192 research outputs found
Continuous Layout Editing of Single Images with Diffusion Models
Recent advancements in large-scale text-to-image diffusion models have
enabled many applications in image editing. However, none of these methods have
been able to edit the layout of single existing images. To address this gap, we
propose the first framework for layout editing of a single image while
preserving its visual properties, thus allowing for continuous editing on a
single image. Our approach is achieved through two key modules. First, to
preserve the characteristics of multiple objects within an image, we
disentangle the concepts of different objects and embed them into separate
textual tokens using a novel method called masked textual inversion. Next, we
propose a training-free optimization method to perform layout control for a
pre-trained diffusion model, which allows us to regenerate images with learned
concepts and align them with user-specified layouts. As the first framework to
edit the layout of existing images, we demonstrate that our method is effective
and outperforms other baselines that were modified to support this task. Our
code will be freely available for public use upon acceptance
Few-shot Object Detection in Remote Sensing: Lifting the Curse of Incompletely Annotated Novel Objects
Object detection is an essential and fundamental task in computer vision and
satellite image processing. Existing deep learning methods have achieved
impressive performance thanks to the availability of large-scale annotated
datasets. Yet, in real-world applications the availability of labels is
limited. In this context, few-shot object detection (FSOD) has emerged as a
promising direction, which aims at enabling the model to detect novel objects
with only few of them annotated. However, many existing FSOD algorithms
overlook a critical issue: when an input image contains multiple novel objects
and only a subset of them are annotated, the unlabeled objects will be
considered as background during training. This can cause confusions and
severely impact the model's ability to recall novel objects. To address this
issue, we propose a self-training-based FSOD (ST-FSOD) approach, which
incorporates the self-training mechanism into the few-shot fine-tuning process.
ST-FSOD aims to enable the discovery of novel objects that are not annotated,
and take them into account during training. On the one hand, we devise a
two-branch region proposal networks (RPN) to separate the proposal extraction
of base and novel objects, On another hand, we incorporate the student-teacher
mechanism into RPN and the region of interest (RoI) head to include those
highly confident yet unlabeled targets as pseudo labels. Experimental results
demonstrate that our proposed method outperforms the state-of-the-art in
various FSOD settings by a large margin. The codes will be publicly available
at https://github.com/zhu-xlab/ST-FSOD
Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks
Uplink sensing in perceptive mobile networks (PMNs), which uses uplink
communication signals for sensing the environment around a base station, faces
challenging issues of clock asynchronism and the requirement of a line-of-sight
(LOS) path between transmitters and receivers. The channel state information
(CSI) ratio has been applied to resolve these issues, however, current research
on the CSI ratio is limited to Doppler estimation in a single dynamic path.
This paper proposes an advanced parameter estimation scheme that can extract
multiple dynamic parameters, including Doppler frequency, angle-of-arrival
(AoA), and delay, in a communication uplink channel and completes the
localization of multiple moving targets. Our scheme is based on the
multi-element Taylor series of the CSI ratio that converts a nonlinear function
of sensing parameters to linear forms and enables the applications of
traditional sensing algorithms. Using the truncated Taylor series, we develop
novel multiple-signal-classification grid searching algorithms for estimating
Doppler frequencies and AoAs and use the least-square method to obtain delays.
Both experimental and simulation results are provided, demonstrating that our
proposed scheme can achieve good performances for sensing both single and
multiple dynamic paths, without requiring the presence of a LOS path
New Developments in Aromatic Halogenation, Borylation, and Cyanation
Several green procedures have been developed for synthesizing functionalized aromatics: i) AuCl3-catalyzed halogenation of aromatic compounds, including aryl boronates; ii) Fe2O3-catalyzed direct aromatic C–H bond borylation; iii) Pd-catalyzed
direct cyanation of indoles; iv) direct conversion of arylamines to pinacol boronates
Identification of Potential Key Genes Associated With the Pathogenesis and Prognosis of Gastric Cancer Based on Integrated Bioinformatics Analysis
Background and Objective: Despite striking advances in multimodality management, gastric cancer (GC) remains the third cause of cancer mortality globally and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The study aimed to identify potential key genes associated with the pathogenesis and prognosis of GC.Methods: Differentially expressed genes between GC and normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GC were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.Results: We identified nine hub genes (TOP2A, COL1A1, COL1A2, NDC80, COL3A1, CDKN3, CEP55, TPX2, and TIMP1) which might be tightly correlated with the pathogenesis of GC. A prognostic gene signature consisted of CST2, AADAC, SERPINE1, COL8A1, SMPD3, ASPN, ITGBL1, MAP7D2, and PLEKHS1 was constructed with a good performance in predicting overall survivals.Conclusion: The findings of this study would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GC
Dynamic Responses of Embedded Rock Pile Groups due to Rock Burst considering Coupled Pile-to-Pile Interaction
AbstractThis note presents an analytical solution to investigate the dynamic behavior of pile groups of embedded rock due to rock burst, which takes into account the interaction between piles. The energy generated by the rock burst propagates through the soil in the form of stress waves. It is transmitted to the pile foundation through the interaction between the soil around the pile and the pile. For rock-socketed piles, the condition of pile tip fixing is considered. The horizontal dynamic response calculation model of the pile group is established, and the analytical forms of the pile group stiffness and pile group interaction factor are obtained. In addition, the effect of saturated soil parameters on the dynamic response of pile groups are discussed
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