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1. Deperturbation analysis of an avoided crossing in the A1 v = 0 and B1 v = 0 states of WO

   https://doi.org/10.1016/j.jms.2025.112022  

   Bales, Kristin N; Swarna, Akhila; O'Brien, James J; O'Brien, Leah C (June 2025, Journal of Molecular Spectroscopy)

   The (0,0) and (0,1) bands of the A1 – X0+ and B1 – X0+ transitions of tungsten monoxide (WO) have been recorded in high resolution using intracavity laser absorption spectroscopy techniques (ILS and ILS-FTS). Lines for all four abundant isotopologues, 182WO, 183WO, 184WO, and 186WO, are observed and have been rotationally analyzed and fit using a constrained-variables approach in PGOPHER. While these transitions have been previously described in the literature, this new analysis incorporates all four isotopologues and presents evidence for an avoided crossing interaction between the v = 0 vibrational levels of the A1 and B1 states. New values for spectroscopic constants that incorporate the perturbation interactions are presented and compared to literature results. 

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2. Approximate Positive Correlated Distributions and Approximation Algorithms for D-optimal Design

   https://doi.org/10.1137/1.9781611975031.145  

   Singh, Mohit; Xie, Weijun (January 2018, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms)

   Experimental design is a classical area in statistics and has also found new applications in machine learning. In the combinatorial experimental design problem, the aim is to estimate an unknown m-dimensional vector x from linear measurements where a Gaussian noise is introduced in each measurement. The goal is to pick k out of the given n experiments so as to make the most accurate estimate of the unknown parameter x. Given a set S of chosen experiments, the most likelihood estimate x0 can be obtained by a least squares computation. One of the robust measures of error estimation is the D-optimality criterion which aims to minimize the generalized variance of the estimator. This corresponds to minimizing the volume of the standard confidence ellipsoid for the estimation error x − x0. The problem gives rise to two natural variants depending on whether repetitions of experiments is allowed or not. The latter variant, while being more general, has also found applications in geographical location of sensors. We show a close connection between approximation algorithms for the D-optimal design problem and constructions of approximately m-wise positively correlated distributions. This connection allows us to obtain first approximation algorithms for the D-optimal design problem with and without repetitions. We then consider the case when the number of experiments chosen is much larger than the dimension m and show one can obtain asymptotically optimal algorithms in this case. 

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3. Testing Symmetric Markov Chains from a Single Trajectory

   Daskalakis, Constantinos; Dikkala, Nishanth; Gravin, Nick (January 2018, 31st Annual Conference on Learning Theory)

   Classical distribution testing assumes access to i.i.d. samples from the distribution that is being tested. We initiate the study of Markov chain testing, assuming access to a single trajectory of a Markov Chain. In particular, we observe a single trajectory X0,...,Xt,... of an unknown, symmetric, and finite state Markov Chain M. We do not control the starting state X0, and we cannot restart the chain. Given our single trajectory, the goal is to test whether M is identical to a model Markov Chain M0 , or far from it under an appropriate notion of difference. We propose a measure of difference between two Markov chains, motivated by the early work of Kazakos [Kaz78], which captures the scaling behavior of the total variation distance between trajectories sampled from the Markov chains as the length of these trajectories grows. We provide efficient testers and information-theoretic lower bounds for testing identity of symmetric Markov chains under our proposed measure of difference, which are tight up to logarithmic factors if the hitting times of the model chain M0 is O(n) in the size of the state space n. 

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4. Search for single production of a vector-like T quark decaying to a top quark and a Z boson in the final state with jets and missing transverse momentum at $$ \sqrt{s} $$ = 13 TeV

   https://doi.org/10.1007/JHEP05(2022)093  

   Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Escalante Del Valle, A.; Frühwirth, R.; Jeitler, M.; Krammer, N.; et al (May 2022, Journal of High Energy Physics)

   A bstract A search is presented for single production of a vector-like T quark with charge 2 / 3 e , in the decay channel featuring a top quark and a Z boson, with the top quark decaying hadronically and the Z boson decaying to neutrinos. The search uses data collected by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 137 fb − 1 recorded at the CERN LHC in 2016–2018. The search is sensitive to a T quark mass between 0.6 and 1.8 TeV with decay widths ranging from negligibly small up to 30% of the T quark mass. Reconstruction strategies for the top quark are based on the degree of Lorentz boosting of its final state. At 95% confidence level, the upper limit on the product of the cross section and branching fraction for a T quark of small decay width varies between 15 and 602 fb, depending on its mass. For a T quark with decay widths between 10 and 30% of its mass, this upper limit ranges between 16 and 836 fb. For most of the studied range, the results provide the best limits to date. This is the first search for single T quark production based on the full Run 2 data set of the LHC. 

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5. Testing Symmetric Markov Chains from a Single Trajectory

   Constantinos Daskalakis, Nishanth Dikkala (July 2018, Conference on Learning Theory (COLT))

   The paper’s abstract in valid LaTeX, without non-standard macros or \cite commands. Classical distribution testing assumes access to i.i.d. samples from the distribution that is being tested. We initiate the study of Markov chain testing, assuming access to a {\em single trajectory of a Markov Chain.} In particular, we observe a single trajectory X0,…,Xt,… of an unknown, symmetric, and finite state Markov Chain M. We do not control the starting state X0, and we cannot restart the chain. Given our single trajectory, the goal is to test whether M is identical to a model Markov Chain M′, or far from it under an appropriate notion of difference. We propose a measure of difference between two Markov chains, motivated by the early work of Kazakos [78], which captures the scaling behavior of the total variation distance between trajectories sampled from the Markov chains as the length of these trajectories grows. We provide efficient testers and information-theoretic lower bounds for testing identity of symmetric Markov chains under our proposed measure of difference, which are tight up to logarithmic factors if the hitting times of the model chain M′ is O~(n) in the size of the state space n. 

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