Who and what links to the Internet Archive

The Internet Archive’s (IA) Wayback Machine is the largest and oldest public Web archive and has become a significant repository of our recent history and cultural heritage. Despite its importance, there has been little research about how it is discovered and used. Based on Web access logs, we analyze what users are looking for, why they come to IA, where they come from, and how pages link to IA. We find that users request English pages the most, followed by the European languages. Most human users come to Web archives because they do not find the requested pages on the live Web. About 65 % of the requested archived pages no longer exist on the live Web. We find that more than 82 % of human sessions connect to the Wayback Machine via referrals from other Web sites, while only 15 % of robots have referrers. Most of the links (86 %) from Websites are to individual archived pages at specific points in time, and of those 83 % no longer exist on the live Web. Finally, we find that users who come from search engines browse more pages than users who come from external Web sites.     

Preface

Annals of Mathematics and Artificial Intelligence -     

A lagrangian propagator for artificial neural networks in constraint programming

This paper discusses a new method to perform propagation over a (two-layer, feed-forward) Neural Network embedded in a Constraint Programming model. The method is meant to be employed in Empirical Model Learning, a technique designed to enable optimal decision making over systems that cannot be modeled via conventional declarative means. The key step in Empirical Model Learning is to embed a Machine Learning model into a combinatorial model. It has been showed that Neural Networks can be embedded in a Constraint Programming model by simply encoding each neuron as a global constraint, which is then propagated individually. Unfortunately, this decomposition approach may lead to weak bounds. To overcome such limitation, we propose a new network-level propagator based on a non-linear Lagrangian relaxation that is solved with a subgradient algorithm. The method proved capable of dramatically reducing the search tree size on a thermal-aware dispatching problem on multicore CPUs. The overhead for optimizing the Lagrangian multipliers is kept within a reasonable level via a few simple techniques. This paper is an extended version of [27], featuring an improved structure, a new filtering technique for the network inputs, a set of overhead reduction techniques, and a thorough experimentation.     

Contention-based forwarding with multi-hop connectivity awareness in vehicular ad-hoc networks

Recent vehicular routing proposals use real-time road traffic density estimates to dynamically select forwarding paths. Estimating the traffic density in vehicular ad hoc networks requires the transmission of additional dedicated messages increasing the communications load. These proposals are generally based on unicast sender-based forwarding schemes. The greedy nature of sender-based forwarding can result in the selection of forwarders with weak radio links that might compromise the end-to-end performance. To overcome these limitations, this paper presents TOPOCBF, a novel contention-based broadcast forwarding protocol that dynamically selects forwarding paths based on their capability to route packets between anchor points. Such capability is estimated by means of a multi-hop connectivity metric. The obtained results demonstrate that TOPOCBF can provide good packet delivery ratios while reducing the communications load compared to unicast sender-based forwarding schemes using road traffic density estimates.     

Approximation processes for resolvent operators

We introduce general sequences of linear operators obtained from classical approximation processes which are useful in the approximation of the resolvent operators of the generators of suitable C ₀-semigroups. The main aim is the representation of the resolvent operators in terms of classical approximation operators. Work performed under the auspices of PRIN 2006–07 “Kolmogorov equations” (coordinator G. Da Prato)     

Exploiting probability density function of deep convolutional autoencoders’ latent space for reliable COVID-19 detection on CT scans

The Journal of Supercomputing - We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an...     

Hepatic cysts: a case report and anatomicopathological and clinico-therapeutic considerations

The recent observation of a case of non-parasitic cyst of the liver brought the authors to a literature review. A 62 year old male, affected by type 2 diabetes and hypertension, after a CT scan and ETG, underwent resection and "capitonnage" of the cyst. Three months after surgery a CT scan showed a complete repletion of the cavity previously occupied by the cyst as a consequence of regeneration and reorganization of the hepatic parenchyma. In conclusion, hepatic cysts are rare and clinically relevant only when huge. The diagnosis is possible with the use of ETG and CT scan of the abdomen, however, in some cases angiography is also useful. The intervention of choice is the "capitonnage" of the cyst.     

Optimal-tuning PID control of adaptive materials for structural efficiency

The objective of this paper was solving the optimization problem of lightweight stiffened structures modelled as a two-dimensional domain in an efficient computational way. The underlying premise was that mass should be distributed in an efficient way, so as to use a minimum amount of material to accomplish the mechanical function. This premise was expressed as a global, multi-objective optimization problem in which stiffness and mass were conflicting objectives. Alternative local evolution rules were implemented to update mass density or Young’s modulus at each step of the iterative procedure. The solution of the structural optimization problem was accomplished by a novel automatic procedure consisting of two consecutive stages of control and optimization. In the first stage of Proportional Integral Derivative (PID) control gains were manually selected whereas in the second stage the finding of optimal values of control gains, target, and cost indices was allowed. In this study a bone-like material was adopted and a thin slab was analysed as a sample problem.     

A declarative two-level framework to specify and verify workflow and authorization policies in service-oriented architectures

The specification of distributed service-oriented applications spans several levels of abstraction, e.g., the protocol for exchanging messages, the set of interface functionalities, the types of the manipulated data, the workflow, the access policy, etc. Many (even executable) specification languages are available to describe each level in separation. However, these levels may interact in subtle ways (for example, the control flow may depend on the values of some data variables) so that a precise abstraction of the application amounts to more than the sum of its per level components. This problem is even more acute in the design phase when automated analysis techniques may greatly help the difficult task of building “correct” applications faced by designers. To alleviate this kind of problems, this paper introduces a framework for the formal specification and automated analysis of distributed service-oriented applications in two levels: one for the workflow and one for the authorization policies. The former allows one to precisely describe the control and data parts of an application with their mutual dependencies. The latter focuses on the specification of the criteria for granting or denying third-party applications the possibility to access shared resources or to execute certain interface functionalities. These levels can be seen as abstractions of one or of several levels of specification mentioned above. The novelty of our proposal is the possibility to unambiguously specify the—often subtle—interplay between the workflow and policy levels uniformly in the same framework. Additionally, our framework allows us to define and investigate verification problems for service-oriented applications (such as executability and invariant checking) and give sufficient conditions for their decidability. These results are non-trivial because their scope of applicability goes well beyond the case of finite state spaces allowing for applications manipulating variables ranging over infinite domains. As proof of concept, we show the suitability and flexibility of our approach on two quite different examples inspired by industrial case studies.     

Modified HSS iteration methods for a class of complex symmetric linear systems

In this paper, we introduce and analyze a modification of the Hermitian and skew-Hermitian splitting iteration method for solving a broad class of complex symmetric linear systems. We show that the modified Hermitian and skew-Hermitian splitting (MHSS) iteration method is unconditionally convergent. Each iteration of this method requires the solution of two linear systems with real symmetric positive definite coefficient matrices. These two systems can be solved inexactly. We consider acceleration of the MHSS iteration by Krylov subspace methods. Numerical experiments on a few model problems are used to illustrate the performance of the new method.