RGBT Tracking via Progressive Fusion Transformer with Dynamically Guided Learning

Existing Transformer-based RGBT tracking methods either use cross-attention to fuse the two modalities, or use self-attention and cross-attention to model both modality-specific and modality-sharing information. However, the significant appearance gap between modalities limits the feature representation ability of certain modalities during the fusion process. To address this problem, we propose a novel Progressive Fusion Transformer called ProFormer, which progressively integrates single-modality information into the multimodal representation for robust RGBT tracking. In particular, ProFormer first uses a self-attention module to collaboratively extract the multimodal representation, and then uses two cross-attention modules to interact it with the features of the dual modalities respectively. In this way, the modality-specific information can well be activated in the multimodal representation. Finally, a feed-forward network is used to fuse two interacted multimodal representations for the further enhancement of the final multimodal representation. In addition, existing learning methods of RGBT trackers either fuse multimodal features into one for final classification, or exploit the relationship between unimodal branches and fused branch through a competitive learning strategy. However, they either ignore the learning of single-modality branches or result in one branch failing to be well optimized. To solve these problems, we propose a dynamically guided learning algorithm that adaptively uses well-performing branches to guide the learning of other branches, for enhancing the representation ability of each branch. Extensive experiments demonstrate that our proposed ProFormer sets a new state-of-the-art performance on RGBT210, RGBT234, LasHeR, and VTUAV datasets.Comment: 13 pages, 9 figure

A Two-stage Multiband Radar Sensing Scheme via Stochastic Particle-Based Variational Bayesian Inference

Multiband fusion is an important technique for radar sensing, which jointly utilizes measurements from multiple non-contiguous frequency bands to improve the sensing performance. In the multi-band radar sensing signal model, there are many local optimums in the associated likelihood function due to the existence of high frequency component, which makes it difficult to obtain high-accuracy parameter estimation. To cope with this challenge, we divide the radar target parameter estimation into two stages equipped with different but equivalent signal models, where the first-stage coarse estimation is used to narrow down the search range for the next stage, and the second-stage refined estimation is based on the Bayesian approach to avoid the convergence to a bad local optimum of the likelihood function. Specifically, in the coarse estimation stage, we employ a weighted root MUSIC algorithm to achieve initial estimation. Then, we apply the block stochastic successive convex approximation (SSCA) approach to derive a novel stochastic particle-based variational Bayesian inference (SPVBI) algorithm for the Bayesian estimation of the radar target parameters in the refined stage. Unlike the conventional particle-based VBI (PVBI) in which only the probability of each particle is optimized and the per-iteration computational complexity increases exponentially with the number of particles, the proposed SPVBI optimizes both the position and probability of each particle, and it adopts the block SSCA to significantly improve the sampling efficiency by averaging over iterations. As such, it is shown that the proposed SPVBI can achieve a better performance than the conventional PVBI with a much smaller number of particles and per-iteration complexity. Finally, extensive simulations verify the advantage of the proposed algorithm over various baseline algorithms

miRecords: an integrated resource for microRNA–target interactions

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords

Roles of arabidopsis WRKY18, WRKY40 and WRKY60 transcription factors in plant responses to abscisic acid and abiotic stress

<p>Abstract</p> <p>Background</p> <p>WRKY transcription factors are involved in plant responses to both biotic and abiotic stresses. Arabidopsis WRKY18, WRKY40, and WRKY60 transcription factors interact both physically and functionally in plant defense responses. However, their role in plant abiotic stress response has not been directly analyzed.</p> <p>Results</p> <p>We report that the three WRKYs are involved in plant responses to abscisic acid (ABA) and abiotic stress. Through analysis of single, double, and triple mutants and overexpression lines for the WRKY genes, we have shown that <it>WRKY18 </it>and <it>WRKY60 </it>have a positive effect on plant ABA sensitivity for inhibition of seed germination and root growth. The same two WRKY genes also enhance plant sensitivity to salt and osmotic stress. <it>WRKY40</it>, on the other hand, antagonizes <it>WRKY18 </it>and <it>WRKY60 </it>in the effect on plant sensitivity to ABA and abiotic stress in germination and growth assays. Both <it>WRKY18 </it>and <it>WRKY40 </it>are rapidly induced by ABA, while induction of <it>WRKY60 </it>by ABA is delayed. ABA-inducible expression of <it>WRKY60 </it>is almost completely abolished in the <it>wrky18 </it>and <it>wrky40 </it>mutants. WRKY18 and WRKY40 recognize a cluster of W-box sequences in the <it>WRKY60 </it>promoter and activate WRKY60 expression in protoplasts. Thus, <it>WRKY60 </it>might be a direct target gene of WRKY18 and WRKY40 in ABA signaling. Using a stable transgenic reporter/effector system, we have shown that both WRKY18 and WRKY60 act as weak transcriptional activators while WRKY40 is a transcriptional repressor in plant cells.</p> <p>Conclusions</p> <p>We propose that the three related WRKY transcription factors form a highly interacting regulatory network that modulates gene expression in both plant defense and stress responses by acting as either transcription activator or repressor.</p

Neural Global Shutter: Learn to Restore Video from a Rolling Shutter Camera with Global Reset Feature

Most computer vision systems assume distortion-free images as inputs. The widely used rolling-shutter (RS) image sensors, however, suffer from geometric distortion when the camera and object undergo motion during capture. Extensive researches have been conducted on correcting RS distortions. However, most of the existing work relies heavily on the prior assumptions of scenes or motions. Besides, the motion estimation steps are either oversimplified or computationally inefficient due to the heavy flow warping, limiting their applicability. In this paper, we investigate using rolling shutter with a global reset feature (RSGR) to restore clean global shutter (GS) videos. This feature enables us to turn the rectification problem into a deblur-like one, getting rid of inaccurate and costly explicit motion estimation. First, we build an optic system that captures paired RSGR/GS videos. Second, we develop a novel algorithm incorporating spatial and temporal designs to correct the spatial-varying RSGR distortion. Third, we demonstrate that existing image-to-image translation algorithms can recover clean GS videos from distorted RSGR inputs, yet our algorithm achieves the best performance with the specific designs. Our rendered results are not only visually appealing but also beneficial to downstream tasks. Compared to the state-of-the-art RS solution, our RSGR solution is superior in both effectiveness and efficiency. Considering it is easy to realize without changing the hardware, we believe our RSGR solution can potentially replace the RS solution in taking distortion-free videos with low noise and low budget.Comment: CVPR2022, https://github.com/lightChaserX/neural-global-shutte

Enhanced superconductivity and electron correlations in intercalated ZrTe3

Charge density waves (CDWs) with superconductivity, competing Fermi surface instabilities, and collective orders have captured much interest in two-dimensional van der Waals (vdW) materials. Understanding the CDW suppression mechanism, its connection to the emerging superconducting state, and electronic correlations provides opportunities for engineering the electronic properties of vdW heterostructures and thin-film devices. Using a combination of the thermal transport, x-ray photoemission spectroscopy, Raman measurements, and first-principles calculations, we observe an increase in electronic correlations of the conducting states as the CDW is suppressed in ZrTe3 with 5% Cu and Ni intercalation in the vdW gap. As superconductivity emerges, intercalation brings not only decoupling of quasi-one-dimensional conduction electrons with phonons as a consequence of intercalation-induced lattice expansion but also a drastic increase in Zr2+ at the expense of Zr4+ metal atoms. These observations not only demonstrate the potential of atomic intercalates in the vdW gap for ground-state tuning but also illustrate the crucial role of the Zr metal valence in the formation of collective electronic orders

The prognostic value of the tertiary lymphoid structure in gastrointestinal cancers

BackgroundNumerous studies and research papers have provided evidence suggesting that tertiary lymphoid structures (TLS) play a crucial role in combating and suppressing tumor growth and progression. Despite the wealth of information on the significance of TLS in various types of cancer, their prognostic value in gastrointestinal (GI) cancers remains uncertain. Therefore, this meta-analysis investigated the prognostic value of TLS in GI cancers.MethodsWe searched Web of science, Pubmed, Embase and Cochrane Library for studies that met the requirements as of May 1, 2023, and the hazard ratio (HR) and the corresponding 95% confidence interval (CI) were included in the analysis. The bioinformatics analysis results based on the TCGA database are used to supplement our research.ResultsThe meta-analysis included 32 studies involving 5778 patients. The results of comprehensive analysis showed that TLS-High is associated with prolonged OS (HR=0.525,95%CI:0.447-0.616 (P &lt; 0.001), RFS (HR=0.546,95%CI:0.461-0.647, P &lt; 0.001), DFS (HR=0.519,95%CI:0.417-0.646, P &lt; 0.001) and PFS (HR=0.588,95%CI:0.406-0.852, P=0.005) in GI cancer. Among the patients who received immunotherapy, TLS-High is associated with significantly prolonged OS (HR=0.475, 95%CI:0.282-0.799, P=0.005) and PFS(HR=0.576, 95%CI:0.381-0.871, P=0.009). It is worth noting that subgroup analysis showed that there was no significant relationship between TLS and OS(HR=0.775, 95%CI:0.570-1.053,P=0.103) in CRC. And when Present is used as the cut-off criteria of TLS, there is no significant correlation between TLS and OS (HR=0.850, 95%CI:0.721-1.002, P=0.053)in HCC.ConclusionTLS is a significant predictor of the prognosis of GI cancers and has the potential to become a prognostic biomarker of immunotherapy-related patients.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42023443562