Efficient Bayesian Nonparametric Modelling of Structured Point Processes

This paper presents a Bayesian generative model for dependent Cox point processes, alongside an efficient inference scheme which scales as if the point processes were modelled independently. We can handle missing data naturally, infer latent structure, and cope with large numbers of observed processes. A further novel contribution enables the model to work effectively in higher dimensional spaces. Using this method, we achieve vastly improved predictive performance on both 2D and 1D real data, validating our structured approach.Comment: Presented at UAI 2014. Bibtex: @inproceedings{structcoxpp14_UAI, Author = {Tom Gunter and Chris Lloyd and Michael A. Osborne and Stephen J. Roberts}, Title = {Efficient Bayesian Nonparametric Modelling of Structured Point Processes}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)}, Year = {2014}

Self Supervision Does Not Help Natural Language Supervision at Scale

Self supervision and natural language supervision have emerged as two exciting ways to train general purpose image encoders which excel at a variety of downstream tasks. Recent works such as M3AE and SLIP have suggested that these approaches can be effectively combined, but most notably their results use small pre-training datasets (<50M samples) and don't effectively reflect the large-scale regime (>100M examples) that is commonly used for these approaches. Here we investigate whether a similar approach can be effective when trained with a much larger amount of data. We find that a combination of two state of the art approaches: masked auto-encoders, MAE and contrastive language image pre-training, CLIP provides a benefit over CLIP when trained on a corpus of 11.3M image-text pairs, but little to no benefit (as evaluated on a suite of common vision tasks) over CLIP when trained on a large corpus of 1.4B images. Our work provides some much needed clarity into the effectiveness (or lack thereof) of self supervision for large-scale image-text training

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature

We propose a novel sampling framework for inference in probabilistic models: an active learning approach that converges more quickly (in wall-clock time) than Markov chain Monte Carlo (MCMC) benchmarks. The central challenge in probabilistic inference is numerical integration, to average over ensembles of models or unknown (hyper-)parameters (for example to compute the marginal likelihood or a partition function). MCMC has provided approaches to numerical integration that deliver state-of-the-art inference, but can suffer from sample inefficiency and poor convergence diagnostics. Bayesian quadrature techniques offer a model-based solution to such problems, but their uptake has been hindered by prohibitive computation costs. We introduce a warped model for probabilistic integrands (likelihoods) that are known to be non-negative, permitting a cheap active learning scheme to optimally select sample locations. Our algorithm is demonstrated to offer faster convergence (in seconds) relative to simple Monte Carlo and annealed importance sampling on both synthetic and real-world examples

A methylotrophic origin of methanogenesis and early divergence of anaerobic multicarbon alkane metabolism

Methanogens are considered as one of the earliest life forms on Earth, and together with anaerobic methane-oxidizing archaea, they have crucial effects on climate stability. However, the origin and evolution of anaerobic alkane metabolism in the domain Archaea remain controversial. Here, we present evidence that methylotrophic methanogenesis was the ancestral form of this metabolism. Carbon dioxide–reducing methanogenesis developed later through the evolution of tetrahydromethanopterin S-methyltransferase, which linked methanogenesis to the Wood-Ljungdahl pathway for energy conservation. Anaerobic multicarbon alkane metabolisms in Archaea also originated early, with genes coding for the activation of short-chain or even long-chain alkanes likely evolving from an ethane-metabolizing ancestor. These genes were likely horizontally transferred to multiple archaeal clades including Candidatus (Ca.) Bathyarchaeia, Ca. Lokiarchaeia, Ca. Hadarchaeia, and the methanogenic Ca. Methanoliparia

Attention as Practice:Buddhist Ethics Responses to Persuasive Technologies

The “attention economy” refers to the tech industry’s business model that treats human attention as a commodifiable resource. The libertarian critique of this model, dominant within tech and philosophical communities, claims that the persuasive technologies of the attention economy infringe on the individual user’s autonomy and therefore the proposed solutions focus on safeguarding personal freedom through expanding individual control. While this push back is important, current societal debates on the ethics of persuasive technologies are informed by a particular understanding of attention, rarely posited explicitly yet assumed as the default. They share the same concept of attention, namely an individualistic and descriptive concept of attention that is a cognitive process, an expendable resource, something that one should control individually. We step away from a negative analysis in terms of external distractions and aim for positive answers, turning to Buddhist ethics to formulate a critique of persuasive technology from a genuinely ethical perspective. Buddhist ethics points at our attention’s inescapable ethical and ontological embeddedness. Attention as practice requires “the right effort” to distinguish desirable and undesirable states, the “right concentration” to stop the flow we are caught in, and the “right mindfulness” to fortify the ability to attend to the present situation and keep in mind a general sense of life’s direction. We offer input for further philosophical inquiry on attention as practice and attention ecology. We put forward comfort/effort and individualism/collectivism as two remaining central tensions in need of further research.</p

Experimental evidence for Tayler instability in a liquid metal column

In the current-driven, kink-type Tayler instability (TI) a sufficiently strong azimuthal magnetic field becomes unstable against non-axisymmetric perturbations. The TI has been discussed as a possible ingredient of the solar dynamo mechanism and a source of the helical structures in cosmic jets. It is also considered as a size limiting factor for liquid metal batteries. We report on a liquid metal TI experiment using a cylindrical column of the eutectic alloy GaInSn to which electrical currents of up to 8 kA are applied. We present results of external magnetic field measurements that indicate the occurrence of the TI in good agreement with numerical predictions. The interference of TI with the competing large scale convection, resulting from Joule heating, is also discussed.Comment: 4 pages, 5 figure