Miracle-dz/UniFAR-FTUs huggingface dataset:
PRICING DISCRETELY MONITORED BARRIER OPTIONS AND DEFAULTABLE BONDS IN LÉVY PROCESS MODELS: A FAST HILBERT TRANSFORM APPROACH

This paper presents a novel method to price discretely monitored single‐ and double‐barrier options in Lévy process‐based models. The method involves a sequential evaluation of Hilbert transforms of the product of the Fourier transform of the value function at the previous barrier monitoring date and the characteristic function of the (Esscher transformed) Lévy process. A discrete approximation with exponentially decaying errors is developed based on the Whittaker cardinal series (Sinc expansion) in Hardy spaces of functions analytic in a strip. An efficient computational algorithm is developed based on the fast Hilbert transform that, in turn, relies on the FFT‐based Toeplitz matrix–vector multiplication. Our method also provides a natural framework for credit risk applications, where the firm value follows an exponential Lévy process and default occurs at the first time the firm value is below the default barrier on one of a discrete set of monitoring dates.
3D Active Brownian Motion of Single Dust Particles Induced by a Laser in a DC Glow Discharge

The active Brownian motion of single dust particles of various types in the 3D electrostatic DC discharge trap under the action of laser radiation is studied experimentally. Spherical dust particles with a homogeneous surface, as well as Janus particles, are used in the experiment. The properties of the active Brownian motion of all types of dust particles are studied. In particular, the 3D analysis of trajectories of microparticles is carried out, well as an analysis of their root mean square displacement. The mean kinetic energy of motion of the dust particle of various types in a 3D trap is determined for different laser powers. Differences in the character of active Brownian motion in electrostatic traps with different spatial dimensions are found.
Effect of dislocations on electron mobility in AlGaN/GaN and AlGaN/AlN/GaN heterostructures

AlxGa1−xN/GaN (x = 0.06, 0.12, 0.24) and AlGaN/AlN/GaN heterostructures were grown on 6 H-SiC, GaN-on-sapphire, and free-standing GaN, resulting in heterostructures with threading dislocation densities of ∼2 × 1010, ∼5 × 108, and ∼5 × 107 cm−2, respectively. All growths were performed under Ga-rich conditions by plasma-assisted molecular beam epitaxy. Dominant scattering mechanisms with variations in threading dislocation density and sheet concentration were indicated through temperature-dependent Hall measurements. The inclusion of an AlN interlayer was also considered. Dislocation scattering contributed to reduced mobility in these heterostructures, especially when sheet concentration was low or when an AlN interlayer was present.
Duality and Derivative Pricing

The aim of this work is to use a duality approach to study the pricing of derivatives depending on two stocks driven by a bidimen- sional Levy process. The main idea is to apply Girsanov's Theorem for Levy processes, in order to reduce the posed problem to the pricing of a one Levy driven stock in an auxiliary market, baptized as \dual market". In this way, we extend the results obtained by Gerber and Shiu (1996) for two dimensional Brownian motion. Also we examine an existing relation between prices of put and call options, of both the European and the American type. This relation, based on a change of numeraire corresponding to a change of the probability measure through Girsanov's Theorem, is called put{call duality. It includes as a particular case, the relation known as put{call symmetry. Necessary and su-cient conditions for put{call symmetry to hold are obtained, in terms of the triplet of predictable characteristic of the Levy process.
Simultaneous selection and estimation for the some zeros family of normal models

Consider the canonical form of the Stein problem of estimating a vector θ on the basis of the observed vector T which is N(θ,I)-distributed. Suppose also that some of the components of θ may be zero and that we wish to simultaneously select and estimate the non-zero components. Although existing Stein estimators perform very well in terms of total squared error loss risk, they are not attuned well to this selection requirement. We introduce two new estimators which effectively address the selection requirement while also having risk performance reasonably comparable to that of Stein estimators.
Estimates of surface normal and curvature, reconstruction of continuum surface force model, and elimination of spurious currents

A reconstructed continuum surface force (CSF) model is presented for the volume‐of‐fluid (VOF) method in numerical simulations of surface tension‐driven flow. The gradient of VOF function yielded by the one‐direction difference (ODD) algorithm has second‐order accuracy in one direction and exhibits unit pulse function in the other direction. Use of our ODD algorithm for surface normal and curvature improves the accuracy of surface tension force estimate and meets the necessary condition of a circular droplet free of spurious currents. The immersed length, a physical depth of grid cell center introduced in this paper, is computed from VOF function and surface orientation. It is chosen instead of VOF function as weight parameters in the evaluations of density and volumetric surface tension force between interfacial grid cells. Grid cells and control cells are classified into interfacial, sublayer, and interior cells according to their distances to interface. Surface tension is thus constructed within interfacial or sublayer cells. The numerical approach is modified so that flow pressure solutions are sought only at those immersed grid cell centers. The transitional region from one fluid to the other is compressed. Spurious currents by no means grow on free surface in the numerical simulations using our algorithms and reconstructed CSF model. The simulation results agree with the Laplace pressure jump across a static circular free surface or experimental observations. Copyright © 2014 John Wiley & Sons, Ltd.
Investigation of distinct gene expression profile patterns that can improve the classification of intermediate-risk prognosis in AML patients

Background: Acute myeloid leukemia (AML) is a heterogeneous type of blood cancer that generally affects the elderly. AML patients are categorized with favorable-, intermediate-, and adverse-risks based on an individual’s genomic features and chromosomal abnormalities. Despite the risk stratification, the progression and outcome of the disease remain highly variable. To facilitate and improve the risk stratification of AML patients, the study focused on gene expression profiling of AML patients within various risk categories. Therefore, the study aims to establish gene signatures that can predict the prognosis of AML patients and find correlations in gene expression profile patterns that are associated with risk groups. Methods: Microarray data were obtained from Gene Expression Omnibus (GSE6891). The patients were stratified into four subgroups based on risk and overall survival. Limma was applied to screen for differentially expressed genes (DEGs) between short survival (SS) and long survival (LS). DEGs strongly related to general survival were discovered using Cox regression and LASSO analysis. To assess the model’s accuracy, Kaplan-Meier (K-M) and receiver operating characteristic (ROC) were used. A one-way ANOVA was performed to assess for differences in the mean gene expression profiles of the identified prognostic genes between the risk subcategories and survival. GO and KEGG enrichment analyses were performed on DEGs. Results: A total of 87 DEGs were identified between SS and LS groups. The Cox regression model selected nine genes CD109, CPNE3, DDIT4, INPP4B, LSP1, CPNE8, PLXNC1, SLC40A1, and SPINK2 that are associated with AML survival. K-M illustrated that the high expression of the nine-prognostic genes is associated with poor prognosis in AML. ROC further provided high diagnostic efficacy of the prognostic genes. ANOVA also validated the difference in gene expression profiles of the nine genes between the survival groups, and highlighted four prognostic genes to provide novel insight into risk subcategories poor and intermediate-poor, as well as good and intermediate-good that displayed similar expression patterns. Conclusion: Prognostic genes can provide more accurate risk stratification in AML. CD109, CPNE3, DDIT4, and INPP4B provided novel targets for better intermediate-risk stratification. This could enhance treatment strategies for this group, which constitutes the majority of adult AML patients.
Channel direction, effective field, and temperature dependencies of hole mobility in (110)-oriented Ge-on-insulator p-channel metal-oxide-semiconductor field-effect transistors fabricated by Ge condensation technique

This paper experimentally reports the channel direction (θ), effective field (Eeff), and temperature (T) dependencies of hole mobility in (110)-oriented 12-nm-thick accumulation mode Ge-on-insulator (GOI) p-channel metal-oxide-semiconductor field-effect transistors (pMOSFETs) fabricated by the Ge condensation technique. It is found that, the hole mobility on (110)-oriented GOI surfaces increases with the channel direction tilted from ⟨100⟩ to ⟨110⟩ direction, in contrast to (100)-oriented conventional GOI surfaces. By low temperature measurements, the extracted phonon-limited mobilities (μph) of (110)-oriented GOI surfaces along ⟨110⟩ direction occupy 2.1 and 7.1 of enhancement against (100)-oriented GOI and Si surfaces, respectively, at any T. Through physical insights into the present analyses, μph dependence on T−1.8 suggests the suppression of intervalley phonon scattering at low T as in Si. Also, μph is found to increase with Eeff, which can be regarded as an inherent property of hole mobility on (11.
Statistical mechanics of a fluid drop

We obtain a statistical mechanical expression for the surface tension of a drop, involving the direct correlation function, by extending the work on the planar liquid-vapour interface, both from the fluctuation theory approach of Triezenburg & Zwanzig (Phys. Rev. Lett. 28, 1183 (1972)) and from the method of Wertheim (J. chem. Phys. 65, 2377 (1976)) based on a spectral analysis of the density-density correlation function. The result does not apply beyond the leading-order difference from the planar limit.
Large particle segregation, transport and accumulation in granular free-surface flows

Particle size segregation can have a significant feedback on the motion of many hazardous geophysical mass flows such as debris flows, dense pyroclastic flows and snow avalanches. This paper develops a new depth-averaged theory for segregation that can easily be incorporated into the existing depth-averaged structure of typical models of geophysical mass flows. The theory is derived by depth-averaging the segregation-remixing equation for a bi-disperse mixture of large and small particles and assuming that (i) the avalanche is always inversely graded and (ii) there is a linear downslope velocity profile through the avalanche depth. Remarkably, the resulting ‘large particle transport equation’ is very closely related to the segregation equation from which it is derived. Large particles are preferentially transported towards the avalanche front and then accumulate there. This is important, because when this is combined with mobility feedback effects, the larger less mobile particles at the front can be continuously shouldered aside to spontaneously form lateral levees that channelize the flow and enhance run-out. The theory provides a general framework that will enable segregation-mobility feedback effects to be studied in detail for the first time. While the large particle transport equation has a very simple representation of the particle size distribution, it does a surprisingly good job of capturing solutions to the full theory once the grains have segregated into inversely graded layers. In particular, we show that provided the inversely graded interface does not break it has precisely the same solution as the full theory. When the interface does break, a concentration shock forms instead of a breaking size segregation wave, but the net transport of large particles towards the flow front is exactly the same. The theory can also model more complex effects in small-scale stratification experiments, where particles may either be brought to rest by basal deposition or by the upslope propagation of a granular bore. In the former case the resulting deposit is normally graded, while in the latter case it is inversely graded. These completely opposite gradings in the deposit arise from a parent flow that is inversely graded, which raises many questions about how to interpret geological deposits.
Small-scale structure of spacetime as the origin of the gravitational constant

We suggest a means of incorporating the Planck length as a fundamental constant determined by the structure of spacetime. In this scheme the spacetime symmetry group is taken as the de Sitter group with radius of curvature proportional to the Planck length, but we argue that the effects of this curvature are not apparent at elementary-particle length scales. To make the connection with gravity we present a formulation of the gravitational interaction as the gauge theory of the de Sitter group. We obtain an action containing the Einstein-Cartan action, but in which the dimensional gravitational constant G appears naturally as the consequence of the commutation relation of the de Sitter group. We also find a cosmological term, higher-derivative couplings of the gravitational field, and a propagating torsion field, which we discuss.
A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.
Molecular Signature of Biological Aggressiveness in Clear Cell Sarcoma of the Kidney (CCSK)

Clear cell sarcoma of the kidney (CCSK) is a rare pediatric renal tumor with a worse prognosis than Wilms’ tumor. Although recently, BCOR internal tandem duplication (ITD) has been found as a driver mutation in more than 80% of cases, a deep molecular characterization of this tumor is still lacking, as well as its correlation with the clinical course. The aim of this study was to investigate the differential molecular signature between metastatic and localized BCOR-ITD-positive CCSK at diagnosis. Whole-exome sequencing (WES) and whole-transcriptome sequencing (WTS) were performed on six localized and three metastatic BCOR-ITD-positive CCSKs, confirming that this tumor carries a low mutational burden. No significant recurrences of somatic or germline mutations other than BCOR-ITD were identified among the evaluated samples. Supervised analysis of gene expression data showed enrichment of hundreds of genes, with a significant overrepresentation of the MAPK signaling pathway in metastatic cases (p < 0.0001). Within the molecular signature of metastatic CCSK, five genes were highly and significantly over-expressed: FGF3, VEGFA, SPP1, ADM, and JUND. The role of FGF3 in the acquisition of a more aggressive phenotype was investigated in a cell model system obtained by introducing the ITD into the last exon of BCOR by Crispr/Cas9 gene editing of the HEK-293 cell line. Treatment with FGF3 of BCOR-ITD HEK-293 cell line induced a significant increase in cell migration versus both untreated and scramble cell clone. The identification of over-expressed genes in metastatic CCSKs, with a particular focus on FGF3, could offer new prognostic and therapeutic targets in more aggressive cases.
High Fat-to-Muscle Ratio Was Associated with Increased Clinical Severity in Patients with Abdominal Trauma

Overweight and moderate obesity confer a survival benefit in chronic diseases such as coronary artery disease and chronic kidney disease, which has been termed the “obesity paradox”. However, whether this phenomenon exists in trauma patients remains controversial. We performed a retrospective cohort study in abdominal trauma patients admitted to a Level I trauma center in Nanjing, China between 2010 and 2020. In addition to the traditional body mass index (BMI) based measures, we further examined the association between body composition-based indices with clinical severity in trauma populations. Body composition indices including skeletal muscle index (SMI), fat tissue index (FTI), and total fat-to-muscle ratio (FTI/SMI) were measured using computed tomography. Our study found that overweight was associated with a four-fold risk of mortality (OR, 4.47 [95% CI, 1.40–14.97], p = 0.012) and obesity was associated with a seven-fold risk of mortality (OR, 6.56 [95% CI, 1.07–36.57], p = 0.032) compared to normal weight. Patients with high FTI/SMI had a three-fold risk of mortality (OR, 3.06 [95% CI, 1.08–10.16], p = 0.046) and double the risk of an intensive care unit length of stay ≥ 5 d (OR, 1.75 [95% CI, 1.06–2.91], p = 0.031) compared to patients with low FTI/SMI. The obesity paradox was not observed in abdominal trauma patients, and high FTI/SMI ratio was independently associated with increased clinical severity.
Torsional vibration of coaxially segmented, tangentially polarized piezoelectric ceramic tubes

Piezoelectric ceramic materials are often used in ultrasonic and underwater sound transducers by stacking a number of identical pieces together. In this paper, the theoretical behavior is derived for a piezoelectric ceramic cylindrical tube consisting of any number of tangentially polarized coaxial segments. The resonance frequency equation of the torsional tube is obtained and the electromechanical equivalent circuits are developed that greatly simplify and improve the analysis of composite resonators in transducer design. A new parameter which is defined as the cross‐sectional torsional coefficient is presented. The relations between the theoretical parameters and the experimentally measured quantities of the segmented electromechanical system in torsional vibration are obtained so that the material parameters can be evaluated even though the lateral dimensions are large relative to the length of each individual segment and the analysis of the vibrational characteristics of the torsional transducer can .
LARGE EDDY SIMULATION OF A CONFINED COAXIAL JET WITH SWIRL AND HEAT RELEASE

Recent progress in large eddy simulation of complex reacting flows is presented. Topics include: the governing equations for low Mach number combustion, assumed PDF subgrid-scale models, subgrid-scale modeling for the variance and dissipation rate of a conserved scalar, inflow and exit boundary conditions for confined swirling flows, simulation results for isothermal swirling flow with experimental validation, and results from a preliminary reacting flow simulation.
Occupancy-Based Energy Consumption Estimation Improvement through Deep Learning

The energy consumed in buildings constitutes more than half of the total electricity consumption and is highly correlated with the number of occupants; therefore, it is necessary to use occupancy information in energy consumption analysis. However, the number of occupants may not be accurate owing to measurement errors caused by various factors, such as the locations of sensors or cameras and the communication environment. In this study, occupancy was measured using an object recognition camera, the number of people was additionally collected by manual aggregation, measurement error in occupancy count was analyzed, and the true count was estimated using a deep learning model. The energy consumption based on occupancy was predicted using the measured and estimated values. To this end, deep learning was used to predict energy consumption after analyzing the correlation between occupancy and energy consumption. Results showed that the performance of occupancy estimation was 1.9 based on RMSE, which is a 71.1% improvement compared to the original occupancy sensing. The RMSE of predicted energy consumption based on the estimated occupancy was 56.0, which is a 5.2% improvement compared to the original energy estimation.
Consumer sentiment analysis with aspect fusion and GAN‐BERT aided adversarial learning

The sentiment analysis (SA) from the user‐generated reviews is the latest research topic in natural language processing. Nowadays, the extraction of consumer sentiment from the content of the consumer reviews is getting much attention because of its importance in understanding consumers' experiences regarding services or products. Consumer sentiment may be helpful for both consumers and organizations; a consumer can refer to previous consumers' feedback while making their purchase decisions, and organizations can use it in service improvements. For the consumer SA, this article proposed the BERT‐GAN model with review aspect fusion, which improves the fine‐tuning performance of the BERT model by introducing semi‐supervised adversarial learning. For our objective, we extracted various service aspects from consumer reviews and fused them with the word sequences before feeding them into the model. That helps in incorporating aspect representation as well as position information in context with the sentences. Our results analysis and their demonstration show the contribution of the presented model in terms of accuracy compared with the existing models found in the previous work.