Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of the Kusuoka approximation to pricing barrier options

The main theme of this research is numerical verification of applicability of a higher-order approximation to pricing barrier options, which is a both mathematically and practically important path-dependent type problem in mathematical finance. The authors successfully extend two types of algorithms called NV and NN. Both algorithms are based on a higher-order approximation scheme called Kusuoka approximation and have been shown to attain second-order approximation of stochastic differential equations as long as applied to European-type problems. In extending the algorithms, the authors apply a function representing the probability of hitting a boundary. In the numerical experiments, these two algorithms are compared with the Euler–Maruyama scheme which is one of the most popular first-order approximation schemes. As a result, it is demonstrated that the speed of calculation of these two algorithms could be much higher than that of the Euler–Maruyama scheme extended by the same way. It is concluded that one of the keys to improvement of the results is the construction of the function for calculation of the probability of hitting a boundary.
Exploration of the Regulatory Pathways and Key Genes Involved in the Response to Saline–Alkali Stress in Betula platyphylla via RNA-Seq Analysis

The pH of saline–alkali soil is high because of carbonate salts, and the deleterious effects of saline–alkali soil on the growth of plants are greater than those of saline soil. Few studies have examined the saline–alkali tolerance of Betula platyphylla at the molecular level. To clarify the regulatory mechanism underlying saline–alkali tolerance in B. platyphylla, RNA sequencing analysis of B. platyphylla seedlings treated with NaHCO3 was conducted. Differences in gene expression in the roots of B. platyphylla seedlings under saline–alkali stress (induced via NaHCO3) for 3 h and 6 h were characterized, and a total of 595 and 607 alkali stress-responsive genes were identified, respectively. Most differentially expressed genes were involved in stress, signal transduction, secondary metabolic process, regulation of jasmonic acid, and the abiotic stimulus signaling pathway. The single nucleotide polymorphism loci in the differentially expressed genes were associated with the alkaline-salt tolerance in birch germplasm. In addition, birch plants overexpressing WRKY70 and NAC9 were obtained using the A. tumefaciens-mediated transient transformation method, and these two genes were found to play key roles in saline–alkali tolerance. Additional study revealed that WRKY70 and NAC9 can increase resistance to saline–alkali stress by enhancing reactive oxygen species scavenging and inhibiting cell death in birch plants. The results of this study enhance our understanding of the saline–alkali stress tolerance of B. platyphylla at the molecular level, and provide several key genes that could be used in the breeding of birch plants in the future.
Global Challenges: personal reflections on the Stockholm "New Shape" Competition

The Stockholm-based Global Challenges “New Shape” competition, which attracted 2,702 entrants from 122 countries, aimed to promote new ideas for the governance of global catastrophic risks. Here I tell the story of my role as one of 14 eventual finalists. It is a story of ideas — ideas that formed the background, ideas that emerged in the course of the finals, and ideas about how we might take things forward in the future. As Sir William Bragg put it in his famous introduction to The Double Helix (Watson 1967), this is not a history, but an autobiographical contribution to the history that may someday be written.
A Comparative Study of Social Anxiety of Blind and Visually-impaired People With Sighted People

Background and Objectives: Social anxiety is a type of disorder in which a person becomes anxious and afraid in social interactions and situations. It is well known that 90% of a person’s social interactions are related to their vision, so blind people definitely have problems in all their social interactions. Methods: Leibovitz’s social anxiety scale is the only tool that is based on social situations. Participants were asked to answer the questions of the data collection tool. Normal subjects were also randomly selected from the normal population who are similar to the patient group in terms of age and gender. The results were analyzed with relevant statistical software. Results: The results show that the average score of social anxiety in the blind group is 43.27 and 44.25 in the sighted group. The average score of avoidance in the blind group is 39.83 and 41.66 in the sighted group. The amount of social anxiety between the blind and the visually impaired group has a significant difference compared to normal people. There is also a significant difference in the comparison between totally blind and mildly impaired people (P=0.027). The amount of avoidance is also different between totally blind and sighted people. There is a significance (P=0.024) and there is a statistical difference between the totally blind and mildly visually impaired (P=0.022). Conclusion: People with normal vision or mild vision loss experience more degrees of social anxiety as well as avoidance than totally blind people.
Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality

Electrophysiological Source Imaging (ESI) refers to reconstructing the underlying brain source activation from non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) measurements on the scalp. Estimating the source locations and their extents is a fundamental tool in clinical and neuroscience applications. However, the estimation is challenging because of the ill-posedness and high coherence in the leadfield matrix as well as the noise in the EEG/MEG data. In this work, we proposed a combinatorial search framework to address the ESI problem with a provable optimality guarantee. Specifically, by exploiting the graph neighborhood information in the brain source space, we converted the ESI problem into a graph search problem and designed a combinatorial search algorithm under the framework of A* to solve it. The proposed algorithm is guaranteed to give an optimal solution to the ESI problem. Experimental results on both synthetic data and real epilepsy EEG data demonstrated that the proposed algorithm could faithfully reconstruct the source activation in the brain.
Anomaly Manufacturing Product Detection using Unregularized Anomaly Score on Deep Generative Models

One of the most common needs in manufacturing plants is rejecting products not coincident with the standards as anomalies. Manufacturing companies usually employ numerous inspectors for anomaly detection and it takes a high cost. Accurate and automatic anomaly detection reduces inspection cost and improves product reliability. In unsupervised anomaly detection, a probabilistic model detects test samples with lower likelihoods as anomalies. Recently, a probabilistic model called deep generative model (DGM) has been proposed for end-to-end modeling of natural images and already achieved a certain success. However, anomaly detection of machine components with complicated structures is still challenging because they produce a wide variety of the normal image patches with lower likelihoods. For overcoming this difficulty, we propose unregularized score for the DGM. As its name implies, the unregularized score is the anomaly score of the DGM without the regularization terms. The unregularized score is robust to the inherent complexity of a sample and has a smaller risk of rejecting a sample appearing less frequently but being coincident with the standards. Experimental results of anomaly detection on the real manufacturing datasets show better performance of the unregularized score compared to existing approaches.
Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model

Around the world, brain tumors are becoming the leading cause of mortality. The inability to undertake a timely tumor diagnosis is the primary cause of this pandemic. Brain cancer diagnosis is a crucial procedure that relies on the expertise and experience of the doctor. Radiologists must use an automated tumor classification model to find brain cancers. The current model’s accuracy has to be improved to get suitable therapies. Radiologists can consult various computer-aided diagnostic (CAD) models in the literature on medical imaging to assist them with their patients. Previous research has widely used CNN models for tumor detection and classification, which typically require large datasets. This research proposed the Caps-VGGNet hybrid model, which integrates the CapsNet model with the VGGNet model by adding the layers of VGGNet. The presented model addresses the challenge of requiring large datasets by automatically extracting and classifying features. The suggested algorithm’s effectiveness was assessed using the Brats-2020 and Brats-2019 dataset, which contains high-quality images of brain tumors. Compared to other conventional and hybrid models, the empirical outcomes of the suggested model indicate that it exhibited the highest level of effectiveness and superior efficacy in terms of accuracy, specificity, and sensitivity. Specifically, the presented hybrid model attained an accuracy of 0.99, a specificity of 0.99, and a sensitivity of 0.98 on the Brats20 dataset.
Articulatory Kinematic Differences During Adaptation to a Bite Block

Articulatory Kinematic Differences During Adaptation to a Bite Block Madison Ann McHaley Department of Communication Disorders, BYU Master of Science The current study examined the effects of bite blocks on articulatory kinematics when producing /r/ within a phrase. Participants consisted of 20 young adults (10 males, 10 females) with no speech, language or hearing disorders. Participants produced the carrier phrase, I say __ with the nonsense words /əri/ (high front vowel), /əræ/ (low front vowel), /əru/ (high back vowel), /ərɑ/ (low back vowel). A Northern Digital Instruments Wave electromagnetic articulograph measured the articulatory movements while the speaker produced the stimuli in two conditions (Pre bite block and post bite block). Bilateral bite blocks were made using Express dental putty, which is a silicone impression material, in order to create an inter-incisal gap of 10 mm. The hull area (i.e., a boundary enclosing the total distance the sensor traveled during the target phrase) of the data for each sensor (i.e., tongue back, tongue mid, tongue front, lower lip, mandibular central incisor) was calculated for the individual nonsense words /e͜ɪərɑ/, /e͜ɪəræ/, /e͜ɪəri/, and /e͜ɪəru/. Results revealed kinematic differences across vowel phrases and between genders. The hull area of the tongue and jaw were significantly different for the vowel phrases /e͜ɪəræ/, /e͜ɪəri/, and /e͜ɪəru/ compared to /e͜ɪərɑ/. The hull area for the jaw for /e͜ɪərɑ/ was significantly larger than the other vowel phrases. The between-gender analyses showed larger hull areas for males than females. Different motor equivalent strategies for tongue movements were observed when speakers produced /e͜ɪərɑ/ and there were individual differences in compensating for the presence of the bite block.
Prevalencia de anemia en la población mexicana: análisis de la Ensanut Continua 2022

OBJETIVO Describir la magnitud de la prevalencia de anemia en la población mexicana participante en la Encuesta Nacional de Salud y Nutrición Continua 2022 (Ensanut Continua 2022). Material y métodos. La Ensanut 2022 es probabilística. Mediante sangre venosa y Hemocué (201+) se midió hemoglobina (Hb) y se ajustó por altitud. Se definió anemia según criterios de la Organización Mundial de la Salud (OMS). Considerando el diseño muestral de la encuesta, se obtuvieron prevalencias e IC95%. RESULTADOS Las prevalencias de anemia fueron 6.8% en niños de 1-4 años, 3.8% en niños de 5-11 años, 10.1% en adolescentes (12-19 años), 15.8% en mujeres (20-49 años) y 10.3% en adultos mayores (≥60 años). CONCLUSIONES La anemia afectó principalmente a las mujeres adultas. Resulta necesario identificar sus causas para focalizar acciones y evitar el ciclo intergeneracional del riesgo de anemia.
The Link between Gender Gaps in School Enrollment and School Achievement

Gender gaps in school enrollment and achievement are usually investigated as separate facets of gender inequality in education. This study suggests that they might be linked by design. If boys and girls differ in their school enrollment rates, the student population will be gender biased. Since out-of-school adolescents tend to be less advantaged than in-school ones, school-based large-scale assessments effectively compare a more fully represented group of one gender with a less represented and more advantaged group of the other one. This should shift student gender achievement gaps so that they favor the latter group. In country-level regression models using data from all PISA cycles, we indeed found evidence of a small, negative association between gender gaps in secondary school enrollment and gender gaps in student achievement. This finding is robust across different achievement domains and specifications. We discuss the study’s limitations and implications for applied research with PISA data.
Numerical investigation of a cold bubbling bed throughout a dense discrete phase model with KTGF collisional closure

Abstract A hybrid Euleran-Lagrangian Dense Discrete Particle Model (DDPM) was used to numerically simulate the bubbling behavior of a fluidized bed reactor. The model exploits the parcels concept to reduce the number of particles to simulate while exploiting the Kinetic Theory of Granular Flow (KTGF) to account for their repulsive interactions. The DDPM-KTGF was explored throughout a model sensitivity analysis to identify the most influent parameters impacting on the numerical accuracy and performances to ultimately assess its potential use for industrial purposes. Because of the measurement simplicity as well as its strong connection with the bed fluid-dynamic, pressure-drop data was used and processed to obtain the power spectral density (PSD) distribution to empirically and numerically characterize the behavior of this system under a bubbling fluidization regime. The DDPM-KTGF model was found to be sensitive to mesh size, restitution coefficients but mostly to the drag law. However, poor sensitivity to the kinetic viscosity, solid pressure, radial distribution function as well as to the number of parcels was revealed. Besides having an effect on the physical outputs, the mesh refinement was also required to numerically verify the model which also had a significant impact on the simulation time-performance. Moreover, a major barrier was found when using this model to simulate fixed bed regime, showing the limitation of the KTGF approach to high particle density regions as a result of a poor estimation of particles force interactions.
Intravenous thrombolysis for acute ischemic stroke: From alteplase to tenecteplase

Stroke is one of the primary causes of morbidity and death worldwide. While intravenous (IV) thrombolysis with alteplase has been widely proven to be beneficial for acute ischemic stroke patients, it still has many limitations. Tenecteplase, a revised version of alteplase, is a potential alternative IV thrombolytic agent that has benefits over alteplase. The aim of this mini-review is to summarize the advancements in IV thrombolysis for severe ischemic stroke, specifically the development and transition from alteplase to tenecteplase.
Fast Large-Integer Matrix Multiplication

This thesis provides two perspectives on fast large-integer matrix multiplication. First, we will cover the complexity theory underlying recent developments in fast matrix multiplication algorithms by developing the theory behind, and proving, Schönhage’s τ -theorem. The theorems will be proved for matrix multiplication over commutative rings. Afterwards, we will discuss two newly developed programs for large-integer matrix multiplication using Strassen’s algorithm, one on the CPU and one on the GPU. Code from the GMP library is used on both of these platforms. We will discuss these implementations and evaluate their performance, and find that multi-core CPU platforms remain the most suited for large-integer matrix multiplication.
Environmental Information Disclosure, Technology Innovation and Enterprise Competitiveness: Empirical Data Based on Heavily Polluting Listed Companies

— China's economy is at a stage of high-quality development, and environmental information disclosure is valued by all sectors of society. This paper selects data on heavily polluting listed companies from 2016-2020 as the research sample to study the relationship between environmental information disclosure, technological innovation, and enterprise competitiveness. The results of the study show that environmental information disclosure and technological innovation are conducive to enhancing enterprise competitiveness, but environmental information disclosure weakens the effect of technological innovation in enhancing enterprise competitiveness. Further analysis found that environmental information disclosure has a more significant impact on the relationship between technological innovation and enterprise competitiveness among non-state enterprises and enterprises in the Eastern region. Based on the above, this paper puts forward three suggestions: Heavy polluting enterprises should improve the level of environmental information disclosure and disclose high-quality information; pay attention to R&D investment to enhance technological innovation; and allocate corporate funds reasonably to avoid the investment in environmental information disclosure crowding out the funds needed for R&D and innovation.
Minimizing the Maximum End-to-End Network Delay: Hardness, Algorithm, and Performance.

We consider the scenario where a source streams a flow at a fixed rate to a receiver across the network, possibly using multiple paths. Each link has a finite capacity. Transmission over a link incurs an integer delay if the rate is within the link capacity, and an unbounded delay otherwise. The objective is to minimize the maximum end-to-end delay experienced by the flow. The problem, denoted as Min-Max-Delay, appears in various practical scenarios, e.g., delay-critical video conferencing using inter-datacenter networks. In this paper, we first show Min-Max-Delay is weakly NP-hard and develop an exact algorithm with pseudo-polynomial time complexity. We then propose a Fully Polynomial Time Approximation Scheme (FPTAS). These results reveal fundamental difference between Min-Max-Delay problem and a similar maximum latency problem studied in the literature, for which is APX-hard and no PTAS exists unless P = NP. Moreover, there exists no exact pseudo-polynomial-time algorithm or constant-approximate algorithm for the maximum latency problem. We evaluate our algorithms in the scenario of routing delay-critical video-conferencing traffic over multiple paths of inter-datacenter networks, using simulations based on Amazon EC2 topology. Our algorithms both achieve the optimal maximum delay performance in all simulation instances, consistently outperforming all state-of-the-art solutions which only obtain sub-optimal maximum delay performance in certain instances. Furthermore, simulation results show that our achieved optimal delay always meet the delay requirement for video conferencing, while sub-optimal delay obtained by the alternatives fail to satisfy video-conferencing delay requirement for up to 15% of simulation instances between certain cross-continental source-receiver pair.
Offshore Electrical Grid Layout Optimization for Floating Wind—A Review

Electrical grid layout optimization should consider the placements of turbines and substations and include effects such as wake losses, power losses in cables, availability of different cable types, reliability-based power losses and operational/decommissioning cost besides the initial investment cost. Hence, optimizing the levelized cost of energy is beneficial capturing long-term effects. The main contribution of this review paper is to identify the current works and trends on electrical layout optimization for offshore wind farms as well as to analyze the applicability of the found optimization approaches to commercial-scale floating wind farms which have hardly been investigated so far. Considering multiple subproblems (i.e., micrositing and cabling), simultaneous or nested approaches are advantageous as they avoid sequential optimization of the individual problems. To cope with this combinatorial problem, metaheuristics seems to offer optimal or at least close-to-optimal results while being computationally much less expensive than deterministic methods. It is found that floating wind brings new challenges which have not (or only insufficiently) been considered in present optimization works. This will also be reflected in a higher complexity and thus influence the suitability of applicable optimization techniques. New aspects include the mobility of structures, the configurations and interactions of dynamic cables and station-keeping systems, the increased likelihood of prevailing heterogeneous seabeds introducing priority zones regarding anchor and riser installation, the increased importance of reliability and maintainability due to stricter weather limits, and new floating specific wind farm control methods to reduce power losses. All these facets are crucial to consider when thoroughly optimizing the levelized cost of energy of commercial-scale floating offshore wind farms.
A Finite Difference Scheme for the Time-Fractional Landau-Lifshitz-Bloch Equation

In this paper, we study a finite difference scheme for temporal discretization of the time-fractional Landau-Lifshitz-Bloch equation, such as the fractional time derivative of order α is taken in the sense of Caputo. An existence result is established for the semi-discrete problem by Schaefer's fixed point theorem. Stability and error analysis are then provided, showing that the temporal accuracy is of order 2 − α.
Lotfi Zadeh: el genio creador de la lógica borrosa

espanolLotfi Asker Zadeh: matematico, ingeniero electricista, inf ormatico y profesor Irani- estadounidense de la Universida d de California en Berkeley. Creo la teoria de conjuntos borrosos y la logica bo rrosa. Zadeh es uno de los mas notables genios de la epoca actu al. Entre sus otras creaciones estan el metodo de la transformada Z, el enfoque de la teoria de sistemas lineales basado en estados, variables linguisticas, control borroso, razonamiento aproximado, teoria de la posibilidad, “soft computing” , computacion con palabras, teoria computacional de las percepciones, teoria de la granulacio n de la informacion borrosa, teoria generalizada de la incer tidumbre, y numeros Z. EnglishLotfi Asker Zadeh: mathematician, electrical engineer, com puter scientist and Iranian-American professor at the Univ ersity of Cali- fornia in Berkeley. He created the theory of fuzzy sets and fu zzy logic. Zadeh is one of the most remarkable geniuses of the current era. Among his other creations are the Z-transform method, t he approach of the theory of linear systems based on states, l inguistic variables, fuzzy control, approximate reasoning, theory o f possibility, "soft computing", computation with words, c omputational the- ory of perceptions, theory of the granulation of fuzzy infor mation, generalized theory of uncertainty, and Z-numbers.