Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical analysis of structural changes in emerging market volatility

In this paper, two different stability tests in linear frameworks are used to examine the presence of structural changes in the GARCH-based conditional volatility of emerging market countries. We particularly relate this issue to the market liberalization reforms undertaken by these countries over the last three decades. Empirical results show that structural breaks detected in emerging market volatility do not happen together with official liberalization dates, but they rather coincide with dates of the first American Depository Receipt (ADR) and Country Fund introduction, and with dates of huge increases in the US capital flows into emerging countries. This leads to reinforce the findings of related literature on that emerging markets do react essentially to alternative events of official liberalizations.
A novel semi-supervised learning for SMS classification

In this paper, we propose a novel semi-supervised methodology to detect spam or ham SMSs, using frequent item set mining algorithm Apriori, probabilistic model Naive Bayes and ensemble learning. This paper considers the unbalanced data set problem which means designing of two class SMS classifier using small number of ham and unlabeled dataset only. Using only a few labeled examples with Semi-supervised training is typically unreliable. However, by applying user-specified minimum support and minimum confidence on ham and unlabeled dataset, we gained significant accuracy on classifying SMSs, experimenting on UCI data Repository.
Enforcement of applet boundaries in Java card systems

In multi-application Java Cards, applet’s sensitive data must be protected against unauthorized accesses. Applet isolation is normally achieved through the firewall mechanism. The firewall allows an applet to access external objects only through an object sharing mechanism, called shareable interface. Firewall is based on the access control policy and does not control information propagation. This paper shows how extending the firewall with the inspection of call-stack of shareable interface methods contributes to avoid unwanted information flow that can arise as a result of method callback from one applet to another. This control detects the illegal information flow of the well-known Electronic Purse case study.
Saralasin‐Induced Renin Release: Its Blockade by Prostaglandin Synthesis Inhibitors in the Conscious Rat

SUMMARY The angiotensin antagonist, saralasin, (10 and 30 rag/kg), increased serum renin activity (SRA) in normal, conscious rats from 2.7 ± 0.4 to 16.2 ± 3.7 and 22.5 ± 2.4 ng/ml/hr (p < 0.001), respectively, without markedly altering blood pressure or heart rate. Indomethacin, in a dose which inhibited the urinary excretion of prostaglandin E2 (PGE,) by 75%, and arachidonate-induced hypotension by 83%, failed to alter basal SRA but inhibited saralasin-induced renin release by 99% and 87% at the 10 and 30 mg/kg doses, respectively. Indomethacin failed to alter basal hemodynamics or the hemodynamic response to saralasin. Prorjranolol (1.5 mg/kg) inhibited saralasin-induced renin release by 93% and enhanced the suppressant effect of indomethacin from 79% to 100%. Meclofenamate, another prostaglandin synthesis inhibitor, also blocked saralasin-induced renin release by 99% and 72% at the 10 and 30 mg/kg doses, respectively (p < 0.001). In sodium-depleted rats, saralasin (03 mg/kg) increased SRA from 12 ± 2 to 119 ± 6 ng/ml/hr (p < 0.001) and decreased blood pressure by 6% (p < 0.01). In these animals, indomethacin failed to alter basal SRA, but inhibited saralasin-induced renin release by 82%, urinary excretion of PGE, by 79%, and arachidonate-induced hypotension by 81%. These findings suggest 1) that saralasin-induced renin release is mediated by renal prostaglandins, and 2) an interrelationship exists between the receptor controlling All-mediated inhibition of renin release, which is blocked by saralasin, and the juxtaglomerular beta-adrenergic receptor.
SR146131: a new potent, orally active, and selective nonpeptide cholecystokinin subtype 1 receptor agonist. II. In vivo pharmacological characterization.

SR146131 is a potent and selective agonist at cholecystokinin subtype 1 (CCK1) receptors in vitro. The present study evaluates the activity of the compound in vivo. SR146131 completely inhibited gastric and gallbladder emptying in mice (ED50 of 66 and 2.7 micrograms/kg p.o., respectively). SR146131 dose dependently reduced food intake in fasted rats (from 0.1 mg/kg p.o.), in nonfasted rats in which food intake had been highly stimulated by the administration of neuropeptide Y (1-36) (from 0.3 mg/kg p.o.), in fasted gerbils (from 0.1 mg/kg p.o.), and in marmosets maintained on a restricted diet (from 3 mg/kg p.o.). SR146131 (10 mg/kg p.o.) also increased the number of Fos-positive cells in the hypothalamic paraventricular nucleus of rats. Locomotor activity of mice was reduced by orally administered SR146131 (from 0.3 mg/kg p.o.). When administered intrastriatally, SR146131 elicited contralateral turning behavior in mice. Furthermore, orally administered SR146131 (0.3-10 mg/kg), also reduced the levels of cerebellar cyclic GMP. Finally, SR146131 (0.1 microgram/kg to 1 mg/kg, p.o.) significantly and dose dependently antagonized fluphenazine-induced mouth movements in rats. The CCK1 antagonist SR27897B prevented all the effects of SR146131. Conversely, SR146131 was unable to elicit any agonist or antagonist effects in a model of CCK2 receptor stimulation in vivo. SR146131 is a very potent and selective nonpeptide CCK1 agonist in vivo. SR146131 is more potent than any other CCK1 agonists reported to date. Because pharmacodynamic studies suggest that SR146131 should have a high absolute bioavailability, it may be a promising drug for the treatment of eating and motor disorders in humans.
Ville Probabilistic Mapping of Human Visual Attention from Head Pose Estimation

Powered by TCPDF (www.tcpdf.org) This material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you for your research use or educational purposes in electronic or print form. You must obtain permission for any other use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user. Veronese, Andrea; Racca, Mattia; Pieters, Roel; Kyrki, Ville
The innate immune receptor MDA5 limits rotavirus infection but promotes cell death and pancreatic inflammation

Melanoma differentiation‐associated protein 5 (MDA5) mediates the innate immune response to viral infection. Polymorphisms in IFIH1, the gene coding for MDA5, correlate with the risk of developing type 1 diabetes (T1D). Here, we demonstrate that MDA5 is crucial for the immune response to enteric rotavirus infection, a proposed etiological agent for T1D. MDA5 variants encoded by minor IFIH1 alleles associated with lower T1D risk exhibit reduced activity against rotavirus infection. We find that MDA5 activity limits rotavirus infection not only through the induction of antiviral interferons and pro‐inflammatory cytokines, but also by promoting cell death. Importantly, this MDA5‐dependent antiviral response is specific to the pancreas of rotavirus‐infected mice, similar to the autoimmunity associated with T1D. These findings imply that MDA5‐induced cell death and inflammation in the pancreas facilitate progression to autoimmune destruction of pancreatic β‐cells.
DYNAMIC DENSITY AND COMPLEXITY METRICS FOR REALTIME TRAFFIC FLOW MANAGEMENT

This study’s goal was to model airspace Dynamic Density and complexity (and hence controller workload) using traffic characteristic metrics. The focus was on metrics that could eventually enable Traffic Flow Management (TFM) personnel to strategically prevent overloads using triggers other than predicted sector traffic count. Potential metrics from past studies were assessed in terms of how well they could be predicted at time horizons required for TFM decision support (up to 120 minutes), and their face validity. Also, proportional odds logistic regression determined the metrics’ usefulness for predicting subjective complexity ratings collected in an FAA-NASA study. Based on these analyses, a subset of 12 metrics was chosen (from the original 41). Further multiple regression analyses were conducted with this reduced model, to determine which metrics provided unique contributions to the prediction of subjective complexity, and to see the extent to which the same complexity factors related to subjective workload in different airspaces.
ALTERNATIVE CHARACTERIZATIONS OF AMERICAN PUT OPTIONS

We derive alternative representations of the McKean equation for the value of the American put option. Our main result decomposes the value of an American put option into the corresponding European put price and the early exercise premium. We then represent the European put price in a new manner. This representation allows us to alternatively decompose the price of an American put option into its intrinsic value and time value, and to demonstrate the equivalence of our results to the McKean equation. Copyright 1992 Blackwell Publishers.
Prospectus Words , Parts of Speech , and Uncertainty in IPO Pricing

We investigate how uncertainty is shaped from words in the risk factor section of IPO prospectuses using a new method to identify words that predict IPO first-day returns. We find that sentiment lists represent only a small fraction of the prospectus words associated with underpricing. When sentiment words do overlap our new words, there is a significantly greater impact on returns to such sentiment words. We investigate whether parts of speech help explain the development of investor sentiment. Nouns are found to be important determinants of first-day returns, but are under-represented on sentiment lists. Such nouns are often connected to an industry or technical function and are not really tonal. Verbs and adjectives appear as the most meaningful words on existing sentiment lists.
Giving the User Control over Android Permissions

In this project, we investigate the possible options for users to restrict application permissions in a more fine-grained way and provide a proof of concept for a command-line tool that can remove permissions from applications before installation. We tested our tool with Android 2.2 (Froyo) on an emulated NexusS and a real Samsung Galaxy S GT-I9000.
A 3-O-Sulfated Heparan Sulfate Binding Peptide Preferentially Targets Herpes Simplex Virus 2-Infected Cells

ABSTRACT Herpes simplex virus 2 (HSV-2) is the primary cause of genital herpes, which is one of the most common sexually transmitted viral infections worldwide and a major cofactor for human immunodeficiency virus infection. The lack of an effective vaccine or treatment and the emergence of drug-resistant strains highlight the need for developing new antivirals for HSV-2. Here, we demonstrate that a low-molecular-weight peptide isolated against 3-O-sulfated heparan sulfate (3-OS HS) can efficiently block HSV-2 infection. Treatment with the peptide inhibited viral entry and cell-to-cell spread both in vitro and in vivo using a mouse model of genital HSV-2 infection. Quite interestingly, the peptide showed a preferential binding to HSV-2-infected cells, with more than 200% increased binding compared to uninfected cells. Our additional results show that heparan sulfate expression is upregulated by 25% upon HSV-2 infection, which is a significant new finding that could be exploited for designing new diagnostic tests and treatment strategies against HSV-2-infected cells. In addition, our results also raise the possibility that 3-OS HS modifications within HS may be upregulated even more to accommodate for a significantly higher increase in the peptide binding to the infected cells.
Computing k-Regret Minimizing Sets

Regret minimizing sets are a recent approach to representing a dataset D by a small subset R of size r of representative data points. The set R is chosen such that executing any top-1 query on R rather than D is minimally perceptible to any user. However, such a subset R may not exist, even for modest sizes, r. In this paper, we introduce the relaxation to k-regret minimizing sets, whereby a top-1 query on R returns a result imperceptibly close to the top-k on D. We show that, in general, with or without the relaxation, this problem is NP-hard. For the specific case of two dimensions, we give an efficient dynamic programming, plane sweep algorithm based on geometric duality to find an optimal solution. For arbitrary dimension, we give an empirically effective, greedy, randomized algorithm based on linear programming. With these algorithms, we can find subsets R of much smaller size that better summarize D, using small values of k larger than 1.
EVALUATIO OF A TIDIABETIC A D A TIHYPERLIPIDEMIC EFFECTS OF ARTEMISIA DRACU CULUS EXTRACTS I STREPTOZOTOCI -I DUCED-DIABETIC RATS

The aerial and root extracts of Artemisia dracunculus were subjected to screening of antidiabetic activity in Streptozotocin (STZ)-induced diabetic rats. The ethanol and aqueous extracts (250 mg/kg body weight) were administered orally in diabetic rats. After the oral administration of ethanol and aqueous extracts of aerial and root of Artemisia dracunculus, blood glucose levels were monitored at specific intervals and it was found that they were significant lowered. In the same study the action of the extracts on diabetes induced hyperlipidemia was analyzed where the extracts significantly lowered the elevated total cholesterol, triglycerides (TGL) and low density lipoprotein (LDL) level while increased the High density lipoprotein (HDL). Glibenclamide was used as a standard drug at a dose of 0.25 mg/kg. The experimental data revealed that extracts of aerial and root of Artemisia dracunculus has significant antihyperglycemic activity in Streptozotocin-induced rats compared to the standard drug. The drug has the potential to act as antidiabetic as well as antihyperlipidemic.
Efficient FDTD Implementation of the ADE-Based CN-PML for the Two-Dimensional TMz Waves

─ An efficient, unsplit-field and unconditional stable implementation of the stretched coordinate perfectly matched layer (SC-PML) is proposed for terminating the finite-difference time-domain (FDTD) method. Via incorporating the Crank-Nicolson Douglas-Gunn (CNDG) and the auxiliary differential equation (ADE) methods, respectively, the proposed PML formulations can take advantage of the unconditional stability of the CNDG method which has smaller numerical anisotropy than the existing alternately direction implicit (ADI) method. A numerical test carried out in a 2D free space FDTD domain is provided to validate the proposed CNDGbased PML. It has been shown that the proposed PML can not only overcome the Courant-Friedrich-Levy (CFL) stability constraint, but attenuate the propagating waves efficiently. Index Terms ─ Auxiliary differential equation (ADE), Crank-Nicolson Douglas-Gunn (CNDG), finitedifference time-domain (FDTD), perfectly matched layer (PML).
Environmental impact assessment of online advertising

There are no commonly agreed ways to assess the total energy consumption of the Internet. Estimating the Internet's energy footprint is challenging because of the interconnectedness associated with even seemingly simple aspects of energy consumption. The first contribution of this paper is a common modular and layered framework, which allows researchers to assess both energy consumption and CO2e emissions of any Internet service. The framework allows assessing the energy consumption depending on the research scope and specific system boundaries. Further, the proposed framework allows researchers without domain expertise to make such an assessment by using intermediate results as data sources, while analyzing the related uncertainties. The second contribution is an estimate of the energy consumption and CO2e emissions of online advertising by utilizing our proposed framework. The third contribution is an assessment of the energy consumption of invalid traffic associated with online advertising. The second and third contributions are used to validate the first. The online advertising ecosystem resides in the core of the Internet, and it is the sole source of funding for many online services. Therefore, it is an essential factor in the analysis of the Internet's energy footprint. As a result, in 2016, online advertising consumed 20–282 TWh of energy. In the same year, the total infrastructure consumption ranged from 791 to 1334 TWh. With extrapolated 2016 input factor values without uncertainties, online advertising consumed 106 TWh of energy and the infrastructure 1059 TWh. With the emission factor of 0.5656 kg CO2e/kWh, we calculated the carbon emissions of online advertising, and found it produces 60 Mt CO2e (between 12 and 159 Mt of CO2e when considering uncertainty). The share of fraudulent online advertising traffic was 13.87 Mt of CO2e emissions (between 2.65 and 36.78 Mt of CO2e when considering uncertainty). The global impact of online advertising is multidimensional. Online advertising affects the environment by consuming significant amounts of energy, leading to the production CO2e emissions. Hundreds of billions of ad dollars are exchanged yearly, placing online advertising in a significant role economically. It has become an important and acknowledged component of the online-bound society, largely due to its integration with the Internet and the amount of revenue generated through it.
Organising for Co-Production: Local Interaction Platforms for Urban Sustainability

Urban sustainability is a wicked issue unsuited to management through traditional decision-making structures. Co-productive arrangements, spaces and processes are inscribed in new organisational forms to bridge between diverse forms of knowledge and expertise. This article suggests that local interaction platforms (LIPs) are innovative responses to these challenges, developed in two African and two European cities between 2010 and 2014. Through elaborating the design and practice of the LIPs, the article concludes that the value of this approach lies in its context-sensitivity and iterative flexibility to articulate between internationally shared challenges and distinctive local practices. Six necessary conditions for the evolution of LIPs are presented: anchorage, co-constitution, context-sensitivity, alignment, connection and shared functions. In the context of increased uncertainty, complexity and the demand for transdisciplinary knowledge production, the platform concept has wider relevance in surfacing the challenges and possibilities for more adaptive urban governance.
Formation of dendrimer nanotubes by layer-by-layer deposition.

[*] P. Karan, Dr. P. Gçring, Prof. U. Gçsele, Dr. M. Steinhart Max Planck Institute of Microstructure Physics Weinberg 2, 06120 Halle (Germany) Fax: (+49)345-551-1223 E-mail: steinhart@mpi-halle.de Dr. D. H. Kim, Prof. W. Knoll Max Planck Institute for Polymer Research Ackermannweg 10, 55128 Mainz (Germany) Fax: (+49)6131-379-100 E-mail: kimdh@mpip-mainz.mpg. de Dr. J. Leclaire, Dr. A. -M. Caminade, Prof. J.-P. Majoral Laboratoire de Chimie de Coordination Centre National de la Recherche Scientifique 205 Route de Narbonne, 31077 Toulouse Cedex 4 (France) [**] The authors thank the German Ministry of Education and Science (Nanobiotechnologie FKZ 031 200 2A) as well as the CNRS (France) for financial support and Dr. J.-L. Hernandez-Lopez for fruitful discussions.
Works Title A Method to Constrain Genome-Scale Models with 13 C Labeling Data Permalink

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from C labeling experiments and genome-scale models. The data from C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by C labeling data. A comparison shows that the results of this new method are similar to those found through C Metabolic Flux Analysis (C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.
Mitigating the Effects of Structural Complexity on Open Source Software Maintenance through Accountability

In this research, we investigate the relationships between structural complexity, accountability, and software maintenance performance in Open Source Software development projects. Additionally, we investigate the moderating role of monetary incentives on various relationships. We collected data on 5,000 bug reports from the SourceForge database and perceptual data from 181 open source software developers registered on SourceForge for model validation. Results support our hypotheses. The important implications of the results are discussed.
A type theoretic approach to information state update in issue based dialogue management ∗

Although Barwise came from the model theoretic tradition in logic he was sceptical of the kind of model theoretic analysis of natural language that Montague proposed based on the notion of natural languages as sets of model theoretically interpreted sentences, i.e. as formal languages. Instead Barwise sought to build a mathematical approach to language which incorporated an Austinian view of language as speech events relating to small parts of the world (situations). The classic book in situation semantics (Barwise and Perry, 1983) was thought provoking and ground breaking although it did not give a mathematically precise theory of the kind of compositional semantics that Montague had proposed. It also suffered in many parts of the linguistic community because it was trying to address questions about communication rather than a theory of meaning in the classical Montagovian sense. Barwise was interested in characterising the information which an agent can extract from an utterance event rather than the meaning of an expression. This did not sit easily with the linguistic tradition based on the formal language approach which followed Montague. Such questions of communication and information (rather than meaning) do, however, arise when we try to create theories relating to what is known as dialogue management in computational dialogue systems. In this paper we try to show how a formal theory of dialogue management could be approached by using type theory as a tool to characterise a kind of dialogue management (issue-based dialogue management) developed by Larsson (2002) using the information state update approach and based on Ginzburg’s (1996a,b) work on “questions under discussion” which was carried out in a situation semantic framework. The exercise leads us to some conclusions about the possibility of unifying the formal language approach with the Austinian approach that Barwise advocated.