An ideal vision model accounts for behavior and neurophysiology in both naturalistic conditions and designed lab experiments. Unlike psychological theories, artificial neural networks (ANNs) actually perform visual tasks and generate testable predictions for arbitrary inputs. These advantages enable ANNs to engage the entire spectrum of the evidence. Failures of particular models drive progress in a vibrant ANN research program of human vision.

Sample materials such as sediments and soils contain complex mixtures of different carbon-containing compounds. These bulk samples can be split into individual fractions, based on the temperature of thermal decomposition of their components. When coupled with radiocarbon (14C) measurement of the isolated fractions, this approach offers the advantage of directly investigating the residence time, turnover time, source, or age of the different components within a mixed sample, providing important insights to better understand the cycling of carbon in the environment. Several laboratories have previously reported different approaches to separate radiocarbon samples based on temperature in what is a growing area of interest within the research community. Here, we report the design and operation of a new ramped oxidation facility for separation of sample carbon on the basis of thermal resistance at the NEIF Radiocarbon Laboratory in East Kilbride, UK. Our new instrumentation shares some characteristics with the previously-reported systems applying ramped oxidation and/or ramped pyrolysis for radiocarbon measurement, but also has several differences which we describe and discuss. We also present the results of a thorough program of testing of the new system, which demonstrates both the reproducibility of the thermograms generated during sample combustion, and the reliability of the radiocarbon measurements obtained on individual sample fractions. This is achieved through quantification of the radiocarbon background and analysis of multiple standards of known 14C content during standard operation of the instrumentation.

The ability to accurately identify human gait intent is a challenge relevant to the success of many applications in robotics, including, but not limited to, assistive devices. Most existing intent identification approaches, however, are either sensor-specific or use a pattern-recognition approach that requires large amounts of training data. This paper introduces a real-time walking speed intent identification algorithm based on the Mahalanobis distance that requires minimal training data. This data efficiency is enabled by making the simplifying assumption that each time step of walking data is independent of all other time steps. The accuracy of the algorithm was analyzed through human-subject experiments that were conducted using controlled walking speed changes on a treadmill. Experimental results confirm that the model used for intent identification converges quickly (within 5 min of training data). On average, the algorithm successfully detected the change in desired walking speed within one gait cycle and had a maximum of 87% accuracy at responding with the correct intent category of speed up, slow down, or no change. The findings also show that the accuracy of the algorithm improves with the magnitude of the speed change, while speed increases were more easily detected than speed decreases.

Coarse spatial resolution in gridded precipitation datasets, reanalysis, and climate model outputs restricts their ability to characterize the localized extreme rain events and limits the use of the coarse resolution information for local to regional scale climate management strategies. Deep learning models have recently been developed to rapidly downscale the coarse resolution precipitation to the high local scale resolution at a much lower cost than dynamic downscaling. However, these existing super-resolution deep learning modeling studies have not rigorously evaluated the model’s skill in producing fine-scale spatial variability, particularly over topographic features. These current deep-learning models also have difficulty predicting the complex spatial structure of extreme events. Here, we develop a model based on super-resolution deconvolution neural network (SRDN) to downscale the hourly precipitation and evaluate the predictions. We apply three versions of the SRDN model: (a) SRDN (no orography), (b) SRDN-O (orography only at final resolution enhancement), and (c) SRDN-SO (orography at each step of resolution enhancement). We assess the ability of SRDN-based models to reproduce the fine-scale spatial variability and compare it with the previously used deep learning model (DeepSD). All the models are trained and tested using the Conformal Cubic Atmospheric Model (CCAM) data to perform a 100 to 12.5 km of hourly precipitation downscaling over the Australian region. We found that SRDN-based models, including orography, deliver better fine-scale spatial structures of both climatology and extremes, and significantly improved the deep-learning downscaling. The SRDN-SO model performs well both qualitatively and quantitatively in reconstructing the fine-scale spatial variability of climatology and rainfall extremes over complex orographic regions.

We introduce new data resources to enable spatial and nonspatial research on Canadian elections, electoral history and political geography. These include a comprehensive set of distinct identification codes for every federal electoral district in Canada from 1867 to the present, a complete set of digital boundary files for these electoral districts, historical census data aggregated to federal electoral districts, and tools to connect our district identification codes to federal election results. After describing the construction and content of these new resources, we provide an example of their use in a comparative-historical analysis of district compactness in Canada and the United States. We find that, in contrast to the United States, postwar institutional changes to district boundary-drawing processes had little effect on district compactness in Canada.

Inclusion in nasogastric tube feeds (NGTF) of acid-sensitive, seaweed-derived alginate, expected to form a reversible gel in the stomach, may create a more normal intragastric state and modified gastrointestinal responses. This may ameliorate NGTF-associated risk of diarrhoea, upper gastrointestinal symptoms and appetite suppression. In a randomised, crossover, comparison study, undertaken in twelve healthy males, an alginate-containing feed (F + ALG) or one that was alginate-free (F-ALG) (300 ml) was given over 1 h with a 7–14-d washout period between treatments. Baseline and for 4-h post-feed initiation, MRI measurements were made to establish small bowel water content (SBWC), gastric contents volume (GCV) and appearance, and superior mesenteric artery blood flux. Blood glucose and gut peptides were measured. Subjective appetite and upper gastrointestinal symptoms scores were obtained. Ad libitum pasta consumption 3-h post-feeding was measured. F + ALG exhibited a gastric appearance consistent with gelling surrounded by a freely mobile water halo. Significant main effects of feed were seen for SBWC (P = 0·03) and peptide YY (PYY) (P = 0·004) which were attributed to generally higher values for SBWC with F + ALG (max difference between adjusted means 72 ml at 210 min) and generally lower values for PYY with F + ALG. GCV showed a faster reduction with F + ALG, less between-participant variation and a feed-by-time interaction (P = 0·04). Feed-by-time interactions were also seen with glucagon-like-peptide 1 (GLP-1) (P = 0·02) and glucose-dependent insulinotropic polypeptide (GIP) (P = 0·002), both showing a blunted response with F + ALG. Apparent intragastric gelling with F + ALG and subsequent differences in gastrointestinal and endocrine responses have been demonstrated between an alginate-containing and alginate-free feed.

In this article, we argue that landesque capital was integral to the development of complexity in the Maya Lowlands. Such features involved permanent investments in the landscape that supported material and ideological practices, resulting in increased sustainability and well-being. We contend that these developments stemmed from accretional modifications to soils in the Preceramic/Early Preclassic, as well as intentional investments of labor in agricultural features, large public works, and select civic complexes during the Middle Preclassic. Capital improvements were particularly important during the Middle Preclassic, when sedentary occupations and civic life were established. The timing and location of the investments strongly correlate with other aspects of Middle Preclassic lifeways, such as the transition to sedentism, acquisition and control of resources, changes in lithic production, and the emergence of an elite class. We note that some of the largest investments in landscape management during the Middle Preclassic occurred in the Central Karstic Uplands, where substantial cities rose in the Late Preclassic. We conclude that during the Middle Preclassic an ontology of landesque capital developed, based on the synergistic fusing of daily and ritual practices with physical features, which provided a foundation for resilience, sustainability, and well-being in subsequent generations.

For a pseudo-Anosov flow $\varphi $ without perfect fits on a closed $3$-manifold, Agol–Guéritaud produce a veering triangulation $\tau $ on the manifold M obtained by deleting the singular orbits of $\varphi $. We show that $\tau $ can be realized in M so that its 2-skeleton is positively transverse to $\varphi $, and that the combinatorially defined flow graph $\Phi $ embedded in M uniformly codes the orbits of $\varphi $ in a precise sense. Together with these facts, we use a modified version of the veering polynomial, previously introduced by the authors, to compute the growth rates of the closed orbits of $\varphi $ after cutting M along certain transverse surfaces, thereby generalizing the work of McMullen in the fibered setting. These results are new even in the case where the transverse surface represents a class in the boundary of a fibered cone of M. Our work can be used to study the flow $\varphi $ on the original closed manifold. Applications include counting growth rates of closed orbits after cutting along closed transverse surfaces, defining a continuous, convex entropy function on the ‘positive’ cone in $H^1$ of the cut-open manifold, and answering a question of Leininger about the closure of the set of all stretch factors arising as monodromies within a single fibered cone of a $3$-manifold. This last application connects to the study of endperiodic automorphisms of infinite-type surfaces and the growth rates of their periodic points.

We evaluate the cross-sectional predictive ability of a forward-looking monetary policy reaction function, or Taylor rule, in both statistical and economic terms. We find that investors require a premium for holding currency portfolios with high implied interest rates while currency portfolios with low implied rates offer negative currency excess returns. Our forward-looking Taylor rule signals are orthogonal to current nominal interest rates and disconnected from carry trade portfolios and other currency investment strategies. The profitability of the Taylor rule portfolio spread is mainly driven by inflation forecasts rather than the output gap and is robust to data snooping and a wide range of robustness checks.

In this review, the relevance of selenium (Se) to viral disease will be discussed paying particular attention to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease (COVID-19). Se, the active centre in selenoproteins has an ongoing history of reducing the incidence and severity of viral infections. Host Se deficiency increased the virulence of RNA viruses such as influenza A and coxsackievirus B3, the latter of which is implicated in the development of Keshan disease in north-east China. Significant clinical benefits of Se supplementation have been demonstrated in HIV-1, in liver cancer linked to hepatitis B, and in Chinese patients with hantavirus that was successfully treated with oral sodium selenite. China is of particular interest because it has populations that have both the lowest and the highest Se status in the world. We found a significant association between COVID-19 cure rate and background Se status in Chinese cities; the cure rate continued to rise beyond the Se intake required to optimise selenoproteins, suggesting an additional mechanism. Se status was significantly higher in serum samples from surviving than non-surviving COVID-19 patients. As regards mechanism, SARS-CoV-2 may interfere with the human selenoprotein system; selenoproteins are important in scavenging reactive oxygen species, controlling immunity, reducing inflammation, ferroptosis and endoplasmic reticulum (ER) stress. We found that SARS-CoV-2 significantly suppressed mRNA expression of GPX4, of the ER selenoproteins, SELENOF, SELENOM, SELENOK and SELENOS and down-regulated TXNRD3. Based on the available data, both selenoproteins and redox-active Se species (mimicking ebselen, an inhibitor of the main SARS-CoV-2 protease that enables viral maturation within the host) could employ their separate mechanisms to attenuate virus-triggered oxidative stress, excessive inflammatory responses and immune-system dysfunction, thus improving the outcome of SARS-CoV-2 infection.