Dark-Ages Reionisation & Galaxy Formation Simulation XVI: The Thermal Memory of Reionisation

Intergalactic medium temperature is a powerful probe of the epoch of reionisation, as information is retained long after reionisation itself. However, mean temperatures are highly degenerate with the timing of reionisation, with the amount heat injected during the epoch, and with the subsequent cooling rates. We post-process a suite of semi-analytic galaxy formation models to characterise how different thermal statistics of the intergalactic medium can be used to constrain reionisation. Temperature is highly correlated with redshift of reionisation for a period of time after the gas is heated. However as the gas cools, thermal memory of reionisation is lost, and a power-law temperature-density relation is formed, $T = T_0(1+\delta)^{1-\gamma}$ with $\gamma \approx 1.5$. Constraining our model against observations of electron optical depth and temperature at mean density, we find that reionisation likely finished at $z_{\rm{reion}} = 6.8 ^{+ 0.5} _{-0.8}$ with a soft spectral slope of $\alpha = 2.8 ^{+ 1.2} _{-1.0}$. By restricting spectral slope to the range $[0.5,2.5]$ motivated by population II synthesis models, reionisation timing is further constrained to $z_{\rm{reion}} = 6.9 ^{+ 0.4} _{-0.5}$. We find that, in the future, the degeneracies between reionisation timing and background spectrum can be broken using the scatter in temperatures and integrated thermal history.Comment: 17 pages, 17 figures, Accepted for publication in MNRA

A Spectral Condition for Feature Learning

The push to train ever larger neural networks has motivated the study of initialization and training at large network width. A key challenge is to scale training so that a network's internal representations evolve nontrivially at all widths, a process known as feature learning. Here, we show that feature learning is achieved by scaling the spectral norm of weight matrices and their updates like $\sqrt{\texttt{fan-out}/\texttt{fan-in}}$, in contrast to widely used but heuristic scalings based on Frobenius norm and entry size. Our spectral scaling analysis also leads to an elementary derivation of \emph{maximal update parametrization}. All in all, we aim to provide the reader with a solid conceptual understanding of feature learning in neural networks

Solution phase, solid state, and theoretical investigations on the MacMillan imidazolidinone

A combination of soln. phase NMR, X-ray crystallog. studies, and DFT calcns. provide a consistent structural conformation for iminium ions derived from the MacMillan imidazolidinone

Images in cardiovascular medicine : multiphoton microscopy for three-dimensional imaging of lymphocyte recruitment into apolipoprotein-E-deficient mouse carotid artery

Two recent elegant studies have shown that in apolipoprotein-E– deficient mice, the lamina adventitia is a major site of arterial wall inflammation associated with lymphocyte infiltration into atherosclerotic arteries and with formation of adventitial lymphoid-like tissues.1,2 These results suggest that lymphocyte responses in the lamina adventitia may play a crucial role in atherosclerosis development.1,

An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression

We study the cost of overfitting in noisy kernel ridge regression (KRR), which we define as the ratio between the test error of the interpolating ridgeless model and the test error of the optimally-tuned model. We take an "agnostic" view in the following sense: we consider the cost as a function of sample size for any target function, even if the sample size is not large enough for consistency or the target is outside the RKHS. We analyze the cost of overfitting under a Gaussian universality ansatz using recently derived (non-rigorous) risk estimates in terms of the task eigenstructure. Our analysis provides a more refined characterization of benign, tempered and catastrophic overfitting (qv Mallinar et al. 2022)

SGD with a Constant Large Learning Rate Can Converge to Local Maxima

Previous works on stochastic gradient descent (SGD) often focus on its success. In this work, we construct worst-case optimization problems illustrating that, when not in the regimes that the previous works often assume, SGD can exhibit many strange and potentially undesirable behaviors. Specifically, we construct landscapes and data distributions such that (1) SGD converges to local maxima, (2) SGD escapes saddle points arbitrarily slowly, (3) SGD prefers sharp minima over flat ones, and (4) AMSGrad converges to local maxima. We also realize results in a minimal neural network-like example. Our results highlight the importance of simultaneously analyzing the minibatch sampling, discrete-time updates rules, and realistic landscapes to understand the role of SGD in deep learning.Comment: ICLR 2022 Spotligh

Wrestling with objectivity and fairness: U.S. environment reporters and the business community

Environment reporters have been criticized for allegedly having an antibusiness bias. This study, based on a series of regional surveys including 364 U.S. environment reporters, found the journalists commonly used a business or economics framework for their stories. The reporters used some business organizations as sources more often than some environmental groups. They acknowledged the need to be fair to both corporations and environmental activists. Nevertheless, a substantial minority of these environment reporters said they struggled with the issue of whether their peers are “too green.

Environment reporters and U.S. journalists: A comparative analysis

This study provides baseline data regarding environment reporters in the twenty-first century, and then compares this baseline information about a specialized journalism beat to existing studies of U.S. journalists in general. This comparison between 652 environmental journalists working at daily newspapers and television stations and more than 1,000 U.S. journalists in general found that these reporters share many individual and work-related characteristics, perhaps due in part to their similar backgrounds and to the basic professional training received by most journalists. The authors propose a uniform theory of journalism education, arguing that journalists are journalists first because they are linked by their studies, training, and experience, and that differences among reporters may be related to variations in their education. The researchers also found that newspapers employ more specialized reporters than do television stations, and that the bigger the newspaper, the more specialists, suggesting that bigger is better for specialized reporting