Domain Adaptive Semantic Segmentation by Optimal Transport

Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. Therefore, how to use a small size of labeled data to achieve semantic segmentation becomes more and more important. In this paper, we propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue. Concretely, first we generate the output space via CNN due to its superiority of feature representation. Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model. In particular, with OT, the number of network parameters has been reduced and the network has been better interpretable. Third, to better describe the multi-scale property of features, we construct a multi-scale segmentation network to perform domain adaptation. Finally, in order to verify the performance of our proposed method, we conduct experimental comparison with three benchmark and four SOTA methods on three scene datasets, and the mean intersection-over-union (mIOU) has been significant improved, and visualization results under multiple domain adaptation scenarios also show that our proposed method has better performance than compared semantic segmentation methods

Computational Design of Wiring Layout on Tight Suits with Minimal Motion Resistance

An increasing number of electronics are directly embedded on the clothing to monitor human status (e.g., skeletal motion) or provide haptic feedback. A specific challenge to prototype and fabricate such a clothing is to design the wiring layout, while minimizing the intervention to human motion. We address this challenge by formulating the topological optimization problem on the clothing surface as a deformation-weighted Steiner tree problem on a 3D clothing mesh. Our method proposed an energy function for minimizing strain energy in the wiring area under different motions, regularized by its total length. We built the physical prototype to verify the effectiveness of our method and conducted user study with participants of both design experts and smart cloth users. On three types of commercial products of smart clothing, the optimized layout design reduced wire strain energy by an average of 77% among 248 actions compared to baseline design, and 18% over the expert design.Comment: This work is accepted at SIGGRAPH ASIA 2023(Conference Track

TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes

Video data and algorithms have been driving advances in multi-object tracking (MOT). While existing MOT datasets focus on occlusion and appearance similarity, complex motion patterns are widespread yet overlooked. To address this issue, we introduce a new dataset called BEE23 to highlight complex motions. Identity association algorithms have long been the focus of MOT research. Existing trackers can be categorized into two association paradigms: single-feature paradigm (based on either motion or appearance feature) and serial paradigm (one feature serves as secondary while the other is primary). However, these paradigms are incapable of fully utilizing different features. In this paper, we propose a parallel paradigm and present the Two rOund Parallel matchIng meChanism (TOPIC) to implement it. The TOPIC leverages both motion and appearance features and can adaptively select the preferable one as the assignment metric based on motion level. Moreover, we provide an Attention-based Appearance Reconstruct Module (AARM) to reconstruct appearance feature embeddings, thus enhancing the representation of appearance features. Comprehensive experiments show that our approach achieves state-of-the-art performance on four public datasets and BEE23. Notably, our proposed parallel paradigm surpasses the performance of existing association paradigms by a large margin, e.g., reducing false negatives by 12% to 51% compared to the single-feature association paradigm. The introduced dataset and association paradigm in this work offers a fresh perspective for advancing the MOT field. The source code and dataset are available at https://github.com/holmescao/TOPICTrack

WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications

Enhancing the specificity and efficiency of polymerase chain reaction using polyethyleneimine-based derivatives and hybrid nanocomposites

There is a general necessity to improve the specificity and efficiency of the polymerase chain reaction (PCR), and exploring the PCR-enhancing mechanism still remains a great challenge. In this paper we report the use of branched polyethyleneimine (PEI)-based derivatives and hybrid nanocomposites as a novel class of enhancers to improve the specificity and efficiency of a nonspecific PCR system. We show that the surface-charge polarity of PEI and PEI derivatives plays a major role in their effectiveness to enhance the PCR. Positively charged amine-terminated pristine PEI, partially (50%) acetylated PEI (PEI-Ac50), and completely acetylated PEI (PEI-Ac) are able to improve PCR efficiency and specificity with an optimum concentration order of PEI < PEI-Ac50 < PEI-Ac, whereas negatively charged carboxyl-terminated PEI (PEI-SAH; SAH denotes succinamic acid groups) and neutralized PEI modified with both polyethylene glycol (PEG) and acetyl (Ac) groups (PEI-PEG-Ac) are unable to improve PCR specificity and efficiency even at concentrations three orders of magnitude higher than that of PEI. Our data clearly suggests that the PCR-enhancing effect is primarily based on the interaction between the PCR components and the PEI derivatives, where electrostatic interaction plays a major role in concentrating the PCR components locally on the backbones of the branched PEI. In addition, multiwalled carbon nanotubes modified with PEI and PEI-stabilized gold nanoparticles are also able to improve the PCR specificity and efficiency with an optimum PEI concentration less than that of the PEI alone, indicating that the inorganic component of the nanocomposites may help improve the interaction between PEI and the PCR components. The developed PEI-based derivatives or nanocomposites may be used as efficient additives to enhance other PCR systems for different biomedical applications

Complete mitochondrial genome sequence and phylogenetic analysis of Procambarus clarkii and Cambaroides dauricus from China

To enhance the management and protection of crayfish genetic diversity and germplasm resources in Cambaroides dauricus (C. dauricus), a common species of Procambarus clarkii (P. clarkii) was used as a control group to compare the whole mitochondrial genome sequence using Illumina sequencing technology. This study found that the mitochondrial genome of C. dauricus is 15580 bp in length, with a base composition of A (31.84%), G (17.66%), C (9.42%), and T (41.08%) and a C + G content of 27.08%. The C + G in the D-loop is rich in 17.06%, indicating a significant preference. The mitochondrial genome of C. dauricus contains 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes, with most of the genes labeled in the negative direction, except for a few genes that are labeled in the positive direction. The start codons of the ten coding sequences are ATG, and the quintessential TAA and TAG are the stop codons. This study also found that the Ka/Ks ratios of most protein-coding genes in the mitochondria of both shrimps are lower than 1, indicating weak natural selection, except for nad 2, nad 5, and cox 1. The Ka/Ks ratio of cox 3 is the lowest (less than 0.1), indicating that this protein-coding gene bears strong natural selection pressure and functional constraint in the process of mitochondrial genetic evolution of both shrimps. Furthermore, we constructed phylogenetic analyses based on the entire sequence, which effectively distinguishes the high body from other shrimp species of the genus based on the mitochondrial genome. This study provides molecular genetic data for the diversity investigation and protection of fishery resources with Chinese characteristics and a scientific reference for the evolutionary study of Procambarus.This research was funded by the Natural Science Foundation of Heilongjiang Province (NO. LH2023C058) and the Central Public-interest Scientific Institution Basal Research Fund, Chinese Academy of Fishery Sciences (NO. 2020TD56)info:eu-repo/semantics/publishedVersio