Recurrent Autoregressive Networks for Online Multi-Object Tracking

The main challenge of online multi-object tracking is to reliably associate object trajectories with detections in each video frame based on their tracking history. In this work, we propose the Recurrent Autoregressive Network (RAN), a temporal generative modeling framework to characterize the appearance and motion dynamics of multiple objects over time. The RAN couples an external memory and an internal memory. The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory. We conduct experiments on the MOT 2015 and 2016 datasets to demonstrate the robustness of our tracking method in highly crowded and occluded scenes. Our method achieves top-ranked results on the two benchmarks.Comment: 10 pages, 3 figures, 6 table

Periodically Aligned Si Nanopillar Arrays as Efficient Antireflection Layers for Solar Cell Applications

Periodically aligned Si nanopillar (PASiNP) arrays were fabricated on Si substrate via a silver-catalyzed chemical etching process using the diameter-reduced polystyrene spheres as mask. The typical sub-wavelength structure of PASiNP arrays had excellent antireflection property with a low reflection loss of 2.84% for incident light within the wavelength range of 200–1,000 nm. The solar cell incorporated with the PASiNP arrays exhibited a power conversion efficiency (PCE) of ~9.24% with a short circuit current density (JSC) of ~29.5 mA/cm2 without using any extra surface passivation technique. The high PCE of PASiNP array-based solar cell was attributed to the excellent antireflection property of the special periodical Si nanostructure

Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications

Large language models (LLMs) exhibit superior performance on various natural language tasks, but they are susceptible to issues stemming from outdated data and domain-specific limitations. In order to address these challenges, researchers have pursued two primary strategies, knowledge editing and retrieval augmentation, to enhance LLMs by incorporating external information from different aspects. Nevertheless, there is still a notable absence of a comprehensive survey. In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications. In addition, we conduct an in-depth analysis of different methods and point out potential research directions in the future. We hope this survey offers the community quick access and a comprehensive overview of this research area, with the intention of inspiring future research endeavors.Comment: Work in progress; 22 pages. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Durable superhydrophobic polyvinylidene fluoride membranes via facile spray-coating for effective membrane distillation

Membrane wetting and fouling substantially limits application and deployment of membrane distillation process. Designing high-performance superhydrophobic membranes offers an effective solution to solve the challenge. In this work, a highly durable superhydrophobic surface (water contact angle of 170.8 ± 1.3°) was constructed via a facile and rapid spray-coating of extremely hydrophobic SiO2 nanoparticles onto a porous polyvinylidene fluoride (PVDF) substrate for membrane distillation. The superhydrophobic membrane coated by fluorinated SiO2 nanoparticles exhibited a superior physicochemical stability in a wide range of extreme environments (i.e., NaOH, HCl, hot water, rust water, humic acid solution, ultrasonication, and high-speed water scouring). During 8-h continuous membrane distillation desalination experiment, the coated superhydrophobic membrane experienced a consistently stable water vapor flux (ca. 19.1 kg·m−2·h−1) and desalination efficiency (99.99 %). Additionally, such a stable superhydrophobicity endowed the spray-coated PVDF membrane to overcome membrane wetting and fouling during membrane distillation of highly saline solutions containing foulants (i.e., humic acid and rust). Results reported in this study provides a useful concept and strategy in facile construction of robust superhydrophobic membranes via spray-coating for effective membrane distillation.</p

UPDP: A Unified Progressive Depth Pruner for CNN and Vision Transformer

Traditional channel-wise pruning methods by reducing network channels struggle to effectively prune efficient CNN models with depth-wise convolutional layers and certain efficient modules, such as popular inverted residual blocks. Prior depth pruning methods by reducing network depths are not suitable for pruning some efficient models due to the existence of some normalization layers. Moreover, finetuning subnet by directly removing activation layers would corrupt the original model weights, hindering the pruned model from achieving high performance. To address these issues, we propose a novel depth pruning method for efficient models. Our approach proposes a novel block pruning strategy and progressive training method for the subnet. Additionally, we extend our pruning method to vision transformer models. Experimental results demonstrate that our method consistently outperforms existing depth pruning methods across various pruning configurations. We obtained three pruned ConvNeXtV1 models with our method applying on ConvNeXtV1, which surpass most SOTA efficient models with comparable inference performance. Our method also achieves state-of-the-art pruning performance on the vision transformer model

Molecular identification of methane monooxygenase and quantitative analysis of methanotrophic endosymbionts under laboratory maintenance in<i>Bathymodiolus platifrons</i>from the South China Sea

Deep-sea mussels of the genusBathymodiolusare numerically dominant macrofauna in many cold seep and hydrothermal vent ecosystems worldwide, and they depend on organic carbon produced by symbionts present in the epithelial cells of the gills. AlthoughBathymodiolus platifronsrepresents typical methanotrophic endosymbiosis, our understanding of molecular mechanisms of methane oxidization and carbon fixation is still in its infancy. Moreover, the laboratory maintenance ofB. platifronsand the symbiont abundance dynamics during maintenance has not been reported. In the present study, we report the first systematic identification and phylogenetic analysis of three subunits of methane monooxygenase (pmoA, pmoB, and pmoC) obtained from the endosymbiotic bacteria found inB. platifrons. The coding sequences (CDS) of the three genes in theB. platifronsendosymbiont were 750, 1,245, and 753 bp, encoding 249, 414, and 250 amino acids, respectively. Sequence alignment and phylogenetic analysis revealed that the symbiont ofB. platifronsbelongs to the type I methanotrophs. In order to clarify the impact of environmental methane on symbiont abundance, a 34-day laboratory maintenance experiment was conducted in whichB. platifronsindividuals were acclimatized to methane-present and methane-absent environments. Symbiont abundance was evaluated by calculating the relative DNA content of the methane monooxygenase gene using quantitative real-time PCR. We found that symbiont quantity immediately decreased from its initial level, then continued to gradually decline during maintenance. At 24 and 34 days of maintenance, symbiont abundance in the methane-absent environment had significantly decreased compared to that in the methane-present environment, indicating that the maintenance of symbionts relies on a continuous supply of methane. Our electron microscopy results validated the qPCR analysis. This study enriches our knowledge of the molecular basis and the dynamic changes of the methanotrophic endosymbiosis inB. platifrons, and provides a feasible model biosystem for further investigation of methane oxidization, the carbon fixation process, and environmental adaptations of deep-sea mussels.</jats:p

KIAA1199 Correlates With Tumor Microenvironment and Immune Infiltration in Lung Adenocarcinoma as a Potential Prognostic Biomarker

Background: KIAA1199 has been considered a key regulator of carcinogenesis. However, the relationship between KIAA1199 and immune infiltrates, as well as its prognostic value in lung adenocarcinoma (LUAD) remains unclear.Methods: The expression of KIAA1199 and its influence on tumor prognosis were analyzed using a series of databases, comprising TIMER, GEPIA, UALCAN, LCE, Prognoscan and Kaplan-Meier Plotter. Further, immunohistochemistry (IHC), western blot (WB) and receiver operating characteristic (ROC) curve analyses were performed to verify our findings. The cBioPortal was used to investigate the genomic alterations of KIAA1199. Prediction of candidate microRNA (miRNAs) and transcription factor (TF) targeting KIAA1199, as well as GO and KEGG analyses, were performed based on LinkedOmics. TIMER and TISIDB databases were used to explore the relationship between KIAA1199 and tumor immune infiltration.Results: High expression of KIAA1199 was identified in LUAD and Lung squamous cell carcinoma (LUSC) patients. High expression of KIAA1199 indicated a worse prognosis in LUAD patients. The results of IHC and WB analyses showed that the expression level of KIAA1199 in tumor tissues was higher than that in adjacent tissues. GO and KEGG analyses indicated KIAA1199 was mainly involved in extracellular matrix (ECM)-receptor interaction and extracellular matrix structure constituent. KIAA1199 was positively correlated with infiltrating levels of CD4+ T cells, macrophages, neutrophil cells, dendritic cells, and showed positive relationship with immune marker subsets expression of a variety of immunosuppressive cells.Conclusion: High expression of KIAA1199 predicts a poor prognosis of LUAD patients. KIAA1199 might exert its carcinogenic role in the tumor microenvironment via participating in the extracellular matrix formation and regulating the infiltration of immune cells in LUAD. The results indicate that KIAA1199 might be a novel biomarker for evaluating prognosis and immune cell infiltration in LUAD