Miracle-dz/UniFAR-FTUs huggingface dataset:
Force calibration using errors-in-variables regression and Monte Carlo uncertainty evaluation

An errors-in-variables regression method is presented as an alternative to the ordinary least-squares regression computation currently employed for determining the calibration function for force measuring instruments from data acquired during calibration. A Monte Carlo uncertainty evaluation for the errors-in-variables regression is also presented. The corresponding function (which we call measurement function, often called analysis function in gas metrology) necessary for the subsequent use of the calibrated device to measure force, and the associated uncertainty evaluation, are also derived from the calibration results. Comparisons are made, using real force calibration data, between the results from the errors-in-variables and ordinary least-squares analyses, as well as between the Monte Carlo uncertainty assessment and the conventional uncertainty propagation employed at the National Institute of Standards and Technology (NIST). The results show that the errors-in-variables analysis properly accounts for the uncertainty in the applied calibrated forces, and that the Monte Carlo method, owing to its intrinsic ability to model uncertainty contributions accurately, yields a better representation of the calibration uncertainty throughout the transducer’s force range than the methods currently in use. These improvements notwithstanding, the differences between the results produced by the current and by the proposed new methods generally are small because the relative uncertainties of the inputs are small and most contemporary load cells respond approximately linearly to such inputs. For this reason, there will be no compelling need to revise any of the force calibration reports previously issued by NIST.
Deep CNN-Based Planthopper Classification Using a High-Density Image Dataset

Rice serves as the primary food source for nearly half of the global population, with Asia accounting for approximately 90% of rice production worldwide. However, rice farming faces significant losses due to pest attacks. To prevent pest infestations, it is crucial to apply appropriate pesticides specific to the type of pest in the field. Traditionally, pest identification and counting have been performed manually using sticky light traps, but this process is time-consuming. In this study, a machine vision system was developed using a dataset of 7328 high-density images (1229 pixels per centimetre) of planthoppers collected in the field using sticky light traps. The dataset included four planthopper classes: brown planthopper (BPH), green leafhopper (GLH), white-backed planthopper (WBPH), and zigzag leafhopper (ZIGZAG). Five deep CNN models—ResNet-50, ResNet-101, ResNet-152, VGG-16, and VGG-19—were applied and tuned to classify the planthopper species. The experimental results indicated that the ResNet-50 model performed the best overall, achieving average values of 97.28% for accuracy, 92.05% for precision, 94.47% for recall, and 93.07% for the F1-score. In conclusion, this study successfully classified planthopper classes with excellent performance by utilising deep CNN architectures on a high-density image dataset. This capability has the potential to serve as a tool for classifying and counting planthopper samples collected using light traps.
How do government orders affect the media agenda? On the example of tengrinews.kz and nur.kz

Agenda setting is an index of how the media prioritizes the topics and how it wants to influence the significance to the themes of public agenda. It also has the factors affecting the agenda: type of issue, type of medium and type of coverage and those are directly correlated with demographics, psychographics and behavior. Consequently, the public agenda changes towards the concern. Despite its importance of revealing the crucial aspects, some of the channels might be affected by different influencers and events. The problem is that if one media channel covers only the negative aspects happening, others are supporting and fading the actual problems. Thus, it obviously could be seen that the second category linearly depends on government orders while setting the news agenda. Here, we have identified some critical issues and decided to focus on case study. During this unbiased application, we have classified two media channels as the objects of research: Nur.kz and Tengrinews. It helped us to indicate that the agenda setting is not only depending on what the public demands and needs to know, but on tenders, benefits for the third parties, posting paid content and also could be stirred by government offers.
Atom Interferometry on Sounding Rockets

Atom interferometry in microgravity o�ers the possibility to perform high precision measurements of inertial forces complementary to experiments based on classical test masses. The ultimate goal is to perform these quantum measurements in space on board dedicated satellite missions. To reach this, a series of pathfinder microgravity experiments with cold atoms were build. The latest installment of these are conducted on sounding rockets. Here we give a short motivation of atom interferometry in space, an overview of the techniques used, and an introduction of the current mission MAIUS-1.
Higher-Order Sub-Poissonian statistics in interaction of two two-level atoms with a single mode coherent radiation

In the present paper we study higher-order sub-Poissonian photon statistics in interaction of a single mode radiation initially in the coherent state Ŧα〉 with an assembly of two excited two-level atoms using the Hamiltonian, H=ω(a+a+Sz)+g(aS++a+S.), in the natural system of units. Here, α=|α|eiθα, a is the annihilation operator for radiation, Sz and S± are the collective Dicke operators, g is the coupling constant, ω is the energy of the photons and the energy difference between the two atomic levels. We solve it exactly and conclude that higher-order sub-Poissonian photon statistics can be obtained for an arbitrary order by choosing suitably the square root of mean photon number |α| of the coherent radiation and coupling time gt. Variations of higher-order sub-Poissonian photon statistics with parameters |α| and gt have also been discussed.
Evaluation of an alternation design for studying transfer of training

iii LIST OF. TABLES . v LIST OF F I G U R E S . vi Chapter I. INTRODUCTION 1 II. METHOD . 21 III. R E S U L T S . 27 IV. DISCUSSION . 56 V. S U M M A R Y • • • . • • 6? REFERENCES . 69 APPENDIX A 1 PERCENT TIME ON TARGET AND TRANSFER SCORES . 73 APPENDIX B: PERFORMANCE CURVES FOR INDIVIDUAL MINUTES OF PRACTICE . 83
Credit Ratings and Investments

We study how inflated credit ratings affect investment decisions in bond markets using experimental coordination games. Theoretical models that feature a feedback effect between capital markets and the real economy suggest that inflated ratings can have both positive and negative real effects. We compare markets with and without a credit rating agency and find that ratings significantly impact investor behaviour and capital allocation to firms. We show that the main mechanism through which these real effects materialize is a shift in investors’ beliefs about the behaviour of other investors rather than firms’ underlying fundamentals. Our experimental results suggest that the positive impact of inflated ratings is likely to dominate in the presence of feedback effects since ratings act as a strong coordination mechanism resulting in enhanced market outcomes. JEL: D81; D82; D83; G24
An Assessment of In-the-Wild Datasets for Multimodal Emotion Recognition

Multimodal emotion recognition implies the use of different resources and techniques for identifying and recognizing human emotions. A variety of data sources such as faces, speeches, voices, texts and others have to be processed simultaneously for this recognition task. However, most of the techniques, which are based mainly on Deep Learning, are trained using datasets designed and built in controlled conditions, making their applicability in real contexts with real conditions more difficult. For this reason, the aim of this work is to assess a set of in-the-wild datasets to show their strengths and weaknesses for multimodal emotion recognition. Four in-the-wild datasets are evaluated: AFEW, SFEW, MELD and AffWild2. A multimodal architecture previously designed is used to perform the evaluation and classical metrics such as accuracy and F1-Score are used to measure performance in training and to validate quantitative results. However, strengths and weaknesses of these datasets for various uses indicate that by themselves they are not appropriate for multimodal recognition due to their original purpose, e.g., face or speech recognition. Therefore, we recommend a combination of multiple datasets in order to obtain better results when new samples are being processed and a good balance in the number of samples by class.
An Interactive Differential Evolution Algorithm Based on Backtracking Strategy Applied in Interior Layout Design

The Interior layout model is to optimize the arrangement position of each room to maximize the comfort and quality of life of residents. Due to the complexity of the Interior layout problem, the computation of fitness function costs lots of time. To reduce the high computational cost while maintaining the solution performance. An interactive differential evolution algorithm based on Backtracking operator (IDE-BO) is proposed as the solver of the Interior layout model. The human-computer interaction mechanism of IDE benefits the automatic adjustment of fitness parameters that best meet the user’s subjective preferences to achieve the optimal solution. At the same time, the backtracking strategy can also help jump out when the algorithm falls into local optimization. The IDE is compared to other two conventional optimization methods based on two different layout scenarios. The experimental results show that in interior layout model IDE-BO is better than conventional interactive genetic algorithm (IGA) and IDE which do not use BO strategy, the super-performance of IDE-BO in complex situations in terms of execution time and convergence rate.
BHR equations re-derived with immiscible particle effects

Compressible and variable density turbulent flows with dispersed phase effects are found in many applications ranging from combustion to cloud formation. These types of flows are among the most challenging to simulate. While the exact equations governing a system of particles and fluid are known, computational resources limit the scale and detail that can be simulated in this type of problem. Therefore, a common method is to simulate averaged versions of the flow equations, which still capture salient physics and is relatively less computationally expensive. Besnard developed such a model for variable density miscible turbulence, where ensemble-averaging was applied to the flow equations to yield a set of filtered equations. Besnard further derived transport equations for the Reynolds stresses, the turbulent mass flux, and the density-specific volume covariance, to help close the filtered momentum and continuity equations. We re-derive the exact BHR closure equations which include integral terms owing to immiscible effects. Physical interpretations of the additional terms are proposed along with simple models. The goal of this work is to extend the BHR model to allow for the simulation of turbulent flows where an immiscible dispersed phase is non-trivially coupled with the carrier phase.
Effect of Blend-Pelleted Products Based on Carinata Meal or Canola Meal in Combination with Lignosulfonate on Ruminal Degradation and Fermentation Characteristics, Intestinal Digestion, and Feed Milk Value When Fed to Dairy Cows

The objectives of this study were to investigate the effect of newly developed blend-pelleted products based on carinata meal (BPPCR) or canola meal (BPPCN) in combination with peas and lignosulfonate on ruminal fermentation characteristics, degradation kinetics, intestinal digestion and feed milk values (FMV) when fed to high-producing dairy cows. Three dietary treatments were Control = control diet (common barley-based diet in western Canada); BPPCR = basal diet supplemented with 12.3%DM BPPCR (carinata meal 71.4% + pea 23.8% + lignosulfonate4.8%DM), and BPPCN = basal diet supplemented with 13.3%DM BPPCN (canola meal 71.4% + pea 23.8% + lignosulfonate 4.8%DM). In the whole project, nine mid-lactating Holstein cows (body weight, 679 ± 124 kg; days in milk, 96 ± 22) were used in a triplicated 3 × 3 Latin square study for an animal production performance study. For this fermentation and degradation kinetics study, the experiment was a 3 × 3 Latin square design with three different dietary treatments in three different periods with three available multiparous fistulated Holstein cows. The results showed that the control diet was higher (p < 0.05) in total VFA rumen concentration (138 mmol/L) than BPPCN. There was no dietary effect (p > 0.10) on the concentration of rumen ammonia and ruminal degradation kinetics of dietary nutrients. There was no significant differences (p > 0.10) among diets on the intestinal digestion of nutrients and metabolizable protein. Similarly, the feed milk values (FMV) were not affected (p > 0.10) by diets. In conclusion, the blend-pelleted products based on carinata meal for a new co-product from the bio-fuel processing industry was equal to the pelleted products based on conventional canola meal for high producing dairy cattle.
Stochastic Subgradient MCMC Methods

Many Bayesian models involve continuous but non-differentiable log-posteriors, including the sparse Bayesian methods with a Laplace prior and the regularized Bayesian methods with maxmargin posterior regularization that acts like a likelihood term. In analogy to the popular stochastic subgradient methods for deterministic optimization, we present the stochastic subgradient MCMC for efficient posterior inference in such Bayesian models in order to deal with largescale applications. We investigate the variants that use adaptive stepsizes and thermostats to improve mixing speeds. Experimental results on a wide range of problems demonstrate the effectiveness of our approach.
Geologically Constrained Full-Waveform Inversion Using an Anisotropic Diffusion Based Regularization Scheme : Application to a 3D Offshore Brazil Dataset

Full-waveform inversion (FWI) has now become a standard part of the workflow for building earth-models in the industry given its ability to provide high-resolution high-fidelity earth-models especially in complex geological settings. Inverse problems like FWI are ill-posed and ill-conditioned, which means that their solution is not unique and unstable. Some kind of regularization, usually in the form of smoothing of the model updates, is essential to obtain a stable solution. The most common approaches used in FWI previously, have been ad hoc in the sense that the smoothing parameters have not been influenced by the geology of the subsurface, which results in smoothing across major geological interfaces, such as, between salt and sediment. Here we present a method to regularize the FWI problem using an anisotropic diffusion-based regularization scheme to conform the earth model updates along geological structure automatically derived from a seismic image. We apply this method on a 3D offshore Brazil field dataset and show that it leads to a more geologically plausible model.
Genomes, Transcriptomes, Proteomes, Metabolomes in Bioinformatics Investigating Alzheimers Disease

Alzheimers disease is affecting many people, especially people older than 65, living in the world. However, the cause and cure of these disease continue to confuse the scientists. Omics analysis, which includes four levels: genomes, transcriptomes, proteomes and metabolomes analysis, can dig out the information lying behind large amount of data and its really useful for complex disease like AD (Alzheimers Disease). In this article, I talked about the four levels of omics analysis investigating AD in details. In each level, the general workflow, the information we can get and the examples in AD have been involved. This paper will give the readers a general view of the function of omics analysis in AD.
Tracking and Describing Deformable Objects Using Active Contour Models

Technical Report TR-CIM-90-9 In this thesis we consider a number of issues in developing techniques and algorithms to automate the visual tracking of deformable objects in the plane. We have applied these techniques in cell locomotion and tracking studies. We examine two classes of computer vision problems. First, we consider the segmentation of a noisy intensity image and the tracking of a nonrigid object. Second, we consider the shape analysis of an amorphous object. In evaluating these problems, we explore a new technique based on an active contour model commonly called a "snake. " The snake permits us to simultaneously solve, in constrained cases, both the segmentation and tracking problems. We present a detailed analysis of the snake model, emphasizing its limitations and shortcomings, and propose various improvements to the original description of the model. Then, we study the two complementary types of shape descriptors: boundary- and region-based. We propose to combine these within the context of the grassfire transform. Two new algorithms are described. First, we present a contour segmentation technique using mathematical morphology on the curvature function, we call curvature morphology. Accurate localization for different scales of curvature features is achieved, leading to a Morphological Curvature Scale-space or MCS. Second, the snake model is used to simulate the grassfire transform using the previously extracted contour features. This permits us to produce a multiscale skeleton representation of shape which is based on the Euclidean distance metric. New significance criteria for our shape descriptors, such as the ``region-support'' of curvature extrema and the ``ridge-support'' of skeleton branches are also proposed. Finally, numerous implementation details are discussed; for example, the description of an efficient sequential Euclidean distance transform.
TABU SEARCH ALGORITHM FOR FEATURE SELECTION

In this paper,an algorithm based on tabu search for selecting an optimal subset from original large scale feature set is presented.The role and effect of the parameters in tabu search,such as the tabu list length,the neighbor size and the number of candidate solutions are analyzed.For two forms of feature selection problem,tabu search is compared with classic algorithms,such as sequential and generalized sequential methods,branch and bound methods,plus l and take away r method,etc.,and other methods proposed recently,such as genetic algorithm and sequential floating forward(backward) search methods.The experimental results have shown that tabu search has good performance in both the quality of obtained feature subset and computation efficiency.
Urban-Rural Differences in the Prevalence of Depressive Symptoms in Korean Adults

This study aimed to investigate the prevalence of depression among Koreans living in urban and rural areas, stratified by socioeconomic status. The study included 216,765 participants from the 2017 Korean Community Health Survey. Depressive symptoms were assessed using the PHQ-9, with a score of 10 or higher indicating depressive symptoms. Residences with the words Eup and Myeon in their addresses were categorized as rural areas, and residences with Dong in their addresses as urban areas. Socioeconomic status was evaluated by household income and education level. A Poisson regression analysis with sampling weights was conducted and adjusted for demographic, lifestyle, socioeconomic status, and comorbidity. The adjusted prevalence rate of depressive symptoms was 3.33% (95% CI, 3.21-3.45) in urban areas and 2.59% (95% CI, 2.43-2.74) in rural areas. The prevalence of depressive symptoms in urban areas was 1.29 times (95% CI, 1.20-1.38) higher than in rural areas. The prevalence rate ratio for depressive symptoms in urban areas compared to rural areas sorted by monthly incomes was 1.39 (95% CI, 1.28-1.51) for less than 2 million won, 1.22 (95% CI, 1.06-1.41) for 2 to 3.99 million won, and 1.09 (95% CI, 0.90-1.32) for more than 4 million won, and the urban-rural difference was more evident in lower household income subjects (p for interaction=0.033). However, urban-rural differences did not differ according to sex, age, or education level. In conclusion, we found urban-rural differences in depressive symptoms in a representative sample of Koreans, and revealed that these differences may vary according to income level. These results suggest that mental health policy must consider the health disparities according to residence and income.
High Deep-Ultraviolet Quantum Efficiency GaN P?I?N Photodetectors with Thin P-GaN Contact Layer

GaN ultraviolet (UV) p—i—n photodetectors (PDs) with a 40 nm thin p-GaN contact layer are fabricated on sapphire substrates, which exhibit enhanced quantum efficiency especially in a deep-UV wavelength range. The PDs show good rectification behavior and low dark current in pA level for reverse bias up to −10 V. Under zero bias, the maximum quantum efficiency of the PD at 360 nm is close to 59.4% with a UV/visible rejection ratio more than 4 orders of magnitude. Even at a short wavelength of 280 nm, the quantum efficiency of the PD is still around 47.5%, which is considerably higher than that of a control device with a thicker p-GaN contact layer. The room temperature thermal noise limited detectivity of the PD is calculated to be ~4.96 × 1014 cmHz1/2 W−1.
Approximation of Reliability of Direct Genomic Breeding Values

Two methods to efficiently approximate theoretical genomic reliabilities are presented. The first relies on the direct inverse of the left hand side (LHS) of mixed model equations. It uses the genomic relationship matrix for a small subset of individuals with the highest genomic relationship with the individual of interest. The second is a ridge-regression method using the direct inverse of LHS for a small subset of SNP. The performance of the methods was evaluated for the North American genomic data set, consisting of 228,168 genotyped individuals. The ridge-regression method gives very high correlations between theoretical and estimated reliabilities for subsets of 5k SNP and above. It is easily applicable to large data sets. Both methods lead to some biases in the mean and SD of reliabilities but these can be corrected by pegging to theoretical values or values from validation studies.
The stochastic collocation Monte Carlo sampler: highly efficient sampling from ‘expensive’ distributions

In this article, we propose an efficient approach for inverting computationally expensive cumulative distribution functions. A collocation method, called the Stochastic Collocation Monte Carlo sampler (SCMC sampler), within a polynomial chaos expansion framework, allows us the generation of any number of Monte Carlo samples based on only a few inversions of the original distribution plus independent samples from a standard normal variable. We will show that with this path-independent collocation approach the exact simulation of the Heston stochastic volatility model, as proposed in Broadie and Kaya [Oper. Res., 2006, 54, 217–231], can be performed efficiently and accurately. We also show how to efficiently generate samples from the squared Bessel process and perform the exact simulation of the SABR model.