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1. Singularity of sparse random matrices: simple proofs

   https://doi.org/10.1017/S0963548321000146  

   Ferber, Asaf; Kwan, Matthew; Sauermann, Lisa (January 2020, Combinatorics, Probability and Computing)

   null (Ed.)

   Consider a random n×n zero-one matrix with ‘sparsity’ p, sampled according to one of the following two models: either every entry is independently taken to be one with probability p (the ‘Bernoulli’ model) or each row is independently uniformly sampled from the set of all length-n zero-one vectors with exactly pn ones (the ‘combinatorial’ model). We give simple proofs of the (essentially best-possible) fact that in both models, if min (p, 1− p) ≥ (1+ε) log n/n for any constant ε >0, then our random matrix is nonsingular with probability 1− o(1). In the Bernoulli model, this fact was already well known, but in the combinatorial model this resolves a conjecture of Aigner-Horev and Person. 

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2. Benchmarking embedded chain breaking in quantum annealing ^*

   https://doi.org/10.1088/2058-9565/ac26d2  

   Thinh V. Le, Manh V. (March 2022, Quantum Science and Technology)

   Abstract Quantum annealing solves combinatorial optimization problems by finding the energetic ground states of an embedded Hamiltonian. However, quantum annealing dynamics under the embedded Hamiltonian may violate the principles of adiabatic evolution and generate excitations that correspond to errors in the computed solution. Here we empirically benchmark the probability of chain breaks and identify sweet spots for solving a suite of embedded Hamiltonians. We further correlate the physical location of chain breaks in the quantum annealing hardware with the underlying embedding technique and use these localized rates in a tailored post-processing strategies. Our results demonstrate how to use characterization of the quantum annealing hardware to tune the embedded Hamiltonian and remove computational errors. 

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3. New perspectives on the evolution of women's cooperation

   https://doi.org/10.1098/rstb.2021.0424  

   Fox, Stephanie A; Scelza, Brooke; Silk, Joan; Kramer, Karen L (January 2023, Philosophical Transactions of the Royal Society B: Biological Sciences)

   A holistic, evolutionary framework about human cooperation must incorporate information about women's cooperative behaviour. Yet, most empirical research on human cooperation has centered on men's behaviour or been derived from experimental studies conducted in western, industrialized populations. These bodies of data are unlikely to accurately represent human behavioural diversity. To address this gap and provide a more balanced view of human cooperation, this issue presents substantial new data and multi-disciplinary perspectives to document the complexity of women's cooperative behaviour. Research in this issue 1) challenges narratives about universal gender differences in cooperation, 2) reconsiders patrilocality and access to kin as constraints on women's cooperation, 3) reviews evidence for a connection between social support and women's health and 4) examines the phylogenetic roots of female cooperation. Here, we discuss the steps taken in this issue toward a more complete and evidence-based understanding of the role that cooperation plays in women's and girls' lives and in building human sociality. This article is part of the theme issue ‘Cooperation among women: evolutionary and cross-cultural perspectives’. 

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4. Socio-hydrological modeling of the tradeoff between flood control and hydropower provided by the Columbia River Treaty

   https://doi.org/10.5194/hess-26-4893-2022  

   Shrestha, Ashish; Souza, Felipe Augusto; Park, Samuel; Cherry, Charlotte; Garcia, Margaret; Yu, David J.; Mendiondo, Eduardo Mario (January 2022, Hydrology and Earth System Sciences)

   Abstract. The Columbia River Treaty (CRT) signed between the United States and Canada in 1961 is known as one of the most successful transboundary watertreaties. Under continued cooperation, both countries equitably share collective responsibilities of reservoir operations and flood control andhydropower benefits from treaty dams. As the balance of benefits is the key factor of cooperation, future cooperation could be challenged byexternal social and environmental factors which were not originally anticipated or change in the social preferences of the two actors. To understandthe robustness of cooperation dynamics, we address two research questions. (i) How does social and environmental change influence cooperationdynamics? (ii) How do social preferences influence the probability of cooperation for both actors? We analyzed infrastructural, hydrological,economic, social, and environmental data to inform the development of a socio-hydrological system dynamics model. The model simulates the dynamicsof flood control and hydropower benefit sharing as a function of the probability to cooperate, which in turn is affected by the share ofbenefits. The model is used to evaluate scenarios that represent environmental and institutional change and changes in political characteristicsbased on social preferences. Our findings show that stronger institutional capacity ensures equitable sharing of benefits over the long term. Under the current CRT, the utility of cooperation is always higher for Canada than non-cooperation, which is in contrast to the United States. The probability tocooperate for each country is lowest when they are self-interested but fluctuates in other social preference scenarios. 

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5. Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

   https://doi.org/10.1002/aic.18605  

   Chowdhury, Myisha A; Al‐Wahaibi, Saif SS; Lu, Qiugang (September 2024, AIChE Journal)

   Abstract Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions. 

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