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1. The GIGANTES Data Set: Precision Cosmology from Voids in the Machine-learning Era

   https://doi.org/10.3847/1538-4357/ac7d4b  

   Kreisch, Christina D. ; Pisani, Alice ; Villaescusa-Navarro, Francisco ; Spergel, David N. ; Wandelt, Benjamin D. ; Hamaus, Nico ; Bayer, Adrian E. ( August 2022 , The Astrophysical Journal)

   We presentGIGANTES, the most extensive and realistic void catalog suite ever released—containing over 1 billion cosmic voids covering a volume larger than the observable universe, more than 20 TB of data, and created by running the void finderVIDEonQUIJOTE’s halo simulations. TheGIGANTESsuite, spanning thousands of cosmological models, opens up the study of voids, answering compelling questions: Do voids carry unique cosmological information? How is this information correlated with galaxy information? Leveraging the large number of voids in theGIGANTESsuite, our Fisher constraints demonstrate voids contain additional information, critically tightening constraints on cosmological parameters. We use traditional void summary statistics (void size function, void density profile) and the void autocorrelation function, which independently yields an error of 0.13 eV on ∑m_νfor a 1h⁻³Gpc³simulation, without cosmic microwave background priors. Combining halos and voids we forecast an error of 0.09 eV from the same volume, representing a gain of 60% compared to halos alone. Extrapolating to next generation multi-Gpc³surveys such as the Dark Energy Spectroscopic Instrument, Euclid, the Spectro-Photometer for the History of the Universe and Ices Explorer, and the Roman Space Telescope, we expect voids should yield an independent determination of neutrino mass. Crucially,GIGANTESis the first void catalog suite expressly built for intensive machine-learning exploration. We illustrate this by training a neural network to perform likelihood-free inference on the void size function, giving a ∼20% constraint on Ω_m. Cosmology problems provide an impetus to develop novel deep-learning techniques. WithGIGANTES, machine learning gains an impressive data set, offering unique problems that will stimulate new techniques.

    

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2. Scaling for Saturated Moist Quasigeostrophic Turbulence

   https://doi.org/10.1175/JAS-D-22-0215.1  

   Brown, Marguerite L. ; Pauluis, Olivier ; Gerber, Edwin P. ( June 2023 , Journal of the Atmospheric Sciences)

   Abstract Much of our conceptual understanding of midlatitude atmospheric motion comes from two-layer quasigeostrophic (QG) models. Traditionally, these QG models do not include moisture, which accounts for an estimated 30%–60% of the available energy of the atmosphere. The atmospheric moisture content is expected to increase under global warming, and therefore, a theory for how moisture modifies atmospheric dynamics is crucial. We use a two-layer moist QG model with convective adjustment as a basis for analyzing how latent heat release and large-scale moisture gradients impact the scalings of a midlatitude system at the synoptic scale. In this model, the degree of saturation can be tuned independently of other moist parameters by enforcing a high rate of evaporation from the surface. This allows for study of the effects of latent heat release at saturation, without the intrinsic nonlinearity of precipitation. At saturation, this system is equivalent to the dry QG model under a rescaling of both length and time. This predicts that the most unstable mode shifts to smaller scales, the growth rates increase, and the inverse cascade extends to larger scales. We verify these results numerically and use them to verify a framework for the complete energetics of a moist system. We examine the spectral features of the energy transfer terms. This analysis shows that precipitation generates energy at small scales, while dry dynamics drive a significant broadening to larger scales. Cascades of energy are still observed in all terms, albeit without a clearly defined inertial range. Significance Statement The effect of moist processes, especially the impact of latent heating associated with condensation, on the size and strength of midlatitude storms is not well understood. Such insight is particularly needed in the context of global warming, as we expect moisture to play a more important role in a warmer world. In this study, we provide intuition into how including condensation can result in midlatitude storms that grow faster and have features on both larger and smaller scales than their dry counterparts. We provide a framework for quantifying these changes and verify it for the special case where it is raining everywhere. These findings can be extended to the more realistic situation where it is only raining locally. 

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3. SCANN: Side Channel Analysis of Spiking Neural Networks

   https://doi.org/10.3390/cryptography7020017  

   Nagarajan, Karthikeyan ; Roy, Rupshali ; Topaloglu, Rasit Onur ; Kannan, Sachhidh ; Ghosh, Swaroop ( June 2023 , Cryptography)

   Spiking neural networks (SNNs) are quickly gaining traction as a viable alternative to deep neural networks (DNNs). Compared to DNNs, SNNs are computationally more powerful and energy efficient. The design metrics (synaptic weights, membrane threshold, etc.) chosen for such SNN architectures are often proprietary and constitute confidential intellectual property (IP). Our study indicates that SNN architectures implemented using conventional analog neurons are susceptible to side channel attack (SCA). Unlike the conventional SCAs that are aimed to leak private keys from cryptographic implementations, SCANN (SCA̲ of spiking n̲eural n̲etworks) can reveal the sensitive IP implemented within the SNN through the power side channel. We demonstrate eight unique SCANN attacks by taking a common analog neuron (axon hillock neuron) as the test case. We chose this particular model since it is biologically plausible and is hence a good fit for SNNs. Simulation results indicate that different synaptic weights, neurons/layer, neuron membrane thresholds, and neuron capacitor sizes (which are the building blocks of SNN) yield distinct power and spike timing signatures, making them vulnerable to SCA. We show that an adversary can use templates (using foundry-calibrated simulations or fabricating known design parameters in test chips) and analysis to identify the specifications of the implemented SNN. 

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4. Performance study of statistical and deterministic channel models for mmWave wi-fi networks in ns-3

   https://doi.org/10.1145/3460797.3460802  

   Liu, Yuchen ; Crisp, Shelby K. ; Blough, Douglas M. ( May 2021 , Proceedings of the Workshop on ns-3)

   null (Ed.)

   To address the needs of emerging bandwidth-intensive applications in 5G and beyond era, the millimeter-wave (mmWave) band with very large spectrum availability have been recognized as a promising choice for future wireless communications. In particular, IEEE 802.11ad/ay operating on 60 GHz carrier frequency is a highly anticipated wireless local area network (WLAN) technology for supporting ultra-high-rate data transmissions. In this paper, we describe additions to the ns-3 802.11ad simulator that include 3D obstacle specifications, line-of-sight calculations, and a sparse cluster-based channel model, which allow researchers to study complex mmWave Wi-Fi network deployments under more realistic conditions. We also study the performance accuracy and simulation efficiency of the implemented statistical channel model as compared to a deterministic ray-tracing based channel model. Through extensive ns-3 simulations, the results show that the implemented channel model has the potential to achieve good accuracy in performance evaluation while improving simulation efficiency. We also provide a detailed parametric analysis on the statistical channel model, which yields insight on how to properly tune the model parameters to further improve performance accuracy. 

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5. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

   https://doi.org/10.1093/bioinformatics/btac254  

   Edrisi, Mohammadamin ; Valecha, Monica V. ; Chowdary, Sunkara B. V. ; Robledo, Sergio ; Ogilvie, Huw A. ; Posada, David ; Zafar, Hamim ; Nakhleh, Luay ( June 2022 , Bioinformatics)

   Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data.

   Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases.

   Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.

    

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