AIR-DA: Adversarial Image Reconstruction for Unsupervised Domain Adaptive Object Detection

Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invariance in the feature space. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level feature alignment module to enhance the adaptation performance. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings.Comment: Accepted at IEEE Robotics and Automation Letters 202

MATE: Masked Autoencoders are Online 3D Test-Time Learners

We propose MATE, the first Test-Time-Training (TTT) method designed for 3D data. It makes deep networks trained in point cloud classification robust to distribution shifts occurring in test data, which could not be anticipated during training. Like existing TTT methods, which focused on classifying 2D images in the presence of distribution shifts at test-time, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: Each test point cloud has a large portion of its points removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. Further, we show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, which reduces its memory footprint and makes it lightweight. We also highlight that MATE achieves competitive performance by adapting sparingly on the test data, which further reduces its computational overhead, making it ideal for real-time applications.Comment: Minor fix in citation

Chiral edge waves in a dance-based human topological insulator

Topological insulators are insulators in the bulk but feature chiral energy propagation along the boundary. This property is topological in nature and therefore robust to disorder. Originally discovered in electronic materials, topologically protected boundary transport has since been observed in many other physical systems. Thus, it is natural to ask whether this phenomenon finds relevance in a broader context. We choreograph a dance in which a group of humans, arranged on a square grid, behave as a topological insulator. The dance features unidirectional flow of movement through dancers on the lattice edge. This effect persists when people are removed from the dance floor. Our work extends the applicability of wave physics to the performance arts

UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging

With the development of our society, unmanned aerial vehicles (UAVs) appear more frequently in people’s daily lives, which could become a threat to public security and privacy, especially at night. At the same time, laser active imaging is an important detection method for night vision. In this paper, we implement a UAV detection model for our laser active imaging system based on deep learning and a simulated dataset that we constructed. Firstly, the model is pre-trained on the largest available dataset. Then, it is transferred to a simulated dataset to learn about the UAV features. Finally, the trained model is tested on real laser active imaging data. The experimental results show that the performance of the proposed method is greatly improved compared to the model not trained on the simulated dataset, which verifies the transferability of features learned from the simulated data, the effectiveness of the proposed simulation method, and the feasibility of our solution for UAV detection in the laser active imaging domain. Furthermore, a comparative experiment with the previous method is carried out. The results show that our model can achieve high-precision, real-time detection at 104.1 frames per second (FPS)

CycDA: Unsupervised Cycle Domain Adaptation from Image to Video

Although action recognition has achieved impressive results over recent years, both collection and annotation of video training data are still time-consuming and cost intensive. Therefore, image-to-video adaptation has been proposed to exploit labeling-free web image source for adapting on unlabeled target videos. This poses two major challenges: (1) spatial domain shift between web images and video frames; (2) modality gap between image and video data. To address these challenges, we propose Cycle Domain Adaptation (CycDA), a cycle-based approach for unsupervised image-to-video domain adaptation by leveraging the joint spatial information in images and videos on the one hand and, on the other hand, training an independent spatio-temporal model to bridge the modality gap. We alternate between the spatial and spatio-temporal learning with knowledge transfer between the two in each cycle. We evaluate our approach on benchmark datasets for image-to-video as well as for mixed-source domain adaptation achieving state-of-the-art results and demonstrating the benefits of our cyclic adaptation.Comment: Accepted at ECCV202

Robotic-assisted tumor enucleation versus robotic-assisted partial nephrectomy for intermediate and high complexity renal cell carcinoma: a single-institution experience

Abstract Objectives To compare the perioperative and oncological outcomes of robotic-assisted tumor enucleation (RATE) and robotic-assisted partial nephrectomy (RAPN) in the treatment of intermediate and high complexity renal cell carcinoma (RCC). Methods We retrospectively collected the data of 359 patients with intermediate and high complexity RCC who underwent RATE and RAPN. The perioperative, oncological, and pathological outcomes of the two groups were compared, and univariate and multivariate analyses were used to evaluate the risk factors for warm ischemia time (WIT) > 25 min. Results Compared with RAPN group, patients in RATE group had shorter operative time (P  25 min (both P < 0.001). The rate of positive surgical margin was similar between the two groups, but the local recurrence rate of the RATE group was higher than that of the RAPN group (P = 0.027). Conclusions RATE and RAPN have similar oncological outcomes for the treatment of intermediate and high complexity RCC. In addition, RATE was superior to RAPN in perioperative outcomes

Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study

BackgroundAntimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. ObjectiveThe aim of this paper is to identify and characterize “no prescription” online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. MethodsWe first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. ResultsA total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. ConclusionsOur study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients

Three-Dimensional Ni₂P Nanoarray: An Efficient Catalyst Electrode for Sensitive and Selective Nonenzymatic Glucose Sensing with High Specificity

It is highly attractive to construct a natural enzyme-free electrode for sensitive and selective detection of glucose. In this Letter, we report that a Ni₂P nanoarray on conductive carbon cloth (Ni₂P NA/CC) behaves as an efficient three-dimensional catalyst electrode for glucose electrooxidation under alkaline conditions. Electrochemical measurements demonstrate that the Ni₂P NA/CC, when used as a nonenzymatic glucose sensor, offers superior analytical performances with a short response time of 5 s, a wide detection range of 1 μM to 3 mM, a low detection limit of 0.18 μM (S/N = 3), a response sensitivity of 7792 μA mM^–1 cm^–2, and satisfactory selectivity, specificity, and reproducibility. Moreover, it can also be used for glucose detection in human blood serum, promising its application toward determination of glucose in real samples

Hierarchical and lamellar porous carbon as interconnected sulfur host and polysulfide-proof interlayer for Li–S batteries

A robust three-dimensional (3D) interconnected sulfur host and a polysulfide-proof interlayer are key components in high-performance Li–S batteries. Herein, cellulose-based 3D hierarchical porous carbon (HPC) and two-dimensional (2D) lamellar porous carbon (LPC) are employed as the sulfur host and polysulfide-proof interlayer, respectively, for a Li–S battery. The 3D HPC displays a cross-linked macroporous structure, which allows high sulfur loading and restriction capability and provides unobstructed electrolyte diffusion channels. With a stackable carbon sheet of 2D LPC that has a large plane view size and is ultrathin and porous, the LPC-coated separator effectively inhibits polysulfides. An optimized combination of the HPC and LPC yields an electrode structure that effectively protects the lithium anode against corrosion by polysulfides, giving the cell a high capacity of 1339.4 mAh g−1 and high stability, with a capacity decay rate of 0.021% per cycle at 0.2C. This work provides a new understanding of biomaterials and offers a novel strategy to improve the performance of Li–S batteries for practical applications