Lazy algorithms for exact real arithmetic

In this article we propose a new representation for the real numbers. This representation can be conveniently used to implement exact real number computation with a lazy programming languages. In fact the new representation permits the exploitation of hardware implementation of arithmetic functions without generating the granularity problem. Moreover we present a variation of the Karatsuba algorithm for multiplication of integers. The new algorithm performs exact real number multiplication in a lazy way and has a lower complexity than the standard algorithm. \ua9 2004 Elsevier B.V. All rights reserved

Improvement in 6-Minute Walking Distance after Supervised Exercise Training Is Related to Changes in Quality of Life in Patients with Lower Extremity Peripheral Artery Disease.

This study aimed to investigate the relationship between supervised exercise training (SET)-induced changes in treadmill performance and 6 min walking distance, and changes in general (physical and mental) self-perceived health-related quality of life (HRQoL) in symptomatic patients with lower extremity peripheral artery disease (PAD). This is an observational study investigating Fontaine stage II PAD patients participating in 3-month SET. Before and following SET, treadmill performance (pain-free (PFWD) and maximal (MWD)), and 6 min walking distance (6MWD) were assessed. Self-perceived HRQoL was assessed with the Medical Outcomes Study Short-Form 36 (SF-36). Ankle- and toe-brachial indexes were also measured. One-hundred forty-seven patients with PAD were included (64.9 ± 9.6 y, 70% men). After SET, PFWD (+102%, p ≤ 0.001), MWD (+87%, p ≤ 0.001), and 6MWD (+14%, p ≤ 0.001) significantly increased. All eight SF-36 subscale scores significantly improved following SET (p ≤ 0.04). SET significantly improved physical and mental component summaries of the SF-36 (p ≤ 0.001). Larger increases in 6MWD were associated with greater improvements in physical (β = 0.19; p = 0.02) and mental (β = 0.24; p = 0.005) component summaries of the SF-36. No significant relationship was observed between changes in treadmill performance and changes in physical and mental component summaries of the SF-36. These results show that improvements in 6MWD following SET are related to improvements in general self-perceived HRQoL in patients with symptomatic lower extremity PAD. On the contrary, changes in treadmill performance were not related to improvements in HRQoL. These results suggest that the 6 min walking test is an essential outcome measure to assess overall patient functional status following interventions in patients with PAD

Detecting (Absent) App-to-app authentication on cross-device short-distance channels

Short-distance or near-field communication is increasingly used by mobile apps for interacting or exchanging data in a cross-device fashion. In this paper, we identify a security issue, namely cross-device app-to-app communication hijacking (or CATCH), that affect Android apps using short-distance channels (e.g., Bluetooth and Wi-Fi-Direct). This issue causes unauthenticated or malicious app-to-app interactions even when the underlying communication channels are authenticated and secured. In addition to discovering the security issue, we design an algorithm based on data-flow analysis for detecting the presence of CATCH in Android apps. Our algorithm checks if a given app contains an app-to-app authentication scheme, necessary for preventing CATCH. We perform experiments on a set of Android apps and show the CATCH problem is always present on the whole analyzed applications set. We also discuss the impact of the problem in real scenarios by presenting two real case studies. At the end of the paper we reported limitations of our model along with future improvements

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Sustainability perspectives: a new methodological approach for quantitative assessment

This paper proposes a new tool to assess sustainability and make the concept of sustainable development operational. It considers its multi-dimensional structure combining the information deriving from a selection of relevant sustainability indicators belonging to economic, social and environmental pillars. The main novelties of this approach are the modelling framework, a recursive-dynamic computable general equilibrium used to calculate the trend of all indicators over time throughout the world, and the aggregation methodology to reconcile them in one aggregate index to measure overall sustainability. The former allows capturing the sector and regional interactions and higher-order effects driven by background assumptions on relevant variables to depict future scenarios. The latter makes it possible to compare sustainability performances, under alternative scenarios, across countries and over time. Main results show that the current sustainability at world level differs from what the traditional measure of well-being, the GDP, depicts, highlighting the trade-offs among different components of sustainability. Moreover, in the next decade a slight decrease in world sustainability may occur, in spite of an expected increase in world domestic product. Finally, dedicated policies increase overall sustainability, showing that social and environmental benefits may be greater than the correlated economic costs

Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%

Targeting of RET oncogene by naphthalene diimide-mediated gene promoter G-quadruplex stabilization exerts anti-tumor activity in oncogene-addicted human medullary thyroid cancer

Medullary thyroid cancer (MTC) relies on the aberrant activation of RET proto-oncogene. Though targeted approaches (i.e., tyrosine kinase inhibitors) are available, the absence of complete responses and the onset of resistance mechanisms indicate the need for novel therapeutic interventions. Due to their role in regulation of gene expression, G-quadruplexes (G4) represent attractive targets amenable to be recognized or stabilized by small molecules. Here, we report that exposure of MTC cells to a tri-substituted naphthalene diimide (NDI) resulted in a significant antiproliferative activity paralleled by inhibition of RET expression. Biophysical analysis and gene reporter assays showed that impairment of RET expression was consequent to the NDI-mediated stabilization of the G4 forming within the gene promoter. We also showed for the first time that systemic administration of the NDI in mice xenotransplanted with MTC cells resulted in a remarkable inhibition of tumor growth in vivo. Overall, our findings indicate that NDI-dependent RET G4 stabilization represents a suitable approach to control RET transcription and delineate the rationale for the development of G4 stabilizing-based treatments for MTC as well as for other tumors in which RET may have functional and therapeutic implications