Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Heston Model with Stochastic Volatility: Analytic Solutions and Complete Markets

We study the Heston model for pricing European options on stocks with stochastic volatility. This is a Black\--Scholes\--type equation whose spatial domain for the logarithmic stock price $x\in \RR$ and the variance $v\in (0,\infty)$ is the half\--plane $\HH = \RR\times (0,\infty)$. The {\it volatility\/} is then given by $\sqrt{v}$. The diffusion equation for the price of the European call option $p = p(x,v,t)$ at time $t\leq T$ is parabolic and degenerates at the boundary $\partial \HH = \RR\times \{0\}$ as $v\to 0+$. The goal is to hedge with this option against volatility fluctuations, i.e., the function $v\mapsto p(x,v,t)\colon (0,\infty)\to \RR$ and its (local) inverse are of particular interest. We prove that $\frac{\partial p}{\partial v}(x,v,t) \not= 0$ holds almost everywhere in $\HH\times (-\infty,T)$ by establishing the analyticity of $p$ in both, space $(x,v)$ and time $t$ variables. To this end, we are able to show that the Black\--Scholes\--type operator, which appears in the diffusion equation, generates a holomorphic $C^0$-semigroup in a suitable weighted $L^2$-space over $\HH$. We show that the $C^0$-semigroup solution can be extended to a holomorphic function in a complex domain in $\CC^2\times \CC$, by establishing some new a~priori weighted $L^2$-estimates over certain complex "shifts" of $\HH$ for the unique holomorphic extension. These estimates depend only on the weighted $L^2$-norm of the terminal data over $\HH$ (at $t=T$).
A method of measuring stacked objects volume based on laser sensing

Stacked objects volume measurement is now widely used in the fields of enterprise material management. It is significant to improve the efficiency of enterprise management and to reduce the cost of management and operation. The method based on laser sensing is one of the key methods to measure the stacked objects volume. This paper presents a laser sensing measurement method of stacked objects based on bottom plane extraction and real-time calibration. A calibration method for a laser scanning sensor and inertial measurement sensor is proposed. Three-dimensional reconstructions of stacked objects and volume calculations are carried out after acquisition and processing of point clouds. Volume measurement experiments of the single box and stacked boxes have been conducted respectively. Experimental results show that the measurement method is feasible and valid with good accuracy.
Stabilization and reference tracking for constrained switching systems: A predictive control approach

In this paper, the problem of designing a compensator capable of ensuring constraint satisfaction and tracking performance for a plant switching among a finite family of constrained linear time‐invariant configurations is investigated. We assume that switching occurrences are governed by a logical rule, which prescribes that any information pertaining to time or state dependence of the system mode transitions is unavailable to the controller side. The novelty of the proposed approach, based on the exploitation of command governor ideas in connection with the receding horizon control philosophy, relies on the capability to online manage unpredictable mode switching by jointly preserving uniformly ultimate boundedness and constraint fulfillment despite any admissible disturbance effect.
Direct-diode laser illumination and pointing at eye-safe SWIR wavelengths

Military, industrial, and medical applications have expressed interest in using ~1550nm laser diodes as efficient laser sources for reduced eye safety concerns, especially where free-space propagation is concerned. In addition, covert military applications, including pointers and illuminators, benefit from the spectral insensitivity of common sensor types to ~1.5um wavelengths. While more efficient than other sources, high-power broad-area laser diodes can sometimes require complex packaging and beam shaping/combining optics in order to meet requirements for brightness, uniformity, stray light, and environmental insensitivity. This paper will highlight and discuss a range of developments exploring laser diode components near 1.5um for free-space military applications, including broadarea high-power illuminators and highly-collimated pointers. Options for tailored beam conditioning and performance results will also be presented.
submitter : Semi-analytic off-axis X-ray source model

Spectral computed tomography (CT) systems are employed with energy-resolving photon counting detectors. Incorporation of a spectrally accurate x-ray beam model in image reconstruction helps to improve material identification and quantification by these systems. Using an inaccurate x-ray model in spectral reconstruction can lead to severe image artifacts, one of the extreme cases of this is the well-known beam-hardening artifacts. An often overlooked spectral feature of x-ray beams in spectral reconstruction models is the angular dependence of the spectrum with reference to the central beam axis. To address these factors, we have developed a parameterized semi-analytical x-ray source model in the diagnostic imaging range (30-120 kVp) by applying regression techniques to data obtained from Monte Carlo simulations (EGSnrc). This x-ray beam model is generalized to describe the off-axis spectral information within ±17o along θ (vertical direction), ±5o along (horizontal direction) of the central axis, and can be parameterized for specific x-ray tube models. Comparisons of our model with those generated by SpekCalc, TOPAS, and IPEM78 at central axis show good agreement (within 2 %). We have evaluated the model with experimental data collected with a small animal spectral scanner.
Spatio-Temporal Deformation of Kink-Breather to the (2+1)-Dimensional Potential Boiti-Leon-Manna-Pempinelli Equation

In the paper, the rational breather soliton and kink solitary wave solution of the (2+1)-dimensional PBLMP equation are obtained by adopting Hirota bilinear method and selecting different test functions. Furthermore, it has been found that the fusion and degeneration of the kink solitary wave occur when interaction between the rational breather soliton and the kink solitary wave happens. These phenomena are very helpful in researching soliton dynamical complexity in the higher dimensional systems.
As-grown domain structure in lithium tantalate with spatially nonuniform composition

ABSTRACT We investigated the as-grown domain structure in LiTaO3 with different nonuniform spatial distributions of deviation from stoichiometry. The as-grown structure formed due to cooling below Curie temperature after vapor transport equilibration (VTE) process. The formation of wide charged domain boundary consisting of smooth head-to-head and zig-zag tail-to-tail charged domain walls and flat neutral domain walls was observed. The mechanisms of domain structure formation due to phase transition and subsequent cooling were explained. The different type of charged domain walls in ferroelectrics attracts interest due to possibilities of its applications.
A Decentralized Information Marketplace Preserving Input and Output Privacy

Data-driven applications are engines of economic growth and essential for progress in many domains. The data involved is often of a personal nature. We propose a decentralized information marketplace where data held by data providers, such as individual users can be made available for computation to data consumers, such as government agencies, research institutes, or companies who want to derive actionable insights or train machine learning models with the data while (1) protecting input privacy, (2) protecting output privacy, and (3) compensating data providers for making their sensitive information available for secure computation. We enable this privacy-preserving data exchange through a novel and carefully designed combination of a blockchain that supports smart contracts and two privacy-enhancing technologies: (1) secure multi-party computations, and (2) robust differential privacy guarantees.
Efficient Solution of a Class of Universally Quantified Constraints

∀ (y1, . . . , ys)∈B1 . φ1(x1, . . . , xr, y1, . . . ys) ∧ · · · ∧ ∀ (y1, . . . , ys)∈Bn . φn(x1, . . . , xr, y1, . . . ys) where each of the B1, . , Bn is a box in R s and each of the φ1, . , φn is a Boolean combination of inequalities. Moreover, for each φ1, . , φn • at most one of the inequalities contains a variable from y1, . , ys (all of them may contain x1, . . . , xr), and • this one inequality contains the variables y1, . , ys only linearly (but may contain x1, . . . , xr nonlinearly). Such an algorithm can, for example, be used to
Effects of Short-Term Presalting and Salt Level on the Development of Pink Color in Cooked Chicken Breasts

The objective of this study was to determine the effects of short-term presalting on pink color and pigment characteristics in ground chicken breasts after cooking. Four salt levels (0%, 1%, 2%, and 3%) were presalted and stored for 0 and 3 d prior to cooking. Cooking yield was increased as salt level was increased. However, no significant differences in pH values or oxidation reduction potential (ORP) of cooked chicken breasts were observed. Cooked products with more than 2% of salt level had less redder (lower CIE a* value) on day 3 than on those on day 0. As salt level was increased to 2%, myoglobin was denatured greatly. Myoglobin denaturation was leveled off when samples had 3% of salt. With increasing salt levels, residual nitrite contents were increased while nitrosyl hemochrome contents were decreased. These results demonstrate that salt addition to a level of more than 2% to ground meat may reduce the redness of cooked products and that presalting storage longer than 3 d should be employed to develop a natural pink color of ground chicken products when less than 1% salt is added to ground chicken meat.
Propagation of trust and distrust

A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.
Analysis of chaotic and regular motion of an autoparametric system by recurrence plots application

The purpose of this study is to use recurrence plot s to investigate the dynamics of an autoparametric system with an attached pendulum. Recurrence is a f undamental property of dynamical systems, which can be exploited to characterise the system’s behaviour in phase space. The Recurrence Plots method (RPs) and Recurrence Quantification Analysis (RQA) is used fo r analysis of relatively short time series for detection different types of behaviour including chaotic mot ions of the considered nonlinear system.
Community Detection with Graph Neural Networks

We study data-driven methods for community detection in graphs. This estimation problem is typically formulated in terms of the spectrum of certain operators, as well as via posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the Stochastic Block Model, recent research has unified these two approaches, and identified both statistical and computational signal-to-noise detection thresholds. We embed the resulting class of algorithms within a generic family of graph neural networks and show that they can reach those detection thresholds in a purely data-driven manner, without access to the underlying generative models and with no parameter assumptions. The resulting model is also tested on real datasets, requiring less computational steps and performing significantly better than rigid parametric models.
The Effect of Macroeconomic Variables On the Saudi Stock Market

This study aimed at exploring the effect of macroeconomic variables on the activity of the Saudi stock market for the period 1997-2017. Macroeconomic variables were: GDP, interest rate on time deposits, inflation rate. The variables of the Saudi stock market activity were: stock price index, market value of shares, value of traded shares. To achieve this objective, the researcher used the ARDL model for the self-regression of the lagged distributed time gaps. The most important results of the research are: The effect of macroeconomic variables on the performance indicators in the Saudi stock market is not important in the short term and is statistically significant in the long term according to the proposed models, so investors in this market can rely on macroeconomic variables in Predict the movement of the stock market and predict long-term profits and losses.
Prediction and error in multi-stage models for spread of aquatic non-indigenous species 1 2

20 Aim 21 Predictions of spread of nonindigenous species allow for greater efficiency in 22 managing invasions by targeting areas for preventative measures. The invasion 23 sequence is a useful concept in predictions of spread as it allows us to test hypotheses 24 about the transport and establishment of propagules in novel habitats. Our aims are 25 two-fold: 1) to develop and validate multi-stage invasion models for the introduced 26 fishhook waterflea, Cercopagis pengoi, and 2) assess how variability in the transport 27 patterns of the propagules influences the accuracy and spatial extent for predictions of 28 spread. 29 Location 30 New York State 31 Methods 32 We developed a two-stage model for the spread of C. pengoi. First, we 33 developed a stochastic gravity model for dispersal based on surveys of recreational 34 boat traffic in New York State as a proxy for propagule pressure. We then modeled the 35 probability of establishment based on predicted levels of propagule pressure and 36 measures of lakes’ physicochemistry. In addition, we used Monte Carlo simulations 37 based on the gravity model to propagate variability in boater traffic through the 38 establishment model to assess how uncertainty in dispersal influenced predictions of 39 spread. 40 Results 41
Journal of Emerging Trends in Computing and Information Sciences a Survey of Real-time Fault-detection Schemes in Mobile Computing

The recent past two decades have witnessed a phenomenal technological and architectural evolution in wireless communication. However, the development has also created certain challenges under resolution, one of which is fault detection in Mobile Computing. Wireless protocols must have the ability to detect faults in the mobile computing environment and respond in real-time. Inability of wireless protocols to detect and respond to faults in real-time impacts quality of service and can lead to consequential financial loss. Efficient and quick response to faults in the long run affects the overall system performance. An appropriate detector of factors leading to fault can avoid degraded quality-of-service. Real-time fault detection in mobile computing enables the system to respond to fault indicating situations within certain time constraints. This paper surveys the literature on fault detection and tolerance schemes used in mobile networks. Our survey reveals that, most of the currently existing algorithms do not fulfill the needed real-time constraints to maintain a certain service quality.
New High-order Methods using Gaussian Processes for Computational Fluid Dynamics Simulations

We present an entirely new class of high-order numerical algorithms for computational fluid dynamics simulations. The new method is based on the Gaussian Processes (GP) modeling that generalizes the Gaussian probability distribution. Our approach is to adapt the idea of the GP prediction technique that utilizes the covariance kernel functions, and use the GP prediction to interpolate/reconstruct a high-order approximations for solving hyperbolic PDEs. We propose the GP high-order method as a new class of numerical high-order formulations, alternative to the conventional polynomial-based approaches.
Journal of Emerging Trends in Computing and Information Sciences Wireless Senor Networks Health Monitoring: Trends and Challenges

Wireless sensor network has undergone tremendous development in the last one decade. The reduced size of the sensors coupled with enhanced capability made the dream of unobtrusive monitoring closer to reality. Sensors are cheaper and readily available in the market in these days. These sensors, when deployed on the human body, forms a Body Area Network (BAN) that can detect and forward various physiological parameters to a central node. BAN is under focus by most of the researchers and different applications are underway. BAN is confronted with various challenges due to which it is not yet accepted by majority of people. This paper carries out in depth analysis of technical and functional requirements of BAN. Research challenges to BAN like scalability, power consumption, data rate etc. along with technologies for transferring the data from BAN to central node have also been discussed in brief.
OPTICAL CHARACTER RECOGNITION TECHNIQUES IN MAIL SORTING: A REVIEW OF ALGORITHMS

A study of Optical Character Recognition (OCR) techniques employed in automatic mail sorting equipment is presented. Methods and algorithms for image preprocessing, character recognition, and contextual postprocessing are discussed and compared. The objective of the study is to provide a background in the state-of-the-art of this equipment as the first element in a search for techniques to significantly improve the capabilities of postal address recognition.
University Ofhawaìi at Mānoa Department of Economics Working Paper Series the Effect of Plan B on Teen Abortions: Evidence from the 2006 Fda Ruling by the Effect of Plan B on Teen Abortions: Evidence from the 2006 Fda Ruling

An increase in the availability of emergency contraception (EC) may lead to a decrease in the abortion rate. The 2006 FDA ruling, which relaxed the prescription requirement for EC for women 18 and older, allows us to apply the difference-indifference methodology on the age-by-year-by-state abortion data to test this hypothesis. Contrary to the literature, we find a moderate reduction in abortion rates among women aged 18 and 19 after 2006 in states that were affected by the change, compared to changes in the control group. These results are robust in a number of specifications and pass the event specification test. (J13, J18)
University Ofhawaìi at Mānoa Department of Economics Working Paper Series Intra-industry Reallocations and Long-run Impacts of Environmental Regulations Intra-industry Reallocations and Long-run Impacts of Environmental Regulations 1

We investigate the long-run impact of environmental regulations on the intra-industry distribution of firm-level productivity and the resulting aggregate variables. In a general-equilibrium model that accounts for endogenous entry/exit of heterogeneous firms, neither the average productivity of firms nor the mass of firms is independent of the choice of policy instruments (i.e. emissions tax vs. emissions trading) or permit allocation rules. The equilibrium price of permits under emissions trading is lower than the emissions tax rate that would support the same aggregate emissions. An incomplete emissions market results in a net increase in combined aggregate emissions.
Treatment Adherence of Patients with Rheumatoid Arthritis during COVID-19 Pandemic

India is no exception to the economic setback due to coronavirus disease 2019 (COVID-19). Loss of jobs and income dramatically impacts the health care cost of chronic disease management. Rheumatoid arthritis is a chronic condition with a high-cost implication. With the outbreak of COVID-19, there is uncertainty about continuing immunosuppressive therapy for rheumatoid arthritis for several reasons. In this milieu, we undertook a prospective observational study to observe the use of Janus Kinase (JAK) inhibitors in a hospital-based rheumatology service in Eastern India during the pandemic period (21 March 2020 to 31 July 2020). Forty-two patients with rheumatoid arthritis were receiving treatment with JAK inhibitors. Twenty-four patients visited the Outpatient Department (OPD) during the COVID-19 pandemic. All of them were COVID-negative, but few of the patients had influenza-like symptoms. Patients faced up to a 25% reduction in their annual income during the COVID-19 pandemic. Of 24 patients, four patients had stopped treatment with JAK inhibitors owing to financial constraints or initial non-availability of medications during the lockdown. In this study, adherence to JAK inhibitors was substantially high even in the face of income curtailment during the COVID-19 pandemic.
Model compression as constrained optimization, with application to neural nets. Part I: general framework

Compressing neural nets is an active research problem, given the large size of state-of-the-art nets for tasks such as object recognition, and the computational limits imposed by mobile devices. We give a general formulation of model compression as constrained optimization. This includes many types of compression: quantization, low-rank decomposition, pruning, lossless compression and others. Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization. This results in a "learning-compression" algorithm, which alternates a learning step of the uncompressed model, independent of the compression type, with a compression step of the model parameters, independent of the learning task. This simple, efficient algorithm is guaranteed to find the best compressed model for the task in a local sense under standard assumptions. We present separately in several companion papers the development of this general framework into specific algorithms for model compression based on quantization, pruning and other variations, including experimental results on compressing neural nets and other models.
An improved adaptive dynamic surface control approach for uncertain nonlinear systems

Dynamic surface control (DSC) was developed to eliminate the “explosion of complexity” problem in backstepping procedure. However, as demonstrated in this paper, the obtained results by the existing DSC technique are somewhat conservative, which may pose difficulties in system debugging for realistic applications. This work addresses a modification that yields an improved adaptive DSC approach for tracking control of a class of semi‐strict feedback systems. The new method introduces nonlinear adaptive filters instead of the first‐order low pass ones to avoid repeatedly differentiating the virtual control signals. Meanwhile, novel flat zone introduced Lyapunov functions, which have dead zones in the prespecified neighborhood of the origin, are employed to design and analyze the improved robust adaptive control law. As a result, the developed control scheme exhibits three distinct features in comparison with the existing DSC strategy as follows: (1) global rather than semiglobal tracking is achieved even in the presence of nonlinear function nonlinearities; (2) the ultimate tracking accuracy can be exactly known before the controller is implemented; and (3) the ranges of the design parameters to guarantee the closed‐loop stability and ultimate tracking accuracy can be completely determined a priori, and the design parameters can be freely chosen from the feasible ranges to improve the control performance. Finally, two examples are presented to confirm the effectiveness of the established approach.