Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Inference and Learning in Modelling Financial Volatility *

This paper uses the Specific-to-General methodological approach that is widely used in science, in which problems with existing theories are resolved as the need arises, to illustrate a number of important developments in the modelling of univariate and multivariate financial volatility. Some of the difficulties in analysing time-varying univariate and multivariate conditional volatility and stochastic volatility include the number of parameters to be estimated, and the computational complexities associated with multivariate conditional volatility models and both univariate and multivariate stochastic volatility models. For these reasons, among others, automated inference in its present state requires modifications and extensions for modelling in empirical financial econometrics. As a contribution to the development of automated inference in modelling volatility, twenty important issues in the specification, estimation and testing of conditional and stochastic volatility models are discussed. A “Potential for Automation Rating” (PAR) index and recommendations regarding the possibilities for automated inference in modelling financial volatility are given in each case. *The author wishes to acknowledge helpful discussions with Manabu Asai, Massimiliano Caporin, Felix Chan, Rob Engle, Jiti Gao, Suhejla Hoti, Thierry Jeantheau, Shiqing Ling, Adrian Pagan, Peter Phillips, Peter Robinson, Ruey Tsay, Neil Shephard and Jun Yu, and the constructive comments and suggestions of the Editor and two referees. Some of the ideas in the paper were presented at the International Conference on Threshold Models and New Developments in Time Series, Hong Kong, July 2004, and in seminars at the Bank of Italy, Carlos III University of Madrid, Chinese University of Hong Kong, Complutense University, Curtin University of Technology, Ente Einaudi Rome, Fondazione Eni Enrico Mattei Milan, Hong Kong University of Science and Technology, National University of Singapore, University of Adelaide, University of Auckland, University of Canterbury, University of Las Palmas de Gran Canaria, University of Melbourne, University of Milan-Bicocca, University Pompeu Fabra, University of Venice “Ca’ Foscari”, and University of Vigo. The financial support of the Australian Research Council is greatly appreciated.
Intracellular pH in single motile cells

Cytoplasmic pH in single living specimens of Chaos carolinensis is determined microfluorometrically by measuring the ratio of fluorescence intensity of microinjected fluorescein-thiocarbamyl (FTC)-ovalbumin at two different excitation wavelengths. The probe is evenly distributed throughout, and confined to, the cytoplasm, and the fluorescence intensity ratio depends only upon pH. It is independent of pathlength, concentration of probe, divalent cations, and ionic strength. Ratios are calibrated with a standard curve generated in situ by adjusting internal pH of FTC-ovalbumin-containing amebae with weak acid and weak base or by injection of strong buffers. With this technique, the average cytoplasmic pH of freely moving ameba is found to be 6.75 (SD +/- 0.3). The pH of a given spot relative to the morphology of a moving ameba remains fairly constant (+/- 0.05 U), whereas the pH of two different spots in the same cell may differ by as much as 0.4 U, and average pH in different amebae ranges from 6.3 to 7.4, with a suggestion of clustering about pH 6.5 and 6.8. During wound healing, there is a local, transient drop in pH (as great as 0.35 U) at the wound site upon puncture, proportional in extent to the degree of damage. Comparison of tails and advancing pseudopod tips reveals no significant difference in cytoplasmic pH at this level of spatial (50 microns diameter spot) and temporal (1.3 s) resolution. Fluctuations in intracellular pH and/or intracellular free Ca++ may be involved in regulation of cytoplasmic structure and contractility.
Wireless Integrated Biosensors for Point-of-Care Diagnostic Applications

Recent advances in integrated biosensors, wireless communication and power harvesting techniques are enticing researchers into spawning a new breed of point-of-care (POC) diagnostic devices that have attracted significant interest from industry. Among these, it is the ones equipped with wireless capabilities that drew our attention in this review paper. Indeed, wireless POC devices offer a great advantage, that of the possibility of exerting continuous monitoring of biologically relevant parameters, metabolites and other bio-molecules, relevant to the management of various morbid diseases such as diabetes, brain cancer, ischemia, and Alzheimer’s. In this review paper, we examine three major categories of miniaturized integrated devices, namely; the implantable Wireless Bio-Sensors (WBSs), the wearable WBSs and the handheld WBSs. In practice, despite the aforesaid progress made in developing wireless platforms, early detection of health imbalances remains a grand challenge from both the technological and the medical points of view. This paper addresses such challenges and reports the state-of-the-art in this interdisciplinary field.
Testing for the degree of commitment via set-identification

We propose a new procedure for testing optimal monetary policy relying on set-identification. The identified set is characterized by means of moment inequality conditions nesting optimal policy under commitment and discretion. The approach is based on the derivation of bounds for in ation that are consistent with both forms of optimal policy and provide set identification of the economy's structural parameters. We derive testable implications that allow for specification tests and discrimination between the two alternative policy regimes. The methodology allows us to study the level of commitment exhibited by the United States monetary authority, and to set-identify the economy's structural parameters such that the target variables of the monetary authority (in ation and welfare relevant output gap) are consistent with optimal monetary policy.
Activation of the cnidarian oxidative stress response by ultraviolet radiation, polycyclic aromatic hydrocarbons and crude oil

Organisms are continuously exposed to reactive chemicals capable of causing oxidative stress and cellular damage. Antioxidant enzymes, such as superoxide dismutases (SODs) and catalases, are present in both prokaryotes and eukaryotes and provide an important means of neutralizing such oxidants. Studies in cnidarians have previously documented the occurrence of antioxidant enzymes (transcript expression, protein expression and/or enzymatic activity), but most of these studies have not been conducted in species with sequenced genomes or included phylogenetic analyses, making it difficult to compare results across species due to uncertainties in the relationships between genes. Through searches of the genome of the sea anemone Nematostella vectensis Stephenson, one catalase gene and six SOD family members were identified, including three copper/zinc-containing SODs (CuZnSODs), two manganese-containing SODs (MnSODs) and one copper chaperone of SOD (CCS). In 24 h acute toxicity tests, juvenile N. vectensis showed enhanced sensitivity to combinations of ultraviolet radiation (UV) and polycyclic aromatic hydrocarbons (PAHs, specifically pyrene, benzo[a]pyrene and fluoranthene) relative to either stressor alone. Adult N. vectensis exhibited little or no mortality following UV, benzo[a]pyrene or crude oil exposure but exhibited changes in gene expression. Antioxidant enzyme transcripts were both upregulated and downregulated following UV and/or chemical exposure. Expression patterns were most strongly affected by UV exposure but varied between experiments, suggesting that responses vary according to the intensity and duration of exposure. These experiments provide a basis for comparison with other cnidarian taxa and for further studies of the oxidative stress response in N. vectensis.
A membrane-inspired algorithm with a memory mechanism for knapsack problems

Membrane algorithms are a class of distributed and parallel algorithms inspired by the structure and behavior of living cells. Many attractive features of living cells have already been abstracted as operators to improve the performance of algorithms. In this work, inspired by the function of biological neuron cells storing information, we consider a memory mechanism by introducing memory modules into a membrane algorithm. The framework of the algorithm consists of two kinds of modules (computation modules and memory modules), both of which are arranged in a ring neighborhood topology. They can store and process information, and exchange information with each other. We test our method on a knapsack problem to demonstrate its feasibility and effectiveness. During the process of approaching the optimum solution, feasible solutions are evolved by rewriting rules in each module, and the information transfers according to directions defined by communication rules. Simulation results showed that the performance of membrane algorithms with memory cells is superior to that of algorithms without memory cells for solving a knapsack problem. Furthermore, the memory mechanism can prevent premature convergence and increase the possibility of finding a global solution.
IRAS 04000+5052: A Not So Compact, Not So Metal‐poor H ii Region

We present new observations of IRAS 04000+5052, a Galactic H ii region that is associated with a young stellar cluster and is possibly located in the Perseus arm. We have found that contrary to previous claims, this object is not a compact metal‐poor H ii region. The electron density and chemical composition of the nebula are similar to those of normal H ii regions of the Galactic disk. The radial velocity of the ionized gas coincides with that obtained from CO observations, indicating that the nebula is associated with a molecular cloud. Probably, the ionizing source of the H ii region is a Herbig Be star of spectral type B0.5.
A comparison of classification methods for gene prediction in metagenomics

Metagenomics is an emerging field in which the power of genome analysis is applied to entire communities of microbes. It is focused on the understanding of the mixture of genes (genomes) in a community as whole. The gene prediction task is a well-known problem in genomics, and it remains an interesting computational challenge in metagenomics too. A large variety of classifiers has been developed for gene prediction though there is lack of an empirical evaluation regarding the core machine learning techniques implemented in these tools. In this work we present an empirical performance comparison of different classification strategies for gene prediction in metagenomic data. This comparison takes into account distinct supervised learning strategies: one lazy learner, two eager-learners and one ensemble learner. The ensemble-based strategy has achieved the overall best result and it is competitive with the prediction baselines of well-known metagenomics tools.
How To Tame Your Sparsity Constraints

We show that designing sparse $H_\infty$ controllers, in a discrete (LTI) setting, is easy when the controller is assumed to be an FIR filter. In this case, the problem reduces to a static output feedback problem with equality constraints. We show how to obtain an initial guess, for the controller, and then provide a simple algorithm that alternates between two (convex) feasibility programs until converging, when the problem is feasible, to a suboptimal $H_\infty$ controller that is automatically stable. As FIR filters contain the information of their impulse response in their coefficients, it is easy to see that our results provide a path of least resistance to designing sparse robust controllers for continuous-time plants, via system identification methods.
Expression and Localization of the CDC 34 Ubiquitin-conjugating Enzyme in Pediatric Acute Lymphoblastic Leukemia 1

Ubiquitin-dependent protein degradation impacts many cellular processes. However, the regulation of ubiquitin-conjugating enzymes (UBCs) in cancer is unknown. We find that the human CDC34 UBC protein is expressed at a 3–4 fold higher level (P < 0.001) in pediatric T cell than in pre-B-cell acute lymphoblastic leukemia (ALL) before treatment in two independent patient sets. The level of CDC34 mRNA was similar in both types of leukemia. CDC34 expression levels in normal resting T cells, B cells, and activated T lymphocytes was comparable with pre-B-cell ALL. CDC34 protein (but not mRNA) was also increased in T-cell ALL compared with pre-B-cell ALL cell lines. The difference in expression was not attributable to mutation or associated with altered CDC34 stability. Immunohistochemistry and cellular fractionation reveals a heterogeneous CDC34 expression pattern including cells containing primarily cytoplasmic or nuclear protein. Thus, a feature of pediatric T-cell ALL is posttranscriptional up-regulation and heterogeneous localization of the human CDC34 UBC.
A Lemma-Based Approach to a Maximum Entropy Word Sense Disambiguation System for Dutch

In this paper, we present a corpus-based supervised word sense disambiguation (WSD) system for Dutch which combines statistical classification (maximum entropy) with linguistic information. Instead of building individual classifiers per ambiguous wordfomi, we introduce a lemma-based approach. The advantage of this novel method is that it clusters all inflected forms of an ambiguous word in one classifier, therefore augmenting the training material available to the algorithm. Testing the lemmabased model on the Dutch Senseval-2 test data, we achieve a significant increase in accuracy over the wordform model. Also, the WSD system based on lemmas is smaller and more robust.
A Survey of Biometric keystroke Dynamics: Approaches, Security and Challenges

Biometrics technologies are gaining popularity today since they provide more reliable and efficient means of authentication and verification. Keystroke Dynamics is one of the famous biometric technologies, which will try to identify the authenticity of a user when the user is working via a keyboard. The authentication process is done by observing the change in the typing pattern of the user. A comprehensive survey of the existing keystroke dynamics methods, metrics, different approaches are given in this study. This paper also discusses about the various security issues and challenges faced by keystroke dynamics.
Linguistic Resources and Technologies for Romanian Language

This paper revises notions related to Language Resources and Technologies (LRT), including a brief overview of some resources developed worldwide and with a special focus on Romanian language. It then describes a joined Romanian, Moldavian, English initiative aimed at developing electronically coded resources for Romanian language, tools for their maintenance and usage, as well as for the creation of applications based on these resources.
On-1 ine Identification of HMMs with Grouped State Values

This paper presents a signal processing scheme for the class of hidden Markov models (HMMs) which have state values clustered into groups. Attention is focussed not only on state estimation for known models, but also on on-line model identification, The approach taken employs a new technique whereby separate state estimators are used for each group of state-values. The state estimator for each cluster estimates the discrete state-values in that cluster, together with an associated flag state which represents all the other groups. The result is that computational complexity is greatly reduced. Hidden Markov model parameters can be identified on-line using available techniques such as the recursive prediction error (RPE) approach taken in this paper. These techniques estimate the transition probabilities and the discrete state values of the Markov chainj on-line. Other parameters, such as the noise density associated with the observations, can also be identified.
ESTIMATING THE ESSENTIAL MATRIX :

GOODSAC is a paradigm for estimation of model parameters given measurements that are contaminated by outliers. Thus, it is an alternative to the well known RANSAC strategy. GOODSAC’s search for a proper set of inliers does not only maximize the sheer size of this set, but also takes other assessments for the utility into account. Assessments can be used on many levels of the process to control the search and foster precision and proper utilization of the computational resources. This contribution discusses and compares the two methods. In particular, the estimation of essential matrices is used as example. The comparison is performed on synthetic and real data and is based on standard statistical methods, where GOODSAC achieves higher precision than RANSAC.
CoGrOO - An OpenOffice Grammar Checker

This paper describes a Portuguese language grammar checker project, called CoGrOO - Corretor Gramatical para o OpenOffice (Grammar Checker for OpenOffice). Two of its features are highlighted: 1) hybrid architecture, mixing rules and statistics; 2) free software project. This project aims to check grammatical errors such as nominal and verbal agreement, "erase" (preposition "a" + determiner "a" yielding "a"), nominal and verbal government and other common errors in Brazilian Portuguese language. We present some empirical results based on the first version of the grammar checker and the new approach used in the second version.
A heuristic algorithmic approach to challenging robustness of digital audio watermarking using discrete wavelet transform

Robustness of digital audio watermarking algorithm against serious signal processing attacks is a big challenge in the recent trends. Many works have been reported in this field claiming high payload with good imperceptibility but good robustness at acceptable perceptual rates is still needed to be addressed. In this paper, watermark is embedded in the wavelet transformation of audio signal by using quantization ensuring the robustness of the proposed algorithm. It can be inferred from the experimental results that the quality of the watermarked audio signal can be preserved by using proposed watermarking algorithm and is more robust to various challenging signal processing attacks. 73% of the evaluators rated the proposed algorithm to be excellent and rest 27% as of good quality in the terms of perceptual transparency. NCC and BER values for the proposed algorithm under various attacks such as compression, cropping, resampling, re-quantization, noise etc. are found to be 1 and 0 respectively. Performance of the proposed algorithm has been compared with various existing techniques in the literature exhibiting good robustness and exact extraction of watermark even under various attack situations.
Characterizing the Membrane-Bound State of Cytochrome P450 3A4: Structure, Depth of Insertion, and Orientation

Cytochrome P450 3A4 (CYP3A4) is the most abundant membrane-associated isoform of the P450 family in humans and is responsible for biotransformation of more than 50% of drugs metabolized in the body. Despite the large number of crystallographic structures available for CYP3A4, no structural information for its membrane-bound state at an atomic level is available. In order to characterize binding, depth of insertion, membrane orientation, and lipid interactions of CYP3A4, we have employed a combined experimental and simulation approach in this study. Taking advantage of a novel membrane representation, highly mobile membrane mimetic (HMMM), with enhanced lipid mobility and dynamics, we have been able to capture spontaneous binding and insertion of the globular domain of the enzyme into the membrane in multiple independent, unbiased simulations. Despite different initial orientations and positions of the protein in solution, all the simulations converged into the same membrane-bound configuration with regard to both the depth of membrane insertion and the orientation of the enzyme on the surface of the membrane. In tandem, linear dichroism measurements performed on CYP3A4 bound to Nanodisc membranes were used to characterize the orientation of the enzyme in its membrane-bound form experimentally. The heme tilt angles measured experimentally are in close agreement with those calculated for the membrane-bound structures resulted from the simulations, thereby verifying the validity of the developed model. Membrane binding of the globular domain in CYP3A4, which appears to be independent of the presence of the transmembrane helix of the full-length enzyme, significantly reshapes the protein at the membrane interface, causing conformational changes relevant to access tunnels leading to the active site of the enzyme.
Frequency warping and the Mel scale

We present experimental results that show that the scale-factor relating the formant frequencies of different speakers increases with decreasing values of formant frequency. Based on these results, we experimentally obtain a frequency warping function aimed at separating speaker dependencies from the inherent characterization of the sound. We find that the frequency warping function is similar to the Mel scale, and we believe that this is the first time that a Mel-like scale has been obtained using only speech. Our results and methods may therefore explain, from a speech point of view, the Mel scale, which was obtained historically from hearing based experiments.
A Life-Cycle Approach in the Dutch Occupational Pension System?

The recent global financial crisis reconfirms the importance of risk management in defined benefit pension systems. To enhance sustainability, new forms of integrating the risk profiles of assets and liabilities should be considered. Specifically, by reducing the risk profile of assets, by transferring more risk to the beneficiaries, or through a combination of the two. This article provides a stochastic analysis of different strategies that gradually switch from risky to risk-free investments over the life-cycle. While the study is set in a Dutch institutional context, its findings have broad application.
Refinement operators to facilitate the reuse of interaction laws in open multi-agent systems

As new software demands and requirements appear, the system and its interaction laws must evolve to support these changes. Languages and models should provide the tools for dealing with this evolution. Poor support for evolution has a negative impact on system maintainability. In this paper, we propose some refinement operators to extend the interaction laws in open multi-agent systems. As an example of this idea, we implemented a customizable application in the supply chain management domain as an open system environment.
Construction of high rate run-length limited codes using arithmetic decoding

We suggest an approach to constructing low-redundant RLL (d; k)-codes whose complexity does not depend on the code length and is determined solely by the achievable redundancy r, the time and space complexity being O(log2(1/r)) and O(log(1/r)), respectively, as r → 0. First we select code-words whose combinations may constitute all (d; k)-constrained sequences of any length. Then we use arithmetic decoding to produce these codewords with (or close to) optimal probabilities from an input sequence. The coding algorithms and estimates of performance are provided.
Metabolic Syndrome and DNA Damage: The Interplay of Environmental and Lifestyle Factors in the Development of Metabolic Dysfunction

The Metabolic Syndrome (MetS) is a complex condition which is characterized by increased risk factor for cardiovascular diseases, such as dyslipidemia, hypertension and central obesity, in addition to increased risk for type 2 diabetes mellitus (T2DM). All of these factors alone have a notable relationship with DNA damage. However, when the risks are combined, the extent for major outcomes being related to DNA damage (cancer), the consequence can be accelerated by the metabolic dysfunction. This article will illustrate the scientific evidence of the role of DNA damage in MetS, as well as discuss the interplay of major risks factors (air pollution, physical inactivity and dietary interventions) in genomic stability.