More Like this

1. Discovering conservation laws using optimal transport and manifold learning

   https://doi.org/10.1038/s41467-023-40325-7  

   Lu, Peter Y.; Dangovski, Rumen; Soljačić, Marin (August 2023, Nature Communications)

   Abstract Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, making it hard to analyze their dynamics and build stable predictive models. Current approaches for discovering conservation laws often depend on detailed dynamical information or rely on black box parametric deep learning methods. We instead reformulate this task as a manifold learning problem and propose a non-parametric approach for discovering conserved quantities. We test this new approach on a variety of physical systems and demonstrate that our method is able to both identify the number of conserved quantities and extract their values. Using tools from optimal transport theory and manifold learning, our proposed method provides a direct geometric approach to identifying conservation laws that is both robust and interpretable without requiring an explicit model of the system nor accurate time information. 

   more » « less
2. Machine Learning-Based Diffractive Image Analysis with Subwavelength Resolution

   https://doi.org/10.1021/acsphotonics.1c00205  

   Ghosh, Abantika; Roth, Diane J.; Nicholls, Luke H.; Wardley, William P.; Zayats, Anatoly V.; Podolskiy, Viktor A. (April 2021, ACS Photonics)

   null (Ed.)

   Far-field analysis of small objects is severely constrained by the diffraction limit. Existing tools achieving sub-diffraction resolution often utilize point-by-point image reconstruction via scanning or labelling. Here, we present a new technique capable of fast and accurate characterization of two-dimensional structures with at least wavelength/25 theoretical resolution, based on a single far-field intensity measurement. Experimentally, we realized this technique resolving the smallest-available to us 180-nm-scale features with 845-nm laser light, reaching a resolution of wavelength/5. A comprehensive analysis of machine learning algorithms was performed to gain insight into the learning process and to understand the flow of subwavelength information through the system. Image parameterization, suitable for diffractive configurations and highly tolerant to random noise was developed. The proposed technique can be applied to new optical characterization tools with high spatial resolution, fast data acquisition and artificial intelligence, such as high-speed nanoscale metrology and quality control, and can be further developed to high-resolution spectroscopy 

   more » « less
3. Machine learning and phone data can improve targeting of humanitarian aid

   https://doi.org/10.1038/s41586-022-04484-9  

   Aiken, Emily; Bellue, Suzanne; Karlan, Dean; Udry, Chris; Blumenstock, Joshua E. (March 2022, Nature)

   Abstract The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards 1 . In response to this crisis, governments and humanitarian organizations worldwide have distributed social assistance to more than 1.5 billion people 2 . Targeting is a central challenge in administering these programmes: it remains a difficult task to rapidly identify those with the greatest need given available data 3,4 . Here we show that data from mobile phone networks can improve the targeting of humanitarian assistance. Our approach uses traditional survey data to train machine-learning algorithms to recognize patterns of poverty in mobile phone data; the trained algorithms can then prioritize aid to the poorest mobile subscribers. We evaluate this approach by studying a flagship emergency cash transfer program in Togo, which used these algorithms to disburse millions of US dollars worth of COVID-19 relief aid. Our analysis compares outcomes—including exclusion errors, total social welfare and measures of fairness—under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%. Relative to methods requiring a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine-learning approach increases exclusion errors by 9–35%. These results highlight the potential for new data sources to complement traditional methods for targeting humanitarian assistance, particularly in crisis settings in which traditional data are missing or out of date. 

   more » « less
4. Belief Convergence under Misspecified Learning: A Martingale Approach

   https://doi.org/10.1093/restud/rdac040  

   Frick, Mira; Iijima, Ryota; Ishii, Yuhta (July 2022, The Review of Economic Studies)

   Abstract We present an approach to analyse learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e. from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyse environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning. 

   more » « less
5. Implementing an epistemologically authentic approach to student-centered inquiry learning

   Brookes, David T.; Etkina, Eugenia; Planinsic, Gorazd (December 2020, Physical review)

   This paper discusses the theoretical framework and curriculum materials that form the basis of the Investigative Science Learning Environment (ISLE) approach to learning and teaching physics. ISLE as a philosophical approach to learning, has two core intentionalities: 1. We want students to learn physics by thinking like physicists; by engaging in knowledge-generating activities that mimic the actual practices of physics and using the reasoning tools that physicists use when constructing and applying knowledge. 2. The way in which students learn physics should enhance their well-being. These intentionalities form the basis upon which we build a bricolage of multiple theoretical perspectives. We will show how the ISLE approach and its implementation is shaped by a. the epistemological commitments of physics, b. the findings of cognitive science, c. theories of learning communities, and d. the perspective of universal design. We will present both qualitative and quantitative data that demonstrate the effectiveness of ISLE in helping students to achieve our intentionalities. We conclude with a call to curriculum developers and implementers to explicitly articulate their intentionalities and theoretical perspectives so that we may forge deeper connections between educational theories, curriculum development, and implementation. 

   more » « less