Central bank independence: A paneldata approach

Central Banks;Panel Data

Bloodstream yeast infections: a 15-month survey

A 15-month survey of 412 bloodstream yeast isolates from 54 Belgian hospitals was undertaken. Candida albicans was the most common species (47.3%) followed by C. glabrata (25.7%), C. parapsilosis (8.0%), C. tropicalis (6.8%) and Saccharomyces cerevisiae (5.1%). Common predisposing factors were antibacterial therapy (45%), hospitalization in intensive care units (34%), presence of in-dwelling catheters (32%), underlying cancer (23%) and major surgery (11%). Most patients had more than one predisposing factor. Fluconazole alone or in combination with another antifungal agent was the treatment of choice for 86.6% of the cases. Susceptibility testing revealed that 93.5% were susceptible to amphotericin B, 39.6% to itraconazole, 42.8% to fluconazole and 87% to voriconazole. Resistance to azoles was more common among C. glabrata isolates

Enhanced heat flow in the hydrodynamic-collisionless regime

We study the heat conduction of a cold, thermal cloud in a highly asymmetric trap. The cloud is axially hydrodynamic, but due to the asymmetric trap radially collisionless. By locally heating the cloud we excite a thermal dipole mode and measure its oscillation frequency and damping rate. We find an unexpectedly large heat conduction compared to the homogeneous case. The enhanced heat conduction in this regime is partially caused by atoms with a high angular momentum spiraling in trajectories around the core of the cloud. Since atoms in these trajectories are almost collisionless they strongly contribute to the heat transfer. We observe a second, oscillating hydrodynamic mode, which we identify as a standing wave sound mode.Comment: Sumitted to Phys. Rev. Letters, 4 pages, 4 figure

Secure Sparse Gradient Aggregation in Distributed Architectures

Federated Learning allows multiple parties to train a model collaboratively while keeping data locally. Two main concerns when using Federated Learning are communication costs and privacy. A technique proposed to significantly reduce communication costs and increase privacy is Partial Weight Sharing (PWS). However, this method is insecure due to the possibility to reconstruct the original data from the partial gradients, called inversion attacks. In this paper, we propose a novel method to successfully combine these PWS and Secure Multi-Party Computation, a method for increasing privacy. This is done by making clients share the same part of their gradient, and adding noise to those entries, which are canceled on aggregation. We show that this method does not decrease the accuracy compared to existing methods while preserving privacy

Induction methods used in low temperature physics

A study has been made of induction bridges used in low temperature physics.\ud \ud In Part 1 the design of a mutual inductance bridge of the Hartshorn type is discussed. This design is based on a critical analysis of impurity effects of the different parts of the Hartshorn bridge. With this equipment frequencies up to 0.5 MHz can be used. Two methods have been developed to examine the secondary signal. In one of these use has been made of AD conversion techniques. In the other one, the secondary signal, produced by a superconducting sample, which is generally distorted, is analysed by using a Fourier expansion.\ud \ud In Part 2 equipment is described which enables us to measure the phase and amplitude of the harmonics of the output signal of the bridge. For synchronous detection a reference signal of the same frequency of the harmonic of interest is required. This reference signal is generated from the input signal of the bridge by means of a digital frequency multiplier with programmable multiplication factor N.\ud \ud In Part 3 some experimental results, showing the possibilities of the equipment, on some superconductors are presented

Adaptive Hedge

Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previous analyses the learning rate was carefully tuned to obtain optimal worst-case performance, leading to suboptimal performance on easy instances, for example when there exists an action that is significantly better than all others. We propose a new way of setting the learning rate, which adapts to the difficulty of the learning problem: in the worst case our procedure still guarantees optimal performance, but on easy instances it achieves much smaller regret. In particular, our adaptive method achieves constant regret in a probabilistic setting, when there exists an action that on average obtains strictly smaller loss than all other actions. We also provide a simulation study comparing our approach to existing methods.Comment: This is the full version of the paper with the same name that will appear in Advances in Neural Information Processing Systems 24 (NIPS 2011), 2012. The two papers are identical, except that this version contains an extra section of Additional Materia