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Abstract Energy efficiency in computation is ultimately limited by noise, with quantum limits setting the fundamental noise floor. Analog physical neural networks hold promise for improved energy efficiency compared to digital electronic neural networks. However, they are typically operated in a relatively high-power regime so that the signal-to-noise ratio (SNR) is large (>10), and the noise can be treated as a perturbation. We study optical neural networks where all layers except the last are operated in the limit that each neuron can be activated by just a single photon, and as a result the noise on neuron activations is no longer merely perturbative. We show that by using a physics-based probabilistic model of the neuron activations in training, it is possible to perform accurate machine-learning inference in spite of the extremely high shot noise (SNR  ~ 1). We experimentally demonstrated MNIST handwritten-digit classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to just 0.038 photons per multiply-accumulate (MAC) operation. Our physics-aware stochastic training approach might also prove useful with non-optical ultra-low-power hardware. 

Abstract The HIV reservoir consists of infected cells in which the HIV-1 genome persists as provirus despite effective antiretroviral therapy (ART). Studies exploring HIV cure therapies often measure intact proviral DNA levels, time to rebound after ART interruption, or ex vivo stimulation assays of latently infected cells. This study utilizes barcoded HIV to analyze the reservoir in humanized mice. Using bulk PCR and deep sequencing methodologies, we retrieve 890 viral RNA barcodes and 504 proviral barcodes linked to 15,305 integration sites at the single RNA or DNA molecule in vivo. We track viral genetic diversity throughout early infection, ART, and rebound. The proviral reservoir retains genetic diversity despite cellular clonal proliferation and viral seeding by rebounding virus. Non-proliferated cell clones are likely the result of elimination of proviruses associated with transcriptional activation and viremia. Elimination of proviruses associated with viremia is less prominent among proliferated cell clones. Proliferated, but not massively expanded, cell clones contribute to proviral expansion and viremia, suggesting they fuel viral persistence. This approach enables comprehensive assessment of viral levels, lineages, integration sites, clonal proliferation and proviral epigenetic patterns in vivo. These findings highlight complex reservoir dynamics and the role of proliferated cell clones in viral persistence. 

Abstract Grasslands are subject to climate change, such as severe drought, and an important aspect of their functioning is temporal stability in response to extreme climate events. Previous research has explored the impacts of extreme drought and post‐drought periods on grassland stability, yet the mechanistic pathways behind these changes have rarely been studied.Here, we implemented an experiment with 4 years of drought and 3 years of recovery to assess the effects of drought and post‐drought on the temporal stability of above‐ground net primary productivity (ANPP) and its underlying mechanisms. To do so, we measured community‐weighted mean (CWM) of six plant growth and nine seed traits, functional diversity, population stability and species asynchrony across two cold, semiarid grasslands in northern China. We also performed piecewise structural equation models (SEMs) to assess the relationships between ANPP stability and its underlying mechanisms and how drought and post‐drought periods alter the relative contribution of these mechanisms to ANPP stability.We found that temporal stability of ANPP was not reduced during drought due to grasses maintaining productivity, which compensated for increased variation of forb productivity. Moreover, ANPP recovered rapidly after drought, and both grasses and forbs contributed to community stability during the post‐drought period. Overall, ANPP stability decreased during the combined drought and post‐drought periods because of rapid changes in ANPP from drought to post‐drought. SEMs revealed that the temporal stability of ANPP during drought and post‐drought periods was modulated by functional diversity and community‐weighted mean traits directly and indirectly by altering species asynchrony and population stability. Specifically, the temporal stability of ANPP was positively correlated with functional divergence of plant communities. CWMs of seed traits (e.g. seed width and thickness), rather than plant growth traits (e.g. specific leaf area and leaf nutrient content), stabilized grassland ANPP. Productivity of plant communities with large and thick seeds was less sensitive to precipitation changes over time.These results emphasize the importance of considering both the functional trait distribution among species and seed traits of dominant species since their combined effects can stabilize ecosystem functions under global climate change scenarios. Read the freePlain Language Summaryfor this article on the Journal blog. 

We consider a non-adaptive controlled sensing sce-nario in which the actions of the decision maker are corrupted by an adversary. The objective of the decision maker is to either detect the presence of the corruption or make a correct decision. Accordingly, the performance of a controlled sensing strategy is measured in terms of the error probability when there is no adversary, denoted PE,0 , and the error probability when an adversary is present, denoted PE,1 . Our main result is Stein-lemma like characterization of the optimal achievable error exponent of PE,0 subject to a constraint on PE,1 . We also illustrate the result with numerical examples.