RR-CP: Reliable-Region-Based Conformal Prediction for Trustworthy Medical Image Classification

Conformal prediction (CP) generates a set of predictions for a given test sample such that the prediction set almost always contains the true label (e.g., 99.5\% of the time). CP provides comprehensive predictions on possible labels of a given test sample, and the size of the set indicates how certain the predictions are (e.g., a set larger than one is `uncertain'). Such distinct properties of CP enable effective collaborations between human experts and medical AI models, allowing efficient intervention and quality check in clinical decision-making. In this paper, we propose a new method called Reliable-Region-Based Conformal Prediction (RR-CP), which aims to impose a stronger statistical guarantee so that the user-specified error rate (e.g., 0.5\%) can be achieved in the test time, and under this constraint, the size of the prediction set is optimized (to be small). We consider a small prediction set size an important measure only when the user-specified error rate is achieved. Experiments on five public datasets show that our RR-CP performs well: with a reasonably small-sized prediction set, it achieves the user-specified error rate (e.g., 0.5\%) significantly more frequently than exiting CP methods.Comment: UNSURE2023 (Uncertainty for Safe Utilization of Machine Learning in Medical Imaging) at MICCAI2023; Spotligh

SLoMo: A General System for Legged Robot Motion Imitation from Casual Videos

We present SLoMo: a first-of-its-kind framework for transferring skilled motions from casually captured "in the wild" video footage of humans and animals to legged robots. SLoMo works in three stages: 1) synthesize a physically plausible reconstructed key-point trajectory from monocular videos; 2) optimize a dynamically feasible reference trajectory for the robot offline that includes body and foot motion, as well as contact sequences that closely tracks the key points; 3) track the reference trajectory online using a general-purpose model-predictive controller on robot hardware. Traditional motion imitation for legged motor skills often requires expert animators, collaborative demonstrations, and/or expensive motion capture equipment, all of which limits scalability. Instead, SLoMo only relies on easy-to-obtain monocular video footage, readily available in online repositories such as YouTube. It converts videos into motion primitives that can be executed reliably by real-world robots. We demonstrate our approach by transferring the motions of cats, dogs, and humans to example robots including a quadruped (on hardware) and a humanoid (in simulation). To the best knowledge of the authors, this is the first attempt at a general-purpose motion transfer framework that imitates animal and human motions on legged robots directly from casual videos without artificial markers or labels.Comment: accepted at RA-L 2023, with ICRA 2024 optio

Competitions of magnetism and superconductivity in FeAs-based materials

Using the numerical unrestricted Hartree-Fock approach, we study the ground state of a two-orbital model describing newly discovered FeAs-based superconductors. We observe the competition of a $(0, \pi)$ mode spin-density wave and the superconductivity as the doping concentration changes. There might be a small region in the electron-doping side where the magnetism and superconductivity coexist. The superconducting pairing is found to be spin singlet, orbital even, and mixed s$_{xy}$ + d$_{x^{2}-y^{2}}$ wave (even parity).Comment: 5 pages, 3 figure

Single production of charged gauge bosons from little Higgs models in association with top quark at the LHCLHC

In the context of the little Higgs models, we discuss single production of the new charged gauge bosons in association with top quark at the $CERN$ Large Hadron Collider$(LHC)$. We find that the new charged gauge bosons $W_{H}^{-}$ and $X^{-}$, which are predicted by the littlest Higgs model and the SU(3) simple model, respectively, can be abundantly produced at the $LHC$. However, since the main backgrounds coming from the processes $pp\to t\bar{t}+X$ and $pp\to tW^{-}+X$ are very large, the values of the ratios $N_{W}$ and $N_{X}$ are very small in most of the parameter space. It is only possible to detect the signal of the gauge boson $W_{H}^{-}$ via the process $pp\to gb+X\to tW_{H}^{-}+X$ at the $LHC$ in a small region of the parameter space.Comment: 14pages, 4 figures. To be published in Europhysics Letter

Engineered Cytochrome c-Catalyzed Lactone-Carbene B–H Insertion

Previous work has demonstrated that variants of a heme protein, Rhodothermus marinus cytochrome c (Rma cyt c), catalyze abiological carbene boron–hydrogen (B–H) bond insertion with high efficiency and selectivity. Here we investigated this carbon–boron bond-forming chemistry with cyclic, lactone-based carbenes. Using directed evolution, we obtained a Rma cyt c variant BOR^(LAC) that shows high selectivity and efficiency for B–H insertion of 5- and 6-membered lactone carbenes (up to 24,500 total turnovers and 97.1:2.9 enantiomeric ratio). The enzyme shows low activity with a 7-membered lactone carbene. Computational studies revealed a highly twisted geometry of the 7-membered lactone carbene intermediate relative to 5- and 6-membered ones. Directed evolution of cytochrome c together with computational characterization of key iron-carbene intermediates has allowed us to expand the scope of enzymatic carbene B–H insertion to produce new lactone-based organoborons

Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks

Extracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks

Band theory in the context of the Hamilton-Jacobi formulation

In the one-dimensional periodic potential case, we formulate the condition of Bloch periodicity for the reduced action by using the relation between the wave function and the reduced action established in the context of the equivalence postulate of quantum mechanics. Then, without appealing to the wave function properties, we reproduce the well-known dispersion relations which predict the band structure for the energy spectrum in the Kr\"onig-Penney model.Comment: 10 pages, no figure