Miracle-dz/UniFAR-FTUs huggingface dataset:
Transport coefficients from in-medium quarkonium dynamics

The in medium dynamics of heavy particles are governed by transport coefficients. The heavy quark momentum diffusion coefficient, $\kappa$, is an object of special interest in the literature, but one which has proven notoriously difficult to estimate, despite the fact that it has been computed by weak-coupling methods at next-to-leading order accuracy, and by lattice simulations of the pure SU(3) gauge theory. Another coefficient, $\gamma$, has been recently identified. It can be understood as the dispersive counterpart of $\kappa$. Little is known about $\gamma$. Both $\kappa$ and $\gamma$ are, however, of foremost importance in heavy quarkonium physics as they entirely determine the in and out of equilibrium dynamics of quarkonium in a medium, if the evolution of the density matrix is Markovian, and the motion, quantum Brownian; the medium could be a strongly or weakly coupled plasma. In this paper, using the relation between $\kappa$, $\gamma$ and the quarkonium in medium width and mass shift respectively, we evaluate the two coefficients from existing 2+1 flavor lattice QCD data. The resulting range for $\kappa$ is consistent with earlier determinations, the one for $\gamma$ is the first non-perturbative determination of this quantity.
On the existence of near threshold exotic hadrons containing two heavy quarks

This paper focuses on tetraquark containing two heavy quarks. There are well-established model-independent arguments for the existence of deeply bound exotic $\bar{q}\bar{q}'QQ$ tetraquark in the formal limit of arbitrarily large heavy quark masses. However, these previous arguments did not address the question whether parametrically narrow tetraquark states exist close to the threshold for break up into two heavy mesons. Here we present a model-independent argument that establishes the existence of parametrically narrow tetraquark states that are parametrically close to threshold. The argument here is based on Born-Oppenheimer and semi-classical considerations.
Traumatic events and psychopathological symptoms in university students

Abstract: A non-experimental, cross-sectional and correlational study that aims to analyze the relationship between traumatic events and psychopathological symptoms in 286 university students (76.6% women and 23.4% men) between 18 and 59 years ( M = 24.03; SD 7.14) by non-probabilistic convenience sampling. A large part of the sample did not present any traumatic event and those who did showed levels of neutral and/or negative affectation. The most frequent events are the death of a loved one, robbery or assault, divorce of the parents, nervous breakdown, school failure, rejection or failure in a relationship. The least reported events are severe or chronic illness, imprisonment or torture, loss of a child or second marriage. In general, the sample presented low scores in all the psychopathological dimensions. All the variables are related in a statistically significant way, which supports the interaction between traumatic events and various psychopathological symptoms. Keywords: Traumatic events; psychopathological symptoms; anxiety; schizotypy; university students. Eventos traumaticos y sintomas psicopatologicos en estudiantes universitarios Resumen: Estudio no experimental, transversal y correlacional, que busca analizar la relacion entre eventos traumaticos y sintomas psicopatologicos en 286 estudiantes universitarios (76.6% mujeres y 23.4% hombres) entre 18 y 59 anos ( M = 24.03; DE . 7.14) mediante muestreo por conveniencia. Gran parte de la muestra no presento ningun evento traumatico y quienes si los presentaron manifestaron niveles de afectacion neutra y/o algo negativa. Los eventos mas frecuentes son muerte de un ser querido, robo o asalto, divorcio de los padres, ataque de nervios, fracaso escolar, rechazo o fracaso en una relacion. Los eventos menos reportados son enfermedad grave o cronica, encarcelamiento o tortura, perdida de un hijo o segundo matrimonio. En general la muestra presento puntuaciones bajas en todas las dimensiones psicopatologicas. Todas las variables se relacionan de manera estadisticamente significativa, con lo cual se soporta la interaccion entre eventos traumaticos y diversos sintomas psicopatologicos. Palabras Clave: Eventos traumaticos; sintomas psicopatologicos; ansiedad; esquizotipia; estudiantes universitarios.
Quantitative geography III: Future challenges and challenging futures

In the previous two reports in this series, we discussed the history and current status of quantitative geography. In this final report, we focus on the future. We argue that quantitative geographers are most helpful when we can simplify difficult problems using our distinct domain expertise. To do this, we must clarify the theory underpinning core conceptual problems in quantitative geography. Then, we examine the social forces that are shaping the future of quantitative geography. We conclude with criteria for how quantitative geography might succeed in addressing these challenges.
FACTORS AFFECTING TO THE CONSUMER ATTITUDE TOWARDS GLOBAL LUXURY CAR BRANDS

Consumer attitude towards global luxury brands has been researched by many scholars around the world. The purpose of this paper is to discuss the factors affecting consumer attitude towards global luxury car brands. This study conceptualizes a new model to identify the factors affecting to the consumer attitude towards global luxury car brands. The study was conducted in four major provinces in Sri Lanka by selecting a sample based on the level of income and the aspirations to purchase a global luxury car brand in a time frame of 5 to 10 years. Convenience sampling method was adopted for the data gathering process and among the respondents, a total of 231 questionnaires were taken for the final data analysis. The data were analyzed by using SPSS package. The findings show that the Perceived Luxury Consumption, General Life Value, Self-Identity, Experiential Value, Brand Consciousness, and Brand Love have a positive effect on the consumer attitude towards luxury. Further, the findings show the importance of understanding the factors, which are affecting the consumer attitude, in developing marketing promotional messages. Luxury brand marketers could develop vertically oriented marketing promotional messages, for instance, messages emphasizing power, exclusivity, superiority, experiential value, conformity with the general life values and brand love, etc. would be progressively convincing and successful when directed towards aspirants who have a vertical orientation. Article visualizations:
The Stress and Adversity Inventory for Adolescents (Adolescent STRAIN): associations with mental and physical health, risky behaviors, and psychiatric diagnoses in youth seeking treatment.

BACKGROUND Numerous theories have posited that stressors occurring over the lifespan may exert a cumulative effect on psychological and biological processes that increase individuals' risk for a variety of mental and physical health problems. Given the difficulty associated with assessing lifetime stress exposure, however, few empirical studies have directly tested these cumulative risk models of psychopathology and human health. METHOD To address this issue, we examined the usability, acceptability, concurrent validity, and predictive validity of the recently developed Stress and Adversity Inventory for Adolescents (Adolescent STRAIN) in 338 youth (Mage = 15.64; 229 females) seeking mental health treatment. RESULTS The Adolescent STRAIN achieved high acceptability and was completed in approximately 25 min (interquartile range: 20-32 min). Concurrent associations with other measures of early adversity (Childhood Trauma Questionnaire-Short Form) and interpersonal stress (Revised Peer Experiences Questionnaire) were very good (rs = .50-.59). In analyses that adjusted for participants' age, sex, and race, the STRAIN was significantly associated with depression, anxiety, and anhedonia severity; general mental and physical health complaints; risky behavior engagement; and number of interviewer-based psychiatric diagnoses (βs = .16-.52; risk ratios = 1.006-1.014). Contrary to classic theories of stress which assume that different stressors exert similar effects on health, substantial differences were observed across the two stressor types, twelve life domains, and five core social-psychological characteristics assessed by the Adolescent STRAIN. CONCLUSIONS These data confirm the relevance of lifetime stress exposure for multiple health outcomes in adolescence, which can in turn inform existing theories of lifespan health. Because stress is a common presenting problem in hospitals and clinics, these data also suggest the possibility of using the Adolescent STRAIN to generate stress exposure profiles for case conceptualization and treatment planning purposes.
Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called “Multi-mask Tensorized Self-Attention” (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency.
On the Mean Square Displacement in Lévy Walks

Many physical and biological processes are modeled by "particles" undergoing L\'evy random walks. A feature of significant interest in these systems is the mean square displacement (MSD) of the particles. Long-time asymptotic approximations of the MSD have been established, via the Tauberian Theorem, for systems in which the distribution of the step durations is asymptotically a power law of infinite variance. We extend these results, using elementary analysis, and obtain closed-form expressions as well as power law bounds for the MSD in equilibrium, and representations of the MSD as sums of super-linear, linear, and sub-linear terms. We show that the super-linear components are determined by the mean and asymptotics of the step durations, but that the linear and sub-linear components (whose size has implications for the accuracy of the asymptotic approximation) depend on the entire distribution function.
Approximating Nonnegative Polynomials via Spectral Sparsification

We study polyhedral approximations to the cone of nonnegative polynomials. We show that any constant ratio polyhedral approximation to the cone of nonnegative degree 2d forms in n variables has to have exponentially many facets in terms of n. We also show that for fixed m ≥ 3, all linear m dimensional sections of the nonnegative cone that include (x 1 +x 2 +. . .+x n ) has a costant ratio polyhedral approximation with O(n) many facets. Our approach is convex geometric, and parts of the argument rely on the recent solution of Kadison-Singer problem. We also discuss a randomized polyhedral approximation which might be of independent interest. Dedicated to Nuriye and Semih, and to thousands of souls longing for justice.
The kink-turn in the structural biology of RNA

Abstract The kink-turn (k-turn) is a widespread structural motif found in functional RNA species. It typically comprises a three-nucleotide bulge followed by tandem trans sugar edge-Hoogsteen G:A base pairs. It introduces a sharp kink into the axis of duplex RNA, juxtaposing the minor grooves. Cross-strand H-bonds form at the interface, accepted by the conserved adenine nucleobases of the G:A basepairs. Alternative acceptors for one of these divides the k-turns into two conformational classes N3 and N1. The base pair that follows the G:A pairs (3b:3n) determines which conformation is adopted by a given k-turn. k-turns often mediate tertiary contacts in folded RNA species and frequently bind proteins. Common k-turn binding proteins include members of the L7Ae family, such as the human 15·5k protein. A recognition helix within these proteins binds in the widened major groove on the outside of the k-turn, that makes specific H-bonds with the conserved guanine nucleobases of the G:A pairs. L7Ae binds with extremely high affinity, and single-molecule data are consistent with folding by conformational selection. The standard, simple k-turn can be elaborated in a variety of ways, that include the complex k-turns and the k-junctions. In free solution in the absence of added metal ions or protein k-turns do not adopt the tightly-kinked conformation. They undergo folding by the binding of proteins, by the formation of tertiary contacts, and some (but not all) will fold on the addition of metal ions. Whether or not folding occurs in the presence of metal ions depends on local sequence, including the 3b:3n position, and the −1b:−1n position (5′ to the bulge). In most cases −1b:−1n = C:G, so that the 3b:3n position is critical since it determines both folding properties and conformation. In general, the selection of these sequence matches a given k-turn to its biological requirements. The k-turn structure is now very well understood, to the point at which they can be used as a building block for the formation of RNA nano-objects, including triangles and squares.
Demonstration of quantum controlled teleportation for discrete variables on linear optical devices

We report an experimental implementation of tripartite controlled quantum teleportation on the quantum optical devices. The protocol is performed through bi- and tripartite entangled channels of discrete variables and qubits encoded in polarization of individual photons. The experimental results demonstrate successful controlled quantum teleportation with a fidelity around $83\%$, well above the classical limit. By realizing the controlled quantum teleportation through biseparable state, we show that tripartite entangled is not a necessary resource for controlled quantum teleportation and the controller's capability to allow or prohibit the teleportation cannot be considered to be a manifestation of tripartite entanglement. These results open new possibilities for further application of controlled quantum teleportation by lowering teleportation channel's requirements.
Estimation of the Shapley Value of a Peer-to-Peer Energy Sharing Game using Coalitional Stratified Random Sampling

Various peer-to-peer energy markets have emerged in recent years in an attempt to manage distributed energy resources in a more efficient way. One of the main challenges these models face is how to create and allocate incentives to participants. Cooperative game theory offers a methodology to financially reward prosumers based on their contributions made to the local energy coalition using the Shapley value, but its high computational complexity limits the size of the game. This paper explores a stratified sampling method proposed in existing literature for Shapley value estimation, and modifies the method for a peer-to-peer cooperative game to improve its scalability. Finally, selected case studies verify the effectiveness of the proposed coalitional stratified random sampling method and demonstrate results from large games.
Didactic Framework for Teaching C Programming Language: A Proposal Based on Cooperative and Competitive Learning Techniques

This Research to Practice Full Paper presents a didactic framework for teaching C programming language. The difficulty of understanding abstraction presented by engineering students in computational problems solving is one of the main reasons for numerous pitfalls in the early college semesters. When teaching abstract contents, such as computer programming, it is relevant to include as many practical exercises and laboratories experiments as possible, to facilitate a better understanding. These practical approaches allow the students to achieve, by simulations or by real experiences, good notions of the basic concepts of the subject being studied. To motivate engineering students to be acquainted with formal definitions and high-level abstractions, a Didactic Framework focused on the teaching of the C programming language for embedded systems was developed and tested. Moreover, the students were supposed to develop a software project in teams that work together to solve basic tasks, and that compete at the end of the semester by solving a common programming challenge. This work is divided into two main parts: one focusing on the creation of practical strategies that make easier understanding complex definitions; and another concerning to the motivational strategies through cooperation and competition. The final goal was to increase the students understanding and satisfaction as well as reduce the dropout rates.
The Prediction of Intrinsically Disordered Proteins Based on Feature Selection

Intrinsically disordered proteins perform a variety of important biological functions, which makes their accurate prediction useful for a wide range of applications. We develop a scheme for predicting intrinsically disordered proteins by employing 35 features including eight structural properties, seven physicochemical properties and 20 pieces of evolutionary information. In particular, the scheme includes a preprocessing procedure which greatly reduces the input features. Using two different windows, the preprocessed data containing not only the properties of the surroundings of the target residue but also the properties related to the specific target residue are fed into a multi-layer perceptron neural network as its inputs. The Adam algorithm for the back propagation together with the dropout algorithm to avoid overfitting are introduced during the training process. The training as well as testing our procedure is performed on the dataset DIS803 from a DisProt database. The simulation results show that the performance of our scheme is competitive in comparison with ESpritz and IsUnstruct.
Multimodal framework based on audio-visual features for summarisation of cricket videos

Sports broadcasters generate an enormous amount of video content on the cyberspace due to massive viewership all over the world. Analysis and consumption of this huge repository urges the broadcasters to apply video summarisation to extract the exciting segments from the entire video to capture user's interest and reap the storage and transmission benefits. Therefore, in this study an automatic method for key-events detection and summarisation based on audio-visual features is presented for cricket videos. Acoustic local binary pattern features are used to capture excitement level in the audio stream, which is used to train a binary support vector machine (SVM) classifier. Trained SVM classifier is used to label audio frame as an excited or non-excited frame. Excited audio frames are used to select candidate key-video frames. A decision tree-based classifier is trained to detect key-events in the input cricket videos that are then used for video summarisation. Performance of the proposed framework has been evaluated on a diverse dataset of cricket videos belonging to different tournaments and broadcasters. Experimental results indicate that the proposed method achieves an average accuracy of 95.5%, which signifies its effectiveness.
Simulation in Digital Enterprises

Digital production and logistic enterprise systems are complex systems, in terms of layout variability, control strategies, business processes and system parameter. All of these aspects are not independent and even due to the system dynamics the optimal solution may differ depending on the actual situation applying specific requirements. The planning, optimization and operation of these complex systems requires modern, data and simulation driven multi criteria decision approaches. Simulation based analyses can be uses through all phases on different planning and operating levels. In order to improve operative decision making in production and logistic enterprises the digital twin concept, using virtual clones of real systems or subsystems, can be applied. Due to improved network and computing power operative simulation concepts are starting now to be realized in industrial practice.
Momentous Innovations in the Prospective Method of Drug Development

The innovative work (Research and development) pipeline is a huge cost for pharmaceutical Organizations. In spite of the requirement for more advancement, Research and development profitability has vegetated or decayed over various years.1-3 More present, the industry has not exhaustively evaluated the effect of new developments in pharmaceutical improvement and market get to particularly as far as basic achievement measurements, for example, clinical preliminary productivity, the probability of medication dispatch and patient access. To invigorate activity on this diagnostic issue, we accumulated and translated hard confirmation on the effect of chosen developments estimated against particular achievement measurements. The general objective of the investigation is to invigorate expansive dialog on how the business can utilize inventive methodologies in medicate advancement and market access to enhance proficiency, revive profitability and revitalize supportability. It is unmistakable in openly evaluating the effect of the most encouraging advancements in sedate improvement on preliminary productivity and accomplishment in dispatch and getting model endorsement around the world. We recommend that it makes convincing, information-driven case for expediting the selection of new market get to forms for drugs. In particular, it demonstrates that the four developments assessed---adaptive trial designs, patient-centric trials, precision medicine trials and real-world data trials reliably convey in contrast to industry achievement touchstone.
All-in-Focus Image Generation Using Improved Blind Image Deconvolution Technique

The purpose of this paper is two-fold. First, we propose two new blind image deconvolution (BID) methods by improving Ahmed's BID method [1] in 2014 that is based on techniques of low-rank matrix recovery. The first method is introducing the total variation regularization term into Ahmed's BID method for the single-input-single-output (SISO) imaging model. The second method is extending Ahmed's BID method to the single-input-multiple-output (SIMO) imaging model. The practical iterative algorithm is developed to solve the formulated BID problem in each case when we take so-called iterative singular value thresholding algorithm. In the next part, we apply the new algorithm for the SIMO case, which is more stable than the SISO case, to the problem in generating all-in-focus images. We often have such a kind of problem when we take multiple images with different focal lengths for a 3-D scene holding varying depth. We demonstrate performances of the proposed methods through simulation studies as well as real data experiments.