Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration of Local Correlation Models to Basket Smiles

Allowing correlation to be local, i.e., state-dependent, in multi-asset models allows better hedging by incorporating correlation moves in the Delta. When options on a basket - be it a stock index, a cross-foreign exchange rate or an interest rate spread - are liquidly traded, one may want to calibrate a local correlation to these option prices. Only two particular solutions have been suggested so far in the literature. Both impose a particular dependency of the correlation matrix on the asset values that one has no reason to undergo. They may also fail to be admissible, i.e., positive semi-definite. We explain how, by combining the particle method presented in "The smile calibration problem solved" by Guyon and Henry-Labordere (2011) with a simple affine transform, we can build all the calibrated local correlation models. The two existing models appear as special cases (if admissible). For the first time, one can now choose a calibrated local correlation in order to fit a view on the correlation skew, or reproduce historical correlation, or match some exotic option prices, thus improving the pricing, hedging and risk-management of multi-asset derivatives. This technique is generalized to calibrate models that combine stochastic interest rates, stochastic dividend yield, local stochastic volatility and local correlation. Numerical results show the wide variety of calibrated local correlations and give insight into a difficult (still unsolved) problem: finding lower bounds/upper bounds on general multi-asset option prices given the whole surfaces of implied volatilities of a basket and its constituents.
The Power of Hashtag in the New Era of Social Media Dominance: Study on the Use of Hashtag Trends Among College Students in Albania

Innovative marketing approaches have been developed because of the growth of social networks. A simple tool, the hashtag has grown to take on new functions and has quickly assimilated into our digital language. This study starts with a brief overview on the importance of hashtags. It examines the related literature considering the dominance of social media in the modern world. The paper also focuses on an examination of hashtag usage in Albania. Primary data includes original information gathered through a survey. This study aims to analyze the level of recognition and the trends of hashtag usage among university students. Also, the paper aims to analyze the perception of the hashtag as a tool that can contribute to increasing the visibility of posts on social networks. Received: 20 May 2023 / Accepted: 24 June 2023 / Published: 5 July 2023
Hopf bifurcation for one-phase flow in model porous media

Inertial one-phase flow, in the laminar regime, within porous media is of interest for a wide range of applications including flow in filters, in columns of reactors or near well during hydrocarbon recovery. Formalized existing macroscopic models for this type of flow are restricted to steady conditions [1]. Their validity is hence restricted to a Reynolds number smaller than the critical value at which the flow switches from steady to unsteady condition, a transition that is known as the first Hopf bifurcation [2]. While the critical Reynolds number at the bifurcation was mainly determined for flow around an isolated cylinder [3], the analysis remains to be done for more complex structures. In this work, we address the determination of the critical Reynolds number characterizing the Hopf bifurcation on 2D porous model structures made of cylinders of square cross section by means of direct numerical simulation. This type of structure can be thought of as a relevant representation of streets/buildings network or clusters of trees for which the characterization of steady/unsteady inertial flow is of crucial importance for particle dissemination (pollutant, pollen), prediction of fluid-structure interaction, etc.
Low-rank tensor learning for human action recognition

of the Dissertation Low-Rank Tensor Learning for Human Action Recognition by Chengcheng Jia Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, April 2016 Dr. Advisor: Yun Fu, Adviser In the era of social media, social security is an important topic such as public surveillance. Even tons of web cameras are widely used in public places, e.g., bank, square and airport, it still needs lots of time and labors to keep attention. Therefore, how to recognize human action in a specific circumstance with a complex background is a basic question for social security problem. In this thesis, we focus on the social security problem, in particular human action recognition, and give the analystics in two lines, (1) machine learning algorithms for action recognition, (2) applying algorithms for novel problems in action recognition, e.g., missing-modality problem, dimensionality reduction. These two lines are detailed in following. For machine learning algorithm, extracting features from high-dimensional action data is crucial in human action recognition. The usual approach is finding a subspace, i.e., projecting high-dimensional data into a low-dimensional subspace containing main pattern of original data and fewer variables, for classification. First of all, data representation is crucial for action video which contains spatiotemporal information. To this end, we propose high-order tensor to represent the action videos, and employ tensor decomposition methods for dimensionality reduction. Second, different problems in action recognition tasks are solved by machine learning algorithms, such as transfer learning, low-rank learning, manifold learning. Low-rank tensor learning is appropriate for some problems in action recognition task such as missing-modality problem, intra-class diversity problem and manual dimensional setting problem. In missing-modality problem, training ans testing data are different modalities, which may not obtain good performance in the test phase. Intra-class diversity problem contains three situations, such as multi-subject, multi-modality and sub-action challenges, which will decrease the performance of recognition. Sub-action problems indicates some body poses are different even for the same action, e.g., drinking action by standing person and drinking by sitting person.
Les voisinages réguliers ouverts : critères homotopiques d'identification

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The Bifocal Lens Inhibition of Myopia Progression (BLIMP) Study

ii Dedication . iv Acknowledgments. v Vita . vi List of Tables . ix List of Figures . x Chapter 1: Introduction . 1 1.1 Public Health Significance . 1 1.2 Can Children Wear Contact Lenses? 3 1.3 Methods of Myopia Control . 5 1.4 Soft Bifocal Contact Lenses and Peripheral Retinal Blur . 7 1.5 Purpose . 10 Chapter 2: Methods . 11 2.1 Human Subjects Proctection . 11 2.2 Recruitment . 11
Bat identification with gaussian process learning

sing a machine learning framework to construct an automatic, acoustic classiﬁer for bats is a research and conservation priority for this large and often vulnerable group of animals. Here I use a large dataset of 1,340 calls from 34 of the 40 European species to construct a Gaussian process classiﬁer. I obtain accuracies of 54.6% at the species level and 83.6% at the genus level for the two best Gaussian process classiﬁers. These classiﬁers are less accurate than nearest neighbour or neural network classiﬁers. A hierarchical Gaussian process classiﬁer is suspected to have accuracies comparable to neural networks — this would be a proﬁtable avenue for future research.
Wave propagation and time reversal in random waveguides.

This thesis concerns wave propagation and time reversal of waves in randomly perturbed waveguides. The study of wave propagation phenomena in random waveguides is an interesting subject with numerous domains of applications: for instance in telecommunication, underwater acoustics and geophysics. This thesis is composed of three chapters. In a first Chapter, we are interested in wave propagation in inhomogeneous oceanic waveguides, and we derive effective equations which model wave propagation in such media. These equations describe the role of the propagating, radiating, and evanescent modes, and allow us to quantify the radiative loss of energy in the ocean bottom during the propagation. In a second chapter we study pulse propagation and time-reversal refocusing in the perturbed waveguide model introduced in the first chapter. We get a description of the refocused wave which takes into account the radiative loss in the ocean bottom, and the evolution of the random fluctuations of the medium between the two steps of the time-reversal experiment. In a last chapter, we study time-reversal refocusing in a simple waveguide model. In this model we get superresolution phenomena by inserting a random section with low speed of propagation in the vicinity of the source, that is, we get more concentrated focal spots than in the homogeneous waveguides.
Fast Tuning-Free Distributed Algorithm for Solving the Network-Constrained Economic Dispatch

With the increasing penetration of distributed energy resources (DERs) and their participation in the electricity market, it becomes more desirable to apply distributed algorithms for resource allocation in order to address the resulting computational and communicational challenges. Most of the existing distributed algorithms for solving the network-constrained economic dispatch (NCED) problem require the tuning of certain auxiliary parameters. As a result, the robustness of these algorithms against the varieties in DERs is greatly undermined. In this paper, a new distributed algorithm, optimality condition consensus (OCC), is proposed to solve the NCED problem by using distributed power flow (DPF) and ratio consensus as fundamental tools. It inherits the advantages of existing distributed algorithms for the NCED problem but removes the need for parameter tuning to improve performance in practice. The effectiveness of the proposed distributed algorithm in terms of efficiency, scalability, and robustness is demonstrated through detailed case studies.
MULTIPLE CRITERIA OPTIMISATION APPROACH TO DERIVING PRIORITIES IN THE ANALYTIC HIERARCHY PROCESS

Summary: This paper is concerned with the problem of deriving priority vectors from pairwise comparison judgements, in the framework of the Analytic Hierarchy Process. With the exception of the traditional Eigenvalue method, all other prioritisation methods are based on minimisation of an objective function. However, a single objective function cannot encompass and satisfy all requirements about the quality of solutions. The multiple criteria character of the requirements necessitates a multiple objective approach to prioritisation, rather than optimisation of a single objective function. The paper proposes a new multi-criteria prioritisation approach, minimising the Euclidean norm and the number of rank violations, which measure the most important properties of the solutions. The numerical results show that the proposed two-objective method outperforms the known prioritisation methods, with respect to accuracy and rank preservation requirements.
Local-aware coupled network for hyperspectral image super-resolution

ABSTRACT Despite the unprecedented success of super-resolution (SR) development for natural images, achieving hyperspectral image (HSI) SR with rich spectral characteristics remains a challenging task. Typically, HSI SR is accomplished by fusing low-resolution HSI (LR HSI) with the corresponding high-resolution multispectral image (HR MSI). However, due to the significant spectral difference between MSI and HSI, it is difficult to retain the spatial characteristics of MSI during image fusion. In addition, the spectral response function (SRF) used for simulating MSI is often unknown or unavailable in hyperspectral remote sensing images, further complicating the problem. To address the above issues, a local-aware coupled network (LCNet) is proposed in this paper. In LCNet, the SRF and point spread function (PSF) are adaptively learned in the primary stage of the network to address the issue of unknown prior information. By coupling two reconstruction networks, LCNet effectively preserves both the texture details of MSI and the spectral characteristics of HSI. Furthermore, the spatial local-aware block selectively emphasizes the texture features of MSI. Experimental results on three publicly available HSIs demonstrate whether the proposed LCNet is superior to the state-of-the-art methods with respect to both stability and quality.
Kesadaran Keberlanjutan Siswa pada Aspek Pengetahuan Melalui Penerapan Program Education For Sustainable Developmnet (ESD) di Sekolah Dasar

Education for Sustainable Development (ESD) merupakan program yang diintegrasikan dalam pembelajaran IPA yang diimplementasikan dengan penerapan Green Behavior di sekolah dasar. Tujuan dari penelitian ini adalah untuk mengidentifikasi pengaruh penerapan ESD yang diintegrasikan dalam pembelajaran IPA yang diimplementasikan dengan penerapan Green Behavior terhadap Keterampilan berpikir kritis, disposisi berpikir kritis dan kesadaran keberlanjutan siswa sekolah dasar. Penelitian ini menggunakan metode Pre-Eksperimental dengan desain single group designs (one group pretest-posttest design). Sampel penelitian terdiri dari 47 siswa kelas 4 sekolah dasar, dimana dalam pelaksanaan siswa mengikuti pembelajaran dengan pendekatan ESD yang diikutsertakan dalam kegiatan Green Behavior yang dilakukan secara rutin di sekolah dan di rumah atau lingkungan sekitar. Data penelitian dikumpulkan menggunakan instrumen penelitian berupa instumen skala sikap kesadaran keberlanjutan. Hasil penelitian menunjukkan bahwa penerapan ESD yang diintegrasikan dalam pembelajaran IPA yang diimplementasikan dengan penerapan Green Behavior terdapat peningkatan yang signifikan terhadap kesadaran keberlanjutan siswa sekolah dasar. Penelitian ini berimplikasi pada keaktifan siswa dalam mengikuti proses pembelajaran juga memberikan kesempatan untuk siswa dalam menciptakan kondisi untuk berpikir kritis dan mempertimbangkan solusi ketika berhadapan dengan masalah keberlanjutan
Energy-Efficient Optimization Algorithm Based on Reconfigurable Intelligent Surface and Rate Splitting Multiple Access for 6G Multicell Communication System

Reconfigurable intelligent surface (RIS) is a technology that can intelligently control the signal transmission link to enhance the signal transmission quality. Rate splitting multiple access (RSMA) technology can effectively restrain the same channel interference between users and achieve better system performance by flexibly controlling the rate allocation strategy and beamforming strategy. In this article, we combine RSMA with RIS and propose an energy efficiency optimization algorithm for 6G multicell communication system. We first formulate an optimization problem which is to maximize energy efficiency of the system by jointly optimizing beamforming vectors of base stations (BSs), phase shifts matrix of RISs, and rate allocation matrix of users. Then, in order to solve this nonconvex optimization problem, we decouple the original problem into two subproblems: 1) beamforming optimization and 2) phase shifts optimization. For the former, we use the successive convex approximation (SCA) method to get the solution, and for the latter, we adopt semidefinite relaxation and exterior point methods to solve. In addition, we analyze the complexity of the proposed algorithm. Simulation result shows that compared with space division multiple access (SDMA) and nonorthogonal multiple access (NOMA), the proposed mechanism in this article can significantly improve system energy efficiency.
DETERMINATION OF OVULATION TIME IN GAYO MARES BASED ON IMAGE OF PREOVULATORY FOLLICLE GROWTH

This study aimed to obtain the ultrasound image of preovulatory follicle growth in gayo mares to predict the ovulation time. This study used three Gayo mares and one gayo stallion for teaser aged 5-10 years and weighed 200-250 kg. Preovulatory follicle growth was observed using transrectal ultrasonography which was performed when the dominant follicle size was ≥ 3.5 cm. Observations were made for 3 consecutive estrous periods on gayo mares at relatively the same time. The absence of previous follicular appearance and the presence of hypoechoic area in the same ovary indicate that ovulation has occurred. The result revealed that the diameter of preovulatory follicle of gayo mares was 4.02±0.32 to 5.05±0.04 cm. There was no significant follicle growth in the last 48 hours before ovulation and the preovulatory follicle shape altered from spherical to pearshape. Many small follicles (2 cm in diameter) were observed in peripheral area of the ovary indicated the recruitment of follicles which form the first follicular wave. To conclude, the alteration in the size and pearshape of preovulatory follicle and the recruitment of small follicles can be used as a marker to determine the time of ovulation on gayo mares.
Boundary feedback control in networks of freeway traffic

This article deals with the boundary feedback control of freeway traffic which is modelled by Aw-Rascle traffic flow model. By means of a Riemann invariants approach, we deduce stabilizing control laws of a single freeway segment. The stability condition is extended to a general class of hyperbolic systems which can describe freeway networks. The control laws design based on this conclusion are illustrated with two case studies: the freeway traffic network lies in the free-congestion mode or the congestion-free mode. The proof of the main stability theorem is based on a previous result from Li Ta-tsien concerning the dissipative boundary conditions of hyperbolic systems.
Comparison between the liquidus temperature and triple-point temperature of tin realized by heat pulse-based melting

In this work, the consistency of the heat pulse-based melting technique, which was used to determine the liquidus temperature of tin, was examined by comparing the liquidus temperatures of tin at 101 325 Pa and at the vapour pressure of tin (i.e. the triple-point temperature), both of which were realized by heat pulse-based melting. Periodic square wave-type temperature steps with an amplitude of 0.7 °C were generated in the isothermal region of the pressure-controlled loop heat pipe, and the tin sample, having a segregated impurity distribution established by the prior outward slow freezing, was melted by application of the temperature step-based heat pulses. The triple-point temperature was found to be lower than the liquidus temperature of tin at 101 325 Pa by 3.23 mK with an expanded measurement uncertainty of 0.24 mK (i.e. a coverage factor of k = 2), while the ideal temperature difference calculated from the ITS-90 given pressure coefficient (i.e. 3.3 × 10−8 K Pa−1) is about 3.34 mK. The difference between the measured temperature difference and ideal temperature difference was attributed to the incomplete removal of the gases in the tin triple-point cell. Overall, these results further corroborated the notion that the heat pulse-based melting technique was shown to yield results consistent with the prescription of the ITS-90, and to be a reliable method in terms of the realization of the fixed-point temperatures.
Post-Translational Modification of Drp1 is a Promising Target for Treating Cardiovascular Diseases

Mitochondria are essential for cell growth, fission, differentiation, and survival, particularly in undivided cells with high energy requirements, such as cardiomyocytes. The morphology and position of mitochondria change with the activity of mitochondrial fission proteins and mitochondrial fusion proteins. These regulatory mechanisms substantially affect cardiomyocyte energy supply and normal function. In mitochondrial fission, dynamin-related protein 1 (Drp1) is involved in the separation and degradation of damaged mitochondria, and accurately regulates mitochondrial renewal and number. Recent studies have revealed a variety of post-translational modification (PTMs) of Drp1, including phosphorylation, SUMOylation, acetylation, O-GlcNAcylation, and S-sulfhydration. These modifications ensure that Drp1 continues to function normally in various signaling pathways, by modulating its activity, stability, and subcellular localization. This article provides an overview of the relationship between Drp1 PTMs and cardiovascular diseases such as heart failure, myocardial infarction, and myocardial ischemia-reperfusion, and describes how these modifications can be targeted and regulated, to help guide cardiovascular disease treatment.
Design of an ultra-thin near-eye display with geometrical waveguide and freeform optics

Due to the worldwide portable devices and illumination technology trends, researches interest in laser diodes applications are booming in recent years. One of the popular and potential LDs applications is near-eye display used in VR/AR. An ideal near-eye display needs to provide high resolution, wide FOV imagery with compact magnifying optics, and long battery life for prolonged use. However, previous studies still cannot reach high light utilization efficiency in illumination and imaging optical systems which should be raised as possible to increase wear comfort. To meet these needs, a waveguide illumination system of near-eye display is presented in this paper. We focused on proposing a high efficiency RGB LDs light engine which could reduce power consumption and increase flexibility of mechanism design by using freeform TIR reflectors instead of beam splitters. By these structures, the total system efficiency of near-eye display is successfully increased, and the improved results in efficiency and fabrication tolerance of near-eye displays are shown in this paper.
Reciprocal classes of continuous time Markov Chains

In this work we study reciprocal classes of Markov walks on graphs. Given a continuous time reference Markov chain on a graph, its reciprocal class is the set of all probability measures which can be represented as a mixture of the bridges of the reference walks. We characterize reciprocal classes with two different approaches. With the first approach we found it as the set of solutions to duality formulae on path space, where the differential operators have the interpretation of the addition of infinitesimal random loops to the paths of the canonical process. With the second approach we look at short time asymptotics of bridges. Both approaches allow an explicit computation of reciprocal characteristics, which are divided into two families, the loop characteristics and the arc characteristics. They are those specific functionals of the generator of the reference chain which determine its reciprocal class. We look at the specific examples such as Cayley graphs, the hypercube and planar graphs. Finally we establish the first concentration of measure results for the bridges of a continuous time Markov chain based on the reciprocal characteristics.