Zero-Shot Aerial Object Detection with Visual Description Regularization

Existing object detection models are mainly trained on large-scale labeled datasets. However, annotating data for novel aerial object classes is expensive since it is time-consuming and may require expert knowledge. Thus, it is desirable to study label-efficient object detection methods on aerial images. In this work, we propose a zero-shot method for aerial object detection named visual Description Regularization, or DescReg. Concretely, we identify the weak semantic-visual correlation of the aerial objects and aim to address the challenge with prior descriptions of their visual appearance. Instead of directly encoding the descriptions into class embedding space which suffers from the representation gap problem, we propose to infuse the prior inter-class visual similarity conveyed in the descriptions into the embedding learning. The infusion process is accomplished with a newly designed similarity-aware triplet loss which incorporates structured regularization on the representation space. We conduct extensive experiments with three challenging aerial object detection datasets, including DIOR, xView, and DOTA. The results demonstrate that DescReg significantly outperforms the state-of-the-art ZSD methods with complex projection designs and generative frameworks, e.g., DescReg outperforms best reported ZSD method on DIOR by 4.5 mAP on unseen classes and 8.1 in HM. We further show the generalizability of DescReg by integrating it into generative ZSD methods as well as varying the detection architecture.Comment: 13 pages, 3 figure

ProGroTrack: Deep Learning-Assisted Tracking of Intracellular Protein Growth Dynamics

Accurate tracking of cellular and subcellular structures, along with their dynamics, plays a pivotal role in understanding the underlying mechanisms of biological systems. This paper presents a novel approach, ProGroTrack, that combines the You Only Look Once (YOLO) and ByteTrack algorithms within the detection-based tracking (DBT) framework to track intracellular protein nanostructures. Focusing on iPAK4 protein fibers as a representative case study, we conducted a comprehensive evaluation of YOLOv5 and YOLOv8 models, revealing the superior performance of YOLOv5 on our dataset. Notably, YOLOv5x achieved an impressive mAP50 of 0.839 and F-score of 0.819. To further optimize detection capabilities, we incorporated semi-supervised learning for model improvement, resulting in enhanced performances in all metrics. Subsequently, we successfully applied our approach to track the growth behavior of iPAK4 protein fibers, revealing their two distinct growth phases consistent with a previously reported kinetic model. This research showcases the promising potential of our approach, extending beyond iPAK4 fibers. It also offers a significant advancement in precise tracking of dynamic processes in live cells, and fostering new avenues for biomedical research

VCL Challenges 2023 at ICCV 2023 Technical Report: Bi-level Adaptation Method for Test-time Adaptive Object Detection

This report outlines our team's participation in VCL Challenges B Continual Test_time Adaptation, focusing on the technical details of our approach. Our primary focus is Testtime Adaptation using bi_level adaptations, encompassing image_level and detector_level adaptations. At the image level, we employ adjustable parameterbased image filters, while at the detector level, we leverage adjustable parameterbased mean teacher modules. Ultimately, through the utilization of these bi_level adaptations, we have achieved a remarkable 38.3% mAP on the target domain of the test set within VCL Challenges B. It is worth noting that the minimal drop in mAP, is mearly 4.2%, and the overall performance is 32.5% mAP

A solid state fungal fermentation-based strategy for the hydrolysis of wheat straw

This paper reports a solid-state fungal fermentation-based pre-treatment strategy to convert wheat straw into a fermentable hydrolysate. Aspergillus niger was firstly cultured on wheat straw for production of cellulolytic enzymes and then the wheat straw was hydrolyzed by the enzyme solution into a fermentable hydrolysate. The optimum moisture content and three wheat straw modification methods were explored to improve cellulase production. At a moisture content of 89.5%, 10.2 ± 0.13 U/g cellulase activity was obtained using dilute acid modified wheat straw. The addition of yeast extract (0.5% w/v) and minerals significantly improved the cellulase production, to 24.0 ± 1.76 U/g. The hydrolysis of the fermented wheat straw using the fungal culture filtrate or commercial cellulase Ctec2 was performed, resulting in 4.34 and 3.13 g/L glucose respectively. It indicated that the fungal filtrate harvested from the fungal fermentation of wheat straw contained a more suitable enzyme mixture than the commercial cellulase

Pt 3 Co Concave Nanocubes: Synthesis, Formation Understanding, and Enhanced Catalytic Activity toward Hydrogenation of Styrene

We report a facile synthesis route to prepare high‐quality Pt 3 Co nanocubes with a concave structure, and further demonstrate that these concave Pt 3 Co nanocubes are terminated with high‐index crystal facets. The success of this preparation is highly dependent on an appropriate nucleation process with a successively anisotropic overgrowth and a preservation of the resultant high‐index planes by control binding of oleyl‐amine/oleic acid with a fine‐tuned composition. Using a hydrogenation of styrene as a model reaction, these Pt 3 Co concave nanocubes as a new class of nanocatalysts with more open structure and active atomic sites located on their high‐index crystallographic planes exhibit an enhanced catalytic activity in comparison with low‐indexed surface terminated Pt 3 Co nanocubes in similar size. Anisotropic overgrowth : Pt 3 Co concave nanocubes bounded by high‐index facets were prepared with a facile wet‐chemical method. The formation process for such concave nanostructures was systematically studied, and a plausible mechanism was proposed. These nanocrystals can be used as advanced nanocatalysts, showing high activity and reusability toward hydrogenation of styrene (see figure).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102689/1/chem_201301724_sm_miscellaneous_information.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/102689/2/1753_ftp.pd

Numerical modelling of hydrodynamic responses of Ocean Farm 1 in waves and current and validation against model test measurements

With the continuous growing of the aquaculture industry and increasingly limited fish farming sites at close to shore areas both in Norway and worldwide, there is a need to develop fish farms suitable for aquaculture production in typical offshore environments. For this purpose, SALMAR has developed and deployed the Ocean Farm 1 facility for offshore fish farming. The main purpose of this paper is to develop a reliable numerical model and investigate the motion responses of the Ocean Farm 1 structure in waves and current. The established numerical model consists of the Ocean Farm 1's frame structure (with rigidly-connected circular column components), the net and the mooring system. The hydrodynamic external loads and coefficients of the frame structure are obtained by using potential flow theory. The quadratic drag load on the individual circular columns of the frame structure is formulated by a given drag coefficient. The loads on the net are formulated by using the screen model, where the Reynold number dependent lift and drag forces are formulated as a function of the solidity ratio Sn of the net, relative inflow angle and velocity. The hydrodynamic loads on the mooring lines are formulated using the Morison's equation and the structural responses of the mooring lines are obtained using a nonlinear FE model. With the developed numerical model, time domain simulations are performed. The simulation results are firstly validated against measured data from the decay tests, current tests, and regular wave tests. After the validation, numerical simulations are performed in different irregular wave and current combined weather conditions and the obtained motion response of Ocean Farm 1 are discussed and compared with available measurement data.acceptedVersio

A doubly robust estimator for the Mann Whitney Wilcoxon Rank Sum Test when applied for causal inference in observational studies

The Mann-Whitney-Wilcoxon rank sum test (MWWRST) is a widely used method for comparing two treatment groups in randomized control trials, particularly when dealing with highly skewed data. However, when applied to observational study data, the MWWRST often yields invalid results for causal inference. To address this limitation, Wu et al. (2014) introduced an approach that incorporates inverse probability weighting (IPW) into this rank-based statistics to mitigate confounding effects. Subsequently, Mao (2018), Zhang et al. (2019), and Ai et al. (2020) extended this IPW estimator to develop doubly robust estimators. Nevertheless, each of these approaches has notable limitations. Mao's method imposes stringent assumptions that may not align with real-world study data. Zhang et al.'s (2019) estimators rely on bootstrap inference, which suffers from computational inefficiency and lacks known asymptotic properties. Meanwhile, Ai et al. (2020) primarily focus on testing the null hypothesis of equal distributions between two groups, which is a more stringent assumption that may not be well-suited to the primary practical application of MWWRST. In this paper, we aim to address these limitations by leveraging functional response models (FRM) to develop doubly robust estimators. We demonstrate the performance of our proposed approach using both simulated and real study data