Phase separation and pairing regimes in the one-dimensional asymmetric Hubbard model

We address some open questions regarding the phase diagram of the one-dimensional Hubbard model with asymmetric hopping coefficients and balanced species. In the attractive regime we present a numerical study of the passage from on-site pairing dominant correlations at small asymmetries to charge-density waves in the region with markedly different hopping coefficients. In the repulsive regime we exploit two analytical treatments in the strong- and weak-coupling regimes in order to locate the onset of phase separation at small and large asymmetries respectively.Comment: 13 pages, RevTeX 4, 12 eps figures, some additional refs. with respect to v1 and citation errors fixe

Towards Reinforcement Learning-based Aggregate Computing

Recent trends in pervasive computing promote the vision of Collective Adaptive Systems (CASs): large-scale collections of relatively simple agents that act and coordinate with no central orchestrator to support distributed applications. Engineering global behaviour out of local activity and interaction, however, is a difficult task, typically addressed by try-and-error approaches in simulation environments. In the context of Aggregate Computing (AC), a prominent functional programming approach for CASs based on field-based coordination, this difficulty is reflected in the design of versatile algorithms preserving efficiency in a variety of environments. To deal with this complexity, in this work we propose to apply Machine Learning techniques to automatically devise local actions to improve over manually-defined AC algorithms specifications. Most specifically, we adopt a Reinforcement Learning-based approach to let a collective learn local policies to improve over the standard gradient algorithm—a cornerstone brick of several higher-level self-organisation algorithms. Our evaluation shows that the learned policies can speed up the self-stabilisation of the gradient to external perturbations

Machine Learning for Aggregate Computing: a Research Roadmap

Aggregate computing is a macro-approach for programming collective intelligence and self-organisation in distributed systems. In this paradigm, a single 'aggregate program' drives the collective behaviour of the system, provided that the agents follow an execution protocol consisting of asynchronous sense-compute-act rounds. For actual execution, a proper aggregate computing middleware or platform has to be deployed across the nodes of the target distributed system, to support the services needed for the execution of applications. Overall, the engineering of aggregate computing applications is a rich activity that spans multiple concerns including designing the aggregate program, developing reusable algorithms, detailing the execution model, and choosing a deployment based on available infrastructure. Traditionally, these activities have been carried out through ad-hoc designs and implementations tailored to specific contexts and goals. To overcome the complexity and cost of manually tailoring or fixing algorithms, execution details, and deployments, we propose to use machine learning techniques, to automatically create policies for applications and their management. To support such a goal, we detail a rich research roadmap, showing opportunities and challenges of integrating aggregate computing and learning

Estimating the selection efficiency

The measurement of the efficiency of an event selection is always an important part of the analysis of experimental data. The statistical techniques which are needed to determine the efficiency and its uncertainty are reviewed. Frequentist and Bayesian approaches are illustrated, and the problem of choosing a meaningful prior is explicitly addressed. Several practical use cases are considered, from the problem of combining different samples to complex situations in which non-unit weights or non-independent selections have been used. The Bayesian approach allows to find analytical expressions which solve even the most complicate problems, which make use of the family of Beta distributions, the conjugate priors for the binomial sampling

Effect of mobile carrier on the performance of pvam–nanocellulose facilitated transport membranes for co2 capture

Facilitated transport membranes obtained by coupling polyvinylamine with highly charged carboxymethylated nanocellulose fibers were studied considering both water sorption and gas permeation experiments. In particular, the effect of the L-arginine as a mobile carrier was investigated to understand possible improvements in CO2 transport across the membranes. The results show that L-arginine addition decreases the water uptake of the membrane, due to the lower polyvinylamine content, but was able to improve the CO2 transport. Tests carried on at 35◦ C and high relative humidity indeed showed an increase of both CO2 permeability and selectivity with respect to nitrogen and methane. In particular, the CO2 permeability increased from 160 to about 340 Barrer when arginine loading was increased from 0 to 45 wt%. In the same conditions, selectivity with respect to nitrogen was more than doubled, increasing from 20 to 45. Minor improvements were instead obtained with respect to methane; CO2 /CH4 selectivity, indeed, even in presence of the mobile carrier, was limited to about 20

Computation Against a Neighbour: Addressing Large-Scale Distribution and Adaptivity with Functional Programming and Scala

Recent works in contexts like the Internet of Things (IoT) and large-scale Cyber-Physical Systems (CPS) propose the idea of programming distributed systems by focussing on their global behaviour across space and time. In this view, a potentially vast and heterogeneous set of devices is considered as an “aggregate” to be programmed as a whole, while abstracting away the details of individual behaviour and exchange of messages, which are expressed declaratively. One such a paradigm, known as aggregate programming, builds on computational models inspired by field-based coordination. Existing models such as the field calculus capture interaction with neighbours by a so-called “neighbouring field” (a map from neighbours to values). This requires ad-hoc mechanisms to smoothly compose with standard values, thus complicating programming and introducing clutter in aggregate programs, libraries and domain-specific languages (DSLs). To address this key issue we introduce the novel notion of “computation against a neighbour”, whereby the evaluation of certain subexpressions of the aggregate program are affected by recent corresponding evaluations in neighbours. We capture this notion in the neighbours calculus (NC), a new field calculus variant which is shown to smoothly support declarative specification of interaction with neighbours, and correspondingly facilitate the embedding of field computations as internal DSLs in common general-purpose programming languages—as exemplified by a Scala implementation, called ScaFi. This paper formalises NC, thoroughly compares it with respect to the classic field calculus, and shows its expressiveness by means of a case study in edge computing, developed in ScaFi

Oxidative Molecular Mechanisms Underlying Liver Diseases: From Systems Biology to the Personalized Medicine

N/

Augmented Collective Digital Twins for Self-Organising Cyber-Physical Systems

Context. Self-organising and collective computing approaches are increasingly applied to large-scale cyber-physical systems (CPS), enabling them to adapt and cooperate in dynamic environments. Also, in CPS engineering, digital twins are often leveraged to provide synchronised logical counterparts of physical entities, whereas in sensor networks the different-but-related concept of virtual device is used e.g. to abstract groups of sensors. Vision. We envision the design concept of 'augmented collective digital twin' that captures digital twins at a collective level extended with purely virtual devices. We argue that this concept can foster the engineering of self-organising CPS by providing a holistic, declarative, and integrated system view. Method. From a review and proposed taxonomy of logical devices comprehending both digital twins and virtual devices, we reinterpret a meta-model for self-organising CPSs and discuss how it can support augmented collective digital twins. We illustrate the approach in a crowd-aware navigation scenario, where virtual devices are opportunistically integrated into the system to enhance spatial coverage, improving navigation capabilities. Conclusion. By integrating physical and virtual devices, the novel notion of augmented collective digital twin paves the way to self-improving system functionality and intelligent use of resources in self-organising CPSs. Conclusion. By integrating physical and virtual devices, the novel notion of augmented collective digital twin paves the way to self-improving system functionality and intelligent use of resources in self-organising CPSs

Treatment of squamous cell carcinoma of the anal canal: A new strategies with anti-EGFR therapy and immunotherapy

The incidence of squamous cell carcinoma of the anal canal (SCAC) is increasing in both sexes but the standard treatment remains that of 20 years ago. However, interesting data have recently emerged on the use of anti-epidermal growth factor receptor (EGFR) agents and immunotherapy in advanced disease. Thus, new avenues of research are opening up that will hopefully lead to more effective therapeutic strategies. We provide an overview of the latest studies published on this tumor and discuss the possible future therapeutic options for combination therapy, anti-EGFR treatment and radiotherapy