Casimir meets Poisson: improved quark/gluon discrimination with counting observables by Frye, Christopher, Larkoski, Andrew J, Zhou, Kevin, Thaler, Jesse

“…The key observation is that track multiplicity is approximately Poisson distributed, with more suppressed tails than the Sudakov peak structure from jet mass. By using an iterated version of the soft drop jet grooming algorithm, we can define a “soft drop multiplicity” which is Poisson distributed at leading-logarithmic accuracy. …”
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Computing Maximum Flow with Augmenting Electrical Flows by Madry, Aleksander

“…At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting s-t flow in the current residual graph and to update the dual solution. …”
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Computing Maximum Flow with Augmenting Electrical Flows by Madry, Aleksander

“…At a high level, the presented algorithm takes a primal dual approach in which each iteration uses electrical flows computations both to find an augmenting s-t flow in the current residual graph and to update the dual solution. …”
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Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions by Amato, Christopher, Liu, Miao, Sivakumar, Kavinayan P, Omidshafiei, Shayegan, How, Jonathan P

“…This work addresses these gaps by proposing an iterative sampling based Expectation-Maximization algorithm (iSEM) to learn polices using only trajectory data containing observations, MAs, and rewards. …”
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Simple, efficient, and neural algorithms for sparse coding by Arora, Sanjeev, Ge, Rong, Ma, Tengyu, Moitra, Ankur

“…We believe that our analysis framework will have applications in other settings where simple iterative algorithms are used.…”
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Complexity of Bayesian Belief Exchange over a Network by Mossel, Elchanan, Rahimian, Mohammad Amin, Jadbabaie-Moghadam, Ali

“…In this paper, we will use the framework of iterated eliminations to model the decision problem as well as the thinking process of a Bayesian agent in a group decision/discussion scenario. …”
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Simulation of Dual Mixed Refrigerant Natural Gas Liquefaction Processes Using a Nonsmooth Framework by Vikse, Matias, Gundersen, Truls, Watson, Harry Alexander James, Barton, Paul I

“…Limited degrees of freedom and the inability to solve for stream variables other than outlet temperatures also makes such tools inflexible to use, often requiring the user to resort to a manual iterative procedure to obtain a feasible solution. …”
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Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods by Vanli, Nuri Denizcan, Gurbuzbalaban, Mert, Ozdaglar, Asuman E

“…We consider solving this problem using the proximal incremental aggregated gradient (PIAG) method, which at each iteration moves along an aggregated gradient (formed by incrementally updating gradients of component functions according to a deterministic order) and takes a proximal step with respect to the nonsmooth function. …”
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Systematic Detection of Clustered Seismicity Beneath the Southwestern Alps by Beaucé, Eric, Frank, William B., Paul, Anne, Campillo, Michel, Campillo, Michel Henri Paul, Hilst, Robert D., van der Hilst, Robert D

“…We describe how we address the problem of false positives in energy-based earthquake detection with supervised machine learning and how to best leverage template matching to iteratively refine the templates and the detection. …”
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Rapid prototyping and clinical testing of a reusable face shield for health care workers responding to the COVID-19 pandemic by Mostaghimi, Arash, Antonini, Marc-Joseph, Plana, Deborah, Anderson, Philip D, Beller, Brandon, Boyer, Edward W, Fannin, Amber, Freake, Jacob, Oakley, Richard, Sinha, Michael S, Smith, Leanne, Van, Christopher, Yang, Helen, Sorger, Peter K, LeBoeuf, Nicole R, Yu, Sherry H

“…Methods: We describe a research protocol under Institutional Review Board supervision that allowed clinicians to participate in an iterative design process followed by real-world testing in an emergency department. …”
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Coresets for scalable Bayesian logistic regression by Huggins, Jonathan H., Campbell, Trevor David, Broderick, Tamara A

“…Recent work on scaling Bayesian inference has focused on modifying the underlying algorithms to, for example, use only a random data subsample at each iteration. We leverage the insight that data is often redundant to instead obtain a weighted subset of the data (called a coreset) that is much smaller than the original dataset. …”
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Tactile-Based Insertion for Dense Box-Packing by Dong, Siyuan, Rodriguez, Alberto

“…Based on the estimated positional errors, a heuristic controller iteratively adjusts the position of the object and eventually inserts it successfully without requiring prior knowledge of the geometry of the object. …”
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The lottery ticket hypothesis: Finding sparse, trainable neural networks by Frankle, Jonathan, Carbin, Michael James

“…Based on these results, we articulate the lottery ticket hypothesis: dense, randomly-initialized, feed-forward networks contain subnetworks (winning tickets) that-when trained in isolation-reach test accuracy comparable to the original network in a similar number of iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. …”
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Accelerating recurrent Ising machines in photonic integrated circuits by Prabhu, Mihika, Roques-Carmes, Charles, Shen, Yichen, Harris, Nicholas, Jing, Li, Carolan, Jacques, Hamerly, Ryan, Baehr-Jones, Tom, Hochberg, Michael, Čeperić, Vladimir, Joannopoulos, John D, Englund, Dirk R, Soljačić, Marin

“…Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.…”
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Accelerating recurrent Ising machines in photonic integrated circuits by Prabhu, Mihika, Roques-Carmes, Charles, Shen, Yichen, Harris, Nicholas Christopher, Jing, Li, Carolan, Jacques J, Hamerly, Ryan M, Baehr-Jones, Tom, Hochberg, Michael, Ceperic, Vladimir, Joannopoulos, John, Englund, Dirk R., Soljacic, Marin

“…Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.…”
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AutoTuner: High Fidelity and Robust Parameter Selection for Metabolomics Data Processing by McLean, Craig, Kujawinski, Elizabeth B

“…In contrast, our new parameter optimization algorithm, AutoTuner, obtains parameter estimates from raw data in a single step as opposed to many iterations. Here, we tested the accuracy and the run-time of AutoTuner in comparison to isotopologue parameter optimization (IPO), the most commonly used parameter selection tool, and compared the resulting parameters’ influence on the properties of feature tables after processing. …”
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Integration of Open-Source URBANopt and Dragonfly Energy Modeling Capabilities into Practitioner Workflows for District-Scale Planning and Design by Charan, Tanushree, Mackey, Christopher, Irani, Ali, Polly, Ben, Ray, Stephen, Fleming, Katherine, El Kontar, Rawad, Moore, Nathan, Elgindy, Tarek, Cutler, Dylan, Roudsari, Mostapha Sadeghipour, Goldwasser, David

“…Finally, this study demonstrates how the Dragonfly/URBANopt toolset can integrate with master planning workflows, thereby enabling an iterative performance-based design process.…”
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Multiqubit randomized benchmarking using few samples by Helsen, Jonas, Wallman, Joel J., Flammia, Steven T., Wehner, Stephanie

“…We therefore recommend moving to more sophisticated fitting methods such as iteratively reweighted least squares optimization. Our results bring rigorous randomized benchmarking on systems with many qubits into the realm of experimental feasibility.…”
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Sat2Graph: Road Graph Extraction Through Graph-Tensor Encoding by He, S, Bastani, F, Jagwani, S, Alizadeh, M, Balakrishnan, H, Chawla, S, Elshrif, MM, Madden, S, Sadeghi, MA

“…Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations. …”
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FilterReg: Robust and Efficient Probabilistic Point-Set Registration Using Gaussian Filter and Twist Parameterization by Gao, Wei, Tedrake, Russ

“…However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. …”
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