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Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance
Identifying energy-efficient routes in real-time has significant implications for the energy-optimal operations of electric vehicles (EVs). Here, this study proposes a novel model for EV online eco-routing problem, which obtains the minimal expected energy consumption paths (MECPs) for multiple origin-destination (OD) pairs simultaneously. Specifically, we formulate the routing problem as a bandit problem and solve it with online algorithms. We extend the algorithms by implementing a path elimination mechanism to reduce the candidate path set and introducing the variance and covariance of the energy consumption to reduce the uncertainties. The numerical results show that the proposed algorithms can efficiently obtain near-optimal MECPs, and the solution is significantly better than the widely used shortest trip time path algorithm (STTP) and shortest trip distance path algorithm (SDP). The variation considering link energy covariance and path elimination generates paths that save 4.1% of energy compared to the SDP and 5.4% to the STTP.
Epitaxial ZnGeP 2 Thin Films on Si and GaP by Reactive Combinatorial Sputtering in Phosphine
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Combinatorial Synthesis of Cation-Disordered Manganese Tin Nitride MnSnN 2 Thin Films with Magnetic and Semiconducting Properties
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Autonomous Multistate Nanoencoding Using Combinatorial Ferroelectric Closure Domains in BiFeO 3
Recent advances in ferroic materials have identified topological defects as promising candidates for enabling additional functionalities in future electronic systems. The generation of stable and customizable polar topologies is needed to achieve multistates that enable beyond-binary device architectures. Here, in this study, we show how to autonomously pattern on-demand highly tunable striped closure domains in pristine rhombohedral-phase BiFeO 3 thin films through precise scanning of a biased atomic force microscopy tip along carefully designed paths. By employing this strategy, we generate and manipulate closed-loop structures with high spatial resolution in an automated manner, allowing the creation of highly tunable and intricate topological domain structures that exhibit distinct polarization configurations without the need for electrode deposition or complex heterostructure growth. As a proof-of-concept for ferroelectric beyond-binary memory devices, we use such topological domains as multistates, engineering an alphabet and automating the symbolic writing/reading process using autonomous microscopy. The resulting information density is compared with that of current commercially available memory devices, demonstrating the potential of ferroelectric topological domains for multistate information storage applications.
Guide RNA structure design enables combinatorial CRISPRa programs for biosynthetic profiling
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Quantum computational phase transition in combinatorial problems
Quantum Approximate Optimization algorithm (QAOA) aims to search for approximate solutions to discrete optimization problems with near-term quantum computers. As there are no algorithmic guarantee possible for QAOA to outperform classical computers, without a proof that bounded-error quantum polynomial time (BQP) ≠ nondeterministic polynomial time (NP), it is necessary to investigate the empirical advantages of QAOA. We identify a computational phase transition of QAOA when solving hard problems such as SAT—random instances are most difficult to train at a critical problem density. We connect the transition to the controllability and the complexity of QAOA circuits. Moreover, we find that the critical problem density in general deviates from the SAT-UNSAT phase transition, where the hardest instances for classical algorithms lies. Then, we show that the high problem density region, which limits QAOA’s performance in hard optimization problems (reachability deficits), is actually a good place to utilize QAOA: its approximation ratio has a much slower decay with the problem density, compared to classical approximate algorithms. Indeed, it is exactly in this region that quantum advantages of QAOA over classical approximate algorithms can be identified.
Combinatorial sputter deposition of ultrathick Au-Bi alloy films
We report gold-bismuth alloys are of interest as catalysts and catalytic sensing systems, electrochemical sensors, superconductors, and hohlraums for magnetically assisted inertial confinement fusion implosions. Radiation-hydrodynamics simulations with the Lasnex code of laser-driven hohlraums predict higher x-ray drive from Au-Bi alloys compared with cases of Au-Ta or pure Au and Bi hohlraums. Here, we use direct current magnetron sputtering in Ar gas, with co-sputtering from two elemental targets, to deposit Au-Bi alloys with Bi content of 9–77 at.% and thicknesses up to ~20 µm. Films are characterized by a combination of x-ray diffraction, Rutherford backscattering, scanning electron microscopy, substrate-curvature-based residual stress, and electronic transport measurements. Experiments are complemented by Monte Carlo simulations of ballistic sputtering and gas phase transport of depositing species and Ar gas atoms. Results show that all films are polycrystalline, with three distinct compositional regimes dominated by Au, Au 2 Bi, and Bi crystallographic phases. A metallic behavior of the temperature dependence of electrical resistivity is observed for all the films. Films with Bi content above ~30 at.% exhibit porosity, which is tolerable to hohlraum x-ray drive based on Lasnex simulations.
Combinatorial deposition of Au–Bi alloys via high-rate magnetron sputtering
Gold–bismuth (Au–Bi) alloy films are promising candidate materials for inertial confinement fusion (ICF) hohlraums due to their high laser-to-x-ray conversion efficiency, particularly compared with Au, Ta–Au, and Bi hohlraums. However, the fabrication of uniform and dense Au–Bi alloy films remains a challenge. Here, we use a combination of Monte-Carlo modeling and experiments to demonstrate that the microstructure and properties of Au–Bi alloy films can be greatly improved when direct-current magnetron sputtering with high deposition rates of ⩾5 μm h -1 is used. Resultant films are ~90% of their maximum theoretical densities and have low O content of <1 at.%. Films with Bi content above 40 at. % exhibit high electrical resistivity >100μΩ cm, making them suitable for both magnetized and non-magnetized ICF schemes.
Z 2 topological order and first-order quantum phase transitions in systems with combinatorial gauge symmetry
We study a generalization of the two-dimensional transverse-field Ising model, combining both ferromagnetic and antiferromagnetic two-body interactions, that hosts exact global and local Z 2 gauge symmetries. Using exact diagonalization and stochastic series expansion quantum Monte Carlo methods, we confirm the existence of the topological phase in line with previous theoretical predictions. Our simulation results show that the transition between the confined topological phase and the deconfined paramagnetic phase is of first order, in contrast to the conventional Z 2 lattice gauge model in which the transition maps onto that of the standard Ising model and is continuous. We further generalize the model by replacing the transverse field on the gauge spins with a ferromagnetic X X interaction while keeping the local gauge symmetry intact. We find that the Z 2 topological phase remains stable, while the paramagnetic phase is replaced by a ferromagnetic phase. The topological-ferromagnetic quantum phase transition is also of first order. For both models, we discuss the low-energy spinon and vison excitations of the topological phase and their avoided level crossings associated with the first-order quantum phase transitions.
Separating signal from combinatorial jets in a high-background environment
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Resolving combinatorial ambiguities in dilepton t t ¯ event topologies with neural networks
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Solving combinatorial problems at particle colliders using machine learning
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Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
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Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs
Cancer is a leading cause of death in the US, second only to heart disease. It is primarily a result of a combination of an estimated two-nine genetic mutations (multi-hit combinations). Although a body of research has identified hundreds of cancer-causing genetic mutations, we don’t know the specific combination of mutations responsible for specific instances of cancer for most cancer types. An approximate algorithm for solving the weighted set cover problem was previously adapted to identify combinations of genes with mutations that may be responsible for individual instances of cancer. However, the algorithm’s computational requirement scales exponentially with the number of genes, making it impractical for identifying more than three-hit combinations, even after the algorithm was parallelized and scaled up to a V100 GPU. Since most cancers have been estimated to require more than three hits, we scaled out the algorithm to identify combinations of four or more hits using 1000 nodes (6000 V100 GPUs with ≈48×106 processing cores) on the Summit supercomputer at Oak Ridge National Laboratory. Efficiently scaling out the algorithm required a series of algorithmic innovations and optimizations for balancing an exponentially divergent workload across processors and for minimizing memory latency and inter-node communication. We achieved an average strong scaling efficiency of 90.14% (80.96%–97.96% for 200 to 1000 nodes), compared to a 100 node run, with 84.18% scaling efficiency for 1000 nodes. With experimental validation, the multi-hit combinations identified here could provide further insight into the etiology of different cancer subtypes and provide a rational basis for targeted combination therapy.
Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computers
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Generating Euler Diagrams Through Combinatorial Optimization
Abstract Can a given set system be drawn as an Euler diagram? We present the first method that correctly decides this question for arbitrary set systems if the Euler diagram is required to represent each set with a single connected region. If the answer is yes, our method constructs an Euler diagram. If the answer is no, our method yields an Euler diagram for a simplified version of the set system, where a minimum number of set elements have been removed. Further, we integrate known wellformedness criteria for Euler diagrams as additional optimization objectives into our method. Our focus lies on the computation of a planar graph that is embedded in the plane to serve as the dual graph of the Euler diagram. Since even a basic version of this problem is known to be NP‐hard, we choose an approach based on integer linear programming (ILP), which allows us to compute optimal solutions with existing mathematical solvers. For this, we draw upon previous research on computing planar supports of hypergraphs and adapt existing ILP building blocks for contiguity‐constrained spatial unit allocation and the maximum planar subgraph problem. To generate Euler diagrams for large set systems, for which the proposed simplification through element removal becomes indispensable, we also present an efficient heuristic. We report on experiments with data from MovieDB and Twitter. Over all examples, including 850 non‐trivial instances, our exact optimization method failed only for one set system to find a solution without removing a set element. However, with the removal of only a few set elements, the Euler diagrams can be substantially improved with respect to our wellformedness criteria.