Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Control for an Urban Traffic Network

In this paper, we develop a distributed control method for an urban traffic network in order to improve traffic conditions and guarantee a smooth operation for all roads and intersections. An optimal coordination among traffic flows is determined by a model predictive control idea. The control objective is formulated as a constrained optimization problem in which the cost function to be minimized is a combination of some traffic network-related performance indexes and the constraints are derived from capacities of roads and intersections. The green time assigned to each road link is computed from its optimal downstream traffic flow. In the proposed algorithms, every road link uses only its local information to determine its control decision which corresponds to a global optimal solution. The algorithms are developed by using a gradient projection-based method and properties of the minimal polynomial of a matrix pair. The effectiveness of the proposed algorithms is validated via numerical simulations with MATLAB and VISSIM.
Association between Ruminal pH and Rumen Fatty Acids Concentrations of Holstein Cows during the First Half of Lactation

Ruminal pH in dairy cows follows a diurnal fluctuation; low values are indicative of subacute ruminal acidosis and are associated with alterations of rumen fatty acids concentrations. The objective of the present study was to prospectively study the associations between ruminal pH and the rumen fluid concentrations of short, medium, and long chain fatty acids, under field conditions during the first half of lactation in 53 Holstein cows of a dairy farm. Ruminal fluid was obtained by rumenocentesis, which was performed at 30, 90, and 150 days in milk (DIM). Ruminal pH was measured immediately after collection with a portable pH meter, whereas gas chromatography was used for the determination of ruminal fatty acid concentrations. Mixed linear regression models were used for data analysis. The prevalence of cows with low ruminal pH (≤5.5) was 45.3%, 54.7%, and 66.0% at 30, 90, and 150 DIM, respectively. The concentrations of acetic, propionic, butyric, valeric, isovaleric, caproic and linoleic acids were negatively associated with ruminal pH values, whereas the acetic to propionic ratio was positively associated with rumen pH. Under field conditions and naturally occurring low ruminal pH cases, ruminal concentrations of most fatty acids are negatively related with ruminal pH values
Transformer-Based Data-Driven Video Coding Acceleration for Industrial Applications

With the exploding development of edge intelligence and smart industry, deep learning-based intelligent industrial solutions are promptly applied in the manufacturing process. Many intelligent industrial solutions such as automatic manufacturing inspection are computer vision based and require fast and efficient video encoding techniques so that video streams can be processed as quickly as possible either at the edge cluster or over the cloud. As one of the most popular video coding standards, the high efficiency video coding (HEVC) standard has been applied to various industrial scenes. However, HEVC brings not only a higher compression rate but also a significant increase in encoding complexity, which hinders its practical application in industrial scenarios. Fortunately, a large amount of video coding data makes it possible to accelerate the encoding process in the industry. To speed up the video coding process in some industrial scenes, this paper proposes a data-driven fast approach for coding tree unit (CTU) partitioning in HEVC intracoding. First, we propose a method to represent the partition result of a CTU as a column vector of length 21. Then, we employ lots of encoding data produced in normal industry scenes to train transformer models used to predict the partitioning vector of the CTU. Finally, the final partitioning structure of the CTU is generated from the partitioning vector after a postprocessing operation and used by an industrial encoder. Compared with the original HEVC encoder used by some industrial applications, experiment results show that our approach achieves 58.77% encoding time reduction with 3.9% bit rate loss, which indicates that our data-driven approach for video coding has great capacity working in industrial applications.
Surfing the modeling of PoS taggers in low-resource scenarios

The recent trend toward the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, particularly in low-resource settings. In parallel, model selection has become an essential task to boost performance at reasonable cost, even more so when we talk about processes involving domains where the training and/or computational resources are scarce. Against this backdrop, we evaluate the early estimation of learning curves as a practical mechanism for selecting the most appropriate model in scenarios characterized by the use of non-deep learners in resource-lean settings. On the basis of a formal approximation model previously evaluated under conditions of wide availability of training and validation resources, we study the reliability of such an approach in a different and much more demanding operational environment. Using as a case study the generation of pos taggers for Galician, a language belonging to the Western Ibero-Romance group, the experimental results are consistent with our expectations.
SNPs in lncRNA KCNQ1OT1 Modulate Its Expression and Confer Susceptibility to Salt Sensitivity of Blood Pressure in a Chinese Han Population

Long noncoding RNA (lncRNA) plays an important role in cardiovascular diseases, but the involvement of lncRNA in salt sensitivity of blood pressure (SSBP) is not well-known. We aimed to explore the association of sixteen single-nucleotide polymorphisms (SNPs) in five lncRNA genes (KCNQOT1, lnc-AGAP1-8:1, lnc-IGSF3-1:1, etc.) with their expression and susceptibility to SSBP. A two-stage association study was conducted among 2057 individuals. Quantified expression of the lncRNA was detected using real-time PCR. Genotyping was accomplished using the MassARRAY System. The expression quantitative tra2it loci test and the generalized linear model were utilized to explore the function of SNPs. One-sample Mendelian randomization was used to study the causal relationship between KCNQOT1 and SSBP. Significant effects were observed in KCNQ1OT1 expressions on the SSBP phenotype (p < 0.05). Rs10832417 and rs3782064 in KCNQ1OT1 may influence the secondary structure, miRNA binding, and expression of KCNQ1OT1. Rs10832417 and rs3782064 in KCNQ1OT1 were identified to be associated with one SSBP phenotype after multiple testing corrections and may be mediated by KCNQ1OT1. One-sample Mendelian randomization analyses showed a causal association between KCNQ1OT1 and SSBP. Our findings suggest that rs10832417 and rs3782064 might be associated with a lower risk of SSBP through influencing the KCNQ1OT1 secondary structure and miRNA binding, resulting in changes in KCNQ1OT1 expression.
LINE LISTS FOR THE A2Π–X2Σ+ (RED) AND B2Σ+–X2Σ+ (VIOLET) SYSTEMS OF CN, 13C14N, AND 12C15N, AND APPLICATION TO ASTRONOMICAL SPECTRA

New red and violet system line lists for the CN isotopologues 13C14N and 12C15N have been generated. These new transition data are combined with those previously derived for 12C14N, and applied to the determination of CNO abundances in the solar photosphere and in four red giant stars: Arcturus, the bright, very low-metallicity star HD 122563, and the carbon-enhanced metal-poor stars HD 196944 and HD 201626. When both red and violet system lines are detectable in a star, their derived N abundances are in good agreement. The mean N abundances determined in this work are also generally in accord with published values.
DETECTION OF OCEAN GLINT AND OZONE ABSORPTION USING LCROSS EARTH OBSERVATIONS

The Lunar CRater Observation and Sensing Satellite (LCROSS) observed the distant Earth on three occasions in 2009. These data span a range of phase angles, including a rare crescent phase view. For each epoch, the satellite acquired near-infrared and mid-infrared full-disk images, and partial-disk spectra at 0.26–0.65 μm (λ/Δλ ∼ 500) and 1.17–2.48 μm (λ/Δλ ∼ 50). Spectra show strong absorption features due to water vapor and ozone, which is a biosignature gas. We perform a significant recalibration of the UV–visible spectra and provide the first comparison of high-resolution visible Earth spectra to the NASA Astrobiology Institute's Virtual Planetary Laboratory three-dimensional spectral Earth model. We find good agreement with the observations, reproducing the absolute brightness and dynamic range at all wavelengths for all observation epochs, thus validating the model to within the ∼10% data calibration uncertainty. Data–model comparisons reveal a strong ocean glint signature in the crescent phase data set, which is well matched by our model predictions throughout the observed wavelength range. This provides the first observational test of a technique that could be used to determine exoplanet habitability from disk-integrated observations at visible and near-infrared wavelengths, where the glint signal is strongest. We examine the detection of the ozone 255 nm Hartley and 400–700 nm Chappuis bands. While the Hartley band is the strongest ozone feature in Earth's spectrum, false positives for its detection could exist. Finally, we discuss the implications of these findings for future exoplanet characterization missions.
Magnetic properties of the antiferromagnetic spin-1/2 chain system beta-TeVO4

The magnetic susceptibility and magnetization of high-quality single-crystal beta-TeVO4 are reported. We show that this compound, made of weakly coupled infinite chains of VO5 pyramids sharing corners, behaves as an S = 1/2 one-dimensional Heisenberg antiferromagnet. From magnetic experiments, we deduce the intrachain antiferromagnetic coupling constant, J/k(B) = 21.4 +/- 0.2 K. Below 5 K, a series of three phase transitions at 2.26, 3.28, and 4.65 K is observed.
Hypothetical Visible Bands of Advanced Meteorological Imager Onboard the Geostationary Korea Multi-Purpose Satellite -2A Using Data-To-Data Translation

True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This article presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the advanced meteorological imager (AMI) sensor onboard the geostationary Korea multipurpose satellite. The AMI datasets in the form of albedo and brightness temperature (${{\boldsymbol{T}}}_{\boldsymbol{B}}$) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or ${{\boldsymbol{T}}}_{\boldsymbol{B}}$ differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were −0.006 and 0.047 in albedo, and 0.941 for the blue band; −0.007 and 0.05 in albedo and 0.939 for the green band; −0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea meteorological administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this article could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, meteosat third generation, and geostationary operational environmental satellites.
THE EGNoG SURVEY: GAS EXCITATION IN NORMAL GALAXIES AT z ≈ 0.3

As observations of molecular gas in galaxies are pushed to lower star formation rate (SFR) galaxies at higher redshifts, it is becoming increasingly important to understand the conditions of the gas in these systems to properly infer their molecular gas content. The rotational transitions of the carbon monoxide (CO) molecule provide an excellent probe of the gas excitation conditions in these galaxies. In this paper, we present the results from the gas excitation sample of the Evolution of molecular Gas in Normal Galaxies (EGNoG) survey at the Combined Array for Research in Millimeter-wave Astronomy (CARMA). This subset of the full EGNoG sample consists of four galaxies at z ≈ 0.3 with SFRs of 40–65 M☉ yr−1 and stellar masses of ≈2 × 1011 M☉. Using the 3 mm and 1 mm bands at CARMA, we observe both the CO(J = 1 → 0) and CO(J = 3 → 2) transitions in these four galaxies in order to probe the excitation of the molecular gas. We report robust detections of both lines in three galaxies (and an upper limit on the fourth), with an average line ratio, r31 = L′CO(3-2)/L′CO(1-0), of 0.46 ± 0.07 (with systematic errors ≲ 40%), which implies sub-thermal excitation of the CO(J = 3 → 2) line. We conclude that the excitation of the gas in these massive, highly star-forming galaxies is consistent with normal star-forming galaxies such as local spirals, not starbursting systems like local ultraluminous infrared galaxies. Since the EGNoG gas excitation sample galaxies are selected from the main sequence (MS) of star-forming galaxies, we suggest that this result is applicable to studies of MS galaxies at intermediate and high redshifts, supporting the assumptions made in studies that find molecular gas fractions in star-forming galaxies at z ∼ 1–2 to be an order of magnitude larger than what is observed locally.
Sociodemographic Associations of Dementia Literacy in Older Australians

Recent levels of dementia literacy in older Australian adults remains relatively unexplored. Our purpose was to identify whether dementia literacy has changed in older Australians, sociodemographic characteristics associated with better literacy, and barriers to dementia risk reduction. A 32-item adapted British Social Attitudes Survey was administered to 834 community-dwelling older adults (mean age 73.3, SD = 6.0, range 65–94) on dementia awareness and knowledge of dementia risk and protective factors. Descriptive analyses, logistic, and multiple linear regressions were used to examine sociodemographic factors on dementia awareness and literacy. Most respondents (61%) were aware of the relationship between different lifestyle factors and dementia risk, with the majority reporting cognitive (85.0%) and physical inactivity (83.4%) as key risk factors. Few were able to identify less well-known factors (e.g., chronic kidney disease; 15.8%). Individuals with higher educational attainment were more likely to agree that dementia is modifiable (OR 1.228, 95% CI 1.02–1.47). Younger age (β = −0.089, 95% CI −0.736–−0.065, p = 0.019) was significantly associated with a higher number of correctly-identified dementia risk factors. Lack of knowledge was the key barrier to hindering dementia risk reduction. A tailored, evidence-informed, population-based lifespan approach targeting dementia literacy may help alleviate commonly reported barriers and support dementia risk reduction.
Awareness and Use of Virtual Clinics following the COVID-19 Pandemic in Saudi Arabia

Studies have shown that virtual clinics enjoyed high use and high patient satisfaction during the COVID-19 pandemic. Thus, virtual clinics are expected to be the new normal mode of receiving care after the COVID-19 pandemic. This study aimed to assess public awareness and use of virtual clinics following the pandemic and identify factors associated with virtual clinic use. Methods: A cross-sectional design was employed in which data were collected via a structured online questionnaire based on the Technology Acceptance Model (TAM) domains: perceived usefulness, perceived ease of use, and social influence. Participants were selected based on the non-probability sampling of convenience. Univariate, bivariate, and binary logistic regression models were used for analysis. Results: A total of 405 responses were received; of those, 286 (70.6%) were aware of the existence of virtual clinics and 99 (34.6%) were post-pandemic users. Among users, 50% used virtual clinics more than two times, 72% used virtual clinics to seek care for themselves, with the vast majority using it via voice calls (83.8%), and for visits to the family medicine clinic (55%). Young adults, females, single adults, those with a higher level of education, the employed, and those with lower income were more likely to use virtual clinics (p < 0.05). The logistic regression model showed that 20% of the variation in virtual clinic use was explained by perceived usefulness and perceived use (p < 0.001). Conclusion: This study showed high awareness of virtual clinics among the population following the COVID-19 pandemic, with one-third being active users. Age, gender, marital status, education, income, employment status, perceived usefulness, and ease of use are associated with virtual clinics’ awareness and use. Considering those factors is important when planning for sustained use of e-health and virtual care.
Harnessing abilities and talents: parents’ perspectives on responsive inclusive early childhood education

ABSTRACT Research endorses accessing the insights of parents whose children have attended inclusive programs as requisite for the continued expansion and evaluation of inclusive education. In this study, researchers follow up with parents of children with disabilities who had attended a university-based inclusive preschool program. It draws on research and theory from both the Early Childhood Education and Inclusive Education disciplines. Through semi-structured interviews parents share their family’s educational stories and reflect on how their understandings of inclusion have continued to evolve over time. Participants describe what was meaningful about their child’s early childhood inclusive education experiences, how they made decisions about their children’s ongoing educational services and settings, and the ways philosophies and practice sometimes clashed. Findings include the importance parents place on reciprocal partnerships with professionals, how they carry professionals’ words with them, and what they value most about inclusive education. Recommendations for practice and teacher preparation are discussed.
Tracking disclosure change trajectories for financial fraud detection

The global economic disruption brought by COVID‐19 crisis can set a stage for the prevalence of financial statement frauds, which jeopardize the efficient functioning of capital markets. In this paper, we propose a nuanced method to detect frauds by tracking granular changes in disclosures over time. Specifically, we first align paragraphs between consecutive disclosures by their similarities. This alignment can be solved as an optimization‐based matching problem. Then we identify three types of changed contents: recurrent, newly added, and deleted contents. For each type, we measure the changes in terms of fraud‐relevant linguistics features, such as sentiment and uncertainties. Further, we formulate a firm's Management Discussion and Analysis change trajectory over years as a multivariate time series composed of these granular metrics. We implement a deep learning model to predict frauds using the change trajectory as an input. Extensive experiments demonstrate that our model significantly outperforms benchmark models, and its performance increases with the length of the change trajectory. Moreover, we found specific types of changes are strongly associated with frauds, including weak modal or reward words in newly added or deleted contents. Our work provides an optimization‐based method to define change trajectories and trace information mutation in narratives. Finally, our study contributes to the fraud detection literature with a new predictive signal—disclosure change trajectories with an effective deep learning architecture.
METAL-POOR STARS OBSERVED WITH THE MAGELLAN TELESCOPE. III. NEW EXTREMELY AND ULTRA METAL-POOR STARS FROM SDSS/SEGUE AND INSIGHTS ON THE FORMATION OF ULTRA METAL-POOR STARS

We report the discovery of one extremely metal-poor (EMP; [ Fe / H ] ? > < − 3 ? > ) and one ultra metal-poor (UMP; [ Fe / H ] ? > < − 4 ? > ) star selected from the Sloan Digital Sky Survey/Sloan Extension for Galactic Understanding and Exploration survey. These stars were identified as EMP candidates based on their medium-resolution ( R ∼ 2000 ? > ) spectra, and were followed up with high-resolution ( R ∼ ? > 35,000) spectroscopy with the Magellan/Clay Telescope. Their derived chemical abundances exhibit good agreement with those of stars with similar metallicities. We also provide new insights on the formation of the UMP stars, based on comparisons with a new set of theoretical models of supernovae (SNe) nucleosynthesis. The models were matched with 20 UMP stars found in the literature, together with one of the program stars (SDSS J1204+1201), with [ Fe / H ] = − 4.34 ? > . From fitting their abundances, we find that the SNe progenitors, for stars where carbon and nitrogen are measured, had masses ranging from 20.5 M ⊙ ? > to 28 M ⊙ ? > and explosion energies from 0.3 to 0.9 × 10 51 erg ? > . These results are highly sensitive to the carbon and nitrogen abundance determinations, which is one of the main drivers for a future high-resolution follow-up of UMP candidates. In addition, we are able to reproduce the different CNO abundance patterns found in UMP stars with a single progenitor type by varying its mass and explosion energy.
Swift and Chandra Detections of Supernova 2006jc: Evidence for Interaction of the Supernova Shock with a Circumstellar Shell

The peculiar Type Ib supernova (SN) 2006jc has been observed with the UV/Optical Telescope (UVOT) and X-Ray Telescope (XRT) on board the Swift observatory over a period of 19-183 days after the explosion. Signatures of interaction of the outgoing SN shock with dense circumstellar material (CSM) are detected, such as strong X-ray emission (L0.2–10 > 1039 erg s−1) and the presence of Mg II 2800 Å line emission visible in the UV spectra. In combination with a Chandra observation obtained on day 40 after the explosion, the X-ray light curve is constructed, which shows a unique rise of the X-ray emission by a factor of ~5 over a period of ~4 months, followed by a rapid decline. We interpret the unique X-ray and UV properties as a result of the SN shock interacting with a shell of material that was deposited by an outburst of the SN progenitor 2 years prior to the explosion. Our results are consistent with the explosion of a Wolf-Rayet star that underwent an episodic mass ejection qualitatively similar to those of luminous blue variable stars prior to its explosion. This led to the formation of a dense (~107 cm−3) shell at a distance of ~1016 cm from the site of the explosion, which expands with the WR wind at a velocity of 1300 ± 300 km s−1.