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Abstract Animals must flexibly adjust their behavior to respond appropriately in changing behavioral situations, in order to survive and thrive. The posterior parietal cortex (PPC) has been implicated in flexible decision-making, as it encodes sensory stimuli depending on their behavioral relevance [1] and is required to adapt to changes in stimulus-response contingencies in behavioral tasks [2]. Here, we show that while mice performed an auditory decision-making task, the temporal organization of neural activity differed across excitatory and inhibitory interneuron subtypes, with pyramidal neurons sparsely encoding task-related information. Sensory responses of all cell types were modulated according to behavioral relevance in the task, and signatures of this modulation were evident even before the presentation of sensory stimuli. Population activity patterns preceding stimulus presentation predicted the strength of sensory responses and even the mouse's behavioral accuracy in the task. A network model revealed that randomly organized inhibitory connectivity could replicate the selective filtering of sensory responses, but that context-dependent inputs to the network must be targeted to neurons responding to behaviorally relevant cues, and avoid those responding to irrelevant cues. Our results reveal a selective filtering mechanism in cortical circuits underlying flexible decision-making. 

We review the status of the US neutron monitor network, the science activities that utilize the network, the long-standing and permanent need for the network, its key role in the national Space Weather Strategy, future scientific and space weather activities and objectives and, lastly, plans for expanding the public profile and improving the security and scientific function of the network 

The Pacific Ocean region presents a significant gap in the equatorial coverage of the global Neutron Monitor (NM) network, hindering the detection of Solar Neutron Particles (SNP) and Galactic Cosmic Rays (GCR). To address this issue, we are redeploying the Haleakala Neutron Monitor (HLEA) on the island of Maui. HLEA was established in 1991 but was subsequently decommissioned in 2006 due to funding constraints. Its strategic location at a high altitude on Haleakala mountain, situated in the middle of the Pacific Ocean, offers unique advantages for SNP detection. The reinstatement of HLEA represents an invaluable opportunity to extend ground coverage for SNP and GCR detection, enhance the global NM network, and contribute to a deeper understanding of high-energy particle interactions. By harnessing the potential of this revitalized NM station, we aim to enrich space weather research and improve the efficacy of space weather monitoring systems, thereby enhancing our preparedness and resilience against space weather hazards. 

With the advent of automated speaker verifcation (ASV) systems comes an equal and opposite development: malicious actors may seek to use voice spoofng attacks to fool those same systems. Various counter measures have been proposed to detect these spoofing attacks, but current oferings in this arena fall short of a unifed and generalized approach applicable in real-world scenarios. For this reason, defensive measures for ASV systems produced in the last 6-7 years need to be classifed, and qualitative and quantitative comparisons of state-of-the-art (SOTA) counter measures should be performed to assess the efectiveness of these systems against real-world attacks. Hence, in this work, we conduct a review of the literature on spoofng detection using hand-crafted features, deep learning, and end-to-end spoofng countermeasure solutions to detect logical access attacks, such as speech synthesis and voice conversion, and physical access attacks, i.e., replay attacks. Additionally, we review integrated and unifed solutions to voice spoofng evaluation and speaker verifcation, and adversarial and anti-forensic attacks on both voice counter measures and ASV systems. In an extensive experimental analysis, the limitations and challenges of existing spoofng counter measures are presented, the performance of these counter measures on several datasets is reported, and cross-corpus evaluations are performed, something that is nearly absent in the existing literature, in order to assess the generalizability of existing solutions. For the experiments, we employ the ASVspoof2019, ASVspoof2021, and VSDC datasets along with GMM, SVM, CNN, and CNN-GRU classifers. For reproducibility of the results, the code of the testbed can be found at our GitHub Repository (https://github.com/smileslab/Comparative-Analysis-Voice-Spoofing). 

Abstract With extreme weather events becoming more frequent and severe, accelerating progress in Earth system predictability is urgently needed to deepen fundamental understanding, improve predictive tools, and provide reliable, actionable information for societal resilience. Building on prior and ongoing efforts by the broader community and informed by discussions at a National Science Foundation’s National Center for Atmospheric Research (NSF NCAR) workshop on Earth System Predictability Across Time Scales, this essay articulates a perspective on the scientific and structural priorities needed to advance Earth system predictability from short-range weather forecasts to century-scale projections, underscoring the urgency of a comprehensive, integrative approach capable of meeting emerging societal needs. Three scientific grand challenges are highlighted: understanding interactions across spatial and temporal scales, across interconnected Earth system components, and the influence of external forcing on predictability. To address these grand challenges, we identify potential implementation priorities across five key areas: 1) enhancing observations and data accessibility, 2) advancing data assimilation techniques, 3) improving modeling frameworks, 4) developing artificial intelligence (AI) and machine learning (ML) methods, and 5) applying convergence research. To support these areas, we outline four intersecting pillars of an integrated strategy: (i) a multiscale and multidisciplinary approach; (ii) closer coordination across modeling, observations, data assimilation, and AI/ML; (iii) intentional convergence research; and (iv) codevelopment of science with users. We also propose a collaborative path forward focused on strengthening scientific and technical connections, rewarding interdisciplinary and team-based science, expanding support for engagement with users, and investing in relationship building, shared language, and trust across scientific and societal domains. Significance StatementThis perspective highlights the need to advance Earth system predictability to enhance societal resilience amid increasingly frequent and severe extreme weather events. Grounded in broader community insights, including those from a National Science Foundation’s National Center for Atmospheric Research (NSF NCAR)–hosted workshop, it outlines a strategy to deepen understanding, improve predictive tools, and enable actionable outcomes across time scales from days to decades. This perspective identifies key scientific grand challenges: understanding scale interactions, Earth system component interactions, and the influence of external forcing. It also outlines corresponding implementation needs—including improved observations, data assimilation, modeling tools, and artificial intelligence and machine learning. Presented ideas emphasize an integrative strategy built on four intersecting pillars: a multiscale and multidisciplinary approach, coordination across tools, intentional convergence research, and codevelopment with users. 

Abstract Ocean warming is increasingly affecting marine ecosystems across the globe. Reef‐building corals are particularly affected by warming, with mass bleaching events increasing in frequency and leading to widespread coral mortality. Yet, some corals can resist or recover from bleaching better than others. Such variability in thermal resilience could be critical to reef persistence; however, the scientific community lacks standardized diagnostic approaches to rapidly and comparatively assess coral thermal vulnerability prior to bleaching events. We present the Coral Bleaching Automated Stress System (CBASS) as a low‐cost, open‐source, field‐portable experimental system for rapid empirical assessment of coral thermal thresholds using standardized temperature stress profiles and diagnostics. The CBASS consists of four or eight flow‐through experimental aquaria with independent water masses, lighting, and individual automated temperature controls capable of delivering custom modulating thermal profiles. The CBASS is used to conduct daily thermal stress exposures that typically include 3‐h temperature ramps to multiple target temperatures, a 3‐h hold period at the target temperatures, and a 1‐h ramp back down to ambient temperature, followed by an overnight recovery period. This mimics shallow water temperature profiles observed in coral reefs and prompts a rapid acute heat stress response that can serve as a diagnostic tool to identify putative thermotolerant corals for in‐depth assessments of adaptation mechanisms, targeted conservation, and possible use in restoration efforts. The CBASS is deployable within hours and can assay up to 40 coral fragments/aquaria/day, enabling high‐throughput, rapid determination of thermal thresholds for individual genotypes, populations, species, and sites using a standardized experimental framework.