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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 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. 

We present a catalog of results of gamma-ray observations made by VERITAS, published from 2008 to 2020. VERITAS is a ground based imaging atmospheric Cherenkov telescope observatory located at the Fred Lawrence Whipple Observatory (FLWO) in southern Arizona, sensitive to gamma-ray photons with energies in the range of ∼ 100 GeV - 30 TeV. Its observation targets include galactic sources such as binary star systems, pulsar wind nebulae, and supernova remnants, extragalactic sources like active galactic nuclei, star forming galaxies, and gamma-ray bursts, and some unidentified objects. The catalog includes in digital form all of the high-level science results published in 112 papers using VERITAS data and currently contains data on 57 sources. The catalog has been made accessible via GitHub and at NASA's HEASARC. 

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.