Deterministic enhancement of coherent photon generation from a nitrogen-vacancy center in ultrapure diamond

The nitrogen-vacancy (NV) center in diamond has an optically addressable, highly coherent spin. However, an NV center even in high quality single-crystalline material is a very poor source of single photons: extraction out of the high-index diamond is inefficient, the emission of coherent photons represents just a few per cent of the total emission, and the decay time is large. In principle, all three problems can be addressed with a resonant microcavity. In practice, it has proved difficult to implement this concept: photonic engineering hinges on nano-fabrication yet it is notoriously difficult to process diamond without degrading the NV centers. We present here a microcavity scheme which uses minimally processed diamond, thereby preserving the high quality of the starting material, and a tunable microcavity platform. We demonstrate a clear change in the lifetime for multiple individual NV centers on tuning both the cavity frequency and anti-node position, a Purcell effect. The overall Purcell factor $F_{\rm P}=2.0$ translates to a Purcell factor for the zero phonon line (ZPL) of $F_{\rm P}^{\rm ZPL}\sim30$ and an increase in the ZPL emission probability from $\sim 3 \%$ to $\sim 46 \%$. By making a step-change in the NV's optical properties in a deterministic way, these results pave the way for much enhanced spin-photon and spin-spin entanglement rates.Comment: 6 pages, 4 figure

Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets

Ideally Open-Domain Question Answering models should exhibit a number of competencies, ranging from simply memorizing questions seen at training time, to answering novel question formulations with answers seen during training, to generalizing to completely novel questions with novel answers. However, single aggregated test set scores do not show the full picture of what capabilities models truly have. In this work, we perform a detailed study of the test sets of three popular open-domain benchmark datasets with respect to these competencies. We find that 60-70% of test-time answers are also present somewhere in the training sets. We also find that 30% of test-set questions have a near-duplicate paraphrase in their corresponding training sets. Using these findings, we evaluate a variety of popular open-domain models to obtain greater insight into what extent they can actually generalize, and what drives their overall performance. We find that all models perform dramatically worse on questions that cannot be memorized from training sets, with a mean absolute performance difference of 63% between repeated and non-repeated data. Finally we show that simple nearest-neighbor models out-perform a BART closed-book QA model, further highlighting the role that training set memorization plays in these benchmark

The Rise of Trichlorides Enabling an Improved Chlorine Technology

Chlorine plays a central role for the industrial production of numerous materials with global relevance. More recently, polychlorides have been evolved from an area of academic interest to a research topic with enormous industrial potential. In this minireview, the value of trichlorides for chlorine storage and chlorination reactions are outlined. Particularly, the inexpensive ionic liquid [NEt3Me][Cl3] shows a similar and sometimes even advantageous reactivity compared to chlorine gas, while offering a superior safety profile. Used as a chlorine storage, [NEt3Me][Cl3] could help to overcome the current limitations of storing and transporting chlorine in larger quantities. Thus, trichlorides could become a key technique for the flexibilization of the chlorine production enabling an exploitation of renewable, yet fluctuating, electrical energy. As the loaded storage, [NEt3Me][Cl3], is a proven chlorination reagent, it could directly be employed for downstream processes, paving the path to a more practical and safer chlorine industry

Interpretation of Natural Language Rules in Conversational Machine Reading

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader's background knowledge. One example is the task of interpreting regulations to answer "Can I...?" or "Do I have to...?" questions such as "I am working in Canada. Do I have to carry on paying UK National Insurance?" after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as "How long have you been working abroad?" when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 32k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.Comment: EMNLP 201

[NEt3Me][O3], Synthesis, Crystal Growth and Crystal Structure Analysis

[NEt3Me][O3] was obtained for the first time by an ion exchange reaction in liquid ammonia. It was thoroughly characterized by X‐Ray diffraction [P21; a=598.51(4) pm, b=1032.03(7) pm, c=723.83(6) pm, β=92.677(3)°, R=0.0384, 15070 reflections] applying non‐spherical and spherical atomic form factors for the refinements. In contrast to previous reported ozonides, [NEt3Me][O3] is the first to adopt the tungsten carbide (WC) motif of cation/ anion arragemnent and shows an untypical hydrogen bond between the central oxygen atom of the ozonide and the cation. Additionally, IR spectroscopy as well as quantum‐chemical calculations were applied to further characterize the compound. The obtained ozonide showed high solubility in ammonia as well as acetonitrile and good properties as a synthon in ozonide chemistry

Cavity-enhanced Raman scattering for in situ alignment and characterization of solid-state microcavities

We report cavity-enhanced Raman scattering from a single-crystal diamond membrane embedded in a highly miniaturized fully-tunable Fabry-P\'{e}rot cavity. The Raman intensity is enhanced 58.8-fold compared to the corresponding confocal measurement. The strong signal amplification results from the Purcell effect. We show that the cavity-enhanced Raman scattering can be harnessed as a narrowband, high-intensity, internal light-source. The Raman process can be triggered in a simple way by using an optical excitation frequency outside the cavity stopband and is independent of the lateral positioning of the cavity mode with respect to the diamond membrane. The strong Raman signal emerging from the cavity output facilitates in situ mode-matching of the cavity mode to single-mode collection optics; it also represents a simple way of measuring the dispersion and spatial intensity-profile of the cavity modes. The optimization of the cavity performance via the strong Raman process is extremely helpful in achieving efficient cavity-outcoupling of the relatively weak emission of single color-centers such as nitrogen-vacancy centers in diamond or rare-earth ions in crystalline hosts with low emitter density

Synthesis and Characterization of Poly(hydrogen halide) Halogenates (–I)

Herein, we report the synthesis and characterization of a variety of novel poly(hydrogen halide) halogenates (−I). The bifluoride ion, which is known to have the highest hydrogen bond energy of ≈160 kJ mol−1, is the most famous among many examples of [X(HX)n]− anions (X=F, Cl) known in the literature. In contrast, little is known about poly(hydrogen halide) halogenates containing two different halogens, ([X(HY)n]−). In this work we present the synthesis of anions of the type [X(HY)n]− (X=Br, I, ClO4; Y=Cl, Br, CN) stabilized by the [PPh4]+ and [PPN]+ cation. The obtained compounds have been characterized by single‐crystal X‐ray diffraction, Raman spectroscopy and quantum‐chemical calculations. In addition, the behavior of halide ions in hydrogen fluoride was investigated by using experimental and quantum‐chemical methods in order to gain knowledge on the acidity of hydrogen halides in HF

Equilibria under Knightian Price Uncertainty

Beißner P, Riedel F. Equilibria under Knightian Price Uncertainty . Center for Mathematical Economics Working Papers. Vol 597. Bielefeld: Center for Mathematical Economics; 2018.We study economies in which agents face Knightian uncertainty about state prices. Knightian uncertainty leads naturally to nonlinear expectations. We introduce a corresponding equilibrium concept with sublinear prices and prove that equilibria exist under weak conditions. In general, such equilibria lead to Pareto inefficient allocations; the equilibria coincide with Arrow-Debreu equilibria only if the values of net trades are ambiguity-free in the mean. In economies without aggregate uncertainty, inefficiencies are generic. We introduce a constrained efficiency concept, uncertainty-neutral efficiency; equilibrium allocations under price uncertainty are efficient in this constrained sense. Arrow-Debreu equilibria turn out to be non-robust with respect to the introduction of Knightian uncertainty