of the IMF sector structure in near-real time by ground magnetic data” by Janzhura and Troshichev (2011)

Abstract. The only published description of the solar wind sector (SS) term used for the reference level in the post-event and real-time derivation of the Polar Cap (PC) indices, PCN (North) and PCS (South), in the version endorsed by the International Association for Geomagnetism and Aeronomy (IAGA) is found in the commented publication, Janzhura and Troshichev (2011): Identification of the IMF sector structure in near-real time by ground magnetic data, Annales Geophysicae, 29, 1491–1500. Actually, the publication has served as basis for the index endorsement by IAGA in 2013. However, neither the illustrations nor the results presented there have been derived by the specified near-real time method. Figs. 1, 6, 7, and 8 display values derived by post-event calculations based on daily medians smoothed over 7 days centred on the day of interest. Figs. 2, 3, and 4 display observed values smoothed over 7 days, while the remaining Fig. 5 displays averages over 4 months. In summary, there are strong disagreements between indications in the title, abstract, and statements in the text compared to the actual results and their illustrations.
What Can Explain Momentum? Evidence From Decomposition

This study comprehensively evaluates and ranks a large number of competing explanations for the momentum anomaly. As a benchmark for evaluation, firm fundamentals are found to be the most promising among well-known explanations of momentum, followed by prospect theory and mental accounting, and anchoring effect. Collectively, all explanations capture 31% of momentum, whereas 69% of momentum remains unexplained. This study thoroughly examines what fractions of the momentum anomaly emerge from the interaction effects between past returns and various firm characteristics. It is further found that strategies based on firm characteristics and residual momentum can significantly alleviate the severity of momentum crashes. Finally, robustness analysis is provided for choosing different formation and holding periods, excluding January observations, and analyze at the level of portfolio rather than individual stock. This paper was accepted by David Simchi-Levi, finance.
Particle swarm optimization for airlines fleet assignment

Airline fleet assignment is the process of assigning aircraft types to scheduled flight legs in order to minimize operating cost and achieve maximize revenue, while satisfying a set of constraints. This paper formulate the fleet assignment problem for airlines that optimization goal is to minimize the total assignment cost. Particle swarm optimization proposed to solve this model. The model successfully applied to Egyptair airline dataset using the particle swarm optimization and mixed integer programming. The proposed method compared with mixed integer programming and current Egyptair assignment methodology. The results showed that the particle swarm optimization is the best method for the Egyptair fleet assignment process. The solution quality is better than mixed integer programming and Egyptair assignment methodology where we saw a daily cost reduction with a percentage of 14.6% and 19.3% respectively.
Efficient TCAM design based on dual port SRAM on FPGA

Ternary content addressable memory (TCAM) is a memory that allows high speed searching for data. Not only it is acknowledged as associative memory/storage but also TCAM can compare input searching content (key) against a collection of accumulated data and return the matching address which compatible with this input search data. SRAM-based TCAM utilizes and allocates blocks RAM to perform application of TCAM on FPGA hardware. This paper presents a design of 480×104 bit SRAM-based TCAM on altera cyclone IV FPGA. Our design achieved lookup rate over 150 millions input search data and update speed at 75 million rules per second. The architecture is configurable, allowing various performance trade-offs to be exploited for different ruleset characteristics.
Ultrafine Fiber Raman Probe with High Spatial Resolution and Fluorescence Noise Reduction

Considerable interest has been shown in fiber Raman probes as powerful tools for in situ biomedical diagnosis and monitoring processes in the materials industry. Miniaturization and high spatial resolution are required for less invasive measurements with accurate locations. In analysis of organs, widespread visible excitation light produces problematic fluorescence backgrounds. Here, we report an ultrafine fiber Raman probe that is thinner than the needle of a mosquito (labrum: 50–80 μm in diameter) with high spatial resolution (23 μm) and with a function of fluorescence background reduction. Due to the fineness and resolution, the distribution of ions in an electrolyte solution in narrow spaces could be measured. Backgrounds in spectra of liquid containing fluorescent impurity were reduced by using the probe. The probe has wide applicability for noninvasive in situ molecular diagnosis of organs and small devices.
Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN

Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.
Climate variability and density-dependent population dynamics: Lessons from a simple high-Arctic ecosystem

Ecologists are still puzzled by the diverse population dynamics of herbivorous small mammals that range from high-amplitude, multi-annual cycles to stable dynamics. Theory predicts that this diversity results from combinations of climatic seasonality, weather stochasticity and density-dependent food web interactions. The almost ubiquitous 3-5-yr cycles in boreal and arctic climates may theoretically result from bottom-up (plant-herbivore) and top-down (predator-prey) interactions. Assessing empirically the roles of such interactions, and how they are influenced by environmental stochasticity, has been hampered by food web complexity. Here, we take advantage of a uniquely simple High-Arctic food web, which allowed us to analyze dynamics of a graminivorous vole population not subjected to top-down regulation. This population exhibited high-amplitude, non-cyclic fluctuations - partly driven by weather stochasticity. However, the predominant driver of the dynamics was direct density dependence, which alternated between being weak in summer and strong (overcompensatory) in winter that the population frequently crashed. Model simulations showed that this season-specific density dependence would yield regular 2-year cycles in absence of stochasticity. While such short cycles have not yet been observed in mammals, they are theoretically plausible if graminivorous vole populations are deterministically bottom-up regulated. When incorporating weather stochasticity in the model simulations, cyclicity became disrupted and the amplitude was increased - akin to the observed dynamics. Our findings contrast with the 3-5-yr population cycles involving delayed density dependence that are typical of graminivorous small mammals in more complex food webs, suggesting that top-down regulation is an important component of such dynamics.
Solar/wind pumping system with forecasting in Sharjah, United Arab Emirates

This paper demonstrates a water pumping hybrid power system design. The proposed system was designed for water related applications in Sharjah (Latitude 25.29 °N and Longitude 55 °E), United Arab Emirates. The proposed water hybrid system has two primary renewable power systems: solar PV panels and wind turbines. The proposed hybrid system considers the changes in weather conditions (humidity, wind speed, and temperature) since wind speed affects the performance of the wind turbines and solar panels are affected by solar irradiance. The following components are involved in the proposed design: battery (to store the power from solar panels), voltage regulator circuit (for getting stable DC voltage), three-phase rectifier (to convert the reduced AC voltage to DC), three-phase transformer (for reducing the obtained AC voltage), and DC electric motor (the main output of the proposed water pumping system). The proposed water pumping system relies on neural network blocks to achieve weather forecasting by obtaining solar irradiance values from the input temperature, wind speed, and humidity in a span of five years. Both MATLAB and Simulink are used simulate the performance of the proposed system under different weather conditions by changing the values according to the measured weather conditions values over five years.