Art and psychoanalysis register at the White Hotel

My title refers to the fact that in D. M. Thomas's remarkable novel, both art, in the form of literary imagination, and psychoanalysis seek to comprehend the life of a woman named Lisa Erdman, and both register certain truths or part truths. The novel traces Lisa's life from the time she enters analysis with Freud in Vienna until her death at Babi Yar at the hands of the Nazis. In a final chapter entitled "the camp" that has troubled many readers we witness a kind of apotheosis in which Lisa and most of the characters we have met survive their own deaths. Most readers find The White Hotel to be a brilliant treatment of human aggression, which it certainly is; and an equally brilliant portrait of Freud, who is presented in his role as the man who first unlocked the secrets of hysteria. But the landscape of hysteria, which is the terrain of the novel, is also the landscape of imagination, and so there is a basic opposition between art and psychoanalysis from the outset

A Structural Model of Tenure and Specific Investments

Though a lot of work has been done on the distribution of job tenures, we are still uncertain about its main determinants. In this paper, we stress random shocks to match productivity after the start of an employment relation. The specificity of investment makes hiring and separation decisions irreversible.These decisions therefore have an option value. Assumptions on riskneutrality, efficient bargaining, and the efficient resolution of hold up problems allow investment and separation decisions to be analyzed separately from wage setting. The tenure profiles in wages implied by the model fit the observed pattern quite well. The model yields a hump shaped pattern in separation rates, similar to learning models, but with a slowerdecline after the peak. Estimation results using job tenure data from the NLSY support this humped shaped pattern and favor this model above the learning model. We develop a methodology to analyze the decomposition of shocks to match productivity into idiosyncratic and macro-level shocks.When assuming a Last-In-First-Out (LIFO) separation rule, this model of individualemployment relations is embedded in a model of firm level employment, that satisfies Gibrat’s law. The LIFO rule is interpreted as an institution protecting the property rights on specific investments of incumbentworkers against hiring new workers by the firm.option value, job tenure, tenure profiles

Effect of green manure crops and organic amendments on incidence of nematode-borne tobacco rattle virus

Tobacco rattle tobravirus (TRV) may infect several ornamental bulb crops and is transmitted by trichodorid nematodes. Paratrichodorus teres, P. pachydermus and Trichodorus similis are the main vectors in the Netherlands. In field experiments the effects of various pre-crops and organic amendments on the TRV Infection Potential of Soils (TRV-IPS) and on disease level in tulip and gladiolus were studied. Organic matter amendment of soil at a rate of 1% dry weight has been shown to reduce the host finding activity of P. teres under laboratory conditions. In a field containing viruliferous P. teres dahlia, italian ryegrass, white mustard and fodder radish were grown or the soil was kept fallow and the resulting TRV-IPS prior to the bulbous test crops was measured by a soil dilution bait test method. The application of organic matter was tested after dahlia as pre-crop. Household waste compost (GFT compost) was applied as a soil mix or planting furrow treatment at 12 tons dry weight per ha for tulip and gladiolus. Spent mushroom compost (Champost) was added as planting furrow treatment at 17 or 12 tons dw/ha, respectively, for tulip and gladiolus. The percentage of TRV diseased plants was determined at flowering in all pre-crop and organic amendment treatments. Champost in the planting furrow and fodder radish as a preceding crop reduced the percentage infection in tulip under favourable conditions for TRV infection. In gladiolus most organic amendments, fodder radish as pre-crop and keeping the soil fallow reduced the TRV infection rate of the plants during the first growing season, but not of the plants grown from the corms in the next year

Spuler, Bertold

Spuler, Bertold (b. Karlsruhe, Germany, 5 December 1911; d. Hamburg, 6 March 1990), scholar of East European history and Oriental studies. Among his many publications are important works on the history of the Iranian lands from the 7th century CE onwards

A Structural Model of Tenure and Specific Investments

Though a lot of work has been done on the distribution of job tenures, we are still uncertain about its main determinants. In this paper, we stress random shocks to match productivity after the start of an employment relation. The specificity of investment makes hiring and separation decisions irreversible.These decisions therefore have an option value. Assumptions on riskneutrality, efficient bargaining, and the efficient resolution of hold up problems allow investment and separation decisions to be analyzed separately from wage setting. The tenure profiles in wages implied by the model fit the observed pattern quite well. The model yields a hump shaped pattern in separation rates, similar to learning models, but with a slowerdecline after the peak. Estimation results using job tenure data from the NLSY support this humped shaped pattern and favor this model above the learning model. We develop a methodology to analyze the decomposition of shocks to match productivity into idiosyncratic and macro-level shocks.When assuming a Last-In-First-Out (LIFO) separation rule, this model of individualemployment relations is embedded in a model of firm level employment, that satisfies Gibrat's law. The LIFO rule is interpreted as an institution protecting the property rights on specific investments of incumbentworkers against hiring new workers by the firm

Automated Seismic Source Characterisation Using Deep Graph Neural Networks

Most seismological analysis methods require knowledge of the geographic location of the stations comprising a seismic network. However, common machine learning tools used in seismology do not account for this spatial information, and so there is an underutilised potential for improving the performance of machine learning models. In this work, we propose a Graph Neural Network (GNN) approach that explicitly incorporates and leverages spatial information for the task of seismic source characterisation (specifically, location and magnitude estimation), based on multi-station waveform recordings. Even using a modestly-sized GNN, we achieve model prediction accuracy that outperforms methods that are agnostic to station locations. Moreover, the proposed method is flexible to the number of seismic stations included in the analysis, and is invariant to the order in which the stations are arranged, which opens up new applications in the automation of seismological tasks and in earthquake early warning systems