May 1963

Massachusetts Turf and Lawn Grass Council Better Turf Through Research and Educatio

Classifying Sequences of Extreme Length with Constant Memory Applied to Malware Detection

Recent works within machine learning have been tackling inputs of ever-increasing size, with cybersecurity presenting sequence classification problems of particularly extreme lengths. In the case of Windows executable malware detection, inputs may exceed $100$ MB, which corresponds to a time series with $T=100,000,000$ steps. To date, the closest approach to handling such a task is MalConv, a convolutional neural network capable of processing up to $T=2,000,000$ steps. The $\mathcal{O}(T)$ memory of CNNs has prevented further application of CNNs to malware. In this work, we develop a new approach to temporal max pooling that makes the required memory invariant to the sequence length $T$. This makes MalConv $116\times$ more memory efficient, and up to $25.8\times$ faster to train on its original dataset, while removing the input length restrictions to MalConv. We re-invest these gains into improving the MalConv architecture by developing a new Global Channel Gating design, giving us an attention mechanism capable of learning feature interactions across 100 million time steps in an efficient manner, a capability lacked by the original MalConv CNN. Our implementation can be found at https://github.com/NeuromorphicComputationResearchProgram/MalConv2Comment: To appear in AAAI 202

February 1966

Massachusetts Turf and Lawn Grass CouncilBetter Turf Through Research and Educatio

Forest gene diversity is correlated with the composition and function of soil microbial communities

The growing field of community and ecosystem genetics indicates that plant genotype and genotypic variation are important for structuring communities and ecosystem processes. Little is known, however, regarding the effects of stand gene diversity on soil communities and processes under field conditions. Utilizing natural genetic variation occurring in Populus spp. hybrid zones, we tested the hypothesis that stand gene diversity structures soil microbial communities and influences soil nutrient pools. We found significant unimodal patterns relating gene diversity to soil microbial community composition, microbial exoenzyme activity of a carbon‐acquiring enzyme, and availability of soil nitrogen. Multivariate analyses indicate that this pattern is due to the correlation between gene diversity, plant secondary chemistry, and the composition of the microbial community that impacts the availability of soil nitrogen. Together, these data from a natural system indicate that stand gene diversity may affect soil microbial communities and soil processes in ways similar to species diversity (i.e., unimodal patterns). Our results further demonstrate that the effects of plant genetic diversity on other organisms may be mediated by plant functional trait variation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147191/1/pope0035.pd

Ecology of Feral Pigeons: Population Monitoring, Resource Selection, and Management Practices

Feral pigeons (Columba livia) are typically ignored by ornithologists but can be found roosting in the thousands within cities across the world. Pigeons have been known to spread zoonoses, through ectoparasites and excrement they produce. Along with disease, feral pigeons have an economic impact due to the cost of cleanup and maintenance of human infrastructure. Many organizations have tried to decrease pigeon abundances through euthanasia or use of chemicals that decrease reproductive output. However, killing pigeons has been unsuccessful in decreasing abundance, and chemical inhibition can be expensive and must be used throughout the year. A case study at Texas Tech University has found that populations fluctuate throughout the year, making it difficult to manage numbers. To successfully decrease populations, it is important to have a multifaceted approach that includes removing necessary resources (i. e. nest sites and roosting areas) and decreasing the number of offspring through humane techniques

Elevated carbon dioxide and ozone alter productivity and ecosystem carbon content in northern temperate forests

Three young northern temperate forest communities in the north‐central United States were exposed to factorial combinations of elevated carbon dioxide ( CO 2 ) and tropospheric ozone (O 3 ) for 11 years. Here, we report results from an extensive sampling of plant biomass and soil conducted at the conclusion of the experiment that enabled us to estimate ecosystem carbon (C) content and cumulative net primary productivity ( NPP ). Elevated CO 2 enhanced ecosystem C content by 11%, whereas elevated O 3 decreased ecosystem C content by 9%. There was little variation in treatment effects on C content across communities and no meaningful interactions between CO 2 and O 3 . Treatment effects on ecosystem C content resulted primarily from changes in the near‐surface mineral soil and tree C, particularly differences in woody tissues. Excluding the mineral soil, cumulative NPP was a strong predictor of ecosystem C content ( r 2  = 0.96). Elevated CO 2 enhanced cumulative NPP by 39%, a consequence of a 28% increase in canopy nitrogen (N) content (g N m −2 ) and a 28% increase in N productivity ( NPP /canopy N). In contrast, elevated O 3 lowered NPP by 10% because of a 21% decrease in canopy N, but did not impact N productivity. Consequently, as the marginal impact of canopy N on NPP (∆ NPP /∆N) decreased through time with further canopy development, the O 3 effect on NPP dissipated. Within the mineral soil, there was less C in the top 0.1 m of soil under elevated O 3 and less soil C from 0.1 to 0.2 m in depth under elevated CO 2 . Overall, these results suggest that elevated CO 2 may create a sustained increase in NPP , whereas the long‐term effect of elevated O 3 on NPP will be smaller than expected. However, changes in soil C are not well‐understood and limit our ability to predict changes in ecosystem C content.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108065/1/gcb12564.pd

Elevated carbon dioxide and ozone alter productivity and ecosystem carbon content in northern temperate forests

Three young northern temperate forest communities in the north-central United States were exposed to factorial combinations of elevated carbon dioxide (CO2) and tropospheric ozone (O3) for 11 years. Here, we report results from an extensive sampling of plant biomass and soil conducted at the conclusion of the experiment that enabled us to estimate ecosystem carbon (C) content and cumulative net primary productivity (NPP). Elevated CO2 enhanced ecosystem C content by 11%, whereas elevated O3 decreased ecosystem C content by 9%. There was little variation in treatment effects on C content across communities and no meaningful interactions between CO2 and O3. Treatment effects on ecosystem C content resulted primarily from changes in the near-surface mineral soil and tree C, particularly differences in woody tissues. Excluding the mineral soil, cumulative NPP was a strong predictor of ecosystem C content (r2 = 0.96). Elevated CO2 enhanced cumulative NPP by 39%, a consequence of a 28% increase in canopy nitrogen (N) content (g N m−2) and a 28% increase in N productivity (NPP/canopy N). In contrast, elevated O3 lowered NPP by 10% because of a 21% decrease in canopy N, but did not impact N productivity. Consequently, as the marginal impact of canopy N on NPP (ΔNPP/ΔN) decreased through time with further canopy development, the O3 effect on NPP dissipated. Within the mineral soil, there was less C in the top 0.1 m of soil under elevated O3 and less soil C from 0.1 to 0.2 m in depth under elevated CO2. Overall, these results suggest that elevated CO2 may create a sustained increase in NPP, whereas the long-term effect of elevated O3 on NPP will be smaller than expected. However, changes in soil C are not well-understood and limit our ability to predict changes in ecosystem C content

SKYSURF-4: Panchromatic HST All-Sky Surface-Brightness Measurement Methods and Results

The diffuse, unresolved sky provides most of the photons that the Hubble Space Telescope (HST) receives, yet remains poorly understood. HST Archival Legacy program SKYSURF aims to measure the 0.2-1.6 $\mu$m sky surface brightness (sky-SB) from over 140,000 HST images. We describe a sky-SB measurement algorithm designed for SKYSURF that is able to recover the input sky-SB from simulated images to within 1% uncertainty. We present our sky-SB measurements estimated using this algorithm on the entire SKYSURF database. Comparing our sky-SB spectral energy distribution (SED) to measurements from the literature shows general agreements. Our SKYSURF SED also reveals a possible dependence on Sun angle, indicating either non-isotropic scattering of solar photons off interplanetary dust or an additional component to Zodiacal Light. Finally, we update Diffuse Light limits in the near-IR based on the methods from Carleton et al. (2022), with values of 0.009 MJy sr$^{-1}$ (22 nW m$^{-2}$ sr$^{-1}$) at 1.25 $\mu$m, 0.015 MJy sr$^{-1}$ (32 nW m$^{-2}$ sr$^{-1}$) at 1.4 $\mu$m, and 0.013 MJy sr$^{-1}$ (25 nW m$^{-2}$ sr$^{-1}$) at 1.6 $\mu$m. These estimates provide the most stringent all-sky constraints to date in this wavelength range. SKYSURF sky-SB measurements are made public on the official SKYSURF website and will be used to constrain Diffuse Light in future papers.Comment: Revised based on helpful comments from the reviewer, and accepted to AJ on April 12th, 2023. Main paper: 18 pages, 9 figures, 4 tables. Appendices: 16 pages, 10 figures, 1 table. Main results shown in Figure 7 and Table