As COVID-19 transitions into an endemic disease that remains constantly present in the population at a stable level, monitoring its prevalence without invasive measures becomes increasingly important. In this paper, we present a deep neural network estimator for the COVID-19 daily case count based on wastewater surveillance data and other confounding factors. This work builds upon the study by Jiang, Kolozsvary, and Li (2024), which connects the COVID-19 case counts with testing data collected early in the pandemic. Using the COVID-19 testing data and the wastewater surveillance data during the period when both data were highly reliable, one can train an artificial neural network that learns the nonlinear relation between the COVID-19 daily case count and the wastewater viral RNA concentration. From a machine learning perspective, the main challenge lies in addressing temporal feature reliability, as the training data has different reliability over different time periods.

Large language models (LLMs) have demonstrated strong instruction-following capabilities in text-based tasks. However, this ability often deteriorates in multimodal models after alignment with non-text modalities such as images or audio. While several recent efforts have investigated instruction-following performance in text and vision-language models, instruction-following in audio-based large language models remains largely unexplored. To bridge this gap, we introduce IFEval-Audio, a novel evaluation dataset designed to assess the ability to follow instructions in an audio LLM. IFEval-Audio contains 280 audio-instruction-answer triples across six diverse dimensions: Content, Capitalization, Symbol, List Structure, Length, and Format. Each example pairs an audio input with a text instruction, requiring the model to generate an output that follows a specified structure. We benchmark state-of-the-art audio LLMs on their ability to follow audio-involved instructions. The dataset is released publicly to support future research in this emerging area.

Causal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of information flow between time series. The theoretical formulation of information flow for stochastic dynamical systems provides a general expression and a data-driven statistic for the rate of entropy transfer between different system units. To advance understanding of information flow rate in terms of intuitive concepts and physically meaningful parameters, we investigate statistical properties of the data-driven information flow rate between coupled stochastic processes. We derive relations between the expectation of the information flow rate statistic and properties of the auto- and cross-correlation functions. Thus, we elucidate the dependence of the information flow rate on the analytical properties and characteristic times of the correlation functions. Our analysis provides insight into the influence of the sampling step, the strength of cross-correlations, and the temporal delay of correlations on information flow rate. We support the theoretical results with numerical simulations of correlated Gaussian processes.

After three decades of computational multibody system (MBS) dynamics, current research is centered at the development of compact and user friendly yet computationally efficient formulations for the analysis of complex MBS. The key to this is a holistic geometric approach to the kinematics modeling observing that the general motion of rigid bodies as well as the relative motion due to technical joints are screw motions. Moreover, screw theory provides the geometric setting and Lie group theory the analytic foundation for an intuitive and compact MBS modeling. The inherent frame invariance of this modeling approach gives rise to very efficient recursive $O\left( n\right) $ algorithms, for which the so-called 'spatial operator algebra' is one example, and allows for use of readily available geometric data. In this paper three variants for describing the configuration of tree-topology MBS in terms of relative coordinates, i.e. joint variables, are presented: the standard formulation using body-fixed joint frames, a formulation without joint frames, and a formulation without either joint or body-fixed reference frames. This allows for describing the MBS kinematics without introducing joint reference frames and therewith rendering the use of restrictive modeling convention, such as Denavit-Hartenberg parameters, redundant. Four different definitions of twists are recalled and the corresponding recursive expressions are derived. The corresponding Jacobians and their factorization are derived. The aim of this paper is to motivate the use of Lie group modeling and to provide a review of the different formulations for the kinematics of tree-topology MBS in terms of relative (joint) coordinates from the unifying perspective of screw and Lie group theory.

China now has more industrial robots per 10,000 workers than the United States, according to the International Federation of Robotics (IFR). This is the first time China has surpassed the United States in robot density. In 2021, China averaged 322 industrial robots for every 10,000 employees. According to the IFR, China saw a huge jump in robot installations in 2021. The country's industrial robotics market saw 243,300 installations last year, a 44% increase from the year before.

The International Federation of Robotics has published what it predicts will be the top five industry trends of 2022. The main theme underlying the trends will be that robots with new features and functions will capture new areas and create new markets. The operational stock of industrial robots hit a new record of about 3 million units worldwide – increasing by 13 percent on average each year (2015-2020). Milton Guerry, president of the IFR, says: "Transformation for robotic automation is picking up speed across traditional and new industries. More and more companies are realizing the numerous advantages robotics provides for their businesses."

Santiago, Chile, is a highly segregated city with distinct zones of affluence and deprivation. This setting offers a window on how social factors propel the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in an economically vulnerable society with high levels of income inequality. Mena et al. analyzed incidence and mortality attributed to SARS-CoV-2 to understand spatial variations in disease burden. Infection fatality rates were higher in lower-income municipalities because of comorbidities and lack of access to health care. Disparities between municipalities in the quality of their health care delivery system became apparent in testing delays and capacity. These indicators explain a large part of the variation in COVID-19 underreporting and deaths and show that these inequalities disproportionately affected younger people. Science , abg5298, this issue p. [eabg5298][1] ### INTRODUCTION The COVID-19 crisis has exposed major inequalities between communities. Understanding the societal risk factors that make some groups particularly vulnerable is essential to ensure more effective interventions for this and future pandemics. Here, we focus on socioeconomic status as a risk factor. Although it is broadly understood that social and economic inequality has a negative impact on health outcomes, the mechanisms by which socioeconomic status affects disease outcomes remain unclear. These mechanisms can be mediated by a range of systemic structural factors, such as access to health care and economic safety nets. We address this gap by providing an in-depth characterization of disease incidence and mortality and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. ### RATIONALE Combining publicly available data sources, we conducted a comprehensive analysis of case incidence and mortality during the first wave of the pandemic. We correlated COVID-19 outcomes with behavioral and health care system factors while studying their interaction with age and socioeconomic status. To overcome the intrinsic biases of incomplete case count data, we used detailed mortality data. We developed a parsimonious Gaussian process model to study excess deaths and their uncertainty and reconstructed true incidence from the time series of deaths with a new regularized maximum likelihood deconvolution method. To estimate infection fatality rates by age and socioeconomic status, we implemented a hierarchical Bayesian model that adjusts for reporting biases while accounting for incompleteness in case information. ### RESULTS We find robust associations between COVID-19 outcomes and socioeconomic status, based on health and behavioral indicators. Specifically, we show in lower–socioeconomic status municipalities that testing was almost absent early in the pandemic and that human mobility was not reduced by lockdowns as much as it was in more affluent locations. Test positivity and testing delays were much higher in these locations, indicating an impaired capacity of the health care system to contain the spread of the epidemic. We also find that 73% more deaths than in a normal year were observed between May and July 2020, and municipalities at the lower end of the socioeconomic spectrum were hit the hardest, both in relation to COVID-19–attributed deaths and excess deaths. Finally, the socioeconomic gradient of the infection fatality rate appeared particularly steep for younger age groups, reflecting worse baseline health status and limited access to health care in municipalities with low socioeconomic status. ### CONCLUSION Together, these findings highlight the substantial consequences of socioeconomic and health care disparities in a highly segregated city and provide practical methodological approaches useful for characterizing the COVID-19 burden and mortality in other urban centers based on public data, even if reports are incomplete and biased. ![Figure][2] Effect of socioeconomic inequalities on COVID-19 outcomes. The map on the left shows the municipalities that were included in this study, colored by their socioeconomic status score. For the comparison between COVID-19 deaths and excess deaths (top right), COVID-19–confirmed deaths are shown in light green and COVID-19–attributed deaths in dark green. Excess deaths, shown in gray, correspond to the difference between observed and predicted deaths. Predicted deaths were estimated using a Gaussian process model. The shading indicates 95% credible intervals for the excess deaths. The infection fatality rates (bottom right) were inferred by implementing a hierarchical Bayesian model, with vertical lines representing credible intervals by age and socioeconomic status. The COVID-19 pandemic has affected cities particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. Our analyses show a strong association between socioeconomic status and both COVID-19 outcomes and public health capacity. People living in municipalities with low socioeconomic status did not reduce their mobility during lockdowns as much as those in more affluent municipalities. Testing volumes may have been insufficient early in the pandemic in those places, and both test positivity rates and testing delays were much higher. We find a strong association between socioeconomic status and mortality, measured by either COVID-19–attributed deaths or excess deaths. Finally, we show that infection fatality rates in young people are higher in low-income municipalities. Together, these results highlight the critical consequences of socioeconomic inequalities on health outcomes. [1]: /lookup/doi/10.1126/science.abg5298 [2]: pending:yes

Robots have been a mainstay in factories for decades, but their use has been expanding everywhere else, from warehouses and hospitals to retail. That trend continued last year, and the novel coronavirus pandemic has accelerated service robot demand for automated logistics, disinfection, and delivery, according to the International Federation of Robotics. The Frankfurt, Germany-based IFR said that the sales value of professional service robots increased by 32% to $11.2 billion (U.S.) worldwide between 2018 and 2019. The organization published its full research in the "World Robotics 2020 – Service Robots" report, which is available for download. Sales of medical robotics accounted for 47% of the total service robot value turnover in 2019, according to the IFR.

To improve the compressive sensing MRI (CS - MRI) approaches in terms of fine structure loss under high acceleration factors, we have propose d an iterative feature refinement model (IFR - CS), equipped with fixed transforms, to restore the meaningful structure s and details. Nevertheless, the proposed IFR - CS still has some limitations, such as the selection of hyper - parameters, a lengthy reconstruction time, and the fixed sparsifying transform . To alleviate these issues, we unroll the iterative feature refinement procedure s in IFR - CS to a supervised model - driven network, dubbed IFR - Net. Equipped with training data pairs, both Additionally, inspired by the powerful representation capability of convolutional neural network (CNN), CNN - based inversion blocks are explored in the sparsity - promoting denoising module to generalize the sparsity - enforcing operator . Extensive experiments on both simulated and in v ivo MR datasets have shown that the proposed network possesses a strong capability to capture image details and preserve well the structural information with fast reconstruction speed. Index terms -- Compressed Sensing; Undersampled image reconstruction; IFR - CS; Deep learning; Model - driven network. Magnetic resonance imaging (MRI) is a non - invasive and widely used imaging technique that can provide both functional and anatomical information for clinic al diagnosis. However, the slow imaging speed may result in patient discomfort and motion artifacts. Therefore, increasing MR imaging speed is an important and worthwhile research goal. During the past decades, compressed sensing (CS) has become a popular and successful strategy for fast MR imaging reconstruction [1] - [6] . Zhang and Q. Yang are with the Department of Electronic Information Engineering, Nanchang Universi ty, Nanchang 330031, China. Liu did the work during her internship at Paul C. Lauterbur Research Center for Biomedical Imaging, Chinese Academy of Sciences, Shenzhen, China. S. Wang and D. Liang are with Paul C. Lauterbur Research Center for Biomedical Imaging and the Medical AI Research Center, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China ( sophiasswang@hotmail.com, dong.liang@siat.ac.cn).

The International Federation of Robotics (IFR), at a press conference here last week, announced preliminary 2018 figures for the industrial sector of the robotics industry. Last year set another record -- but just barely. It was only up 1% over 2017. No information was given about service and field robotics. It's true that 2017 was a banner year, with a 30% year-over-year gain.