Lattice study on kaon nucleon scattering length in the I=1 channel

Using the tadpole improved clover Wilson quark action on small, coarse and anisotropic lattices, $KN$ scattering length in the I=1 channel is calculated within quenched approximation. The results are extrapolated towards the chiral and physical kaon mass region. Finite volume and finite lattice spacing errors are also analyzed and a result in the infinite volume and continuum limit is obtained which is compatible with the experiment and the results from Chiral Perturbation Theory.Comment: 15 pages, 4 figures, typeset by latex using elsart.cls,minor change

I=2 Pion scattering length with improved actions on anisotropic lattices

$\pi\pi$ scattering length in the I=2 channel is calculated within quenched approximation using improved gauge and improved Wilson fermion actions on anisotropic lattices. The results are extrapolated towards the chiral, infinite volume and continuum limit. This result improves our previous result on the scattering length. In the chiral, infinite volume and continuum limit, we obtain $a^{(2)}_0m_\pi=-0.0467(45)$, which is consistent with the result from Chiral Perturbation Theory, the experiment and results from other lattice calculations.Comment: 7 pages, 2 figures, typeset wit elsart.cl

Estimation of distances to stars with stellar parameters from LAMOST

We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and returns a posterior probability density function for each star's absolute magnitude. This technique is tailored specifically to data from the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) survey. Because LAMOST obtains roughly 3000 stellar spectra simultaneously within each ~5-degree diameter "plate" that is observed, we can use the stellar parameters of the observed stars to account for the stellar luminosity function and target selection effects. This removes biasing assumptions about the underlying populations, both due to predictions of the luminosity function from stellar evolution modeling, and from Galactic models of stellar populations along each line of sight. Using calibration data of stars with known distances and stellar parameters, we show that our method recovers distances for most stars within ~20%, but with some systematic overestimation of distances to halo giants. We apply our code to the LAMOST database, and show that the current precision of LAMOST stellar parameters permits measurements of distances with ~40% error bars. This precision should improve as the LAMOST data pipelines continue to be refined.Comment: 11 pages, 12 figures; accepted for publication in A

A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest

The Global Lake Ecological Observatory Network (GLEON): the evolution of grassroots network science

Nine years later, with over 380 members from 40 countries, and 50 publications to its credit, GLEON is growing at a rapid pace and pushing the boundaries of the practice of network science. GLEON is really three networks: a network of lakes, data, and peopl

A new framework to enable equitable outcomes: resilience and nexus approaches combined

Managing integrated social-ecological systems to reduce risks to human and environmental well-being remains challenging in light of the rate and extent of undesirable changes that are occurring. Developing frameworks that are sufficiently integrative to guide research to deliver the necessary insights into all key system aspects is an important outstanding task. Among existing approaches, resilience and nexus framings both allow focus on unpacking relationships across scales and levels in a system and emphasize the involvement of different groups in decision making to different extents. They also suffer weaknesses and neither approach puts social justice considerations explicitly at its core. This has important implications for understanding who wins and loses out from different decisions and how social and ecological risks and trade-offs are shared and distributed, temporally and spatially. This paper conceptually integrates resilience and nexus approaches, developing a combined framework and indicating how it could effectively be operationalized in cases from mountain and mangrove social-ecological systems. In doing so, it advances understanding of complex social-ecological systems framings for risk-based decision making beyond that which could be achieved through use of either resilience or nexus approaches alone. Important next steps in testing the framework involve empirical and field operationalization, requiring interdisciplinary, mixed method approache

Data-Efficient Multimodal Fusion on a Single GPU

The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. The most powerful models in this space have been trained using massive datasets of paired inputs and large-scale computational resources, making them prohibitively expensive to train in many practical scenarios. We surmise that existing unimodal encoders pre-trained on large amounts of unimodal data should provide an effective bootstrap to create multimodal models from unimodal ones at much lower costs. We therefore propose FuseMix, a multimodal augmentation scheme that operates on the latent spaces of arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal alignment, we achieve competitive performance -- and in certain cases outperform state-of-the art methods -- in both image-text and audio-text retrieval, with orders of magnitude less compute and data: for example, we outperform CLIP on the Flickr30K text-to-image retrieval task with $\sim \! 600\times$ fewer GPU days and $\sim \! 80\times$ fewer image-text pairs. Additionally, we show how our method can be applied to convert pre-trained text-to-image generative models into audio-to-image ones. Code is available at: https://github.com/layer6ai-labs/fusemix.Comment: CVPR 2024 (Highlight

Longitudinal T1 relaxation rate (R1) captures changes in short-term Mn exposure in welders

We demonstrated recently that the T1 relaxation rate (R1) captured short-term Mn exposure in welders with chronic, relatively low exposure levels in a cross-sectional study. In the current study, we used a longitudinal design to examine whether R1 values reflect the short-term dynamics of Mn exposure

Increased R2* in the Caudate Nucleus of Asymptomatic Welders

Welding has been associated with neurobehavioral disorders. Welding fumes contain several metals including copper (Cu), manganese (Mn), and iron (Fe) that may interact to influence welding-related neurotoxicity. Although welding-related airborne Fe levels are about 10-fold higher than Mn, previous studies have focused on Mn and its accumulation in the basal ganglia. This study examined differences in the apparent transverse relaxation rates [R2* (1/T2*), estimate of Fe accumulation] in the basal ganglia (caudate nucleus, putamen, and globus pallidus) between welders and controls, and the dose–response relationship between estimated Fe exposure and R2* values. Occupational questionnaires estimated recent and lifetime Fe exposure, and blood Fe levels and brain magnetic resonance imaging (MRI) were obtained. Complete exposure and MRI R2* and R1 (1/T1: measure to estimate Mn accumulation) data from 42 subjects with welding exposure and 29 controls were analyzed. Welders had significantly greater exposure metrics and higher whole-blood Fe levels compared with controls. R2* in the caudate nucleus was significantly higher in welders after controlling for age, body mass index, respirator use, caudate R1, and blood metals of Cu and Mn, whereas there was no difference in R1 values in the basal ganglia between groups. The R2* in the caudate nucleus was positively correlated with whole-blood Fe concentration. This study provides the first evidence of higher R2* in the caudate nucleus of welders, which is suggestive of increased Fe accumulation in this area. Further studies are needed to replicate the findings and determine the neurobehavioral relevance