Compact Markov-modulated models for multiclass trace fitting

Markov-modulated Poisson processes (MMPPs) are stochastic models for fitting empirical traces for simulation, workload characterization and queueing analysis purposes. In this paper, we develop the first counting process fitting algorithm for the marked MMPP (M3PP), a generalization of the MMPP for modeling traces with events of multiple types. We initially explain how to fit two-state M3PPs to empirical traces of counts. We then propose a novel form of composition, called interposition, which enables the approximate superposition of several two-state M3PPs without incurring into state space explosion. Compared to exact superposition, where the state space grows exponentially in the number of composed processes, in interposition the state space grows linearly in the number of composed M3PPs. Experimental results indicate that the proposed interposition methodology provides accurate results against artificial and real-world traces, with a significantly smaller state space than superposed processes

A Unifying Framework for the Approximate Solution of Closed Multiclass Queuing Networks

Queuing network models of modern computing systems must consider a large number of components (e.g., Web servers, DB servers, application servers, firewall, routers, networks) and hundreds of customers with very different resource requirements. The complexity of such models makes the application of exact solution techniques prohibitively expensive, motivating research on approximate methods. This paper proposes an interpolation-matching framework that allows a unified view of approximate solution techniques for closed product-form queuing networks. Depending upon the interpolating functional form and the matching populations selected, a large versatile family of new approximations can be generated. It is shown that all the known approximation strategies, including Linearizer, are instances of the interpolation-matching framework. Furthermore, a new approximation technique, based on a third-order polynomial, is obtained using the interpolation-matching framework. The new technique is shown to be more accurate than other known methods

Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances

In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one limitation of VQAs is their reliance on fixed-structure circuits, which may not be taylored for specific problems or hardware configurations. A leading strategy to address this issue are Adaptative VQAs, which dynamically modify the circuit structure by adding and removing gates, and optimize their parameters during the training. Several Adaptative VQAs, based on heuristics such as circuit shallowness, entanglement capability and hardware compatibility, have already been proposed in the literature, but there is still lack of a systematic comparison between the different methods. In this paper, we aim to fill this gap by analyzing three Adaptative VQAs: Evolutionary Variational Quantum Eigensolver (EVQE), Variable Ansatz (VAns), already proposed in the literature, and Random Adapt-VQE (RA-VQE), a random approach we introduce as a baseline. In order to compare these algorithms to traditional VQAs, we also include the Quantum Approximate Optimization Algorithm (QAOA) in our analysis. We apply these algorithms to QUBO problems and study their performance by examining the quality of the solutions found and the computational times required. Additionally, we investigate how the choice of the hyperparameters can impact the overall performance of the algorithms, highlighting the importance of selecting an appropriate methodology for hyperparameter tuning. Our analysis sets benchmarks for Adaptative VQAs designed for near-term quantum devices and provides valuable insights to guide future research in this area

Feature Selection for Classification with QAOA

Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real-world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step

Content-Based Multimedia Recommendation Systems: Definition and Application Domains

The goal of this work is to formally provide a general definition of a multimedia recommendation system (MMRS), in particular a content-based MMRS (CB-MMRS), and to shed light on different applications of multimedia content for solving a variety of tasks related to recommendation. We would like to disambiguate the fact that multimedia recommendation is not only about recommending a particular media type (e.g., music, video), rather there exists a variety of other applications in which the analysis of multimedia input can be usefully exploited to provide recommendations of various kinds of information

Isolation of intermediate compounds between hemoglobin and carbon monoxide.

A human hemoglobin solution partially saturated with carbon monoxide was rapidly quenched at -25 degrees C into a hydro-organic buffer containing ferricyanide. Under the experimental conditions of pH, ionic strength, and buffer composition used in this work, it was found that the deoxy hemes were rapidly transformed into their met form, whereas practically no carbon monoxide-bound hemes were oxidized before the separation of the mixture from the oxidizing agent. As a preliminary step to the analysis of the resulting solution, carbonylhemoglobin solutions partially oxidized with ferricyanide were studied by isoelectric focusing at -25 degrees C under identical conditions. The relative position in the gel of all nine possible valence hybrids was established as follows (going from the anodic to the cathodic side of the gel) alpha CO2 beta CO2, (alpha CO beta +)(alpha CO beta CO) (alpha CO beta CO), (alpha CO2 beta +2), (alpha + beta CO), (alpha + beta +)-(alpha CO beta CO), (alpha + beta +)(alpha CO beta +), (alpha +2 beta CO2), (alpha + beta +)(alpha + beta CO), alpha +2 beta +2. When carbonylhemoglobin and methemoglobin were mixed in equal proportion at -25 degrees C and then analyzed by isoelectric focusing at the same temperature, it was found that the contribution of valence hybrids other than alpha CO2 beta CO2 and alpha +2 beta +2 to the total amount of hemoglobin in the gel was no more than 6%. When carbonylhemoglobin and deoxyhemoglobin were mixed in the same proportion and incubated at 20 degrees C so to allow the redistribution of the carbon monoxide molecules between all possible binding sites to occur, a substantially higher amount of valence hybrids, derived from the oxidation of intermediate compounds of hemoglobin with carbon monoxide, was found. The isoelectric focusing separation indicated the presence in the original solution of intermediate species other than carbonylhemoglobin and deoxyhemoglobin at a concentration of about 10% of the total

A bolometric measurement of the antineutrino mass

High statistics calorimetric measurements of the beta spectrum of 187Re are being performed with arrays of silver perrhenate crystals operated at low temperature. After a modification of the experimental set-up, which allowed to substantially reduce the background of spurious counts and therefore to increase the sensitivity on the electron antineutrino mass, a new measurement with 10 silver perrhenate microbolometers is running since July 2002. The crystals have masses between 250 and 350 micrograms and their average FWHM energy resolution, constantly monitored by means of fluorescence X-rays, is of 28.3 eV at the beta end-point. The Kurie plot collected during 4485 hours x mg effective running time has an end-point energy of 2466.1 +/- 0.8{stat} +/- 1.5 {syst} eV, while the half lifetime of the decay is found to be 43.2 +/- 0.2{stat} +/- 0.1{syst} Gy. These values are the most precise obtained so far for 187Re. From the fit of the Kurie plot we can deduce a value for the squared electron antineutrino mass m(nu)^2 of 147 +/- 237{stat} +/- 90{syst} eV^2. The corresponding 90% C.L. upper limit for m(nu) is 21.7 eV.Comment: 3 pages, 3 figures. Submitted to Phys. Rev. Let

The 4 K outer cryostat for the CUORE experiment: construction and quality control

The external shell of the CUORE cryostat is a large cryogen-free system designed to host the dilution refrigerator and the bolometers of the CUORE experiment in a low radioactivity environment. The three vessels that form the outer shell were produced and delivered to the Gran Sasso underground Laboratories in July 2012. In this paper, we describe the production techniques and the validation tests done at the production site in 2012.Comment: 11 pages, 13 figures; to appear in NIM