Miracle-dz/UniFAR-FTUs huggingface dataset:
Indonesia should eliminater discriminations towards lgbt..

Stigma towards persons with disability has been highlighted as one predicting factors for overdiagnosis and more negative prognoses. The extent to which disability equality policies and practices are adopted in any given Southeast Asia societies is dependent upon the ability to capture in a real-time the extremely rapid and massive context of current societal dynamics. The mode of communication, with the support of increasingly affordable digital devices, brings communities into a new form of interaction in social media. As a source of data, the patterns derived from these social media are extremely valuable, especially to support decision-making processes. Data-driven policies are capable of suppressing errors because the target is a measurable goal. Characteristics of specific digital data requires a special approach given the huge volume of data (big data). Related to this research, the applied platform is social listening. Big data processing is conducted through the (1) data mining stage (Twitter API) using six technical terms i.e., “disabilitas” (disability), “difabel” (different ability), “cacat fisik” (physical disability), “cacat genetik” (genetic disability), “gangguan mental” (mental disorder), “gangguan jiwa” (soul illness), “penyandang cacat” (persons with disability), “retardasi mental” (mental retardation), and “skizofrenia” (schizophrenia), (2) data processing, (3) pattern evaluation, and (4) data visualization (Tableau and Gephi software). Findings from this study is expected to overcome the limitations of the tradition of stigma measurement with surveys and interviews that are vulnerable to the tendency of the subject to respond normatively.
Didactic Potential of Web Portfolio: Theory and Practice

The urgency of the research is determined by the new stage of the digital environment, the rapid development of web tools, the improvement of the technical capabilities of telecommunications, software and hardware solutions for the effective self-presentation of the student’s work and, consequently, the need to conduct research in the field of pedagogical theory and practice, search for new approaches to objective assessment of their personal and educational achievements, interests, preferences, problem areas, as well as increasing the effective use of web technologies to build a table of the graduates’ competencies for self-promotion, and job search. The scientific problem of the research is the inadequacy of the potential for the implementation of a personcentered, competence-based approach in the educational process of the university due to the creation and maintenance of a web portfolio of students, on the one hand, and an insufficient level of organizational and methodological support, on the other. The scientific toolkit, the basis of the research is an integrated approach to organizing the study of the theory and practice of using the digital portfolio in domestic and foreign education systems. An integrated approach ensures the objectivity of studying problems using the methodology of such sciences as pedagogy, psychology, sociology and computer science. The use of convenient and easily customizable web services for building a portfolio helps in fixing “digital” tracks, shaping the student’s and student’s personal Internet space, provides the opportunity to enter various educational and professional communities to present the completed work, exchange information, present successful work, success. and achievements. The main task of using the portfolio in the educational process is solved it is the diagnosis of the formation of creative ability, professionally important competences of the future specialist, collection, systematization of results in all activities and representation of the graduate in the most advantageous light to employers.
Osteopontin a new probable marker for atherosclerosis in obese women

Obesity is associated with a state of chronic, low-grade inflammation characterized by abnormal cytokine production and macrophage infiltration into adipose tissue, which may contribute to the development of insulin resistance and type 2 diabetes. It was emphasized that inflamation takes part in all stages and complications of atherosclerosis. Osteopontin (OPN) is a multifunctional protein involved in various inflammatory processes, cell migration, and tissue remodeling. This study evaluated the role of OPN in female patients who have at least one risk factor for atherosclerosis such as obesity. The study included 45 morbidly obese normotensive female patients and 22 age and sex-matched control subjects. As well as making physical and antropometric examinations, fasting plasma glucose and insulin, post prandial plasma glucose and insulin, lipid profile, C-reactive protein, lipoprotein a, OPN levels were obtained in all female person. In this study we investigated atherosclerosis indirectly. Our obese group had significantly higher levels in glucose and lipid parameters as well as antropometric measures. Obese group also had significantly higher plasma OPN and C-reactive protein levels than the control group (p < 0.05). We also performed the correlation analysis of the groups and found positive correlations in OPN levels and body mass index, C-reactive protein, fasting insulin. In conclusion, our data may point toward a role of OPN in atherosclerosis and obesity. Key words: Osteopontin, atherosclerosis, obesity.
Stellar cosmic rays as an important source of ionisation in protoplanetary disks: a disk mass dependent process

We assess the ionising effect of low energy protostellar cosmic rays in protoplanetary disks around a young solar mass star for a wide range of disk parameters. We assume a source of low energy cosmic rays located close to the young star which travel diffusively through the protoplanetary disk. We use observationally inferred values from nearby star-forming regions for the total disk mass and the radial density profile. We investigate the influence of varying the disk mass within the observed scatter for a solar mass star. We find that for a large range of disk masses and density profiles that protoplanetary disks are “optically thin” to low energy (∼3 GeV) cosmic rays. At R ∼ 10 au, for all of the disks that we consider (Mdisk = 6.0 × 10−4 − 2.4 × 10−2M⊙), the ionisation rate due to low energy stellar cosmic rays is larger than that expected from unmodulated galactic cosmic rays. This is in contrast to our previous results which assumed a much denser disk which may be appropriate for a more embedded source. At R ∼ 70 au, the ionisation rate due to stellar cosmic rays dominates in ∼50% of the disks. These are the less massive disks with less steep density profiles. At this radius there is at least an order of magnitude difference in the ionisation rate between the least and most massive disk that we consider. Our results indicate, for a wide range of disk masses, that low energy stellar cosmic rays provide an important source of ionisation at the disk midplane at large radii (∼70 au).
Deep Reinforcement Learning with Explicitly Represented Knowledge and Variable State and Action Spaces

We focus on a class of real-world domains, where gathering hierarchical knowledge is required to accomplish a task. Many problems can be represented in this manner, such as network penetration testing, targeted advertising or medical diagnosis. In our formalization, the task is to sequentially request pieces of information about a sample to build the knowledge hierarchy and terminate when suitable. Any of the learned pieces of information can be further analyzed, resulting in a complex and variable action space. We present a combination of techniques in which the knowledge hierarchy is explicitly represented and given to a deep reinforcement learning algorithm as its input. To process the hierarchical input, we employ Hierarchical Multiple-Instance Learning and to cope with the complex action space, we factor it with hierarchical softmax. Our end-to-end differentiable model is trained with A2C, a standard deep reinforcement learning algorithm. We demonstrate the method in a set of seven classification domains, where the task is to achieve the best accuracy with a set budget on the amount of information retrieved. Compared to baseline algorithms, our method achieves not only better results, but also better generalization.
An In-depth Investigation of Faraday Depth Spectrum Using Synthetic Observations of Turbulent MHD Simulations

In this paper, we present a detailed analysis of the Faraday depth (FD) spectrum and its clean components obtained through the application of the commonly used technique of Faraday rotation measure synthesis to analyze spectro-polarimetric data. To directly compare the Faraday depth spectrum with physical properties of a magneto-ionic medium, we generated synthetic broad-bandwidth spectro-polarimetric observations from magnetohydrodynamic (MHD) simulations of a transonic, isothermal, compressible turbulent medium. We find that correlated magnetic field structures give rise to a combination of spiky, localized peaks at certain FD values, and broad structures in the FD spectrum. Although most of these spiky FD structures appear narrow, giving an impression of a Faraday thin medium, we show that they arise from strong synchrotron emissivity at that FD. Strong emissivity at a FD can arise because of both strong spatially local polarized synchrotron emissivity at a FD or accumulation of weaker emissions along the distance through a medium that have Faraday depths within half the width of the rotation measure spread function. Such a complex Faraday depth spectrum is a natural consequence of MHD turbulence when the lines of sight pass through a few turbulent cells. This therefore complicates the convention of attributing narrow FD peaks to the presence of a Faraday-rotating medium along the line of sight. Our work shows that it is difficult to extract the FD along a line of sight from the Faraday depth spectrum using standard methods for a turbulent medium in which synchrotron emission and Faraday rotation occur simultaneously.
Joint Emotion Label Space Modelling for Affect Lexica

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, methodological issues emerge when employing them: lexica are often not very extensive, and the way they are constructed can vary widely -- from lab conditions to crowdsourced approaches and distant supervision. Furthermore, both categorical frameworks and dimensional frameworks coexist, in which theorists provide many different sets of categorical labels or dimensional axes. The heterogenous nature of the resulting emotion detection resources results in a need for a unified approach to utilising them. This paper contributes to the field of emotion analysis in NLP by a) presenting the first study to unify existing emotion detection resources automatically and thus learn more about the relationships between them; b) exploring the use of existing lexica for the above-mentioned task; c) presenting an approach to automatically combining emotion lexica, namely by a multi-view variational auto-encoder (VAE), which facilitates the mapping of datasets into a joint emotion label space. We test the utility of joint emotion lexica by using them as additional features in state-of-the art emotion detection models. Our overall findings are that emotion lexica can offer complementary information to even extremely large pre-trained models such as BERT. The performance of our models is comparable to state-of-the art models that are specifically engineered for certain datasets, and even outperform the state-of-the art on four datasets.
Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Models

We introduce a new strategy designed to help physicists discover hidden laws governing dynamical systems. We propose to use machine learning automatic differentiation libraries to develop hybrid numerical models that combine components based on prior physical knowledge with components based on neural networks. In these architectures, named Deep Neural Numerical Models (DNNMs), the neural network components are used as building-blocks then deployed for learning hidden variables of underlying physical laws governing dynamical systems. In this paper, we illustrate an application of DNNMs to upper ocean dynamics, more precisely the dynamics of a sea surface tracer, the Sea Surface Height (SSH). We develop an advection-based fully differentiable numerical scheme, where parts of the computations can be replaced with learnable ConvNets, and make connections with the single-layer Quasi-Geostrophic (QG) model, a baseline theory in physical oceanography developed decades ago.
On the Complexity of a Charged Quantum Oscillator

In this paper, we study the effect of both the electric and the magnetic fields on the rate of complexity growth. Our system is a charged quantum oscillator and over a period of time, we study the maximum dynamic evolution of quantum states which might lead to a strong bound on the rate of computation. We show that by turning on the electric field, the rate of complexity decreases whereas this rate has increasing behavior when the magnetic field is turned on. In this regard, we also find a critical value of the magnetic field, beyond which this rate changes its behavior drastically.
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU

Log messages are now widely used in software systems. They are important for classification as millions of logs are generated each day. Most logs are unstructured which makes classification a challenge. In this paper, Deep Learning (DL) methods called Auto-LSTM, Auto-BLSTM and Auto-GRU are developed for anomaly detection and log classification. These models are used to convert unstructured log data to trained features which is suitable for classification algorithms. They are evaluated using four data sets, namely BGL, Openstack, Thunderbird and IMDB. The first three are popular log data sets while the fourth is a movie review data set which is used for sentiment classification and is used here to show that the models can be generalized to other text classification tasks. The results obtained show that Auto-LSTM, Auto-BLSTM and Auto-GRU perform better than other well-known algorithms.
HIGMAN’S EMBEDDING THEOREM AND DECISION PROBLEMS

This paper exposits Higman’s Embedding Theorem, which establishes a correspondence between finitely generated subgroups of finitely presented groups and recursively presented groups. To that end we present the basic constructions in combinatorial group theory and the correspondence between Turing machines and their associated semigroups, ultimately combining the concepts to prove Higman’s theorem. Our discussion culminates in a host of applications to decision problems in group theory and topology.
An Empirical Study of Community Detection Algorithms on Social and Road Networks

Community detection in social networks is a problem with considerable interest, since, discovering communities reveals hidden information about networks. There exist many algorithms to detect inherent community structures and recently few of them are investigated on social networks. However, it is non-trivial to decide the best approach in the presence of diverse nature of graphs, in terms of density and sparsity, and inadequate analysis of the results. Therefore, in this study, we analyze and compare various algorithms to detect communities in two networks, namely social and road networks, with varying structural properties. The algorithms under consideration are evaluated with unique metrics for internal and external connectivity of communities that includes internal density, average degree, cut ratio, conductance, normalized cut, and average Jaccard Index. The evaluation results revealed key insights about selected algorithms and underlying community structures.
Error-in-Variables and Tests of Asset Pricing Models with Liquidity Risk∗

This paper diagnoses the impact of error-in-variables (EIV) on inferences in asset pricing models. I test the CAPM and the LCAPM in a manner that explicitly accounts for EIV, without pooling stocks into portfolios. I find that the single-factor CAPM beta is not priced. I prove that the aggregate liquidity risk in the liquidity-adjusted CAPM of Acharya and Pedersen (2005) is priced, and the portfolio-based approach is unable to capture this relationship. The cumulant-based approach to handle EIV enables me to test the effects of the individual components of aggregate liquidity risk, and I document that the risk associated with the commonality in illiquidity has a positive premium and the risk associated with the sensitivity of a stock’s illiquidity to the value-weighted market return has a negative premium. I also show that for microcap stocks, the risk attributable to the covariance between stock return and market-wide liquidity has a negative relationship with average returns. I prove that the LCAPM cannot be rejected when the betas are estimated at the stock-level.
Effect of Acute High Intensity Exercise in Hypoxic Conditions on Plasma IL6, IL-1ra in Male Wistar Rat

The Purpose of this study is effect of acute high intensity exercise in stimulated condition on plasmaIL6 and IL-1ra in male wistar Rats. Therefore, 21 male wistar rats, 4 weeks old with mean weight of 263±20 g were bought from Iran Pasteur Institution. Animal were familiarized with treadmill running for 0ne week, then randomly assign done of three groups: Normoxic Control (NC), Hypoxic Control (HC), and Hypoxic Training (HT). Then subjects of experimental group had an activity session with determined intensity and HC group was just in exposure of stimulated hypoxic condition and NC group was exposure in normal condition of Pasteur institute, Tehran. After familiarized period and staying in specified groups and 8 hours resting immediately before main exercise, After unconsciousness the blood samples of 1 to 1.5 cc were collected. Immediately after high intensity exercise with the speed 25.5m/min of experimental group and exposure control groups in vitro, rats' blood samples were collected for calculating the factors under study. Then they were transferred to centrifuge machine and serum, plasma were separated. Therefore, for each sample IL-1ra, IL-6 was measured. For an analysis of data, descriptive statistics, SPSS 19 software was used at a significance level of p=0.05. The results showed that hypoxia alone and acute high intensity exercise in hypoxic conditions specified was significant on IL-6, IL-1ra plasma concentrations. In conclusion, acute high intensity exercise in hypoxic conditions, affects the immune factors such as IL-6 and IL-1ra in rats. Hence, it seems that more research is needed to investigate different factors in this field.
Defining the topological influencers and predictive principles to engineer the band structure of halide perovskites

Complex quantum coupling phenomena of halide perovskites are examined through ab-initio calculations and exact diagonalization of model Hamiltonians to formulate a set of fundamental guiding rules to engineer the bandgap through strain. The bandgap tuning in halides is crucial for photovoltaic applications and for establishing non-trivial electronic states. Using CsSnI$_3$ as the prototype material, we show that in the cubic phase, the bandgap reduces irrespective of the nature of strain. However, for the tetragonal phase, it reduces with tensile strain and increases with compressive strain, while the reverse is the case for the orthorhombic phase. The reduction can give rise to negative bandgap in the cubic and tetragonal phases leading to normal to topological insulator phase transition. Also, these halides tend to form a stability plateau in a space spanned by strain and octahedral rotation. In this plateau, with negligible cost to the total energy, the bandgap can be varied in a range of 1eV. Furthermore, we present a descriptor model for the perovskite to simulate their bandgap with strain and rotation. Analysis of band topology through model Hamiltonians led to the conceptualization of topological influencers that provide a quantitative measure of the contribution of each chemical bonding towards establishing a normal or topological insulator phase. On the technical aspect, we show that a four orbital based basis set (Sn-$\{s,p\}$ for CsSnI$_3$) is sufficient to construct the model Hamiltonian which can explain the electronic structure of each polymorph of halide perovskites.
Learning to Control Latent Representations for Few-Shot Learning of Named Entities

Humans excel in continuously learning with small data without forgetting how to solve old problems. However, neural networks require large datasets to compute latent representations across different tasks while minimizing a loss function. For example, a natural language understanding (NLU) system will often deal with emerging entities during its deployment as interactions with users in realistic scenarios will generate new and infrequent names, events, and locations. Here, we address this scenario by introducing an RL trainable controller that disentangles the representation learning of a neural encoder from its memory management role. Our proposed solution is straightforward and simple: we train a controller to execute an optimal sequence of reading and writing operations on an external memory with the goal of leveraging diverse activations from the past and provide accurate predictions. Our approach is named Learning to Control (LTC) and allows few-shot learning with two degrees of memory plasticity. We experimentally show that our system obtains accurate results for few-shot learning of entity recognition in the Stanford Task-Oriented Dialogue dataset.
Perceptual loss function for neural modeling of audio systems

This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing. In our previous work, the errorto-signal ratio loss function was used during network training, with a first-order high-pass pre-emphasis filter applied to both the target signal and neural network output. This work considers more perceptually relevant pre-emphasis filters, which include low-pass filtering at high frequencies. We conducted listening tests to determine whether they offer an improvement to the quality of a neural network model of a guitar tube amplifier. Listening test results indicate that the use of an A-weighting pre-emphasis filter offers the best improvement among the tested filters. The proposed perceptual loss function improves the sound quality of neural network models in audio processing without affecting the computational cost.