How much information do language users need to differentiate potentially absolute synonyms into near-synonyms? How consistent must the information be? We present two simple experiments designed to investigate this. After exposure to two novel verbs, participants generalized them to positive or negative contexts. In Experiment 1, there was a tendency across conditions for the verbs to become differentiated by context, even following inconsistent, random, or neutral information about context during exposure. While a subset of participants matched input probabilities, a high proportion did not. As a consequence, the overall pattern was of growth in differentiation that did not closely track input distributions. Rather, there were two main patterns: When each verb had been presented consistently in a positive or negative context, participants overwhelmingly specialized both verbs in their output. When this was not the case, the verbs tended to become partially differentiated, with one becoming specialized and the other remaining less specialized. Experiment 2 replicated and expanded on Experiment 1 with the addition of a pragmatic judgment task and neutral contexts at test. Its results were consistent with Experiment 1 in supporting the conclusion that quality of input may be more important than quantity in the differentiation of synonyms.

The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo algorithm is critical to the quality of the generated samples. Much work has gone into obtaining such results for various Metropolis–Hastings (MH) algorithms. Recently, acceptance probabilities other than MH are being employed in problems with intractable target distributions. There are few resources available on tuning the Gaussian proposal distributions for this situation. We obtain optimal scaling results for a general class of acceptance functions, which includes Barker’s and lazy MH. In particular, optimal values for Barker’s algorithm are derived and found to be significantly different from that obtained for the MH algorithm. Our theoretical conclusions are supported by numerical simulations indicating that when the optimal proposal variance is unknown, tuning to the optimal acceptance probability remains an effective strategy.

We prove polynomial ergodicity for the one-dimensional Zig-Zag process on heavy-tailed targets and identify the exact order of polynomial convergence of the process when targeting Student distributions.

Simulated tempering is a popular method of allowing Markov chain Monte Carlo algorithms to move between modes of a multimodal target density $\pi$ . Tawn, Moores and Roberts (2021) introduces the Annealed Leap-Point Sampler (ALPS) to allow for rapid movement between modes. In this paper we prove that, under appropriate assumptions, a suitably scaled version of the ALPS algorithm converges weakly to skew Brownian motion. Our results show that, under appropriate assumptions, the ALPS algorithm mixes in time $O(d [\log d]^2)$ or O(d), depending on which version is used.

In 2020, HR 6819 was reported to be a triple system containing the closest black hole to Earth. However, these results were contested, with an alternative explanation of a post-interaction binary suggested. Using new integral field spectroscopic and interferometric data, we have been able to determine the true nature of this exotic source.

It is well known that traditional Markov chain Monte Carlo (MCMC) methods can fail to effectively explore the state space for multimodal problems. Parallel tempering is a well-established population approach for such target distributions involving a collection of particles indexed by temperature. However, this method can suffer dramatically from the curse of dimensionality. In this paper we introduce an improvement on parallel tempering called QuanTA. A comprehensive theoretical analysis quantifying the improved efficiency and scalability of the approach is given. Under weak regularity conditions, QuanTA gives accelerated mixing through the temperature space. Empirical evidence of the effectiveness of this new algorithm is illustrated on canonical examples.

In this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.), but to date the framework and methodology proposed in this paper (Monte Carlo fusion) is the first general approach which avoids any form of approximation error in obtaining the unified inference. In this paper we focus on the key theoretical underpinnings of this new methodology, and simple (direct) Monte Carlo interpretations of the theory. There is considerable scope to tailor the theory introduced in this paper to particular application settings (such as the big data setting), construct efficient parallelised schemes, understand the approximation and computational efficiencies of other such unification paradigms, and explore new theoretical and methodological directions.

Uranium incorporation into magnetite and its behaviour during subsequent oxidation has been investigated at high pH to determine the uranium retention mechanism(s) on formation and oxidative perturbation of magnetite in systems relevant to radioactive waste disposal. Ferrihydrite was exposed to U(VI)aq containing cement leachates (pH 10.5–13.1) and crystallization of magnetite was induced via addition of Fe(II)aq. A combination of XRD, chemical extraction and XAS techniques provided direct evidence that U(VI) was reduced and incorporated into the magnetite structure, possibly as U(V), with a significant fraction recalcitrant to oxidative remobilization. Immobilization of U(VI) by reduction and incorporation into magnetite at high pH, and with significant stability upon reoxidation, has clear and important implications for limiting uranium migration in geological disposal of radioactive wastes.

In this paper we consider the optimal scaling of high-dimensional random walk Metropolis algorithms for densities differentiable in the L p mean but which may be irregular at some points (such as the Laplace density, for example) and/or supported on an interval. Our main result is the weak convergence of the Markov chain (appropriately rescaled in time and space) to a Langevin diffusion process as the dimension d goes to ∞. As the log-density might be nondifferentiable, the limiting diffusion could be singular. The scaling limit is established under assumptions which are much weaker than the one used in the original derivation of Roberts et al. (1997). This result has important practical implications for the use of random walk Metropolis algorithms in Bayesian frameworks based on sparsity inducing priors.

In the Antarctic, climate-scale anomalies of surface temperature (Ts) are associated with the atmospheric circulation and also sea-ice conditions. Negative (positive) anomalies of station Ts tend to accompany more (less) extensive sea ice in broadly similar longitudes. However, the relationship between temperature and sea-ice conditions during large interannual variations of the circulation has been little explored, as has its association over longer distances within Antarctica. This study examines the inter-associations between Ts at seven automatic weather stations in East Antarctica and the Ross Sea area, and sea-ice conditions in the sector 30° Ε eastward to 60° W for the three ice-growth seasons (March-October) of 1987-89. Strong between-year differences occur in the intercorrelalions among station Ts, sectoral içe extent and the relationship between the two climate variables, especially for 1988 and 1989. These differences are also expressed in the patterns of cold-air mesoscale cyclogenesis over sub-Antarctic latitudes. The study indicates that the Ts-sea-ice link is modulated strongly in the presence of large-scale interannual anomalies of the atmospheric circulation, including the El Niño-Southern Oscillation (ENSO).

Gurga Chiya and Tepe Marani are small, adjacent mounds located close to the town of Halabja in the southern part of the Shahrizor Plain, one of the most fertile regions of Iraqi Kurdistan. Survey and excavation at these previously unexplored sites is beginning to produce evidence for human settlement spanning the sixth to the fourth millennia, c. 5600–3300 cal. b.c. In Mesopotamian chronology this corresponds to the Late Neolithic through to Chalcolithic periods; the Halaf, Ubaid, and Uruk phases of conventional culture history. In Iraqi Kurdistan, documentation of these periods—which witnessed many important transformations in prehistoric village life—is currently very thin. Here we offer a preliminary report on the emerging results from the Shahrizor Plain, with a particular focus on the description of material culture (ceramic and lithic assemblages), in order to establish a benchmark for further research. We also provide a detailed report on botanical remains and accompanying radiocarbon dates, which allow us to place this new evidence in a wider comparative framework. A further, brief account is given of Late Bronze Age material culture from the upper layers at Gurga Chiya. We conclude with observations on the significance of the Shahrizor Plain for wider research into the later prehistory of the Middle East, and the importance of preserving and investigating its archaeological record.

We introduce exact methods for the simulation of sample paths of one-dimensional diffusions with a discontinuity in the drift function. Our procedures require the simulation of finite-dimensional candidate draws from probability laws related to those of Brownian motion and its local time, and are based on the principle of retrospective rejection sampling. A simple illustration is provided.

We connect known results about diffusion limits of Markov chain Monte Carlo (MCMC) algorithms to the computer science notion of algorithm complexity. Our main result states that any weak limit of a Markov process implies a corresponding complexity bound (in an appropriate metric). We then combine this result with previously-known MCMC diffusion limit results to prove that under appropriate assumptions, the random-walk Metropolis algorithm in d dimensions takes O(d) iterations to converge to stationarity, while the Metropolis-adjusted Langevin algorithm takes O(d1/3) iterations to converge to stationarity.

While one of the IAU's missions is to “serve as the internationally recognized authority for assigning designations to celestial bodies and surface features on them” (†), the participation of the public in the naming of celestial objects has been a little-known, but decade-long tradition of the IAU.