Miracle-dz/UniFAR-FTUs huggingface dataset:
Object tracking in visual surveillance using narrow band-based graph cuts

Tracking deforming objects involves estimating the global motion of the object and its local deformations as a function of time. Tracking algorithms using Graph Cut have been proposed. However, because of its globally optimal nature, it is prone to capture outlying areas similar to the object of interest. Constraint was introduced to confine the standard graph cut technique in many methods. In this paper, narrow band is introduced to constrain the standard graph cut where it is build on. With the narrow band constraint, segmentation is ensured to remain in it, which is useful when the result is not robust using standard graph cut. Secondly, Graph Cut is built based on the band region, compared with traditionally method, calculation cost is reduced. Thirdly, formulations are adjusted to distinguish between different parts of the narrow band, which improve the segmentation quality.
Statistical Significance: Rationale, Validity and Utility

A Litany of Criticisms of NHSTP The Null-Hypothesis Significance-Test Procedure (NHSTP) A Phenomenon and its Quartets of Hypotheses Evidential Support for a Theory and NHSTP Effect Size and Related Issues A Critical Look at Statistical Power Bayesianism A Case for NHSTP
Evaluation of Two Simultaneous Metabolomic and Proteomic Extraction Protocols Assessed by Ultra-High-Performance Liquid Chromatography Tandem Mass Spectrometry

Untargeted multi-omics analysis of plasma is an emerging tool for the identification of novel biomarkers for evaluating disease prognosis, and for developing a better understanding of molecular mechanisms underlying human disease. The successful application of metabolomic and proteomic approaches relies on reproducibly quantifying a wide range of metabolites and proteins. Herein, we report the results of untargeted metabolomic and proteomic analyses from blood plasma samples following analyte extraction by two frequently-used solvent systems: chloroform/methanol and methanol-only. Whole blood samples were collected from participants (n = 6) at University Hospital Sharjah (UHS) hospital, then plasma was separated and extracted by two methods: (i) methanol precipitation and (ii) 4:3 methanol:chloroform extraction. The coverage and reproducibility of the two methods were assessed by ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS). The study revealed that metabolite extraction by methanol-only showed greater reproducibility for both metabolomic and proteomic quantifications than did methanol/chloroform, while yielding similar peptide coverage. However, coverage of extracted metabolites was higher with the methanol/chloroform precipitation.
New fusion features convolutional neural network with high generalization ability on rolling bearing fault diagnosis

This study proposes a feature fusion one‐dimensional convolutional neural network algorithm model with a portable non‐dimensionality reduction attention mechanism to resolve the limitations pertaining to the small training sample capacity of conventional deep learning models, single feature‐extractor size, insufficient feature extraction of the bearing faults, and low recognition rate of the bearing health status under variable loads and strong noise. This model regarded raw vibration signal as input, used the feature fusion module to extract multiscale time information, and the convolution‐pooling alternating layer adaptively overcome the time‐dependent limitations. An adaptive batch normalization was introduced to reduce the divergence in sample distribution between the source and target domains and enhance the model generalization capabilities. Moreover, the non‐dimensionality reduction attention mechanism is a more efficient channel weight distribution algorithm that can enhance the anti‐noise performance of the model. After embedding the non‐dimensionality reduction attention mechanism, the proposed network could enhance the anti‐noise performance of the model through a special channel‐weight learning method. It combined the Softmax classification layer to construct a feature extraction feature classification dual intelligent fault diagnosis algorithm. The experiments were performed on the rolling bearing fault dataset. The results revealed that the method provided a rather high generalization ability and stable anti‐noise ability. Simultaneously, the introduction of t‐SNE dimensionality reduction visualization revealed the strong feature‐extraction ability of the deep network model for large‐volume samples.
Personality and engagement with music: Results from network modeling in three adolescent samples

People who engage in musical activities may, on average, share certain personality features. For example, performing music in front of audiences may require greater extraversion. In contrast, long and solitary practice sessions may require greater introversion and conscientiousness. Research has established some links between dimensions of personality and indicators of engagement with music, for example, specific personality profiles for musicians/non-musicians. For example, openness is usually linked to musical involvement. However, research in the area is scarce and it remains unclear which specific aspects of musical engagement are linked to personality; how these links establish in the course of development; and whether these links are affected by culture. This article reports data collected with several measures of personality (Big Five personality scales) and a comprehensive measure of engagement with music—the musical sophistication index (Gold-MSI) in three countries: Germany (N = 1,114), Russia (N = 346), and the United Kingdom (N = 751). We applied a graphical network modeling approach to investigate the patterns of association among the measures. Our results found a number of consistent musical sophistication-personality associations across the three samples, with the strongest link being between the Gold-MSI emotions subscale and the personality trait openness, which was found in all three samples.
Multi-Omics Reveals the Effect of Population Density on the Phenotype, Transcriptome and Metabolome of Mythimna separata

Simple Summary Mythimna separata living in the environment with high population density show characteristics of melanization and overeating and cause serious damage to crops. In order to explore the impact of population density on M. separata, we studied the gene expression and metabolite accumulation of M. separata under different population densities through transcriptome and metabolome analysis. The results showed that the insulin-like signaling pathway influences the phase change of M. separata. Compared with solitary M. separata, the gregarious type had a faster energy consumption rate and stronger protein digestion and absorption capacity, while the transcription and translation processes were inhibited. This study explains the molecular mechanism of the phase change and the overeating behavior of M. separata under high population density. Abstract Population-density-dependent polymorphism is important in the biology of some agricultural pests. The oriental armyworm (Mythimna separata) is a lepidopteran pest (family Noctuidae). As the population density increases, its body color becomes darker, and the insect eats more and causes greater damage to crops. The molecular mechanisms underlying this phase change are not fully clear. Here, we used transcriptomic and metabolomic methods to study the effect of population density on the differentiation of second-day sixth instar M. separata larvae. The transcriptomic analysis identified 1148 differentially expressed genes (DEGs) in gregarious-type (i.e., high-population-density) armyworms compared with solitary-type (low-population-density) armyworms; 481 and 667 genes were up- and downregulated, respectively. The metabolomic analysis identified 137 differentially accumulated metabolites (DAMs), including 59 upregulated and 78 downregulated. The analysis of DEGs and DAMs showed that activation of the insulin-like signaling pathway promotes the melanization of gregarious armyworms and accelerates the decomposition of saccharides, which promotes the gregarious type to take in more food. The gregarious type is more capable of digesting and absorbing proteins and decreases energy consumption by inhibiting transcription and translation processes. The phase change traits of the armyworm are thus attributable to plasticity of its energy metabolism. These data broaden our understanding of the molecular mechanisms of insect-density-dependent polymorphism.
Research on the application status of image recognition technology in textile and clothing field

With the development of the times, computer, information technology gradually penetrated into all walks of life. Textile and clothing design, production, consumption integration of machine vision, graphic image recognition technology development have become a trend. At present, the intelligence degree of the textile and clothing industry has reached an unprecedented new height. In the production and identification of fabrics, the machine vision gradually replaces the manual work, and realizes the automatic and accurate production line. In the design, manufacturing, consumption and other aspects of clothing, image recognition technology has assumed the historical responsibility. Combined with the Internet of things, cloud computing and other technologies, it has improved the production efficiency and achieved intelligent design and production.
Recurrence and Mortality Risks in Patients with First Incident Acute Stroke or Myocardial Infarction: A Longitudinal Study Using the Korean National Health Insurance Service Database

Background: We aimed to identify the long-term risk of recurrence and mortality in patients who experienced acute ischemic stroke (AIS), acute myocardial infarction (AMI), or acute hemorrhagic stroke (AHS) using a population-level database. Methods: This retrospective cohort study included adults aged ≥55 years diagnosed with AIS, AMI, and AHS in the National Health Insurance Service Database between 2004 and 2007. The target outcomes were secondary AIS, AMI, AHS, and all-cause mortality. Predetermined covariates, such as age, sex, socioeconomic status, hypertension, diabetes, and dyslipidemia, were adjusted. Results: We included 151,181, 49,077, and 41,636 patients in the AIS, AHS, and AMI groups, respectively. The AMI (adjusted hazard ratio [aHR], 0.318; 95% confidence interval [CI], 0.306–0.330; p < 0.001) and AHS (aHR, 0.489; 95% CI, 0.472–0.506; p < 0.001) groups had a significantly lower risk of developing secondary AIS than the AIS group. The risk of developing secondary AMI was significantly lower in the AMI (aHR, 0.388; 95% CI, 0.348–0.433; p < 0.001) and AHS (aHR, 0.711; 95% CI, 0.640–0.790; p < 0.001) groups than in the AIS group. Initial AHS was a decisive risk factor for secondary AHS (aHR, 8.546; 95% CI, 8.218–8.887; p < 0.001). The AMI (aHR, 1.436; 95% CI, 1.412–1.461; p < 0.001) and AHS (aHR, 1.328; 95% CI, 1.309–1.348; p < 0.001) groups were associated with a significantly higher risk of long-term mortality than the AIS group. Conclusion: Our results elucidated that initial AIS was a significant risk factor for recurrent AIS and AMI; initial AHS was a decisive risk factor for developing secondary AHS. Further, AMI and AHS were more closely related to long-term mortality than AIS.
Effects of helicopter net gunning on the survival and movement behaviour of nilgai antelope

ABSTRACT Context. Research on large, terrestrial mammals often requires physical captures to attach tags or collars, collect morphological data, and collect biological samples. Choice of capture method should minimise pain and distress to the animal, minimise risk to personnel, and consider whether the method can achieve study objectives without biasing results. Aims. We studied how capture via helicopter net-gunning affected survival, post-capture movement patterns, and space use of exotic nilgai (Boselaphus tragocamelus) in southern Texas, USA. Methods. We estimated daily survival rates for 101 collared nilgai over 28 days, following 125 captures. We calculated mean daily movement rates and net-squared displacement for 21 recaptured nilgai for 60 days, starting 30 days before capture. Key results. The survival probability of 125 nilgai individuals was 0.97 (95% CI = 0.92–0.99) over the 28 days following capture, with the lowest daily survival for the day after capture (x̄ = 0.99; 95% CI = 0.96–1.00). We observed an increase of ~65% in the mean daily movement rate of 134 m/h on the first 2 days since capture, followed by a period of reduced movement out to the 5th day before returning to pre-capture levels. Analysis of net-squared displacement for 21 nilgai showed that 17 resumed pre-capture space-use patterns within a week, whereas four individuals did not return to the pre-capture range for ≥1 month. Conclusions. Capture-related mortality rates for nilgai using helicopter net-gunning in our study (3%) were similar or lower than those reported for similar species captured using the same method. While we were able to detect a period of elevated movement rates, followed by a recovery period of diminished movement as a result of capture, nilgai appeared to return to typical behaviour ~6 days post-capture. Most nilgai in our study also resumed typical space-use patterns within a week of capture; however, our results suggest high individual variability in their response. Implications. We recommend using net-gunning from a helicopter as a method for capturing nilgai when conditions and where vegetation and topography allow. We suggest censoring data for a minimum of 7 days following capture for analyses related to survival and movement rates. For analyses relating to space use, we suggest inspecting net-squared displacement or some similar displacement analysis for each animal separately to account for individual variation in response and exclude data accordingly.
Welfare Implications of the Gas Stunning of Pigs 1. Determination of Aversion to the Initial Inhalation of Carbon Dioxide or Argon

The aversive effects of 90 per cent argon in air, 30 per cent carbon dioxide in air or 90 per cent carbon dioxide in air were investigated in slaughter weight pigs. Aversion was assessed from their reluctance to enter the three gaseous atmospheres to obtain a reward (apples). The pigs did not show any aversion to the inhalation of 90 per cent argon in air. The majority of the pigs did not show aversion to the presence of 30 per cent carbon dioxide in air. By contrast, the inhalation of 90 per cent carbon dioxide was aversive to the majority of the pigs. Fasting them for up to 24h prior to testing did not overcome the pigs ‘ reluctance to enter an atmosphere containing 90 per cent carbon dioxide.
Selecting Catalysts for Pyrolysis of Lignocellulosic Biomass

The pyrolysis of lignocellulosic biomass is a promising technology for obtaining renewable chemicals and fuels to replace fossil-based products. However, due to the complexity of the lignin, cellulose and hemicellulose molecules, a large variety of compounds are often formed, making commercial implementation difficult. The use of a catalyst during reactions has been recognized as one of the major improvements in pyrolysis, allowing the production of selected compounds. Moreover, the large number of available catalysts opens up a wide range of possibilities for controlling the reaction network. Zeolites, hierarchical zeolites, alkali and alkaline earth oxides, transition metals and carbonaceous materials, among others, have been investigated in the pyrolysis of a variety of biomasses. In addition, bifunctional catalysts play a role in pyrolysis, as well as the addition of plastics as hydrogen donors. This review aims to present and discuss in detail state-of-the-art catalytic pyrolysis, focusing on the relationships between the properties of the catalysts and the obtained products. A guideline for selecting catalysts for lignocellulosic biomass is also provided.
Development of high diversity beech forest in the eastern Carpathians

In recent decades, a surge in the number of significant and uncontrolled wildfires has occurred worldwide. Global warming may amplify this trend and threaten most ecosystems worldwide. Deciduous forests are characterized by high plant diversity, and understanding their long‐term dynamics is crucial to anticipate changes in these ecosystems during ongoing global warming. The aim of this study is to understand how European beech forests have colonized the inner Eastern Carpathians and how changes in fire regime and human activities have affected their biodiversity.
Voxel-wise Mapping of Functional Magnetic Resonance Imaging in Impression Formation

Social interactions often involve encountering inconsistent information about social others. We conducted a functional magnetic resonance imaging (fMRI) study to comprehensively investigate voxel-wise temporal dynamics showing how impressions are anchored and/or adjusted in response to inconsistent social information. The participants performed a social impression task inside an fMRI scanner in which they were shown a male face, together with a series of four adjectives that described the depicted person's personality traits, successively presented beneath the image of the face. Participants were asked to rate their impressions of the person at the end of each trial on a scale of 1 to 8 (where 1 is most negative and 8 is most positive). We established two hypothetical models that represented two temporal patterns of voxel activity: Model 1 featured decreasing patterns of activity towards the end of each trial, anchoring impressions to initially presented information, and Model 2 showed increasing patterns of activity toward the end of each trial, where impressions were being adjusted using new and inconsistent information. Our data-driven model fitting analyses showed that the temporal activity patterns of voxels within the ventral anterior cingulate cortex, medial orbitofrontal cortex, posterior cingulate cortex, amygdala, and fusiform gyrus fit Model 1 (i.e., they were more involved in anchoring first impressions) better than they did Model 2 (i.e., showing impression adjustment). Conversely, voxel-wise neural activity within dorsal ACC and lateral OFC fit Model 2 better than it did Model 1, as it was more likely to be involved in processing new, inconsistent information and adjusting impressions in response. Our novel approach to model fitting analysis replicated previous impression-related neuroscientific findings, furthering the understanding of neural and temporal dynamics of impression processing, particularly with reference to functionally segmenting each region of interest based on relative involvement in impression anchoring as opposed to adjustment.
Mechanical Design, Planning, and Control for Legged Robots in Distillation Columns

Dexterous robots have great potential to execute industrial tasks that are not suited to humans. In this work, a novel robotic mobility platform is proposed for use in the chemical industry to enable autonomous distillation column inspection — a tedious and dangerous task for humans. A roller arm mechanism is designed for a quadrupedal robot that enables moving across the distillation column. Required dynamic behaviors are generated with full-body motion planning and low-level control. The holistic process of mechanical design, planning, and control leads to desired behavior, as demonstrated by high-fidelity simulations. This marks a key step towards operating legged robots inside distillation columns.
Spectrometers consisting of a diffractive lens and a concave diffraction grating

Abstract The concave diffraction grating is used in many spectrometers and monochromators. Since it has both dispersion and focusing properties, one does not need a collimator or camera. In a nearly symmetric setup with a small deviation angle, the second- and third-order aberrations of the spherical concave diffraction grating are less then the respective aberrations of the setup with a plane diffraction grating and spherical mirrors. However, the residual defocusing limits the applicability of concave gratings in high resolution devices. This aberration can only be partially compensated by a grating with nonequidistant grooves. The focal distance of the diffractive lens has a linear dependence on the wavelength. This property is used to design hybrid diffractive-refractive lens systems for a wide spectral region. It is both tempting and promising to compensate the residual defocusing of a concave diffraction grating by means of a diffractive lens. The present paper analyses, both analytically and numerically, the effect of such compensation. The use of the diffractive lenses in the concave grating spectrometers is shown for the flat-field spectrometer and the double monochromator.
Constrained Clustering: General Pairwise and Cardinality Constraints

In this work, we study constrained clustering, where constraints are utilized to guide the clustering process. In existing works, two categories of constraints have been widely explored, namely pairwise and cardinality constraints. Pairwise constraints enforce the cluster labels of two instances to be the same (must-link constraints) or different (cannot-link constraints). Cardinality constraints encourage cluster sizes to satisfy a user-specified distribution. However, most existing constrained clustering models can only utilize one category of constraints at a time. In this paper, we enforce the above two categories into a unified clustering model starting with the integer program formulation of the standard K-means. As these two categories provide useful information at different levels, utilizing both of them is expected to allow for better clustering performance. However, the optimization is difficult due to the binary and quadratic constraints in the proposed unified formulation. To alleviate this difficulty, we utilize two techniques: equivalently replacing the binary constraints by the intersection of two continuous constraints; the other is transforming the quadratic constraints into bi-linear constraints by introducing extra variables. Then we derive an equivalent continuous reformulation with simple constraints, which can be efficiently solved by Alternating Direction Method of Multipliers (ADMM) algorithm. Extensive experiments on both synthetic and real data demonstrate: 1) when utilizing a single category of constraint, the proposed model is superior to or competitive with state-of-the-art constrained clustering models, and 2) when utilizing both categories of constraints jointly, the proposed model shows better performance than the case of the single category. The experimental results show that the proposed method exploits the constraints to achieve perfect clustering performance with improved clustering to $2-5$ % in classical clustering metrics, e.g., Adjusted Random Index (ARI), Mirkin’s Index (MI), and Huber’s Index (HI), outerperfomring all compared-againts methods across the board. Moreover, we show that our method is robust to initialization.