PuTR: A Pure Transformer for Decoupled and Online Multi-Object Tracking

Recent advances in Multi-Object Tracking (MOT) have achieved remarkable success in short-term association within the decoupled tracking-by-detection online paradigm. However, long-term tracking still remains a challenging task. Although graph-based approaches can address this issue by modeling trajectories as a graph in the decoupled manner, their non-online nature poses obstacles for real-time applications. In this paper, we demonstrate that the trajectory graph is a directed acyclic graph, which can be represented by an object sequence arranged by frame and a binary adjacency matrix. It is a coincidence that the binary matrix matches the attention mask in the Transformer, and the object sequence serves exactly as a natural input sequence. Intuitively, we propose that a pure Transformer can naturally unify short- and long-term associations in a decoupled and online manner. Our experiments show that a classic Transformer architecture naturally suits the association problem and achieves a strong baseline compared to existing foundational methods across four datasets: DanceTrack, SportsMOT, MOT17, and MOT20, as well as superior generalizability in domain shift. Moreover, the decoupled property also enables efficient training and inference. This work pioneers a promising Transformer-based approach for the MOT task, and provides code to facilitate further research. https://github.com/chongweiliu/PuT

Preparation and Properties of 1, 3, 5, 7-Tetranitro-1, 3, 5, 7-Tetrazocane-based Nanocomposites

A new insensitive explosive based on octahydro-1, 3, 5, 7-tetranitro-1, 3, 5, 7-tetrazocine (HMX) was prepared by spray drying using Viton A as a binder. The HMX sample without binder (HMX-1) was obtained by the same spray drying process also. The samples were characterised by Scanning Electron Microscope, and X-ray diffraction. The Differential Scanning Calorimetry and the impact sensitivity of HMX-1 and nanocomposites were also being tested. The nanocomposite morphology was found to be microspherical (1 μm to 7 μm diameter) and composed of many tiny particles, 100 nm to 200 nm in size. The crystal type of HMX-1 and HMX/Viton A agrees with raw HMX. The activation energy of raw HMX, HMX-1 and HMX/Viton A is 523.16 kJ mol-1, 435.74 kJ mol-1 and 482.72 kJ mol-1, respectively. The self-ignition temperatures of raw HMX, HMX-1 and HMX/Viton A is 279.01 °C, 277.63 °C, and 279.34 °C, respectively. The impact sensitivity order of samples is HMX/Viton A < HMX-1 < raw HMX from low to high.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.131-134, DOI:http://dx.doi.org/10.14429/dsj.65.784

StyleSeg V2: Towards Robust One-shot Segmentation of Brain Tissue via Optimization-free Registration Error Perception

One-shot segmentation of brain tissue requires training registration-segmentation (reg-seg) dual-model iteratively, where reg-model aims to provide pseudo masks of unlabeled images for seg-model by warping a carefully-labeled atlas. However, the imperfect reg-model induces image-mask misalignment, poisoning the seg-model subsequently. Recent StyleSeg bypasses this bottleneck by replacing the unlabeled images with their warped copies of atlas, but needs to borrow the diverse image patterns via style transformation. Here, we present StyleSeg V2, inherited from StyleSeg but granted the ability of perceiving the registration errors. The motivation is that good registration behaves in a mirrored fashion for mirrored images. Therefore, almost at no cost, StyleSeg V2 can have reg-model itself "speak out" incorrectly-aligned regions by simply mirroring (symmetrically flipping the brain) its input, and the registration errors are symmetric inconsistencies between the outputs of original and mirrored inputs. Consequently, StyleSeg V2 allows the seg-model to make use of correctly-aligned regions of unlabeled images and also enhances the fidelity of style-transformed warped atlas image by weighting the local transformation strength according to registration errors. The experimental results on three public datasets demonstrate that our proposed StyleSeg V2 outperforms other state-of-the-arts by considerable margins, and exceeds StyleSeg by increasing the average Dice by at least 2.4%.Comment: 10 pages, 11 figures, 2 table

A Dataset And Benchmark Of Underwater Object Detection For Robot Picking

Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets basically lack the test set annotations, causing researchers must compare their method with other SOTAs on a self-divided test set (from the training set). Training other methods lead to an increase in workload and different researchers divide different datasets, resulting there is no unified benchmark to compare the performance of different algorithms. Secondly, these datasets also have other shortcomings, e.g., too many similar images or incomplete labels. Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets. DUO contains a collection of diverse underwater images with more rational annotations. The corresponding benchmark provides indicators of both efficiency and accuracy of SOTAs (under the MMDtection framework) for academic research and industrial applications, where JETSON AGX XAVIER is used to assess detector speed to simulate the robot-embedded environment

Case Report: Sequential treatment with rituximab and belimumab in a pediatric patient of type 1 diabetes mellitus complicated with systemic lupus erythematosus

Type 1 diabetes mellitus (T1DM) and systemic lupus erythematosus (SLE) are both autoimmune diseases influenced by multiple genetic and environmental factors, but rarely coexist. This case describes a 13-year-old girl with early onset of T1DM who was diagnosed with SLE 12 years later, highlighting diagnostic and therapeutic challenges, particularly in distinguishing kidney involvement and management without exacerbating hyperglycemia. The patient presented with edema of the eyelids and lower limbs. Urinalysis revealed hematuria and proteinuria. High-titer antinuclear antibody and anti-double-stranded DNA were detected. SLE was diagnosed clinically. As T1DM and SLE both cause kidney damage, kidney biopsy was performed. Deposition of various immune complexes led to a diagnosis of lupus nephritis. To avoid the impact of steroid pulses on glycemic control, conventional dose of steroids with sequential treatment with rituximab and belimumab was initiated. The combined therapy effectively alleviated the SLE condition, reduced steroids dosage, and led to discontinuation of steroids after 13 months. However, due to the prolonged disease course of T1DM, the pancreatic cell function was not reversed

A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing

To boost the object grabbing capability of underwater robots for open-sea farming, we propose a new dataset (UDD) consisting of three categories (seacucumber, seaurchin, and scallop) with 2,227 images. To the best of our knowledge, it is the first 4K HD dataset collected in a real open-sea farm. We also propose a novel Poisson-blending Generative Adversarial Network (Poisson GAN) and an efficient object detection network (AquaNet) to address two common issues within related datasets: the class-imbalance problem and the problem of mass small object, respectively. Specifically, Poisson GAN combines Poisson blending into its generator and employs a new loss called Dual Restriction loss (DR loss), which supervises both implicit space features and image-level features during training to generate more realistic images. By utilizing Poisson GAN, objects of minority class like seacucumber or scallop could be added into an image naturally and annotated automatically, which could increase the loss of minority classes during training detectors to eliminate the class-imbalance problem; AquaNet is a high-efficiency detector to address the problem of detecting mass small objects from cloudy underwater pictures. Within it, we design two efficient components: a depth-wise-convolution-based Multi-scale Contextual Features Fusion (MFF) block and a Multi-scale Blursampling (MBP) module to reduce the parameters of the network to 1.3 million. Both two components could provide multi-scale features of small objects under a short backbone configuration without any loss of accuracy. In addition, we construct a large-scale augmented dataset (AUDD) and a pre-training dataset via Poisson GAN from UDD. Extensive experiments show the effectiveness of the proposed Poisson GAN, AquaNet, UDD, AUDD, and pre-training dataset.Comment: 14 pages, 10 figure

EEG-based approach for recognizing human social emotion perception

Social emotion perception plays an important role in our daily social interactions and is involved in the treatments for mental disorders. Hyper-scanning technique enables to measure brain activities simultaneously from two or more persons, which was employed in this study to explore social emotion perception. We analyzed the recorded electroencephalogram (EEG) to explore emotion perception in terms of event related potential (ERP) and phase synchronization, and classified emotion categories based on convolutional neural network (CNN). The results showed that (1) ERP was significantly different among four emotion categories (i.e., anger, disgust, neutral, and happy), but there was no significant difference for ERP in the comparison of rating orders (the order of rating actions of the paired participants); (2) the intra-brain phase lag index (PLI) was higher than the inter-brain PLI but its number of connections exhibiting significant difference was less in all typical frequency bands (from delta to gamma); (3) the emotion classification accuracy of inter-PLI-Conv outperformed that of intra-PLI-Conv for all cases of using each frequency band (five frequency bands totally). In particular, the classification accuracies averaged across all participants in the alpha band were 65.55% and 50.77% (much higher than the chance level) for the inter-PLI-Conv and intra-PLI-Conv, respectively. According to our results, the emotion category of happiness can be classified with a higher performance compared to the other categories

Hydrodynamic analysis of a heave-hinge wave energy converter combined with a floating breakwater

Research interest in breakwater design has increased recently due to the impetus to develop marine renewable energy systems, as breakwaters can be retrofitted to harness wave energy at the same time as attenuating it. This study investigates a novel system of attaching a hinge baffle under a floating breakwater. The floating breakwater itself acts as a heaving wave energy converter, and meanwhile the hinge rotation provides a second mechanism for wave energy harnessing. A computational model with multi-body dynamics was established to study this system, and a series of simulations were conducted in various wave conditions. Both wave attenuation performance and energy conversion ratio were studied, using an interdisciplinary approach considering both coastal engineering and renewable energy. In particular, the performance of the proposed system is compared with contemporary floating breakwater designs to demonstrate its advantage. Overall, a useful simulation framework with multi-body dynamics is presented and the simulation results provide valuable insights into the design of combined wave energy and breakwater systems

Hydrodynamic analysis of a heave-hinge wave energy converter combined with a floating breakwater

Research interest in breakwater design has increased recently due to the impetus to develop marine renewable energy systems, as breakwaters can be retrofitted to harness wave energy at the same time as attenuating it. This study investigates a novel system of attaching a hinge baffle under a floating breakwater. The floating breakwater itself acts as a heaving wave energy converter, and meanwhile the hinge rotation provides a second mechanism for wave energy harnessing. A computational model with multi-body dynamics was established to study this system, and a series of simulations were conducted in various wave conditions. Both wave attenuation performance and energy conversion ratio were studied, using an interdisciplinary approach considering both coastal engineering and renewable energy. In particular, the performance of the proposed system is compared with contemporary floating breakwater designs to demonstrate its advantage. Overall, a useful simulation framework with multi-body dynamics is presented and the simulation results provide valuable insights into the design of combined wave energy and breakwater systems

GX1-conjugated poly(lactic acid) nanoparticles encapsulating Endostar for improved in vivo anticolorectal cancer treatment

Tumor angiogenesis plays a key role in tumor growth and metastasis; thus, targeting tumor-associated angiogenesis is an important goal in cancer therapy. However, the efficient delivery of drugs to tumors remains a key issue in antiangiogenesis therapy. GX1, a peptide identified by phage-display technology, is a novel tumor vasculature endothelium-specific ligand and possesses great potential as a targeted vector and antiangiogenic agent in the diagnosis and treatment of human cancers. Endostar, a novel recombinant human endostatin, has been shown to inhibit tumor angiogenesis. In this study, we developed a theranostic agent composed of GX1-conjugated poly(lactic acid) nanoparticles encapsulating Endostar (GPENs) and labeled with the near-infrared dye IRDye 800CW to improve colorectal tumor targeting and treatment efficacy in vivo. The in vivo fluorescence molecular imaging data showed that GPENs (IRDye 800CW) more specifically targeted tumors than free IRDye 800CW in colorectal tumor-bearing mice. Moreover, the antitumor efficacy was evaluated by bioluminescence imaging and immunohistology, revealing that GPENs possessed improved antitumor efficacy on subcutaneous colorectal xenografts compared to other treatment groups. Thus, our study showed that GPENs, a novel GX1 peptide guided form of nanoscale Endostar, can be used as a theranostic agent to facilitate more efficient targeted therapy and enable real-time monitoring of therapeutic efficacy in vivo.National Basic Research Program of China (973) [2011CB707702, 2014CB748600, 2015CB755500]; National Natural Science Foundation of China [81227901, 81470083]; State Key Laboratory of Management and Control for Complex Systems [Y3S9021F30]SCI(E)[email protected]