Effects of diversification among assets in an agent-based market model

We extend to the multi-asset case the framework of a discrete time model of a single asset financial market developed in Ghoulmie et al (2005). In particular, we focus on adaptive agents with threshold behavior allocating their resources among two assets. We explore numerically the effect of this diversification as an additional source of complexity in the financial market and we discuss its destabilizing role. We also point out the relevance of these studies for financial decision making.Comment: 12 pages, 5 figures, accepted for publication in the Proceedings of the Complex Systems II Conference at the Australian National University, 4-7 December 2007, Canberra, ACT Australi

Scale-free networks in complex systems

In the past few years, several studies have explored the topology of interactions in different complex systems. Areas of investigation span from biology to engineering, physics and the social sciences. Although having different microscopic dynamics, the results demonstrate that most systems under consideration tend to self-organize into structures that share common features. In particular, the networks of interaction are characterized by a power law distribution, $P(k)\sim k^{-\alpha}$, in the number of connections per node, $k$, over several orders of magnitude. Networks that fulfill this propriety of scale-invariance are referred to as ``scale-free''. In the present work we explore the implication of scale-free topologies in the antiferromagnetic (AF) Ising model and in a stochastic model of opinion formation. In the first case we show that the implicit disorder and frustration lead to a spin-glass phase transition not observed for the AF Ising model on standard lattices. We further illustrate that the opinion formation model produces a coherent, turbulent-like dynamics for a certain range of parameters. The influence, of random or targeted exclusion of nodes is studied.Comment: 9 pages, 4 figures. Proceeding to "SPIE International Symposium Microelectronics, MEMS, and Nanotechnology", 11-15 December 2005, Brisbane, Australi

Applications of physical methods in high-frequency futures markets

In the present work we demonstrate the application of different physical methods to high-frequency or tick-by-tick financial time series data. In particular, we calculate the Hurst exponent and inverse statistics for the price time series taken from a range of futures indices. Additionally, we show that in a limit order book the relaxation times of an imbalanced book state with more demand or supply can be described by stretched exponential laws analogous to those seen in many physical systems.Comment: 14 Pages and 10 figures. Proceeding to the SPIE conference, 4 - 7 December 2007 Australian National Univ. Canberra, ACT, Australi

Bond deterioration effects on corroded RC bridge pier in seismic zone

The effects of corrosion focusing on the consequences of bond strength deterioration for a reinforced concrete bridge pier in a seismic affected area are examined in this research. A bond degradation model based on the local bond stress-slip model presented in FIB Model Code 2010 is chosen. A motorway overpass object of a previous study, which considered the rebars cross-section reduction effect only, has been selected to assess the seismic capacity of the corroded pier in the time domain when bond degradation due to corrosion is also taken into account. The modification of strength capacity and ductility of the structural element is analyzed and the effect of corrosion during the whole service life of the structure is obtained. It is concluded that the effect of bond degradation is more critical for the safety of the pier than the effect of rebars cross-section loss

A Multi Agent Model for the Limit Order Book Dynamics

In the present work we introduce a novel multi-agent model with the aim to reproduce the dynamics of a double auction market at microscopic time scale through a faithful simulation of the matching mechanics in the limit order book. The agents follow a noise decision making process where their actions are related to a stochastic variable, "the market sentiment", which we define as a mixture of public and private information. The model, despite making just few basic assumptions over the trading strategies of the agents, is able to reproduce several empirical features of the high-frequency dynamics of the market microstructure not only related to the price movements but also to the deposition of the orders in the book.Comment: 20 pages, 11 figures, in press European Physical Journal B (EPJB

Study of semi-synthetic plastic objects of historic interest using non-invasive total reflectance FT-IR

A significant proportion of modern and contemporary artifacts and objects of historical interest, are composed of materials in the form of synthetic, semi-synthetic, and natural polymers. Each class of polymer and corresponding composite plastics are subject to different degradation processes. This means that conservators and curators of 20th century collections are faced with varied, nontrivial preservation issues. An unresolved problem is the identification of early plastics based on semi-synthetic polymers such as cellulose nitrate, cellulose acetate, and casein formaldehyde, which were often used to imitate the more valuable natural materials such as ivory, tortoiseshell, ebony, and bone. This exemplifies the need for non-invasive methods specifically tailored for identification of plastic materials in collections, so as to provide conservators with a means of materials classification to support preventive conservation strategies and interventive treatments. The present work is aimed at evaluating the effectiveness of non-invasive Total Reflectance (TR) FT-IR spectroscopy, coupled with a custom reference spectral TR FT-IR library, for the identification of materials comprising a series of unknown objects. A set of ten heritage objects made from early semi-synthetic materials was used as a training test set to validate the proposed methodological approach. The FT-IR data acquired on the test objects were pre-processed and finally classified using commercial software tools used for the automatic classification of spectra in FT-IR spectroscopy. The procedure was successfully applied to several cases, although residual uncertainties remained in a few examples. The results obtained are critically analyzed and discussed in the perspective of proposing a robust method for in-field prescreening of the chemical composition of plastic artistic and historical objects

Self-reported adherence supports patient preference for the single tablet regimen (STR) in the current cART era

Objective: To analyze self-reported adherence to antiretroviral regimens containing ritonavir-boosted protease inhibitors, non-nucleoside reverse transcriptase inhibitors (NNRTI), raltegravir, and maraviroc. Methods: Overall, 372 consecutive subjects attending a reference center for HIV treatment in Florence, Italy, were enrolled in the study, from December 2010 to January 2012 (mean age 48 years). A self-report questionnaire was filled in. Patients were defined as “non-adherent” if reporting one of the following criteria:<90% of pills taken in the last month, ≥1 missed dose in the last week, spontaneous treatment interruptions reported, or refill problems in the last 3 months. Gender, age, CD4, HIV-RNA, years of therapy, and type of antiretroviral regimen were analyzed with respect to adherence. Results: At the time of the questionnaire, 89.8% of patients had <50 copies/mL HIV-RNA and 14.2% were on their first combined antiretroviral therapy. 57% of patients were prescribed a regimen containing ritonavir boosted protease inhibitors (boosted PI), 41.7% NNRTI, 17.2% raltegravir, and 4.8% maraviroc; 49.5% of the subjects were on bis-in-die regimens, while 50.5% were on once-daily regimens, with 23.1% of these on the single tablet regimen (STR): tenofovir/emtricitabine/efavirenz. The non-adherence proportion was lower in NNRTI than in boosted-PI treatments (19.4% vs 30.2%), and even lower in STR patients (17.4%). In multivariable logistic regression, patients with the NNRTI regimen (OR: 0.56, 95% CI: 0.34–0.94) and the STR (OR: 0.45, 95% CI: 0.22–0.92) reported lower non-adherence. Efavirenz regimens were also associated with lower non-adherence (OR: 0.42, 95% CI: 0.21–0.83), while atazanavir/ritonavir regimens were associated with higher non-adherence. No other relation to specific antiretroviral drugs was found. A higher CD4 count, lower HIV-RNA, and older age were also found to be associated with lower non-adherence, while a longer time on combined antiretroviral therapy was related to higher non-adherence. Conclusion: In conclusion, older age, higher CD4 cell counts, lower HIV-RNA viral loads, and the use of STR are all related to lower non-adherence. In particular, the use of STR maintains an advantage in improving adherence with respect to other cARTs, even with the availability of new, well-tolerated antiretroviral drugs and drug classes in recent years

Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces

We propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN