Investigation of focused ion beam induced damage in single crystal diamond tools

In this work, transmission electron microscope (TEM) measurements and molecular dynamics (MD) simulations were carried out to characterise the focused ion beam (FIB) induced damage layer in a single crystal diamond tool under different FIB processing voltages. The results obtained from the experiments and the simulations are in good agreement. The results indicate that during FIB processing cutting tools made of natural single crystal diamond, the energetic Ga+ collision will create an impulse-dependent damage layer at the irradiated surface. For the tested beam voltages in a typical FIB system (from 8 kV to 30 kV), the thicknesses of the damaged layers formed on a diamond tool surface increased from 11.5 nm to 27.6 nm. The dynamic damage process of FIB irradiation and ion-solid interactions physics leading to processing defects in FIB milling were emulated by MD simulations. The research findings from this study provide the in-depth understanding of the wear of nanoscale multi-tip diamond tools considering the FIB irradiation induced doping and defects during the tool fabrication process

Advancing Vision Transformers with Group-Mix Attention

Vision Transformers (ViTs) have been shown to enhance visual recognition through modeling long-range dependencies with multi-head self-attention (MHSA), which is typically formulated as Query-Key-Value computation. However, the attention map generated from the Query and Key captures only token-to-token correlations at one single granularity. In this paper, we argue that self-attention should have a more comprehensive mechanism to capture correlations among tokens and groups (i.e., multiple adjacent tokens) for higher representational capacity. Thereby, we propose Group-Mix Attention (GMA) as an advanced replacement for traditional self-attention, which can simultaneously capture token-to-token, token-to-group, and group-to-group correlations with various group sizes. To this end, GMA splits the Query, Key, and Value into segments uniformly and performs different group aggregations to generate group proxies. The attention map is computed based on the mixtures of tokens and group proxies and used to re-combine the tokens and groups in Value. Based on GMA, we introduce a powerful backbone, namely GroupMixFormer, which achieves state-of-the-art performance in image classification, object detection, and semantic segmentation with fewer parameters than existing models. For instance, GroupMixFormer-L (with 70.3M parameters and 384^2 input) attains 86.2% Top-1 accuracy on ImageNet-1K without external data, while GroupMixFormer-B (with 45.8M parameters) attains 51.2% mIoU on ADE20K

Measurement of the Inclusive Charm Cross Section at 4.03 GeV and 4.14 GeV

The cross section for charmed meson production at $\sqrt{s} = 4.03$ and 4.14 GeV has been measured with the Beijing Spectrometer. The measurement was made using 22.3 $pb^{-1}$ of $e^+e^-$ data collected at 4.03 GeV and 1.5 $pb^{-1}$ of $e^+e^-$ data collected at 4.14 GeV. Inclusive observed cross sections for the production of charged and neutral D mesons and momentum spectra are presented. Observed cross sections were radiatively corrected to obtain tree level cross sections. Measurements of the total hadronic cross section are obtained from the charmed meson cross section and an extrapolation of results from below the charm threshold.Comment: 11 pages, 13 figures. The top level tex file is paper.tex. It builds the paper from other tex files in this .tar and the .eps file

Measurement of the Total Cross Section for Hadronic Production by e+e- Annihilation at Energies between 2.6-5 Gev

Using the upgraded Beijing Spectrometer (BESII), we have measured the total cross section for $e^+e^-$ annihilation into hadronic final states at center-of-mass energies of 2.6, 3.2, 3.4, 3.55, 4.6 and 5.0 GeV. Values of $R$, $\sigma(e^+e^-\to {hadrons})/\sigma(e^+e^-\to\mu^+\mu^-)$, are determined.Comment: Submitted to Phys. Rev. Let

PVTv2: Improved Baselines with Pyramid Vision Transformer

Transformer recently has shown encouraging progresses in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers. With these modifications, our PVTv2 significantly improves PVTv1 on three tasks e.g., classification, detection, and segmentation. Moreover, PVTv2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision. Code is available at https://github.com/whai362/PVT .Comment: Technical Repor

Study of the P-wave charmonium state \chi_{cJ} in \psi(2S) decays

The processes $\psi(2S)\to \gamma \pi^+ \pi^-$, $\gamma K^+ K^-$ and $\gamma p \bar{p}$ have been studied using a sample of $3.7 \times 10^6$ produced $\psi(2S)$ decays. We determine the total width of the $\chi_{c0}$ to be $\Gamma^{tot}_{\chi_{c0}} = 14.3\pm 2.0\pm 3.0$ MeV. We present the first measurement of the branching fraction $B(\chi_{c0} \to p \bar{p}) = (16.3 \pm 4.4 \pm 5.4)\times 10^{-5}$, where the first error is statistical and the second one systematic. Branching fractions of $\chi_{c0,2} \to \pi^+ \pi^-$ and $K^+ K^-$ are also reported.Comment: 10 pages, revtex, 3 figures, 2 table

Lattice Boltzmann simulations of soft matter systems

This article concerns numerical simulations of the dynamics of particles immersed in a continuum solvent. As prototypical systems, we consider colloidal dispersions of spherical particles and solutions of uncharged polymers. After a brief explanation of the concept of hydrodynamic interactions, we give a general overview over the various simulation methods that have been developed to cope with the resulting computational problems. We then focus on the approach we have developed, which couples a system of particles to a lattice Boltzmann model representing the solvent degrees of freedom. The standard D3Q19 lattice Boltzmann model is derived and explained in depth, followed by a detailed discussion of complementary methods for the coupling of solvent and solute. Colloidal dispersions are best described in terms of extended particles with appropriate boundary conditions at the surfaces, while particles with internal degrees of freedom are easier to simulate as an arrangement of mass points with frictional coupling to the solvent. In both cases, particular care has been taken to simulate thermal fluctuations in a consistent way. The usefulness of this methodology is illustrated by studies from our own research, where the dynamics of colloidal and polymeric systems has been investigated in both equilibrium and nonequilibrium situations.Comment: Review article, submitted to Advances in Polymer Science. 16 figures, 76 page

AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing

Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly developed considering constant resource constraints, overlooking the varied and dynamic resource constraints in diverse edge devices, like energy budgets. Consequently, model providers have to design specialized models for different devices, where all of them have to be stored on the edge server, resulting in inefficient deployment. To fill this gap, this work presents AdaPI, a novel approach that achieves adaptive PI by allowing a model to perform well across edge devices with diverse energy budgets. AdaPI employs a PI-aware training strategy that optimizes the model weights alongside weight-level and feature-level soft masks. These soft masks are subsequently transformed into multiple binary masks to enable adjustments in communication and computation workloads. Through sequentially training the model with increasingly dense binary masks, AdaPI attains optimal accuracy for each energy budget, which outperforms the state-of-the-art PI methods by 7.3\% in terms of test accuracy on CIFAR-100. The code of AdaPI can be accessed via https://github.com/jiahuiiiiii/AdaPI.ICCAD 2024 accepted publicatio