Miracle-dz/UniFAR-FTUs huggingface dataset:
Stock Market Analysis with Text Data: A Review

Stock market movements are inﬂuenced by public and private information shared through news articles, company reports, and social media discussions. Analyzing these vast sources of data can give market participants an edge to make proﬁt. However, the majority of the studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. In this study, we provide a review on the immense amount of existing literature of text-based stock market analysis. We present input data types and cover main textual data sources and variations. Feature representation techniques are then presented. Then, we cover the analysis techniques and create a taxonomy of the main stock market forecast models. Importantly, we discuss representative work in each category of the taxonomy, analyzing their respective contributions. Finally, this paper shows the ﬁndings on unaddressed open problems and gives suggestions for future work. The aim of this study is to survey the main stock market analysis models, text representation techniques for ﬁnancial market prediction, shortcomings of existing techniques, and propose promising directions for future research.
On the use of PN Ranging with High-rate Spectrally-efficient Modulations

In this paper, we study the feasibility of coupling the PN ranging with filtered high-order modulations, and investigate the simultaneous demodulation of a high-rate telemetry stream while tracking the PN ranging sequence. Accordingly, we design a receiver scheme that is able to perform a parallel cancellation, in closed-loop, of the ranging and the telemetry signal reciprocally. From our analysis, we find that the non-constant envelope property of the modulation causes an additional jitter on the PN ranging timing estimation that, on the other hand, can be limited by properly sizing the receiver loop bandwidth. Our study proves that the use of filtered high-order modulations combined with PN ranging outperforms the state-of-the-art in terms of spectral efficiency and achievable data rate, while having comparable ranging performance.
PASSAGES: The large millimeter telescope and ALMA observations of extremely luminous high redshift galaxies identified by the Planck

The Planck All-Sky Survey to Analyze Gravitationally-lensed Extreme Starbursts (PASSAGES) project aims to identify a population of extremely luminous galaxies using the Planck All-Sky Survey and to explore the nature of their gas fuelling, induced starburst, and the resulting feedback that shape their evolution. Here, we report the identification of 22 high redshift luminous dusty star forming galaxies (DSFGs) at z = 1.1 − 3.3 drawn from a candidate list constructed using the Planck Catalog of Compact Sources (PCCS) and WISE All-Sky Survey. They are confirmed through follow-up dust continuum imaging and CO spectroscopy using AzTEC and the Redshift Search Receiver (RSR) on the Large Millimeter Telescope Alfonso Serrano (LMT). Their apparent IR luminosities span (0.1 − 3.1) × 1014L⊙ (median of 1.2 × 1014L⊙), making them some of the most luminous galaxies found so far. They are also some of the rarest objects in the sky with a source density of ≲ 0.01 deg−2. Our Atacama Large Millimeter/submillimeter Array (ALMA) 1.1 mm continuum observations with θ ≈ 0.4″ resolution show clear ring or arc morphologies characteristic of strong lensing. Their lensing-corrected luminosity of LIR ≳ 1013L⊙ (SFR ≳ 103M⊙ yr−1) indicates that they are the magnified versions of the most intrinsically luminous DSFGs found at these redshifts. Our spectral energy distribution (SED) analysis finds little detectable AGN activity despite their enormous luminosity, and any AGN activity present must be extremely heavily obscured.
Prevalence of multidrug-resistant bacteria in groundwater supplies in Osogbo, Southwest Nigeria

ABSTRACT The occurrence of multidrug-resistant (MDR) bacteria in groundwater samples from Osogbo, southwest Nigeria was investigated in this study. Bacteria isolated from the water samples were tested against antibiotics by disc diffusion method. Out of the 113 gram-negative bacteria isolated, E. coli (28.32%) was the most predominant and the least was Serratia spp. (2.65%). All the isolates were 100% resistant to cefuroxime and <20% susceptible to ceftazidime and cefixime. More than 99% of the isolates were MDR and 33 (29.20%) were resistant to at least five classes of antibiotics. High prevalence of MDR bacteria in the groundwater indicates that the water sources are reservoirs of antibiotic resistance.
Deep Model Predictive Control for Visual Servoing

In this document, we present a detailed analysis of few crucial components of our approach to demonstrate their efficacy and establish the rationale behind the improvement in performance claimed in this work. We also present an additional quantitative and qualitative study of our pipeline design along with few more comparisons with state-of-the-arts in addition to the ones present in the main manuscript.
Development and Validation of a Modified Multiple Errands Test for Adults with Intellectual Disabilities

BACKGROUND The aims of the current study were to adapt a version of the MET for people with intellectual disabilities and assess its ecological and construct validity. MATERIAL AND METHODS Using a correlational design, 40 participants with intellectual disabilities were invited to complete a battery of neuropsychological assessments and the modified Multiple Errands Test for Intellectual Disabilities (mMET-IDs). RESULTS The ability to successfully complete tasks on the mMET-IDs correlated significantly with measures of the Supervisory Attentional System, namely, the Tower of London Test and the Six Parts Test. However, performance on the mMET-IDs and the Six Parts Test could be accounted for by Verbal IQ and receptive vocabulary. The mMET-IDs failed to correlate with the DEX-IR. CONCLUSIONS The mMET-IDs can be successfully used to assess some aspects of the Supervisory Attentional System in people with intellectual disabilities. Further development is needed, however, to improve the ecological validity of the mMET-IDs.
Development and validation of a modified falls-efficacy scale

Purpose. This study examined the psychometric properties of a modified falls-efficacy scale (FES) that included more challenging activities of daily living items and made reference to the presence or absence of enabling assistive devices that are part of the built environment. Method. Baseline data from a longitudinal study among a cohort of 551 community-living seniors was used to generate data to inform the current report. Data for this study was collected in seniors' homes and apartments in two neighbouring cities in Canada, Ottawa and Gatineau. Measurements included a modified falls self-efficacy scale, various health and demographic measures. Results. Factor analysis of the instrument revealed a two-factor solution, explaining 60.3% of the variance. The two emerging subscales were: Subscale 1 – basic activities of daily living (ADLs), and subscale 2 – challenging ADLs. The modified FES demonstrated greater internal consistency and better response variability than Tinetti's original FES. Conclusions. Adding more challenging ADL items and specifying use of assistive devices while undertaking the ADL may increase the FES' ability to distinguish between participants with varying degrees of mobility or health impairment. Recommendations for future research are offered and implications for use are discussed.
Asymptotic soliton-like and asymptotic peakon-like solutions of the modiﬁed Camassa-Holm equation with variable coeﬃcients and singular perturbation

are two early models that were successfully used to capture the dispersive features of water wave propagation. These equations were studied by various methods and approaches, including, analytical, numerical, algebraic-geometric techniques. The highly intensive research on the KdV equation motivated the developements of the theory of completely integrable, infinitedimensional, dynamical system and of new concepts, like that of soliton [55, 53]. Solitons became a central object of modern mathematical physics, because they are an important peculiarity of many integrable nonlinear evolution equations [1, 41, 3]. Soliton solutions describe wave processes localized in space, propagating with a speed depending on the wave amplitude and interacting according to a nonlinear superposition principle [18, 39]. The Boussinesq equation possesses only one-soliton solutions and is not an integrable system, but the Korteweg-de Vries equation features one-, twoand multi-soliton solutions and is completely integrable. Subsequently, new nonlinear partial differential equations with soliton solutions were found, although not all of them are integrable systems. In this connection, we
Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets

Motivation: To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes. Results: In GSNCA, weight factors are assigned to genes in proportion to the genes’ cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no difference in the weight vectors of the genes between two conditions. In simulation studies and the analyses of experimental data, we demonstrate that GSNCA captures changes in the structure of genes’ cross-correlations rather than differences in the averaged pairwise correlations. Thus, GSNCA infers differences in coexpression networks, however, bypassing method-dependent steps of network inference. As an additional result from GSNCA, we define hub genes as genes with the largest weights and show that these genes correspond frequently to major and specific pathway regulators, as well as to genes that are most affected by the biological difference between two conditions. In summary, GSNCA is a new approach for the analysis of differentially coexpressed pathways that also evaluates the importance of the genes in the pathways, thus providing unique information that may result in the generation of novel biological hypotheses. Availability and implementation: Implementation of the GSNCA test in R is available upon request from the authors. Contact: YRahmatallah@uams.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Is the alignment of nematics on a polymer slab always along the rubbing direction? A molecular dynamics study

ABSTRACT We have studied the alignment and molecular organisation within a thin film of the popular nematic 5CB sandwiched between two flat polymer slabs, examining the effect of polymer chemical nature and morphology with atomistic molecular dynamics simulations. We have chosen two common polymers: polystyrene (PS) and polymethylmethacrylate (PMMA), either with their chains in random coil conformation (disordered) or with chains unidirectionally stretched (ordered). We found that, independently on the arrangement of the chains, both surfaces align planarly the liquid crystal, in accord with experimental observation. Moreover, while 5CB molecules align along the chains stretching direction of the PMMA ordered surface, on the combed PS surface they arrange on average perpendicularly to the stretching direction. This behaviour is attributed to the chemically specific interactions between the respective aromatic moieties of PS and 5CB. GRAPHICAL ABSTRACT
Genetic Predisposition to Neuroblastoma Results from a Regulatory Polymorphism that Promotes the Adrenergic Cell State

Childhood neuroblastomas exhibit plasticity between an undifferentiated neural crest-like “mesenchymal” cell state and a more differentiated sympathetic “adrenergic” cell state. These cell states are governed by autoregulatory transcriptional loops called core regulatory circuitries (CRCs), which drive the early development of sympathetic neuronal progenitors from migratory neural crest cells during embryogenesis. The adrenergic cell identity of neuroblastoma requires LMO1 as a transcriptional co-factor. Both LMO1 expression levels and the risk of developing neuroblastoma in children are associated with a single nucleotide polymorphism G/T that affects a GATA motif in the first intron of LMO1. Here we show that wild-type zebrafish with the GATA genotype develop adrenergic neuroblastoma, while knock-in of the protective TATA allele at this locus reduces the penetrance of MYCN-driven tumors, which are restricted to the mesenchymal cell state. Whole genome sequencing of childhood neuroblastomas demonstrates that TATA/TATA tumors also exhibit a mesenchymal cell state and are low risk at diagnosis. Thus, conversion of the regulatory GATA to a TATA allele in the first intron of LMO1 reduces the neuroblastoma initiation rate by preventing formation of the adrenergic cell state, a mechanism that is conserved over 400 million years of evolution separating zebrafish and humans.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Feb 22, 2022 Revised Aug 18, 2022 Accepted Sep 17, 2022 This paper presents an effective Vietnamese handwritten text recognition model by applying an improved convolutional recurrent neural networks (CRNNs) model to high school enrollment forms in Tay Ninh province, Vietnam. First, the proposed model extracts data areas containing text characters from forms. Then, we connect text boxes on the same row and divide the fields that containing text into three specific regions. Finally, we detect areas containing text characters for handwritten text recognition. We use word error rate (WER) to evaluate the recognition process and obtain a result of 0.3602. This result is one of the best solutions to the Vietnamese handwritten text recognition problem.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received May 30, 2021 Revised Dec 14, 2021 Accepted Dec 30, 2021 Advancements in globalization have significantly seen a rise in road travel. This has also led to increased car accidents and fatalities, which become a global cause of concern. Driver's behavior, including drowsiness, contributes to many of the road deaths. The main objective of this study is to develop a system to diminish mishaps caused by the driver's drowsiness. Recently deep convolutional neural networks have been used in multiple applications, including identifying and anticipate driver drowsiness. However, limited studies investigated the systematic optimization of convolutional neural networks (CNNs) hyperparameters, which could lead to better anticipation of driver drowsiness. To bridge this gap, a holistic approach based on the deep learning method is proposed in this paper to anticipate the drivers' drowsiness and provide an alerting mechanism to prevent drowsiness related accidents. To ensure optimal performance achievement by the system, a database of real-time images preprocessed via Haar cascade's classifiers is used to systematically optimize the CNN model's hyperparameters. Different metrics, including accuracy, precision, recall, F1-score, and confusion matrix, are used to evaluate the performance of the model. The training evaluation results of the optimal model achieved an accuracy of 99.87%, while the testing results accurately classify the drowsy driver with 97.98%.
Localization of Brain Activity Using Permutation Analysis

In this report we study bootstrap theory and permutation analysis as a hypothesis testing method using bootstrap procedure. We investigate asymptotic properties of the bootstrap procedure as well as bootstrap estimate accuracy using Edgeworth and Cornish-Fisher expansions. We show that resampling with replacement from data provides a theoretically sound method that outperforms Normal approximation of data distribution in terms of convergence error and accuracy of estimates. We conclude the report by applying permutation analysis on Magentoencephalography (MEG) brain signals to localize human brain activity in pointing/reaching tasks and find regions that are significantly active.
An asymptotic treatment for non-convex fully nonlinear elliptic equations: Global Sobolev and BMO type estimates

In this paper, we establish global Sobolev a priori estimates for [Formula: see text]-viscosity solutions of fully nonlinear elliptic equations as follows: [Formula: see text] by considering minimal integrability condition on the data, i.e. [Formula: see text] for [Formula: see text] and a regular domain [Formula: see text], and relaxed structural assumptions (weaker than convexity) on the governing operator. Our approach makes use of techniques from geometric tangential analysis, which consists in transporting “fine” regularity estimates from a limiting operator, the Recession profile, associated to [Formula: see text] to the original operator via compactness methods. We devote special attention to the borderline case, i.e. when [Formula: see text]. In such a scenery, we show that solutions admit [Formula: see text] type estimates for their second derivatives.
Chronic diseases: Perceptions about Covid-19 risk and vaccination

Background Individuals with chronic disease may be at higher risk of dying from COVID-19, yet no association has been established between chronic illness and COVID-19 risk perception, engagement with nonpharmaceutical interventions (NPIs), or vaccine acceptance. Methods We surveyed US residents who self-reported a chronic respiratory or autoimmune disease in February 2021. Respondents reported beliefs about the risk of COVID-19 to personal and public health, adoption and support of NPIs, willingness to be vaccinated against COVID-19, and reasons for vaccination willingness. We evaluated the association between disease status and COVID-19 behaviors or attitudes, adjusting for demographic and political factors. Results Compared to healthy controls, chronic disease was associated with increased belief that COVID-19 was a personal (Respiratory = 0.12, 95% confidence interval (CI) = 0.10 - 0.15; Autoimmune = 0.11, CI = 0.08 - 0.14) and public threat (Respiratory = 0.04, CI = 0.02 - 0.06; Autoimmune = 0.03, CI = 0.01 - 0.06), and support for NPIs. Chronic respiratory disease was associated with willingness to be vaccinated (0.6, CI = 0.05 - 0.09). Personal protection was associated with vaccination (Respiratory = 1.08, CI = 1.03 - 1.13; Autoimmune = 1.06, CI = 1.01 - 1.11). Autoimmune disease was associated with fear of a bad vaccine reaction (1.22, CI = 1.06 - 1.41) among those unwilling to be vaccinated. Conclusions In the US, chronic disease status is significantly related to risk perceptions of COVID, support of personal and community risk mitigation measures, and willingness to be vaccinated.
Simulation of plasma turbulence in the periphery of diverted tokamak by using the GBS code

We report on the implementation of diverted magnetic equilibria in GBS and on first simulations in this geometry. GBS is a simulation code used to evolve plasma turbulence in the tokamak periphery by solving the drift-reduced Braginskii's equations. The model equations are written in toroidal coordinates, abandoning flux coordinate systems that are not defined at the X-point. A fourth order finite difference scheme is used for the implementation of the spatial operators on poloidally and toroidally staggered grids. The GBS numerical implementation is verified through the method of manufactured solutions. The code convergence properties are tested on a relatively simple analytical X-point configuration. Finally, the diverted equilibrium from a TCV tokamak discharge is implemented in the new version of GBS. The analysis of the simulation results is focused on blob formation, radial transport, and plasma poloidal rotation mechanisms.We report on the implementation of diverted magnetic equilibria in GBS and on first simulations in this geometry. GBS is a simulation code used to evolve plasma turbulence in the tokamak periphery by solving the drift-reduced Braginskii's equations. The model equations are written in toroidal coordinates, abandoning flux coordinate systems that are not defined at the X-point. A fourth order finite difference scheme is used for the implementation of the spatial operators on poloidally and toroidally staggered grids. The GBS numerical implementation is verified through the method of manufactured solutions. The code convergence properties are tested on a relatively simple analytical X-point configuration. Finally, the diverted equilibrium from a TCV tokamak discharge is implemented in the new version of GBS. The analysis of the simulation results is focused on blob formation, radial transport, and plasma poloidal rotation mechanisms.
ANALISIS PROPAGASI GELOMBANG RADIO MODE ANGKASA SAAT PERISTIWA GERHANA MATAHARI 9 MARET 2016 [SKYWAVE PROPAGATION ANALYSIS DURING SOLAR ECLIPSE ON 9 MARCH 2016]

This paper discusses the analysis of radio wave propagation using Skywave mode during solar eclipse events on March 9, 2016. The analysis was based on the observation results using Automatic Link Establishment (ALE) waveform. The carrier frequency is 10.1455 MHz with Biak (1.16 N; 136.047E) and Manado (1.52N; 124.856E) as radio communication circuits. The working frequency was determined from the model of the ionosphere Biak-Manado circuit during a solar eclipse event and based on the frequency allocation for ALE system in Indonesia. The results showed that the SN index values were different between the day during solar eclipse event compared to the previous day and after. The differences of the SN index value were included the time variations and its distribution, which can be explained as the changes in the absorption levels of the ionosphere D layer. The change of the absorption levels during the solar eclipse event caused the SN index values had a constant tendency with the gradient value of linear equations was 0.0004/seconds. The constant changes of the absorption levels were also shown in the distribution of the SN index with a mean value (μ) was 5.2384 and standard deviation (σ) was 0.74894. Differences with the previous day and the day after the solar eclipse events, the SN index values had a descending trend with a gradient of the linear equations was -0.002/seconds. The differences also shown in the distribution of the SN index with μ values were 4.8316 and 4.6164 and σ were 0.92123 and 0.9096. These results indicated the change of radio wave propagation in the ionosphere during solar eclipse event in March 9, 2016.
Gravitational Radiation: From theory to detection

Gravitational waves are ripples in space and time which locally stretch and compress space. These waves are strongly emitted from the most violent astrophysical systems such as two black holes orbiting each other. However, the stretching and distorting we find on Earth from these sources is extremely small, of the order of 1/1000 the diameter of a proton! This forms a huge challenge to detect these waves however it is possible using many different methods and we explore these within.