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Abstract Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

This paper offers a novel understanding of collective responsibility for AI outcomes that can help resolve the “problem of many hands” and “responsibility gaps” when it comes to AI failure, especially in the context of lethal autonomous weapon systems.

Abstract Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (“integrate and fire”) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary “growth spurts” in brain size, as per punctuated equilibrium theories in evolutionary biology.

We show sharp Vizing-type inequalities for eternal domination. Namely, we prove that for any graphs G and H, where is the eternal domination function, α is the independence number, and is the strong product of graphs. This addresses a question of Klostermeyer and Mynhardt. We also show some families of graphs attaining the strict inequality where is the Cartesian product. For the eviction model of eternal domination, we show a sharp upper bound for

The spatial pattern and global mean values of warming differ between different climate models, an issue that needs to be better understood in order to obtain reliable regional projections. Here, the authors show that ice-albedo and water vapor feedbacks are the key processes that are responsible for this inter-model spread.

We analyze retrospectively/prospectively the transient variations of six different physical parameters in the atmosphere/ionosphere during the M7.8 and M7.3 earthquakes in Nepal, namely: 1) outgoing longwave radiation (OLR) at the top of the atmosphere (TOA); 2) GPS/TEC; 3) the very-low-frequency (VLF/LF) signals at the receiving stations in Bishkek (Kyrgyzstan) and Varanasi (India); 4) Radon observations; 5) Atmospheric chemical potential from assimilation models; and; 6) Air Temperature from NOAA ground stations. We found that in mid-March 2015, there was a rapid increase in the radiation from the atmosphere observed by satellites. This anomaly was located close to the future M7.8 epicenter and reached a maximum on April 21–22. The GPS/TEC data analysis indicated an increase and variation in electron density, reaching a maximum value during April 22–24. A strong negative TEC anomaly in the crest of EIA (Equatorial Ionospheric Anomaly) occurred on April 21, and a strong positive anomaly was recorded on April 24, 2015. The behavior of VLF-LF waves along NWC-Bishkek and JJY-Varanasi paths has shown abnormal behavior during April 21–23, several days before the first, stronger earthquake. Our continuous satellite OLR analysis revealed this new strong anomaly on May 3, which was why we anticipated another major event in the area. On May 12, 2015, an M7.3 earthquake occurred. Our results show coherence between the appearance of these pre-earthquake transient’s effects in the atmosphere and ionosphere (with a short time-lag, from hours up to a few days) and the occurrence of the 2015 M7.8 and M7.3 events. The spatial characteristics of the pre-earthquake anomalies were associated with a large area but inside the preparation region estimated by Dobrovolsky-Bowman. The pre-earthquake nature of the signals in the atmosphere and ionosphere was revealed by simultaneous analysis of satellite, GPS/TEC, and VLF/LF and suggest that they follow a general temporal-spatial evolution pattern that has been seen in other large earthquakes worldwide.

Background: Mental imagery abnormalities feature across affective disorders including bipolar disorder (BD) and unipolar depression (UD). Maladaptive emotional imagery has been proposed as a maintenance factor for affective symptomatology and a target for mechanism-driven psychological treatment developments. Where imagery abnormalities feature beyond acute affective episodes, further opportunities for innovation arise beyond treatments, such as for tertiary/relapse prevention (e.g., in remitted individuals) or primary prevention (e.g., in non-affected but at-risk individuals). The aim of our study was to investigate for the first time the presence of possible mental imagery abnormalities in affected individuals in remission and at-risk individuals for affective disorders using a familial risk design.Methods: A population-based cohort of monozygotic twins was recruited through linkage between the Danish national registries (N=204). Participants were grouped as: affected (remitted BD/UD; n = 115); high-risk (co-twin with history of BD/UD; n = 49), or low-risk (no co-twin history of BD/UD; n = 40). Twins completed mental imagery measures spanning key subjective domains (spontaneous imagery use and emotional imagery) and cognitive domains (imagery inspection and imagery manipulation).Results: Affected twins in remission reported enhanced emotional mental imagery compared to both low- and high-risk twins. This was characterized by greater impact of i) intrusive prospective imagery (Impact of Future Events Scale) and ii) deliberately-generated prospective imagery of negative scenarios (Prospective Imagery Task). There were no significant differences in these key measures between affected BD and UD twins in remission. Additionally, low- and high-risk twins did not significantly differ on these emotional imagery measures. There were also no significant differences between the three groups on non-emotional measures including spontaneous imagery use and cognitive stages of imagery.Conclusions: Abnormalities in emotional prospective imagery are present in monozygotic twins with affective disorders in remission—despite preserved cognitive stages of imagery—but absent in unaffected high-risk twins, and thus do not appear to index familial risk (i.e., unlikely to qualify as “endophenotypes”). Elevated emotional prospective imagery represents a promising treatment/prevention target in affective disorders.