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1. Revisiting the Half-and-Half Functional

   https://doi.org/10.1021/acs.jpca.5c01402  

   Gray, Montgomery; Mandal, Aniket; Herbert, John M (May 2025, The Journal of Physical Chemistry A)
2. Double Half-Heuslers

   https://doi.org/10.1016/j.joule.2019.04.003  

   Anand, Shashwat; Wood, Max; Xia, Yi; Wolverton, Chris; Snyder, G. Jeffrey (May 2019, Joule)
3. HALF-SPACE MACDONALD PROCESSES

   https://doi.org/10.1017/fmp.2020.3  

   BARRAQUAND, GUILLAUME; BORODIN, ALEXEI; CORWIN, IVAN (January 2020, Forum of Mathematics, Pi)

   null (Ed.)

   Macdonald processes are measures on sequences of integer partitions built using the Cauchy summation identity for Macdonald symmetric functions. These measures are a useful tool to uncover the integrability of many probabilistic systems, including the Kardar–Parisi–Zhang (KPZ) equation and a number of other models in its universality class. In this paper, we develop the structural theory behind half-space variants of these models and the corresponding half-space Macdonald processes. These processes are built using a Littlewood summation identity instead of the Cauchy identity, and their analysis is considerably harder than their full-space counterparts. We compute moments and Laplace transforms of observables for general half-space Macdonald measures. Introducing new dynamics preserving this class of measures, we relate them to various stochastic processes, in particular the log-gamma polymer in a half-quadrant (they are also related to the stochastic six-vertex model in a half-quadrant and the half-space ASEP). For the polymer model, we provide explicit integral formulas for the Laplace transform of the partition function. Nonrigorous saddle-point asymptotics yield convergence of the directed polymer free energy to either the Tracy–Widom (associated to the Gaussian orthogonal or symplectic ensemble) or the Gaussian distribution depending on the average size of weights on the boundary. 

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4. Deception through Half-Truths

   https://doi.org/10.1609/aaai.v34i06.6570  

   Estornell, Andrew; Das, Sanmay; Vorobeychik, Yevgeniy (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated “leaks” and fake news about candidates. Typical considerations of deception view it as providing false information. However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked. We consider the problem of how much an adversary can affect a principal's decision by “half-truths”, that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time. 

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5. Deception through Half-Truths

   Estornell, Andrew; Das, Sanmay; Vorobeychik, Yevgeniy (January 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   Deception is a fundamental issue across a diverse array of settings, from cybersecurity, where decoys (e.g., honeypots) are an important tool, to politics that can feature politically motivated "leaks" and fake news about candidates.Typical considerations of deception view it as providing false information.However, just as important but less frequently studied is a more tacit form where information is strategically hidden or leaked.We consider the problem of how much an adversary can affect a principal's decision by "half-truths", that is, by masking or hiding bits of information, when the principal is oblivious to the presence of the adversary. The principal's problem can be modeled as one of predicting future states of variables in a dynamic Bayes network, and we show that, while theoretically the principal's decisions can be made arbitrarily bad, the optimal attack is NP-hard to approximate, even under strong assumptions favoring the attacker. However, we also describe an important special case where the dependency of future states on past states is additive, in which we can efficiently compute an approximately optimal attack. Moreover, in networks with a linear transition function we can solve the problem optimally in polynomial time. 

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