A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques

In recent years, cubic regularization algorithms for unconstrained optimization have been defined as alternatives to trust-region and line search schemes. These regularization techniques are based on the strategy of computing an (approximate) global minimizer of a cubic overestimator of the objective function. In this work we focus on the adaptive regularization algorithm using cubics (ARC) proposed in Cartis et al. [Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results, Mathematical Programming A 127 (2011), pp. 245–295]. Our purpose is to design a modified version of ARC in order to improve the computational efficiency preserving global convergence properties. The basic idea is to suitably combine a Goldstein-type line search and a nonmonotone accepting criterion with the aim of advantageously exploiting the possible good descent properties of the trial step computed as (approximate) minimizer of the cubic model. Global convergence properties of the proposed nonmonotone ARC algorithm are proved. Numerical experiments are performed and the obtained results clearly show satisfactory performance of the new algorithm when compared to the basic ARC algorithm.     

Speeding up the spatial adiabatic passage of matter waves in optical microtraps by optimal control

We numerically investigate the performance of atomic transport in optical microtraps via the so called spatial adiabatic passage technique. Our analysis is carried out by means of optimal control methods, which enable us to determine suitable transport control pulses. We investigate the ultimate limits of the optimal control in speeding up the transport process in a triple well configuration for both a single atomic wave packet and a Bose-Einstein condensate within a regime of experimental parameters achievable with current optical technology.     

Quantum computing implementations with neutral particles

We review quantum information processing with cold neutral particles, that is, atoms or polar molecules. First, we analyze the best suited degrees of freedom of these particles for storing quantum information, and then we discuss both single- and two-qubit gate implementations. We focus our discussion mainly on collisional quantum gates, which are best suited for atom-chip-like devices, as well as on gate proposals conceived for optical lattices. Additionally, we analyze schemes both for cold atoms confined in optical cavities and hybrid approaches to entanglement generation, and we show how optimal control theory might be a powerful tool to enhance the speed up of the gate operations as well as to achieve high fidelities required for fault tolerant quantum computation.     

A pseudo-random network mobile automaton with linear growth

Based on the mobile automaton model, an algorithm is introduced that grows planar, tri-valent graphs by exhibiting a peculiar, twofold dynamics. In a first phase, graph growth appears to be pseudo-random and O(n) then it settles to a very regular behavior and rate. A pseudo-random mobile automaton is already known; the new automaton provides now a finite, but surprisingly long, pseudo-random, linear growth process. Applications of mobile automata to fundamental physics and quantum gravity have been recently suggested.     

The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks

The aim of this paper is to study and predict the effect of a number of critical parameters on the performance of virtual machines (VMs). These parameters include allocation percentages, real-time scheduling decisions and co-placement of VMs when these are deployed concurrently on the same physical node, as dictated by the server consolidation trend and the recent advances in the Cloud computing systems. Different combinations of VM workload types are investigated in relation to the aforementioned factors in order to find the optimal allocation strategies. What is more, different levels of memory sharing are applied, based on the coupling of VMs to cores on a multi-core architecture. For all the aforementioned cases, the effect on the score of specific benchmarks running inside the VMs is measured. Finally, a black box method based on genetically optimized artificial neural networks is inserted in order to investigate the degradation prediction ability a priori of the execution and is compared to the linear regression method.     

Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces

We present a novel logic-based framework to automate multi-issue bilateral negotiation in e-commerce settings. The approach exploits logic as communication language among agents, and optimization techniques in order to find Pareto-efficient agreements. We introduce , a propositional logic extended with concrete domains, which allows one to model relations among issues (both numerical and non-numerical ones) via logical entailment, differently from well-known approaches that describe issues as uncorrelated. Through it is possible to represent buyer’s request, seller’s supply and their respective preferences as formulas endowed with a formal semantics, e.g., “if I spend more than 30000 € for a sedan then I want more than a two-years warranty and a GPS system included”. We mix logic and utility theory in order to express preferences in a qualitative and quantitative way. We illustrate the theoretical framework, the logical language, the one-shot negotiation protocol we adopt, and show we are able to compute Pareto-efficient outcomes, using a mediator to solve an optimization problem. We prove the computational adequacy of our method by studying the complexity of the problem of finding Pareto-efficient solutions in our setting.     

Analysis and implementation of the multiprocessor bandwidth inheritance protocol

The Multiprocessor Bandwidth Inheritance (M-BWI) protocol is an extension of the Bandwidth Inheritance (BWI) protocol for symmetric multiprocessor systems. Similar to Priority Inheritance, M-BWI lets a task that has locked a resource execute in the resource reservations of the blocked tasks, thus reducing their blocking time. The protocol is particularly suitable for open systems where different kinds of tasks dynamically arrive and leave, because it guarantees temporal isolation among independent subsets of tasks without requiring any information on their temporal parameters. Additionally, if the temporal parameters of the interacting tasks are known, it is possible to compute an upper bound to the interference suffered by a task due to other interacting tasks. Thus, it is possible to provide timing guarantees for a subset of interacting hard real-time tasks. Finally, the M-BWI protocol is neutral to the underlying scheduling policy: it can be implemented in global, clustered and semi-partitioned scheduling. After introducing the M-BWI protocol, in this paper we formally prove its isolation properties, and propose an algorithm to compute an upper bound to the interference suffered by a task. Then, we describe our implementation of the protocol for the?LITMUS ^RT real-time testbed, and measure its overhead. Finally, we compare M-BWI against FMLP and OMLP, two other protocols for resource sharing in multiprocessor systems.     

Binding of Gamma-Glutamyl Transferase to TLR4 Signalling Allows Tissue Factor Activation in Monocytes

Gamma-glutamyl transferase (GGT) is involved in the progression of atherosclerosis, since its enzymatic activity promotes the generation of reactive oxygen species (ROS). Besides, GGT may act as a prothrombotic factor by inducing tissue factor (TF) expression, independently of its enzymatic activity. The aim of this study was to assess whether GGT-induced TF stimulation was a consequence of binding to toll-like receptor 4 (TLR4) expressed on monocytes, the precursors of macrophages and foam cells which colocalize with GGT activity within atherosclerotic plaques. Experiments were performed in human peripheral blood mononuclear cells (PBMCs), THP-1 cells (a monocytic cellular model), and HEK293 cells, which were genetically modified to study the activation of TLR4. TF procoagulant activity was assessed by a one-stage clotting time test, and TF protein expression was estimated by western blot. Human recombinant (hr) GGT protein increased TF procoagulant activity and protein expression in both PBMCs and THP-1 cells. The GGT-induced TF stimulation was prevented by cellular pretreatment with TLR4/NF-κB inhibitors (LPS-Rs, CLI-095, and BAY-11-7082), and HEK293 cells lacking TLR4 confirmed that TLR4 is essential for GGT-induced activation of NF-κB. In conclusion, hrGGT induced TF expression in monocytes through a cytokine-like mechanism that involved the activation of TLR4/NF-κB signaling.     

Modeling RTT Syndrome by iPSC-Derived Neurons from Male and Female Patients with Heterogeneously Severe Hot-Spot MECP2 Variants

Rett syndrome caused by MECP2 variants is characterized by a heterogenous clinical spectrum accounted for in 60% of cases by hot-spot variants. Focusing on the most frequent variants, we generated in vitro iPSC-neurons from the blood of RTT girls with p.Arg133Cys and p.Arg255*, associated to mild and severe phenotype, respectively, and of an RTT male harboring the close to p.Arg255*, p.Gly252Argfs*7 variant. Truncated MeCP2 proteins were revealed by Western blot and immunofluorescence analysis. We compared the mutant versus control neurons at 42 days for morphological parameters and at 120 days for electrophysiology recordings, including girls’ isogenic clones. A precocious reduced morphological complexity was evident in neurons with truncating variants, while in p.Arg133Cys neurons any significant differences were observed in comparison with the isogenic wild-type clones. Reduced nuclear size and branch number show up as the most robust biomarkers. Patch clamp recordings on mature neurons allowed the assessment of cell biophysical properties, V-gated currents, and spiking pattern in the mutant and control cells. Immature spiking, altered cell capacitance, and membrane resistance of RTT neurons, were particularly pronounced in the Arg255* and Gly252Argfs*7 mutants. The overall results indicate that the specific markers of in vitro cellular phenotype mirror the clinical severity and may be amenable to drug testing for translational purposes.     

Exploiting ELIOT for Scaffold-Repurposing Opportunities: TRIM33 a Possible Novel E3 Ligase to Expand the Toolbox for PROTAC Design

The field of targeted protein degradation, through the control of the ubiquitin–proteasome system (UPS), is progressing considerably; to exploit this new therapeutic modality, the proteolysis targeting chimera (PROTAC) technology was born. The opportunity to use PROTACs engaging of new E3 ligases that can hijack and control the UPS system could greatly extend the applicability of degrading molecules. To this end, here we show a potential application of the ELIOT (E3 LIgase pocketOme navigaTor) platform, previously published by this group, for a scaffold-repurposing strategy to identify new ligands for a novel E3 ligase, such as TRIM33. Starting from ELIOT, a case study of the cross-relationship using GRID Molecular Interaction Field (MIF) similarities between TRIM24 and TRIM33 binding sites was selected. Based on the assumption that similar pockets could bind similar ligands and considering that TRIM24 has 12 known co-crystalised ligands, we applied a scaffold-repurposing strategy for the identification of TRIM33 ligands exploiting the scaffold of TRIM24 ligands. We performed a deeper computational analysis to identify pocket similarities and differences, followed by docking and water analysis; selected ligands were synthesised and subsequently tested against TRIM33 via HTRF binding assay, and we obtained the first-ever X-ray crystallographic complexes of TRIM33α with three of the selected compounds.