Monogamy and ground-state entanglement in highly connected systems

We consider the ground-state entanglement in highly connected many-body systems consisting of harmonic oscillators and spin-1/2 systems. Varying their degree of connectivity, we investigate the interplay between the enhancement of entanglement, due to connections, and its frustration, due to monogamy constraints. Remarkably, we see that in many situations the degree of entanglement in a highly connected system is essentially of the same order as in a low connected one. We also identify instances in which the entanglement decreases as the degree of connectivity increases.

Reconstructing the quantum state of oscillator networks with a single qubit

We introduce a scheme to reconstruct arbitrary states of networks composed of quantum oscillators-e. g., the motionalstate of trapped ions or the radiation state of coupled cavities. The scheme involves minimal resources and minimal access, in the sense that it (i) requires only the interaction between a one-qubit probe and a single node of the network; (ii) provides the Weyl characteristic function of the network directly from the data, avoiding any tomographic transformation; (iii) involves the tuning of only one coupling parameter. In addition, we show that a number of quantum properties can be extracted without full reconstruction of the state. The scheme can be used for probing quantum simulations of anharmonic many-body systems and quantum computations with continuous variables. Experimental implementation with trapped ions is also discussed and shown to be within reach of current technology.

Coherently Opening a High-Q Cavity

We propose a general framework to effectively `open' a high-Q resonator, that is, to release the quantum state initially prepared in it in the form of a traveling electromagnetic wave. This is achieved by employing a mediating mode that scatters coherently the radiation from the resonator into a one-dimensional continuum of modes such as a waveguide. The same mechanism may be used to `feed' a desired quantum field to an initially empty cavity. Switching between an `open' and `closed' resonator may then be obtained by controlling either the detuning of the scatterer or the amount of time it spends in the resonator. First, we introduce the model in its general form, identifying (i) the traveling mode that optimally retains the full quantum information of the resonator field and (ii) a suitable figure of merit that we study analytically in terms of the system parameters. Then, we discuss two feasible implementations based on ensembles of two-level atoms interacting with cavity fields. In addition, we discuss how to integrate traditional cavity QED in our proposal using three-level atoms.

Nonlinearity Induced Entanglement Stability in a Qubit-Oscillator System

We consider a system composed of a qubit interacting with a quartic (undriven) nonlinear oscillator (NLO) through a conditional displacement Hamiltonian. We show that even a modest nonlinearity can enhance and stabilize the quantum entanglement dynamically generated between the qubit and the NLO. In contrast to the linear case, in which the entanglement is known to oscillate periodically between zero and its maximal value, the nonlinearity suppresses the dynamical decay of the entanglement once it is established. While the entanglement generation is due to the conditional displacements, as noted in several works before, the suppression of its decay is related to the presence of squeezing and other complex processes induced by two- and four-phonon interactions. Finally, we solve the respective Markovian master equation, showing that the previous features are preserved also when the system is open.

The Kepler-10 Planetary System Revisited by HARPS-N: A Hot Rocky World and a Solid Neptune-Mass Planet

Kepler-10b was the first rocky planet detected by the Kepler satellite and confirmed with radial velocity follow-up observations from Keck-HIRES. The mass of the planet was measured with a precision of around 30%, which was
insufficient to constrain models of its internal structure and composition in detail. In addition to Kepler-10b, a second planet transiting the same star with a period of 45 days was statistically validated, but the radial velocities were only
good enough to set an upper limit of 20 M⊕ for the mass of Kepler-10c. To improve the precision on the mass for planet b, the HARPS-N Collaboration decided to observe Kepler-10 intensively with the HARPS-N spectrograph
on the Telescopio Nazionale Galileo on La Palma. In total, 148 high-quality radial-velocity measurements were obtained over two observing seasons. These new data allow us to improve the precision of the mass determination for Kepler-10b to 15%. With a mass of 3.33 ± 0.49 M⊕ and an updated radius of 1.47+0.03 −0.02 R⊕, Kepler-10b has a density of 5.8 ± 0.8 g cm−3, very close to the value predicted by models with the same internal structure and composition as the Earth. We were also able to determine a mass for the 45-day period planet Kepler-10c, with an even better precision of 11%. With a mass of 17.2 ± 1.9 M⊕ and radius of 2.35+0.09 −0.04 R⊕, Kepler-10c has a density of 7.1 ± 1.0 g cm−3. Kepler-10c appears to be the first strong evidence of a class of more massive solid planets with longer orbital periods

Prediction of integral type failures in semiconductor manufacturing through classification methods

Smart management of maintenances has become fundamental in manufacturing environments in order to decrease downtime and costs associated with failures. Predictive Maintenance (PdM) systems based on Machine Learning (ML) techniques have the possibility with low added costs of drastically decrease failures-related expenses; given the increase of availability of data and capabilities of ML tools, PdM systems are becoming really popular, especially in semiconductor manufacturing. A PdM module based on Classification methods is presented here for the prediction of integral type faults that are related to machine usage and stress of equipment parts. The module has been applied to an important class of semiconductor processes, ion-implantation, for the prediction of ion-source tungsten filament breaks. The PdM has been tested on a real production dataset. © 2013 IEEE.

A predictive maintenance system for integral type faults based on support vector machines:An application to ion implantation

In semiconductor fabrication processes, effective management of maintenance operations is fundamental to decrease costs associated with failures and downtime. Predictive Maintenance (PdM) approaches, based on statistical methods and historical data, are becoming popular for their predictive capabilities and low (potentially zero) added costs. We present here a PdM module based on Support Vector Machines for prediction of integral type faults, that is, the kind of failures that happen due to machine usage and stress of equipment parts. The proposed module may also be employed as a health factor indicator. The module has been applied to a frequent maintenance problem in semiconductor manufacturing industry, namely the breaking of the filament in the ion-source of ion-implantation tools. The PdM has been tested on a real production dataset. © 2013 IEEE.

Machine Learning for Predictive Maintenance: A Multiple Classifiers Approach

In this paper a multiple classifier machine learning methodology for Predictive Maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating so called ’health factors’ or quantitative indicators of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance trade-offs in terms of frequency of unexpected breaks and unexploited lifetime and then employing this information in an operating cost based maintenance decision system to minimise expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

Gremlin1 preferentially binds to Bone Morphogenetic Protein-2 (BMP-2) and BMP-4 over BMP-7

Gremlin (Grem1) is a member of the DAN family of secreted bone morphogenetic protein (BMP) antagonists. Bone morphogenetic protein-7 (BMP-7) mediates protective effects during renal fibrosis-associated with diabetes and other renal diseases. The pathogenic mechanism of Grem1 during DN has been suggested to be binding and inhibition of BMP-7. However, the precise interactions between Grem1, BMP-7 and other BMPs have not been accurately defined. Here we show the affinity of Grem1 for BMP-7 is lower than that of BMP-2 and BMP-4, using a combination of surface plasmon resonance and cell culture techniques. Using kidney proximal tubule cells and HEK-293 cell Smad1/5/8 phosphorylation and BMP-dependent gene expression as readout, Grem1 consistently demonstrated a higher affinity for BMP-2>4>7. Cell-associated Grem1 did not inhibit BMP-2 or BMP-4 mediated signalling, suggesting that Grem1-BMP-2 binding occurred in solution, preventing BMP receptor activation. These data suggest that Grem1 preferentially binds to BMP-2 and this may be the dominant complex in a disease situation where levels of Grem1 and BMPs are elevated.

Global Sensitivity Analysis for MAP Inference in Graphical Models

We study the sensitivity of a MAP configuration of a discrete probabilistic graphical model with respect to perturbations of its parameters. These perturbations are global, in the sense that simultaneous perturbations of all the parameters (or any chosen subset of them) are allowed. Our main contribution is an exact algorithm that can check whether the MAP configuration is robust with respect to given perturbations. Its complexity is essentially the same as that of obtaining the MAP configuration itself, so it can be promptly used with minimal effort. We use our algorithm to identify the largest global perturbation that does not induce a change in the MAP configuration, and we successfully apply this robustness measure in two practical scenarios: the prediction of facial action units with posed images and the classification of multiple real public data sets. A strong correlation between the proposed robustness measure and accuracy is verified in both scenarios.

Approximate credal network updating by linear programming with applications to decision making

Credal nets are probabilistic graphical models which extend Bayesian nets to cope with sets of distributions. An algorithm for approximate credal network updating is presented. The problem in its general formulation is a multilinear optimization task, which can be linearized by an appropriate rule for fixing all the local models apart from those of a single variable. This simple idea can be iterated and quickly leads to accurate inferences. A transformation is also derived to reduce decision making in credal networks based on the maximality criterion to updating. The decision task is proved to have the same complexity of standard inference, being NPPP-complete for general credal nets and NP-complete for polytrees. Similar results are derived for the E-admissibility criterion. Numerical experiments confirm a good performance of the method.

On the Complexity of Strong and Epistemic Credal Networks

Credal networks are graph-based statistical models whose parameters take values on a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The result of inferences with such models depends on the irrelevance/independence concept adopted. In this paper, we study the computational complexity of inferences under the concepts of epistemic irrelevance and strong independence. We strengthen complexity results by showing that inferences with strong independence are NP-hard even in credal trees with ternary variables, which indicates that tractable algorithms, including the existing one for epistemic trees, cannot be used for strong independence. We prove that the polynomial time of inferences in credal trees under epistemic irrelevance is not likely to extend to more general models, because the problem becomes NP-hard even in simple polytrees. These results draw a definite line between networks with efficient inferences and those where inferences are hard, and close several open questions regarding the computational complexity of such models.

Generalized Loopy 2U: A New Algorithm for Approximate Inference in Credal Networks

Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal nets. The algorithm is based on an important representation result we prove for general credal nets: that any credal net can be equivalently reformulated as a credal net with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal net is updated by L2U, a loopy approximate algorithm for binary credal nets. Thus, we generalize L2U to non-binary credal nets, obtaining an accurate and scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences is evaluated by empirical tests.

Characterizing K2 Planet Discoveries: A super-Earth transiting the bright K-dwarf HIP 116454

We report the first planet discovery from the two-wheeled Kepler (K2) mission: HIP 116454 b. The host star HIP 116454 is a bright (V = 10.1, K = 8.0) K1 dwarf with high proper motion and a parallax-based distance of 55.2 +/- 5.4 pc. Based on high-resolution optical spectroscopy, we find that the host star is metal-poor with [Fe/H]= -0.16 +/- 0.08 and has a radius R-star = 0.716 +/- 0.024 R-circle dot and mass M-star = 0.775 +/- 0.027M(circle dot). The star was observed by the Kepler spacecraft during its Two-Wheeled Concept Engineering Test in 2014 February. During the 9 days of observations, K2 observed a single transit event. Using a new K2 photometric analysis technique, we are able to correct small telescope drifts and recover the observed transit at high confidence, corresponding to a planetary radius of R-p = 2.53 +/- 0.18 R-circle plus. Radial velocity observations with the HARPS-N spectrograph reveal a 11.82 +/- 1.33 M-circle plus planet in a 9.1 day orbit, consistent with the transit depth, duration, and ephemeris. Follow-up photometric measurements from the MOST satellite confirm the transit observed in the K2 photometry and provide a refined ephemeris, making HIP 116454 b amenable for future follow-up observations of this latest addition to the growing population of transiting super-Earths around nearby, bright stars.

Invited Interview on Network Performance in the "Transient Gestures" issue

An issue on Networked Performance produceed by Neural for which I was invited to deliver an interview on network performance. Neural is a printed magazine established in 1993 dealing with new media art, electronic music and hacktivism. It was founded by Alessandro Ludovico and Minus Habens Records label owner Ivan Iusco in Bari (Italy).