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1. Y-cube model and fractal structure of subdimensional particles on hyperbolic lattices

   Yan, H.; Slagle, K.; Nevidomskyy, Andriy H. (November 2022, arXivorg)

   Unlike ordinary topological quantum phases, fracton orders are intimately dependent on the underlying lattice geometry. In this work, we study a generalization of the X-cube model, dubbed the Y-cube model, on lattices embedded in H2×S1 space, i.e., a stack of hyperbolic planes. The name `Y-cube' comes from the Y-shape of the analog of the X-cube's X-shaped vertex operator. We demonstrate that for certain hyperbolic lattice tesselations, the Y-cube model hosts a new kind of subdimensional particle, treeons, which can only move on a fractal-shaped subset of the lattice. Such an excitation only appears on hyperbolic geometries; on flat spaces treeons becomes either a lineon or a planeon. Intriguingly, we find that for certain hyperbolic tesselations, a fracton can be created by a membrane operator (as in the X-cube model) or by a fractal-shaped operator within the hyperbolic plane. 

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2. CASM: A Deep-Learning Approach for Identifying Collective Action Events with Text and Image Data from Social Media

   https://doi.org/10.1177/0081175019860244  

   Zhang, Han; Pan, Jennifer (July 2019, Sociological Methodology)

   Protest event analysis is an important method for the study of collective action and social movements and typically draws on traditional media reports as the data source. We introduce collective action from social media (CASM)—a system that uses convolutional neural networks on image data and recurrent neural networks with long short-term memory on text data in a two-stage classifier to identify social media posts about offline collective action. We implement CASM on Chinese social media data and identify more than 100,000 collective action events from 2010 to 2017 (CASM-China). We evaluate the performance of CASM through cross-validation, out-of-sample validation, and comparisons with other protest data sets. We assess the effect of online censorship and find it does not substantially limit our identification of events. Compared to other protest data sets, CASM-China identifies relatively more rural, land-related protests and relatively few collective action events related to ethnic and religious conflict. 

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3. Measurement of three-photon excitation cross-sections of fluorescein from 1154 nm to 1500 nm

   https://doi.org/10.1364/BOE.498214  

   LaViolette, Aaron_K; Ouzounov, Dimitre_G; Xu, Chris (July 2023, Biomedical Optics Express)

   Measurements of three-photon action cross-sections for fluorescein (dissolved in water, pH ∼11.5) are presented in the excitation wavelength range from 1154 to 1500 nm in ∼50 nm steps. The excitation source is a femtosecond wavelength tunable non-collinear optical parametric amplifier, which has been spectrally filtered with 50 nm full width at half maximum band pass filters. Cube-law power dependance is confirmed at the measurement wavelengths. The three-photon excitation spectrum is found to differ from both the one- and two-photon excitation spectra. The three-photon action cross-section at 1154 nm is more than an order of magnitude larger than those at 1450 and 1500 nm (approximately three times the wavelength of the one-photon excitation peak), which possibly indicates the presence of resonance enhancement. 

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4. Monotone probability distributions over the Boolean cube can be learned with sublinear samples

   Rubinfeld, Ronitt; Vasilyan, Arsen (January 2020, 11th Innovations in Theoretical Computer Science (ITCS 2020))

   A probability distribution over the Boolean cube is monotone if flipping the value of a coordinate from zero to one can only increase the probability of an element. Given samples of an unknown monotone distribution over the Boolean cube, we give (to our knowledge) the first algorithm that learns an approximation of the distribution in statistical distance using a number of samples that is sublinear in the domain. To do this, we develop a structural lemma describing monotone probability distributions. The structural lemma has further implications to the sample complexity of basic testing tasks for analyzing monotone probability distributions over the Boolean cube: We use it to give nontrivial upper bounds on the tasks of estimating the distance of a monotone distribution to uniform and of estimating the support size of a monotone distribution. In the setting of monotone probability distributions over the Boolean cube, our algorithms are the first to have sample complexity lower than known lower bounds for the same testing tasks on arbitrary (not necessarily monotone) probability distributions. One further consequence of our learning algorithm is an improved sample complexity for the task of testing whether a distribution on the Boolean cube is monotone. 

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5. Monotone Probability Distributions over the Boolean Cube Can Be Learned with Sublinear Samples

   Rubinfeld, Ronitt; Vasilyan, Arsen (January 2020, 11th Innovations in Theoretical Computer Science Conference, ITCS 2020)

   null (Ed.)

   A probability distribution over the Boolean cube is monotone if flipping the value of a coordinate from zero to one can only increase the probability of an element. Given samples of an unknown monotone distribution over the Boolean cube, we give (to our knowledge) the first algorithm that learns an approximation of the distribution in statistical distance using a number of samples that is sublinear in the domain. To do this, we develop a structural lemma describing monotone probability distributions. The structural lemma has further implications to the sample complexity of basic testing tasks for analyzing monotone probability distributions over the Boolean cube: We use it to give nontrivial upper bounds on the tasks of estimating the distance of a monotone distribution to uniform and of estimating the support size of a monotone distribution. In the setting of monotone probability distributions over the Boolean cube, our algorithms are the first to have sample complexity lower than known lower bounds for the same testing tasks on arbitrary (not necessarily monotone) probability distributions. One further consequence of our learning algorithm is an improved sample complexity for the task of testing whether a distribution on the Boolean cube is monotone. 

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