Cascaded information enhancement and cross-modal attention feature fusion for multispectral pedestrian detection

Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian detection because of the lacking take into account the confusion of pedestrian information and background noise in Color and Thermal images. Here we propose a multispectral pedestrian detection algorithm, which mainly consists of a cascaded information enhancement module and a cross-modal attention feature fusion module. On the one hand, the cascaded information enhancement module adopts the channel and spatial attention mechanism to perform attention weighting on the features fused by the cascaded feature fusion block. Moreover, it multiplies the single-modal features with the attention weight element by element to enhance the pedestrian features in the single-modal and thus suppress the interference from the background. On the other hand, the cross-modal attention feature fusion module mines the features of both Color and Thermal modalities to complement each other, then the global features are constructed by adding the cross-modal complemented features element by element, which are attentionally weighted to achieve the effective fusion of the two modal features. Finally, the fused features are input into the detection head to detect and locate pedestrians. Extensive experiments have been performed on two improved versions of annotations (sanitized annotations and paired annotations) of the public dataset KAIST. The experimental results show that our method demonstrates a lower pedestrian miss rate and more accurate pedestrian detection boxes compared to the comparison method. Additionally, the ablation experiment also proved the effectiveness of each module designed in this paper

Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification

Thanks for the cross-modal retrieval techniques, visible-infrared (RGB-IR) person re-identification (Re-ID) is achieved by projecting them into a common space, allowing person Re-ID in 24-hour surveillance systems. However, with respect to the probe-to-gallery, almost all existing RGB-IR based cross-modal person Re-ID methods focus on image-to-image matching, while the video-to-video matching which contains much richer spatial- and temporal-information remains under-explored. In this paper, we primarily study the video-based cross-modal person Re-ID method. To achieve this task, a video-based RGB-IR dataset is constructed, in which 927 valid identities with 463,259 frames and 21,863 tracklets captured by 12 RGB/IR cameras are collected. Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID. Additionally, a novel method is further proposed, which not only projects two modalities to a modal-invariant subspace, but also extracts the temporal-memory for motion-invariant. Thanks to these two strategies, much better results are achieved on our video-based cross-modal person Re-ID. The code and dataset are released at: https://github.com/VCMproject233/MITML

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt

Large Language Models (LLMs), armed with billions of parameters, exhibit exceptional performance across a wide range of Natural Language Processing (NLP) tasks. However, they present a significant computational challenge during inference, especially when deploying on common hardware such as single GPUs. As such, minimizing the latency of LLM inference by curtailing computational and memory requirements, though achieved through compression, becomes critically important. However, this process inevitably instigates a trade-off between efficiency and accuracy, as compressed LLMs typically experience a reduction in predictive precision. In this research, we introduce an innovative perspective: to optimize this trade-off, compressed LLMs require a unique input format that varies from that of the original models. Our findings indicate that the generation quality in a compressed LLM can be markedly improved for specific queries by selecting prompts with precision. Capitalizing on this insight, we introduce a prompt learning paradigm that cultivates an additive prompt over a compressed LLM to bolster their accuracy. Our empirical results imply that through our strategic prompt utilization, compressed LLMs can match, and occasionally even exceed, the accuracy of the original models. Moreover, we demonstrated that these learned prompts have a certain degree of transferability across various datasets, tasks, and compression levels. These insights shine a light on new possibilities for enhancing the balance between accuracy and efficiency in LLM inference. Specifically, they underscore the importance of judicious input editing to a compressed large model, hinting at potential advancements in scaling LLMs on common hardware

Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model

With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually focus on reducing the number of trainable parameters in the network. While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation. Notably, neural networks are usually trained using stochastic gradient descent. We argue that in stochastic optimization, models can handle noisy gradients as long as the gradient estimator is unbiased with reasonable variance. Following this motivation, we propose a new family of unbiased estimators called WTA-CRS, for matrix production with reduced variance, which only requires storing the sub-sampled activations for calculating the gradient. Our work provides both theoretical and experimental evidence that, in the context of tuning transformers, our proposed estimators exhibit lower variance compared to existing ones. By replacing the linear operation with our approximated one in transformers, we can achieve up to 2.7$\times$ peak memory reduction with almost no accuracy drop and enables up to $6.4\times$ larger batch size. Under the same hardware, WTA-CRS enables better down-streaming task performance by applying larger models and/or faster training speed with larger batch sizes

Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (RH) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future

Multiple influences of land transfer in the integration of Beijing-Tianjin-Hebei region in China

Land transfers are an important approach to Chinese farmland management and intensive crop production as well as a primary government strategy to promote Beijing-Tianjin-Hebei region development; these transfers are expected not only to generate social, economic, and ecological benefits but also to further Beijing-Tianjin-Hebei's regional development by means of more efficient and sustainable resource use. However, together with the challenges associated with this process, several contradictions and problems have arisen that are now critical political and social concerns. Therefore, a modern demonstration zone of sustainable agriculture in Yi County, Hebei Province, China, was selected as a case study for emergy-based performance and sustainability evaluation of the associated social, economic, and ecological benefits before and after land transfer. The results suggest that land transfers have induced fundamental changes to land use, which improved performance in terms of resource use and sustainability indicators (based on the emergy approach) and have produced ecological, economic, and social benefits mainly based on the increased link to the surrounding larger scale economic system via the increased demand for labor and services from outside. Therefore, the emergy results, while highlighting the achieved or potential benefits, also indicate that local improvements cannot be fully achieved if the entire supply chain of goods and resources is not suitably improved as well and that the local system is heavily affected by the larger-scale functioning of the economy as a whole, such that all links across scales need to be monitored and carefully addressed

DataSheet1_LNC-ZNF33B-2:1 gene rs579501 polymorphism is associated with organ dysfunction and death risk in pediatric sepsis.docx

Background: Sepsis is a severe systemic reaction disease induced by bacteria and virus invading the bloodstream and subsequently causing multiple systemic organ dysfunctions. For example, the kidney may stop producing urine, or the lungs may stop taking in oxygen. Recent studies have shown that long non-coding RNAs (lncRNAs) are related to the dysfunction of organs in sepsis. This study aims to screen and validate the sepsis-associated lncRNAs and their functional single nucleotide polymorphisms (SNPs).Result: Unconditional multiple logistic regression based on the recessive model (adjusted odds ratio = 2.026, 95% CI = 1.156–3.551, p = 0.0136) showed that patients with the CC genotype of rs579501 had increased risk of sepsis. Stratification analysis by age and gender indicated that patients with the rs579501 CC genotype had higher risk of sepsis among children aged Conclusion: Our findings showed that the lnc-ZNF33B-2:1 rs579501 CC genotype increases the susceptibility to sepsis. From the medical perspective, the lnc-ZNF33B-2:1 rs579501 CC genotype could be serving as a biochemical marker for sepsis.</p

Design and Fabrication of Multifunctional Sericin Nanoparticles for Tumor Targeting and pH-Responsive Subcellular Delivery of Cancer Chemotherapy Drugs

The severe cytotoxicity of cancer chemotherapy drugs limits their clinical applications. Various protein-based nanoparticles with good biocompatibility have been developed for chemotherapy drug delivery in hope of reducing drugs’ side effects. Sericin, a natural protein from silk, has no immunogenicity and possesses diverse bioactivities that have prompted sericin’s application studies. However, the potential of sericin as a multifunctional nanoscale vehicle for cancer therapy have not been fully explored. Here we report the successful fabrication and characterization of <u>f</u>ol<u>a</u>te-conjugated <u>s</u>erici<u>n</u> nanoparticles with cancer-targeting capability for pH-responsive release of <u>d</u>oxorubicin (these nanoparticles are termed “FA-SND”). DOX is covalently linked to sericin through pH-sensitive hydrazone bonds that render a pH-triggered release property. The hydrophobicity of DOX and the hydrophilicity of sericin promote the self-assembly of sericin-DOX (SND) nanoconjugates. Folate (FA) is then covalently grafted to SND nanoconjugates as a binding unit for actively targeting cancer cells that overexpress folate receptors. Our characterization study shows that FA-SND nanoparticles exhibit negative surface charges that would reduce nonspecific clearance by circulation. These nanoparticles possess good cytotoxicity and hemocompatibiliy. Acidic environment (pH 5.0) triggers effective DOX release from FA-SND, 5-fold higher than does a neutral condition (pH 7.4). Further, FA-SND nanoparticles specifically target folate-receptor-rich KB cells, and endocytosed into lysosomes, an acidic organelle. The acidic microenvironment of lysosomes promotes a rapid release of DOX to nuclei, producing cancer specific chemo-cytotoxicity. Thus, FA-mediated cancer targeting and lysosomal-acidity promoting DOX release, two sequentially-occurring cellular events triggered by the designed components of FA-SND, form the basis for FA-SND to achieve its localized and intracellular chemo-cytotoxicity. Together, this study suggests that these FA-SND nanoparticles may be a potentially effective carrier particularly useful for delivering hydrophobic chemotherapeutic agents for treating cancers with high-level expression of folate receptors