Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly efficient pricing of exotic derivatives under mean-reversion, jumps and stochastic volatility

The pricing of exotic derivatives continues to attract much attention from academics and practitioners alike. Despite the overwhelming interest, the task of finding a robust methodology that could derive closed-form solutions for exotic derivatives remains a di cult challenge. In addition, the level of sophistication is greatly enhanced when options are priced in a more realistic framework. This includes, but not limited to, utilising jump-di↵usion models with mean-reversion, stochastic volatility, and/or stochastic jump intensity. More pertinently, these inclusions allow the resulting asset price process to capture the various empirical features, such as heavy tails and asymmetry, commonly observed in financial data. However, under such a framework, the density function governing the underlying asset price process is generally not available. This leads to a breakdown of the classical risk-neutral option valuation method via the discounted expectation of the final payo↵. Furthermore, when an analytical expression for the option pricing formula becomes available, the solution is often complex and in semi closed-form. Hence, a substantial amount of computational time is required to obtain the value of the option, which may not satisfy the e ciency demanded in practice. Such drawbacks may be remedied by utilising numerical integration techniques to price options more e ciently in the Fourier domain instead, since the associated characteristic functions are more readily available. This thesis is concerned primarily with the e cient and accurate pricing of exotic derivatives under the aforementioned framework. We address the research opportunity by exploring the valuation of exotic options with numerical integration techniques once the associated characteristic functions are developed. In particular, we advocate the use of the novel Fourier-cosine (COS) expansions, and the more recent Shannon wavelets inverse Fourier technique (SWIFT). Once the option prices are obtained, the e ciency of the two techniques are benchmarked against the widely-acclaimed fast Fourier transform (FFT) method. More importantly, we perform extensive numerical experiments and error analyses to show that, under our proposed framework, not only is the COS and SWIFT methods more e cient, but are also highly accurate with exponential rate of error convergence. Finally, we conduct a set of sensitivity analyses to evaluate the
Artificial intelligence algorithms for cyberspace security applications: a technological and status review

Three technical problems should be solved urgently in cyberspace security: the timeliness and accuracy of network attack detection, the credibility assessment and prediction of the security situation, and the effectiveness of security defense strategy optimization. Artificial intelligence (AI) algorithms have become the core means to increase the chance of security and improve the network attack and defense ability in the application of cyberspace security. Recently, the breakthrough and application of AI technology have provided a series of advanced approaches for further enhancing network defense ability. This work presents a comprehensive review of AI technology articles for cyberspace security applications, mainly from 2017 to 2022. The papers are selected from a variety of journals and conferences: 52.68% are from Elsevier, Springer, and IEEE journals and 25% are from international conferences. With a specific focus on the latest approaches in machine learning (ML), deep learning (DL), and some popular optimization algorithms, the characteristics of the algorithmic models, performance results, datasets, potential benefits, and limitations are analyzed, and some of the existing challenges are highlighted. This work is intended to provide technical guidance for researchers who would like to obtain the potential of AI technical methods for cyberspace security and to provide tips for the later resolution of specific cyberspace security issues, and a mastery of the current development trends of technology and application and hot issues in the field of network security. It also indicates certain existing challenges and gives directions for addressing them effectively.
Measurements with doubly-charmed hadrons in LHCb

The LHCb experiment has been collecting large amounts of charm data, making it possible to perform precise measurements and search for rare particles. In 2017, the LHCb collaboration reported the first observation of the doubly charmed baryon, the $ \Xi _{cc}^{ + + } $. In 2018, the first measurement of the $ \Xi _{cc}^{ + + } $ lifetime, using the same decay and dataset, was published. This paper summarizes the content of both analyses, as reported in the 9th International Workshop on Charm Physics, in Novosibirsk, Russia.
Cancer Chemoprevention: Current State of the Art

The aim of cancer chemoprevention is disruption or delay of the molecular pathways that lead to carcinogenesis. Chemopreventive blocking and/or suppressing agents disrupt the molecular mechanisms that drive carcinogenesis such as DNA damage by reactive oxygen species, increased signal transduction to NF-κB, epigenomic deregulation, and the epithelial mesenchymal transition that leads to metastatic progression. Numerous dietary phytochemicals have been observed to inhibit the initiation phase of carcinogenesis, and therefore are useful in primary chemoprevention. Moreover, phytochemicals are capable of interfering with the molecular mechanisms of metastasis. Likewise, numerous synthetic compounds are relevant and clinically viable as chemopreventive agents during the fundamental stages of carcinogenesis. While molecularly targeted anti-cancer therapies are in constant stages of development, superior patient outcomes are observed if carcinogenic processes are prevented altogether. This article reviews the role of chemopreventive compounds in inhibition of cancer initiation and their ability to reduce cancer progression.
Robust Self-Triggered MPC With Adaptive Prediction Horizon for Perturbed Nonlinear Systems

This paper proposes a robust self-triggered model predictive control (MPC) with an adaptive prediction horizon scheme for constrained nonlinear discrete-time systems subject to additive disturbances. At each triggering instant, the controller provides an optimal control sequence by solving an optimal control problem (OCP), and at the same time, determines the next triggering time and prediction horizon. By implementing the algorithm, the average sampling frequency is reduced and the prediction horizon is adaptively decreased as the system state approaches a terminal region. Meanwhile, an upper bound of performance loss is guaranteed when compared with a nominal periodic sampling MPC. Feasibility of the OCP and stability of the closed-loop system are established. Simulation results verify the effectiveness of the scheme.
Window in the sea

analyze blood for several sex hormones to help determine the stage in the fish's reproductive cycle. A shrimp (Penaeus aztecus), one of three species commercially harvested in the Gulf, forages at night among epiphytic algae on seagrasses. A bed of the common eastern oyster, Crassostrea virginica.
Selected Papers

We propose an algorithm that improves the quality of intermediate-view images obtained by mapping the average color of multiple-view images onto a visual hull. The algorithm estimates a real image from an intermediate-view image using example-based super-resolution. Estimation is performed using relationships between two kinds of training images: an ideal image whose quality is as good as a real image and an intermediate view image viewed from the same viewpoint. High frequencies of the real image of an intermediate-view are estimated using the relationship between high frequencies of an ideal image and those of a corresponding intermediate-view image. Experimental results show that the quality of the resulting images is almost as good as that of ideal images. Quality Improvement for Intermediate Views Using Example-Based Super-Resolution Polygon Reconstructed model
A Relation Extraction System for Indian Languages

Relation Extraction is an important subtask of Information Extraction that involves extracting signiﬁcant facts from natural language text. Extracting structured information from the plaintext is the ultimate goal of IE systems. The Indian language content on the internet is increasing day to day. Extracting relevant information from this huge unstructured data is a challenging task especially when the business ﬁrms are interested in ascertaining public view on their products and processes. The primary objective of relation extraction systems is to ﬁnd those entities which can be targeted through social networking and digital marketing. Cannibalisation of the product is nowadays done using these Social Networks. Di ﬀ erent methods are proposed and experimented for Relation extraction problems. In this paper, we propose a Relation Extraction system using Convolutional Neural Networks. Deep learning based methods have produced state of the art results in many domains. Training and testing are conducted using the shared corpus provided by ’ARNEKT-IECSIL 2018’ competition organisers. The evaluation results show that the proposed system could outperform most of the reported methods in the competition.
I STITUTO DI A NALISI DEI S ISTEMI ED I NFORMATICA “ Antonio Ruberti

We consider an extension of logic programs, called ω-programs, that can be used to define predicates over infinite lists. ω-programs allow us to specify properties of the infinite behavior of reactive systems and, in general, properties of infinite sequences of events. The semantics of ω-programs is an extension of the perfect model semantics. We present variants of the familiar unfold/fold rules which can be used for transforming ω-programs. We show that these new rules are correct, that is, their application preserves the perfect model semantics. Then we outline a general methodology based on program transformation for verifying properties of ω-programs. We demonstrate the power of our transformation-based verification methodology by proving some properties of Büchi automata and ω-regular languages.
Istituto Di Analisi Dei Sistemi Ed Informatica " Antonio Ruberti " Consiglio Nazionale Delle Ricerche

We consider low-rank semidefinite programming (LRSDP) relaxations of unconstrained {−1, 1} quadratic problems (or, equivalently, of Max-Cut problems) that can be formulated as the nonconvex nonlinear programming problem of minimizing a quadratic function subject to separable quadratic equality constraints. We prove the equivalence of the LRSDP problem with the unconstrained minimization of a new merit function and we define an efficient and globally convergent algorithm, called SpeeDP, for finding critical points of the LRSDP problem. We provide evidence of the effectiveness of SpeeDP by comparing it with other existing codes on an extended set of instances of the Max-Cut problem. We further include SpeeDP within an enhanced version of the Goemans-Williamson algorithm and we show that the corresponding heuristic SpeeDP-MC can generate high-quality cuts for very large, sparse graphs of size up to a million nodes in a few hours.
Complexity and approximation in routing and scheduling

• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
hp-adaptive discontinuous Galerkin methods for simplified PN approximations of frequency-dependent radiative transfer

We investigate the performance of a class of hp-adaptive discontinuous Galerkin methods for the numerical solution of simplified PN approximations of radiative transfer in non-grey semitransparent media. By introducing an optical scale and using asymptotic expansions in the radiative transfer equation we formulate the simplified PN approximations. The optical spectrum is decomposed in frequency bands and the simplified PN equations are solved for each frequency band. As a numerical solver for the simplified PN equations we consider a high-order discontinuous Galerkin method. The solver belongs to a class of finite element methods whose approximate solutions are discontinuous across inter-element boundaries; this property renders the method ideally suited for the hp-adaptivity. An error estimator is shown to provide reliable and practically useful upper bounds for the numerical errors independent of the optical scales used in the simulations. The proposed method is simple, fast and highly accurate. The performance of the method is analyzed on several applications in frequency-dependent radiative transfer. The aim of such a method compared to the conventional finite element methods is to solve the simplified PN equations efficiently and with a high level of accuracy on unstructured meshes with different elements. The obtained results demonstrate the ability of the proposed method to capture the main radiative features.
Comparative analysis of University websites for indicators of sustainability practises: Australia and Malaysia

Globally, sustainability practices are becoming more important as the need increases to respond to the impact of development. However, there are differences on the level of sustainability practices between developed and developing countries. Furthermore, the achievement of sustainable development goals needs participation, integration and collective efforts from both types of country. This study reviewed university websites to explore the sustainability commitment in 39 Australian and 20 Malaysian universities as examples of higher education institutions from developed and developing countries. Content analysis was used to analyse data. The definition for „commitment‟ used was that universities provided information in their website that demonstrated initiatives towards sustainability. Generally, the extent of commitment among Australian universities is higher than Malaysian universities and shows that sustainability issues are well established in the former country. Most universities in Malaysia met only one theme which is „sustainability research and education‟. The preliminary results of the study show a significant gap and differences between both countries regarding sustainability commitments. Furthermore, this study gives insights into questions of the level of awareness in Australia and Malaysia regarding sustainability practices. The results provide initial indicators which may assist Malaysian universities in minimising the sustainability commitment gap compared with Australian universities.
Subset Algebra Lift Operators for 0-1 Integer Programming

We extend the Sherali--Adams, Lovasz--Schrijver, Balas--Ceria--Cornuejols, and Lasserre lift-and-project methods for 0-1 optimization by considering liftings to subset algebras. Our methods yield polynomial-time algorithms for solving a relaxation of a set-covering problem at least as strong as that given by the set of all valid inequalities with small coefficients and, more generally, all valid inequalities where the right-hand side is not very large relative to the positive coefficients in the left-hand side. Applied to generalizations of vertex-packing problems, our methods yield, in polynomial time, relaxations that have unbounded rank using, for example, the N+ operator.
Toxoplasma F-Box Protein 1 is Required for Daughter Cell Scaffold Function 1 During Parasite Replication 2 3

Toxoplasma F-Box Protein 1 is Required for Daughter Cell Scaffold Function 1 During Parasite Replication 2 3 Carlos G. Baptistaa, Agnieszka Lisa, Bowen Dengb, Ashley Dittmara*, Christopher M. 4 Westb, and Ira J. Bladera# 5 6 aDepartment of Microbiology and Immunology, University at Buffalo School of Medicine, 7 Buffalo, NY 14203 8 9 bDepartment of Biochemistry & Molecular Biology, University of Georgia, Athens, GA 10 30602 USA 11 12 # Address correspondence to: iblader@buffalo.edu 13 * Current Address: HarkerBIO, LLC, 700 Ellicott St, Buffalo, NY 14203 14 15 Running Title: Toxoplasma F-Box Protein Functions in Endodyogeny 16
Proteolytic shedding of the prion protein via activation of metallopeptidase ADAM10 reduces cellular binding and toxicity of amyloid- (cid:2) oligomers

The cellular prion protein (PrP C ) is a key neuronal receptor for (cid:2) -amyloid oligomers (A (cid:2) O), mediating their neurotoxicity, which contributes to the neurodegeneration in Alzheimer’s disease (AD). Similarly to the amyloid precursor protein (APP), PrP C is proteolytically cleaved from the cell surface by a disintegrin and metalloprotease,
Detection of Primitive Collective Behaviours in a Crowd Panic Simulation Based on Multi-Agent Approach

In case of emergency and evacuation, it is often impossible to interpret manually the complex behaviour of a crowd, essentially due to the lack of staff and time needed to understand a situation. In the literature, a monitored system using data fusion methods makes it possible to perform automatic situation awareness. Using Swarm Intelligence domain, the authors propose an approach based on multi-agent system to simulate and detect primitive collective behaviours emerging from a crowd panic. It enables anticipating collective behaviours in real-time as well as their anomalies according to specific scenarios. Detection is the possibility to learn, recognize and anticipate different behaviours by a probabilistic model. The collective behaviour detection of a crowd panic in real-time is based on a learning method on an extended model of Hidden Markov Model. This paper presents experiments of simulation and detection using an implementation of a virtual environment.
Software forensics: Can we track code to its authors? Computers & Security

A harness assembly is provided for use with an exerciser and walker apparatus which is mounted on mounting means and accommodates the torso of the user disposed therein, the harness assembly including a chest strap for engagement above the upper extremity of a torso, a waist strap for engagement about the waist of the torso, head and shoulder restraint means, a plurality of suspension straps interconnecting the chest strap, waist strap and head and shoulder restraint means, the suspension straps including connecting means for extending the harness and the lower ends of the suspension straps connected to the waist strap, and a pair of leg straps interconnecting the rear and front portions respectively, of the waist strap, thereby to secure the waist about the lower extremity of the torso.
Designing Convex Combination of Graph Filters

—In this letter, we study the problem of parametric modeling of network-structured signals with graph ﬁlters. Unlike the popular polynomial graph ﬁlters, which are based on a single graph shift operator, we consider convex combinations of graph shift operators particularly adapted to directed graphs. As the resulting modeling problem is not convex, we reformulate it as a convex optimization problem which can be solved efﬁciently. Experiments on real-world data structured by undirected and directed graphs are conducted. The results show the effectiveness of this method compared to other methods reported in the literature.
Semiparametric regression model for complex survey data

A semiparametric regression model is developed for complex surveys. In this model, the explanatory variables are represented separately as a nonparametric part and a parametric linear part. The estimation techniques combine nonparametric local polynomial regression estimation and least squares estimation. Asymptotic results such as consistency and normality of the estimators of regression coefficients and the regression functions have also been developed. Success of the performance of the methods and the properties of estimates have been shown by simulation and empirical examples with the Ontario Health Survey 1990.
Investigating Mitochondrial Influence on the Rate of Anaerobic Glycolysis in an <i>in vitro</i> Model

Investigating Mitochondrial influence on the Rate of Anaerobic Glycolysis in an in vitro Model by Mackenzie Taylor, Master of Science Utah State University, 2023 Major Professor: Dr. Sulaiman Matarneh Department: Nutrition, Dietetics, and Food Sciences The rate of postmortem pH decline is a crucial factor in determining pork quality. A gradual decrease in postmortem muscle pH (~ pH 5.6 by 24 h) is associated with desirable pork quality characteristics. In contrast, rapid pH decline is the immediate cause of the pale, soft, and exudative (PSE) meat defect. The exact biochemical mechanisms controlling the rate of postmortem pH decline are unclear, but recent studies propose that mitochondria could influence the rate of postmortem pH decline by completing lactate dehydrogenase (LDH) for pyruvate. As such, we hypothesized that inhibiting pyruvate dehydrogenase (PDH) and pyruvate carboxylase (PC) may increase the rate of pH decline by providing more substrate for anaerobic glycolysis. We utilized an in vitro system simulating postmortem metabolism and incorporated CPI-613 and Avidin to inhibit PDH and PC, respectively. Four treatments were tested: 400 μM CPI-613, 1.5 U/ml Avidin, 400 μM CPI613 + 1.5 U/ml Avidin, or diluent (control). Our findings indicated that CPI-613-containing treatments, with or without Avidin, resulted in decreased pH levels, increased lactate and glucose-6-phosphate accumulation, and enhanced glycogen degradation compared to the
Parallel Execution of Prolog Programs : a Survey

Since the early days of logic programming, researchers in the field realized the potential for exploitation of parallelism present in the execution of logic programs. Their high-level nature, the presence of non-determinism, and their referential transparency, among other characteristics, make logic programs interesting candidates for obtaining speedups through parallel execution. At the same time, the fact that the typical applications of logic programming frequently involve irregular computations, make heavy use of dynamic data structures with logical variables, and involve search and speculation, makes the techniques used in the corresponding parallelizing compilers and run-time systems potentially interesting even outside the field. The objective of this paper is to provide a comprehensive survey of the issues arising in parallel execution of logic programming languages along with the most relevant approaches explored to date in the field. Focus is mostly given to the challenges emerging from the parallel execution of Prolog programs. The paper describes the major techniques used for shared memory implementation of Or-parallelism, Andparallelism, and combinations of the two. We also explore some related issues, such as memory management, compile-time analysis, and execution visualization.