Quantum optical coherence tomography with dispersion cancellation

We propose a new technique, called quantum optical coherence tomography (QOCT), for carrying out tomographic measurements with dispersion-cancelled resolution. The technique can also be used to extract the frequency-dependent refractive index of the medium. QOCT makes use of a two-photon interferometer in which a swept delay permits a coincidence interferogram to be traced. The technique bears a resemblance to classical optical coherence tomography (OCT). However, it makes use of a nonclassical entangled twin-photon light source that permits measurements to be made at depths greater than those accessible via OCT, which suffers from the deleterious effects of sample dispersion. Aside from the dispersion cancellation, QOCT offers higher sensitivity than OCT as well as an enhancement of resolution by a factor of 2 for the same source bandwidth. QOCT and OCT are compared using an idealized sample.Comment: 19 pages, 4 figure

Greenhouse gas emissions in coffee grown with differing input levels under conventional and organic management

Coffee plays a key role in sustaining millions of livelihoods around the world. Understanding GHG emissions from coffee supply chains is important in evaluating options for climate change mitigation within the sector. We use data from two long-term coffee agroforestry experiments in Costa Rica and Nicaragua to calculate carbon footprints (CF) for coffee and identify emission hotspots within different management systems, levels of inputs and shade types. Management system and input level were the main cause of variation in CFs. Carbon footprints for 1 kg of fresh coffee cherries were between 0.26 and 0.67 kgCO2e for conventional and 0.12 and 0.52 kgCO2e for organic management systems. The main contributor to GHG emissions for all management systems was the inputs of organic and inorganic nitrogen. Nitrous oxide emissions from pruning inputs contributed between 7% and 42 % of CFs. However, these estimates were strongly influenced by the choice of emission factors

Sink or source—The potential of coffee agroforestry systems to sequester atmospheric CO2 into soil organic carbon

Current carbon accounting methodologies often assume interactions between above-ground and below-ground carbon, without considering effects of land management. We used data from two long-term coffee agroforestry experiments in Costa Rica and Nicaragua to assess the effect on total soil organic carbon (SOC) stocks of (i) organic versus conventional management, (ii) higher versus moderate agronomic inputs, (iii) tree shade types. During the first nine years of coffee establishment total 0–40 cm depth SOC stocks decreased by 12.4% in Costa Rica and 0.13% in Nicaragua. Change in SOC differed consistently amongst soil layers: at 0–10 cm SOC stocks increased by 2.14 and 1.26 Mg C ha−1 in Costa Rica and Nicaragua respectively; however much greater reduction occurred at 20–40 cm (9.65 and 2.85 Mg C ha−1 respectively). Organic management caused a greater increase in 0–10 cm SOC but did not influence its reduction at depth. Effects of shade type were smaller, though heavily pruned legume shade trees produced a greater increase in 0–10 cm SOC than unpruned timber trees. No significant differences in SOC stocks were found between shaded and unshaded systems at any depth and SOC was poorly correlated with above-ground biomass stocks highlighting poor validity of “expansion factors” currently used to estimate SOC. SOC stock changes were significantly negatively correlated with initial SOC stock per plot, providing evidence that during establishment of these woody-plant-dominated agricultural systems SOC stocks tend to converge towards a new equilibrium as a function of the change in the quantity and distribution of organic inputs. Therefore it cannot be assumed that tree-based agricultural systems necessarily lead to increases in soil C stocks. While high inputs of organic fertiliser/tree pruning mulch increased surface-layer SOC stocks, this did not affect stocks in deeper soil, where decreases generally exceeded any gains in surface soil. Therefore site- and system-specific sampling is essential to draw meaningful conclusions for climate change mitigation strategies

Non-crossing dependencies: Least effort, not grammar

The use of null hypotheses (in a statistical sense) is common in hard sciences but not in theoretical linguistics. Here the null hypothesis that the low frequency of syntactic dependency crossings is expected by an arbitrary ordering of words is rejected. It is shown that this would require star dependency structures, which are both unrealistic and too restrictive. The hypothesis of the limited resources of the human brain is revisited. Stronger null hypotheses taking into account actual dependency lengths for the likelihood of crossings are presented. Those hypotheses suggests that crossings are likely to reduce when dependencies are shortened. A hypothesis based on pressure to reduce dependency lengths is more parsimonious than a principle of minimization of crossings or a grammatical ban that is totally dissociated from the general and non-linguistic principle of economy.Postprint (author's final draft

Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control

We discuss a recent approach to investigating cognitive control, which has the potential to deal with some of the challenges inherent in this endeavour. In a model-based approach, the researcher defines a formal, computational model that performs the task at hand and whose performance matches that of a research participant. The internal variables in such a model might then be taken as proxies for latent variables computed in the brain. We discuss the potential advantages of such an approach for the study of the neural underpinnings of cognitive control and its pitfalls, and we make explicit the assumptions underlying the interpretation of data obtained using this approach