Infusing Definiteness into Randomness: Rethinking Composition Styles for Deep Image Matting

We study the composition style in deep image matting, a notion that characterizes a data generation flow on how to exploit limited foregrounds and random backgrounds to form a training dataset. Prior art executes this flow in a completely random manner by simply going through the foreground pool or by optionally combining two foregrounds before foreground-background composition. In this work, we first show that naive foreground combination can be problematic and therefore derive an alternative formulation to reasonably combine foregrounds. Our second contribution is an observation that matting performance can benefit from a certain occurrence frequency of combined foregrounds and their associated source foregrounds during training. Inspired by this, we introduce a novel composition style that binds the source and combined foregrounds in a definite triplet. In addition, we also find that different orders of foreground combination lead to different foreground patterns, which further inspires a quadruplet-based composition style. Results under controlled experiments on four matting baselines show that our composition styles outperform existing ones and invite consistent performance improvement on both composited and real-world datasets. Code is available at: https://github.com/coconuthust/composition_stylesComment: Accepted to AAAI 2023; 11 pages, 9 figures; Code is available at https://github.com/coconuthust/composition_style

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapaComment: Accepted to NeurIPS 2022. Code is available at https://github.com/poppinace/sap

A hybrid mixed double-sided incremental forming method for forming Ti6Al4V alloy

Incremental forming can be an alternative manufacturing means in producing biomedical parts characterized by the need of patient-specific geometry. A novel hybrid dieless sheet metal forming process, electrically-assisted mixed double-sided incremental forming (E-MDSIF), is proposed to manufacture difficult-to-form Ti6Al4V sheets. E-MDSIF is a spark-free technique shown to increase the formability and the geometric accuracy while decreasing the forming force. The effects of the electrical process on microstructure, micro-hardness and surface roughness of the formed parts are investigated. Additionally, comparisons between E-MDSIF and the conventional E-ISF processes are illustrated based on fundamental mechanics. Limitations and potential applications are presented.Peer ReviewedPostprint (author's final draft

PowerFusion: A Tensor Compiler with Explicit Data Movement Description and Instruction-level Graph IR

Deep neural networks (DNNs) are of critical use in different domains. To accelerate DNN computation, tensor compilers are proposed to generate efficient code on different domain-specific accelerators. Existing tensor compilers mainly focus on optimizing computation efficiency. However, memory access is becoming a key performance bottleneck because the computational performance of accelerators is increasing much faster than memory performance. The lack of direct description of memory access and data dependence in current tensor compilers' intermediate representation (IR) brings significant challenges to generate memory-efficient code. In this paper, we propose IntelliGen, a tensor compiler that can generate high-performance code for memory-intensive operators by considering both computation and data movement optimizations. IntelliGen represent a DNN program using GIR, which includes primitives indicating its computation, data movement, and parallel strategies. This information will be further composed as an instruction-level dataflow graph to perform holistic optimizations by searching different memory access patterns and computation operations, and generating memory-efficient code on different hardware. We evaluate IntelliGen on NVIDIA GPU, AMD GPU, and Cambricon MLU, showing speedup up to 1.97x, 2.93x, and 16.91x(1.28x, 1.23x, and 2.31x on average), respectively, compared to current most performant frameworks.Comment: 12 pages, 14 figure

Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning

Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct articles. The first part, a "Survey of Surveys" (SoS), outlines the history, surveys, ethics, and future directions of AD and IV technologies. The second part, "Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors" delves into the development of control, computing system, communication, HD map, testing, and human behaviors in IVs. This part, the third part, reviews perception and planning in the context of IVs. Aiming to provide a comprehensive overview of the latest advancements in AD and IVs, this work caters to both newcomers and seasoned researchers. By integrating the SoS and Part I, we offer unique insights and strive to serve as a bridge between past achievements and future possibilities in this dynamic field.Comment: 17pages, 6figures. IEEE Transactions on Systems, Man, and Cybernetics: System

Maternal High Fat Diet Alters Gut Microbiota of Offspring and Exacerbates DSS-Induced Colitis in Adulthood

Background: Accumulating evidence shows that high fat diet is closely associated with inflammatory bowel disease. However, the effects and underlying mechanisms of maternal high fat diet (MHFD) on the susceptibility of offspring to colitis in adulthood lacks confirmation.Methods: C57BL/6 pregnant mice were given either a high fat (60 E% fat, MHFD group) or control diet [10 E% fat, maternal control diet (MCD) group] during gestation and lactation. The intestinal development, mucosal barrier function, microbiota, and mucosal inflammation of 3-week old offspring were assessed. After weaning all mice were fed a control diet until 8 weeks of age when the microbiota was analyzed. Offspring were also treated with 2% DSS solution for 5 days and the severity of colitis was assessed.Results: The offspring in MHFD group were significantly heavier than those in MCD group only at 2–4 weeks of age, while no differences were found in the body weight between two groups at other measured time points. Compared with MCD group, MHFD significantly inhibited intestinal development and disrupted barrier function in 3-week old offspring. Although H&E staining showed no obvious microscopic inflammation in both groups of 3-week old offspring, increased production of inflammatory cytokines indicated low-grade inflammation was induced in MHFD group. Moreover, fecal analysis of the 3-week old offspring indicated that the microbiota compositions and diversity were significantly changed in MHFD group. Interestingly after 5 weeks consumption of control diet in both groups, the microbiota composition of offspring in MHFD group was still different from that in MCD group, although the bacterial diversity was partly recovered at 8 weeks of age. Finally, after DSS treatment in 8-week old offspring, MHFD significantly exacerbated the severity of colitis and increased the production of proinflammatory cytokine.Conclusions: Our data reveal that MHFD in early life can inhibit intestinal development, induce dysbiosis and low-grade inflammation and lead to the disruption of intestinal mucosal barrier in offspring, and enhance DSS-induced colitis in adulthood

Uncooled photodetector at short-wavelength infrared using InAs nanowire photoabsorbers on InP with p-n heterojunctions

In this work, we demonstrate an InAs nanowire photodetector at short-wavelength infrared (SWIR) composed of vertically oriented selective-area InAs nanowire photoabsorber arrays on InP substrates, forming InAs–InP heterojunctions. We measure a rectification ratio greater than 300 at room temperature, which indicates a desirable diode performance. The dark current density, normalized to the area of nanowire heterojunctions, is 130 mA/cm2 at a temperature of 300 K and a reverse bias of 0.5 V, making it comparable to the state-of-the-art bulk InAs p-i-n photodiodes. An analysis of the Arrhenius plot of the dark current at reverse bias yields an activation energy of 175 meV from 190 to 300 K, suggesting that the Shockley–Read–Hall (SRH) nonradiative current is the primary contributor to the dark current. By using three-dimensional electrical simulations, we determine that the SRH nonradiative current originates from the acceptor-like surface traps at the nanowire-passivation heterointerfaces. The spectral response at room temperature is also measured, with a clear photodetection signature observed at wavelengths up to 2.5 μm. This study provides an understanding of dark current for small band gap selective-area nanowires and paves the way to integrate these improved nanostructured photoabsorbers on large band gap substrates for high-performance photodetectors at SWIR

Milestones in autonomous driving and intelligent vehicles: survey of surveys

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future