Towards A Comprehensive Assessment of AI's Environmental Impact

Artificial Intelligence, machine learning (AI/ML) has allowed exploring solutions for a variety of environmental and climate questions ranging from natural disasters, greenhouse gas emission, monitoring biodiversity, agriculture, to weather and climate modeling, enabling progress towards climate change mitigation. However, the intersection of AI/ML and environment is not always positive. The recent surge of interest in ML, made possible by processing very large volumes of data, fueled by access to massive compute power, has sparked a trend towards large-scale adoption of AI/ML. This interest places tremendous pressure on natural resources, that are often overlooked and under-reported. There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle for informing policymakers, stakeholders to adequately implement standards and policies and track the policy outcome over time. For these policies to be effective, AI's environmental impact needs to be monitored in a spatially-disaggregated, timely manner across the globe at the key activity sites. This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations. We present a case study around Northern Virginia, United States that hosts a growing number of datacenters and observe changes in multiple satellite-based environmental metrics. We then discuss the steps to expand this methodology for comprehensive assessment of AI's environmental impact across the planet. We also identify data gaps and formulate recommendations for improving the understanding and monitoring AI-induced changes to the environment and climate

Adaptive Modeling of Satellite-Derived Nighttime Lights Time-Series for Tracking Urban Change Processes Using Machine Learning

Remotely sensed nighttime lights (NTL) uniquely capture urban change processes that are important to human and ecological well-being, such as urbanization, socio-political conflicts and displacement, impacts from disasters, holidays, and changes in daily human patterns of movement. Though several NTL products are global in extent, intrinsic city-specific factors that affect lighting, such as development levels, and social, economic, and cultural characteristics, are unique to each city, making the urban processes embedded in NTL signatures difficult to characterize, and limiting the scalability of urban change analyses. In this study, we propose a data-driven approach to detect urban changes from daily satellite-derived NTL data records that is adaptive across cities and effective at learning city-specific temporal patterns. The proposed method learns to forecast NTL signatures from past data records using neural networks and allows the use of large volumes of unlabeled data, eliminating annotation effort. Urban changes are detected based on deviations of observed NTL from model forecasts using an anomaly detection approach. Comparing model forecasts with observed NTL also allows identifying the direction of change (positive or negative) and monitoring change severity for tracking recovery. In operationalizing the model, we consider ten urban areas from diverse geographic regions with dynamic NTL time-series and demonstrate the generalizability of the approach for detecting the change processes with different drivers and rates occurring within these urban areas based on NTL deviation. This scalable approach for monitoring changes from daily remote sensing observations efficiently utilizes large data volumes to support continuous monitoring and decision making

Global urban activity changes from COVID-19 physical distancing restrictions

During the COVID-19 pandemic changes in human activity became widespread through official policies and organically in response to the virus's transmission, which in turn, impacted the environment and the economy. The pandemic has been described as a natural experiment that tested how social and economic disruptions impacted different components of the global Earth System. To move this beyond hypotheses, locally-resolved, globally-available measures of how, where, and when human activity changed are critically needed. Here we use satellite-derived nighttime lights to quantify and map daily changes in human activity that are atypical for each urban area globally for two years after the onset of the pandemic using machine learning anomaly detectors. Metrics characterizing changes in lights from pre-COVID baseline in human settlements and quality assurance measures are reported. This dataset, TRacking Anomalous COVID-19 induced changEs in NTL (TRACE-NTL), is the first to resolve COVID-19 disruptions for all metropolitan regions globally, daily. It is suitable to support a variety of post-pandemic studies that assess how changes in human activity impact environmental systems

Is GN-z11 powered by a super-Eddington massive black hole?

Observations of $z \sim 6$ quasars powered by supermassive black holes (SMBHs; $M_{\rm BH} \sim 10^{8-10}\, M_\odot$) challenge our current understanding of early black hole (BH) formation and evolution. The advent of the James Webb Space Telescope (JWST) has enabled the study of massive BHs (MBHs; $M_{\rm BH}\sim 10^{6-7} \ \mathrm{M}_\odot$) up to $z\sim 11$, thus bridging the properties of $z\sim 6$ quasars to their ancestors. The JWST spectroscopic observations of GN-z11, a well-known $z=10.6$ star-forming galaxy, have been interpreted with the presence of a super-Eddington (Eddington ratio $\equiv \,\lambda_{\rm Edd}\sim 5.5$) accreting MBH. To test this hypothesis, we used a zoom-in cosmological simulation of galaxy formation and BH co-evolution. We first tested the simulation results against the observed probability distribution function (PDF) of $\lambda_{\rm Edd}$ found in $z\sim 6$ quasars. Then, in the simulation we selected the BHs that satisfy the following criteria: (a) $10 10^6 \ \mathrm{M}_\odot$. Next, we apply the extreme value statistics to the PDF of$\lambda_{\rm Edd}$resulting from the simulation and we find that the probability of observing a$z\sim 10-11$MBH accreting with$\lambda_{\rm Edd} \sim 5.5$in the volume surveyed by JWST is very low ($<0.2\%$). We compared our predictions with those in the literature, and discuss the main limitations of our work. Our simulation cannot explain the JWST observations of GN-z11. This might be due to (i) poor resolution and statistics in simulations, (ii) simplistic sub-grid models (e.g. BH accretion and seeding), (iii) uncertainties in the data analysis and interpretation.Comment: 8 pages, 2 figures; accepted for publication in A&

Is GN-z11 powered by a super-Eddington massive black hole?

Observations of z∼6 quasars powered by super-massive black holes (SMBHs, MBH∼108−10M⊙) challenge our current understanding of early black hole formation and evolution. The advent of the James Webb Space Telescope (JWST) has enabled the study of massive black holes (MBHs, MBH∼106−7 M⊙) up to z∼11, thus bridging the properties of z∼6 quasars to their ancestors. JWST spectroscopic observations of GN-z11, a well-known z=10.6 star forming galaxy, have been interpreted with the presence of a super-Eddington (Eddington ratio ≡λEdd∼5.5) accreting MBH. To test this hypothesis we use a zoom-in cosmological simulation of galaxy formation and BH co-evolution. We first test the simulation results against the observed probability distribution function (PDF) of λEdd found in z∼6 quasars. Then, we select in the simulation those BHs that satisfy the following criteria: (a) 10106 M⊙. Finally we apply the Extreme Value Statistics to the PDF of λEdd resulting from the simulation and find that the probability of observing a z∼10−11 MBH, accreting with λEdd∼5.5, in the volume surveyed by JWST, is very low (<0.5%). We compare our predictions with those in the literature and further discuss the main limitations of our work. Our simulation cannot explain the JWST observations of GN-z11. This might be due to (i) missing physics in simulations, or (ii) uncertainties in the data analysis

Cellular landscaping of COVID-19 and gynaecological cancers: An infrequent correlation

COVID-19 resulted in a mortality rate of 3-6% caused by SARS-CoV-2 and its variant leading to unprecedented consequences of acute respiratory distress septic shock and multiorgan failure. In such a situation, evaluation, diagnosis, treatment, and care for cancer patients are difficult tasks faced by medical staff. Moreover, patients with gynaecological cancer appear to be more prone to severe infection and mortality from COVID-19 due to immunosuppression by chemotherapy and coexisting medical disorders. To deal with such a circumtances oncologists have been obliged to reconsider the entire diagnostic, treatment, and management approach. This review will provide and discuss the molecular link with gynaecological cancer under COVID-19 infection, providing a novel bilateral relationship between the two infections. Moreover, the authors have provided insights to discuss the pathobiology of COVID-19 in gynaecological cancer and their risks associated with such comorbidity. Furthermore, we have depicted the overall impact of host immunity along with guidelines for the treatment of patients with gynaecological cancer under COVID-19 infection. We have also discussed the feasible scope for the management of COVID-19 and gynaecological cancer

Astrophysics with the Laser Interferometer Space Antenna

Laser Interferometer Space Antenna (LISA) will be a transformative experiment for gravitational wave astronomy as it will offer unique opportunities to address many key astrophysical questions in a completely novel way. The synergy with ground-based and other space-based instruments in the electromagnetic domain, by enabling multi-messenger observations, will add further to the discovery potential of LISA. The next decade is crucial to prepare the astrophysical community for LISA's first observations. This review outlines the extensive landscape of astrophysical theory, numerical simulations, and astronomical observations that are instrumental for modeling and interpreting the upcoming LISA datastream. To this aim, the current knowledge in three main source classes for LISA is reviewed: ultra-compact stellar-mass binaries, massive black hole binaries, and extreme or intermediate mass ratio inspirals. The relevant astrophysical processes and the established modeling techniques are summarized. Likewise, open issues and gaps in our understanding of these sources are highlighted, along with an indication of how LISA could help make progress in the different areas. New research avenues that LISA itself, or its joint exploitation with studies in the electromagnetic domain, will enable, are also illustrated. Improvements in modeling and analysis approaches, such as the combination of numerical simulations and modern data science techniques, are discussed. This review is intended to be a starting point for using LISA as a new discovery tool for understanding our Universe

Global Urban Actvity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)

&lt;p&gt;We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.&lt;/p&gt; &lt;p&gt;TRACE-NTL:&lt;/p&gt; &lt;p&gt;├───ancillary&lt;br&gt;├───data&lt;br&gt;└───metrics&lt;br&gt;&nbsp; &nbsp; ├───disruption&lt;br&gt;&nbsp; &nbsp; │ &nbsp; ├───change_segment&lt;br&gt;&nbsp; &nbsp; │ &nbsp; ├───city_uncertainty&lt;br&gt;&nbsp; &nbsp; │ &nbsp; ├───daily_change&lt;br&gt;&nbsp; &nbsp; │ &nbsp; └───daily_qa_flags&lt;br&gt;&nbsp; &nbsp; └───recovery&lt;/p&gt; &lt;p&gt;&lt;a title="Dataset description" href="https://github.com/srijac/covid-19_Nightlights"&gt;https://github.com/srijac/covid-19_Nightlights&lt;/a&gt;&lt;/p&gt; &lt;p&gt;Accompanying paper: "Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions", S Chakraborty, E C Stokes, O Alexander&lt;/p&gt