A Supercooled Spin Liquid State in the Frustrated Pyrochlore Dy2Ti2O7

A "supercooled" liquid develops when a fluid does not crystallize upon cooling below its ordering temperature. Instead, the microscopic relaxation times diverge so rapidly that, upon further cooling, equilibration eventually becomes impossible and glass formation occurs. Classic supercooled liquids exhibit specific identifiers including microscopic relaxation times diverging on a Vogel-Tammann-Fulcher (VTF) trajectory, a Havriliak-Negami (HN) form for the dielectric function, and a general Kohlrausch-Williams-Watts (KWW) form for time-domain relaxation. Recently, the pyrochlore Dy2Ti2O7 has become of interest because its frustrated magnetic interactions may, in theory, lead to highly exotic magnetic fluids. However, its true magnetic state at low temperatures has proven very difficult to identify unambiguously. Here we introduce high-precision, boundary-free magnetization transport techniques based upon toroidal geometries and gain a fundamentally new understanding of the time- and frequency-dependent magnetization dynamics of Dy2Ti2O7. We demonstrate a virtually universal HN form for the magnetic susceptibility, a general KWW form for the real-time magnetic relaxation, and a divergence of the microscopic magnetic relaxation rates with precisely the VTF trajectory. Low temperature Dy2Ti2O7 therefore exhibits the characteristics of a supercooled magnetic liquid; the consequent implication is that this translationally invariant lattice of strongly correlated spins is evolving towards an unprecedented magnetic glass state, perhaps due to many-body localization of spin.Comment: Version 2 updates: added legend for data in Figures 4A and 4B; corrected equation reference in caption for Figure 4

Enhanced and continuous electrostatic carrier doping on the SrTiO3_{3} surface

Paraelectrical tuning of a charge carrier density as high as 10$^{13}$\,cm$^{-2}$ in the presence of a high electronic carrier mobility on the delicate surfaces of correlated oxides, is a key to the technological breakthrough of a field effect transistor (FET) utilising the metal-nonmetal transition. Here we introduce the Parylene-C/Ta$_{2}$O$_{5}$ hybrid gate insulator and fabricate FET devices on single-crystalline SrTiO$_{3}$, which has been regarded as a bedrock material for oxide electronics. The gate insulator accumulates up to $\sim10^{13}$cm$^{-2}$ carriers, while the field-effect mobility is kept at 10\,cm$^2$/Vs even at room temperature. Further to the exceptional performance of our devices, the enhanced compatibility of high carrier density and high mobility revealed the mechanism for the long standing puzzle of the distribution of electrostatically doped carriers on the surface of SrTiO$_{3}$. Namely, the formation and continuous evolution of field domains and current filaments.Comment: Supplementary Information: <http://www.nature.com/srep/2013/130424/srep01721/extref/srep01721-s1.pdf

Statistical mechanics of budget-constrained auctions

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). Based on the cavity method of statistical mechanics, we introduce a message passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.Comment: Minor revisio

Nonfatal reinfarction as an independent riskfactor for subsequent mortality in post-myocardial infarction patients

Pericas, Enric;Ordóñez, Estrell

Global Ultrasound Elastography Using Convolutional Neural Network

Displacement estimation is very important in ultrasound elastography and failing to estimate displacement correctly results in failure in generating strain images. As conventional ultrasound elastography techniques suffer from decorrelation noise, they are prone to fail in estimating displacement between echo signals obtained during tissue distortions. This study proposes a novel elastography technique which addresses the decorrelation in estimating displacement field. We call our method GLUENet (GLobal Ultrasound Elastography Network) which uses deep Convolutional Neural Network (CNN) to get a coarse time-delay estimation between two ultrasound images. This displacement is later used for formulating a nonlinear cost function which incorporates similarity of RF data intensity and prior information of estimated displacement. By optimizing this cost function, we calculate the finer displacement by exploiting all the information of all the samples of RF data simultaneously. The Contrast to Noise Ratio (CNR) and Signal to Noise Ratio (SNR) of the strain images from our technique is very much close to that of strain images from GLUE. While most elastography algorithms are sensitive to parameter tuning, our robust algorithm is substantially less sensitive to parameter tuning.Comment: 4 pages, 4 figures; added acknowledgment section, submission type late

Charge Transfer from Regularized Symmetry-Adapted Perturbation Theory

16 pages, 16 figure

Managing Climate Risk

At the heart of the traditional approach to strategy in the climate change dilemma lies the assumption that the global community, by applying a set of powerful analytical tools, can predict the future of climate change accurately enough to choose a clear strategic direction for it. We claim that this approach might involve underestimating uncertainty in order to lay out a vision of future events sufficiently precise to be captured in a discounted cost flow analysis in integrated assessment models. However, since the future of climate change is truly uncertain, this approach might at best be marginally helpful and at worst downright dangerous: underestimating uncertainty can lead to strategies that do not defend the world against unexpected and sometimes even catastrophic threats. Another danger lies on the other extreme: if the global community can not find a strategy that works under traditional analysis or if uncertainties are too large that clear messages are absent, they may abandon the analytical rigor of their planning process altogether and base their decisions on good instinct and consensus of some future process that is easy to agree upon. In this paper, we try to outline a system to derive strategic decisions under uncertainty for the climate change dilemma. What follows is a framework for determining the level of uncertainty surrounding strategic decisions and for tailoring strategy to that uncertainty. Our core argument is that a robust strategy towards climate change involves the building of a technological portfolio of mitigation and adaptation measures that includes sufficient opposite technological positions to the underlying baseline emission scenarios given the uncertainties of the entire physical and socioeconomic system in place. In the case of mitigation, opposite technological positions with the highest leverage are particular types of sinks. A robust climate risk management portfolio can only work when the opposite technological positions are readily available when needed and therefore have to be prepared in advance. It is precisely the flexibility of these technological options which has to be quantified under the perspective of the uncertain nature of the underlying system and compared to the cost of creating these options, rather than comparing their cost with expected losses in a net present value type analysis. We conclude that climate policy - especially under the consideration of the precautionary principle - would look much different if uncertainties would be taken explicitly into account

Why Are People's Decisions Sometimes Worse with Computer Support?

In many applications of computerised decision support, a recognised source of undesired outcomes is operators' apparent over-reliance on automation. For instance, an operator may fail to react to a potentially dangerous situation because a computer fails to generate an alarm. However, the very use of terms like "over-reliance" betrays possible misunderstandings of these phenomena and their causes, which may lead to ineffective corrective action (e.g. training or procedures that do not counteract all the causes of the apparently "over-reliant" behaviour). We review relevant literature in the area of "automation bias" and describe the diverse mechanisms that may be involved in human errors when using computer support. We discuss these mechanisms, with reference to errors of omission when using "alerting systems", with the help of examples of novel counterintuitive findings we obtained from a case study in a health care application, as well as other examples from the literature