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We report an easy-to-make, resonance-based mechanism to realize negative rotational inertia. The device consists of three parts: a heavy inner core, a lightweight outer shell, and rubber connections between the core and shell. We theoretically predict and experimentally observe the negative rotational inertia in the range of 100–230 Hz. The experimental values are obtained via measurements of vibrational response. We further clarify the relation between the bandwidth of negative inertia and the bandgap in a chain consisting of an array of negative-inertia units. The findings reveal a unique property different from conventional systems in classical physics and offer an opportunity for metamaterial designs. [ABSTRACT FROM AUTHOR]

Abstract: We generalize the recently proposed steady-state density functional theory (SS-DFT) to spin-dependent cases and theoretically investigate the electronic and transport properties of a Mn-phthalocyanine molecule sandwiched between two graphene [...]

Magnetoelectric coupling is achieved near room temperature in a spin crossover FeII molecule‐based compound, [Fe(1bpp)2](BF4)2. Large atomic displacements resulting from Jahn–Teller distortions induce a change in the molecule dipole moment when switching between high‐spin and low‐spin states leading to a step‐wise change in the electric polarization and dielectric constant. For temperatures in the region of bistability, the changes in magnetic and electrical properties are induced with a remarkably low magnetic field of 3 T. This result represents a successful expansion of magnetoelectric spin crossovers towards ambient conditions. Moreover, the observed 0.3–0.4 mC m−2 changes in the H‐induced electric polarization suggest that the high strength of the coupling obtained via this route is accessible not just at cryogenic temperatures but also near room temperature, a feature that is especially appealing in the light of practical applications. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have demonstrated their potential in a variety of domains ranging from computer vision (CV) to natural language processing. Among various NNs, two-dimensional (2-D) and three-dimensional (3-D) convolutional NNs (CNNs) have been widely adopted for a broad spectrum of applications, such as image classification and video recognition, due to their excellent capabilities in extracting 2-D and 3-D features. However, standard 2-D and 3-D CNNs are not able to capture their model uncertainty which is crucial for many safety-critical applications, including healthcare and autonomous driving. In contrast, Bayesian CNNs (BayesCNNs), as a variant of CNNs, have demonstrated their ability to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BayesCNNs have not been widely used in industrial practice due to their compute requirements stemming from sampling and subsequent forward passes through the whole network multiple times. As a result, these requirements significantly increase the amount of computation and memory consumption in comparison to standard CNNs. This article proposes a novel field-programmable gate array (FPGA)-based hardware architecture to accelerate both 2-D and 3-D BayesCNNs based on Monte Carlo dropout (MCD). Compared with other state-of-the-art accelerators for BayesCNNs, the proposed design can achieve up to four times higher energy efficiency and nine times better compute efficiency. An automatic framework capable of supporting partial Bayesian inference is proposed to explore the tradeoff between algorithm and hardware performance. Extensive experiments are conducted to demonstrate that our framework can effectively find the optimal implementations in the design space. [ABSTRACT FROM AUTHOR]

We show that when a molecular junction is under an external bias, its properties cannot be uniquely determined by the total electron density in the same manner as the density functional theory for ground state properties. In order to correctly incorporate bias-induced nonequilibrium effects, we present a dual mean field (DMF) approach. The key idea is that the total electron density together with the density of current-carrying electrons are sufficient to determine the properties of the system. Two mean fields, one for current-carrying electrons and the other one for equilibrium electrons can then be derived. Calculations for a graphene nanoribbon junction show that compared with the commonly used ab initio transport theory, the DMF approach could significantly reduce the electric current at low biases due to the non-equilibrium corrections to the mean field potential in the scattering region. [ABSTRACT FROM AUTHOR]

Interfaces between two topological insulators are of fundamental interest in condensed matter physics. Inspired by experimental efforts, we study interfacial processes between two slabs of BiSbTeSe 2 (BSTS) via first principles calculations. Topological surface states are absent for the BSTS interface in its equilibrium separation, but our calculations show that they appear if the inter-slab distance is greater than 6 Å. More importantly, we find that topological interface states can be preserved by inserting two or more layers of hexagonal boron nitride between the two BSTS slabs. In experiments, the electric current tunneling through the interface is insensitive to back gate voltage when the bias voltage is small. Using a first-principles based method that allows us to simulate the gate field, we show that at low bias, the extra charge induced by a gate voltage resides on the surface that is closest to the gate electrode, leaving the interface almost undoped. This provides clues to understand the origin of the observed insensitivity of transport properties to back voltage at low bias. Our study resolves a few questions raised in experiment, which does not yet offer a clear correlation between microscopic physics and transport data. We provide a road map for the design of vertical tunneling junctions involving the interface between two topological insulators. [ABSTRACT FROM AUTHOR]

High-quality single-crystalline aluminum films have been grown on Si(111) substrates by molecular beam epitaxy. The x-ray diffraction rocking curve of the (111) plane of the Al film shows a full width at half maximum of 564 arc sec for the sample grown at 100 °C, where the surface is atomically flat with a root-mean-square roughness of 0.40 nm in a scanned area of 3 × 3 μm2. By using such a high-quality Al film, we have demonstrated a room temperature ultraviolet surface-plasmon-polariton nanolaser at a wavelength of 360 nm with a threshold as low as ∼0.2 MW/cm2, which provides a powerful evidence for potential application of the single-crystalline Al film in plasmonic devices. [ABSTRACT FROM AUTHOR]

Markov Chain Monte Carlo (MCMC) based methods have been the main tool used for Bayesian Inference by practitioners and researchers due to their flexibility and theoretical properties that guarantee unbiased sampling-based estimates. Nevertheless, with the availability of large data sets and the constant need to develop more complex models that better capture the targeted problem, significant computational challenges have been presented. Current approaches, based on multi-core CPUs, GPUs, and FPGAs, aim to accelerate the execution time of the MCMC methods using subsampling techniques or custom precision arithmetic, resulting to biased estimates. In this work, a novel FPGA-based construction is proposed that utilises the custom precision support of FPGA devices in order to accelerate the computations, guaranteeing at the same time asymptotically unbiased estimates. Key to this approach is the extension of the parameter space by an extra parameter that indicates the required precision in the computation of the likelihood of a data point. The work proposes an FPGA architecture for the above algorithm, as well as discuss its tuning for maximising the performance of the system. The performance of the FPGA-mapped sampler is evaluated using two Bayesian logistic regression case studies of varying complexity, which show significant speedups compared to existing FPGA- and CPU-based works that utilise double floating point arithmetic, without any bias on the sampling-based estimates. [ABSTRACT FROM AUTHOR]