Nanoindentation-induced phase transformation and structural deformation of monocrystalline germanium: a molecular dynamics simulation investigation

Molecular dynamics simulations were conducted to study the nanoindentation of monocrystalline germanium. The path of phase transformation and distribution of transformed region on different crystallographic orientations were investigated. The results indicate the anisotropic behavior of monocrystalline germanium. The nanoindentation-induced phase transformation from diamond cubic structure to β-tin-Ge was found in the subsurface region beneath the tool when indented on the (010) plane, while direct amorphization was observed in the region right under the indenter when the germanium was loaded along the [101] and [111] directions. The transformed phases extend along the < 110 > slip direction of germanium. The depth and shape of the deformed layers after unloading are quite different according to the crystal orientation of the indentation plane. The study results suggest that phase transformation is the dominant mechanism of deformation of monocrystalline germanium film in nanoindentation

Numerical Modeling of Submicron Particles for Acoustic Concentration in Gaseous Flow

This paper intends to explore the rationality and feasibility of modeling dispersed submicron particles in air by a kinetic-based method called the unified gas-kinetic scheme (UGKS) and apply it to the simulation of particle concentration under a transverse standing wave. A gas-particle coupling scheme is proposed where the gas phase is modeled by the two-dimensional linearized Euler equations (LEE) and, through the analogous behavior between the rarefied gas molecules and the air-suspended particles, a modified UGKS is adopted to estimate the particle dynamics. The Stokes\u27 drag force and the acoustic radiation force applied on particles are accounted for by introducing a velocity-dependent acceleration term in the UGKS formulation. To validate this methodology, the computed concentration patterns are compared with experimental results in the literature. The comparison shows that the adopted LEE-UGKS coupling scheme could well capture the concentration pattern of suspended submicron particles in a channel. In addition, numerical simulations with varying standing wave amplitudes, different acoustic radiation force to drag force ratios, and mean flow velocities are conducted. Their respective influences on the particle concentration pattern and efficiency are analyzed

Language Models for Image Captioning: The Quirks and What Works

Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network (CNN) trained on images, and then a maximum entropy (ME) language model is used to arrange these words into a coherent sentence. The second uses the penultimate activation layer of the CNN as input to a recurrent neural network (RNN) that then generates the caption sequence. In this paper, we compare the merits of these different language modeling approaches for the first time by using the same state-of-the-art CNN as input. We examine issues in the different approaches, including linguistic irregularities, caption repetition, and data set overlap. By combining key aspects of the ME and RNN methods, we achieve a new record performance over previously published results on the benchmark COCO dataset. However, the gains we see in BLEU do not translate to human judgments.Comment: See http://research.microsoft.com/en-us/projects/image_captioning for project informatio

Coherent heteronuclear spin dynamics in an ultracold spin-1 mixture

We report the observation of coherent heteronuclear spin dynamics driven by inter-species spin-spin interaction in an ultracold spinor mixture, which manifests as periodical and well correlated spin oscillations between two atomic species. In particular, we investigate the magnetic field dependence of the oscillations and find a resonance behavior which depends on {\em both} the linear and quadratic Zeeman effects and the spin-dependent interaction. We also demonstrate a unique knob for controlling the spin dynamics in the spinor mixture with species-dependent vector light shifts. Our finds are in agreement with theoretical simulations without any fitting parameters.Comment: 13 pages including the supplementary materia

ProphNet: Efficient Agent-Centric Motion Forecasting with Anchor-Informed Proposals

Motion forecasting is a key module in an autonomous driving system. Due to the heterogeneous nature of multi-sourced input, multimodality in agent behavior, and low latency required by onboard deployment, this task is notoriously challenging. To cope with these difficulties, this paper proposes a novel agent-centric model with anchor-informed proposals for efficient multimodal motion prediction. We design a modality-agnostic strategy to concisely encode the complex input in a unified manner. We generate diverse proposals, fused with anchors bearing goal-oriented scene context, to induce multimodal prediction that covers a wide range of future trajectories. Our network architecture is highly uniform and succinct, leading to an efficient model amenable for real-world driving deployment. Experiments reveal that our agent-centric network compares favorably with the state-of-the-art methods in prediction accuracy, while achieving scene-centric level inference latency.Comment: CVPR 2023 (Highlight