Neuro-Inspired Hierarchical Multimodal Learning

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Distinct from most traditional fusion models that aim to incorporate all modalities as input, our model designates the prime modality as input, while the remaining modalities act as detectors in the information pathway. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of downstream tasks. Experimental evaluations on both the MUStARD and CMU-MOSI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks

ClipCrop: Conditioned Cropping Driven by Vision-Language Model

Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially when the composition of the input image is complex. Moreover, labeling of cropping data is costly and hence the amount of data is limited, leading to poor generalization performance of current algorithms in the wild. In this work, we take advantage of vision-language models as a foundation for creating robust and user-intentional cropping algorithms. By adapting a transformer decoder with a pre-trained CLIP-based detection model, OWL-ViT, we develop a method to perform cropping with a text or image query that reflects the user's intention as guidance. In addition, our pipeline design allows the model to learn text-conditioned aesthetic cropping with a small cropping dataset, while inheriting the open-vocabulary ability acquired from millions of text-image pairs. We validate our model through extensive experiments on existing datasets as well as a new cropping test set we compiled that is characterized by content ambiguity

Development and validation of novel immune-inflammation-based clinical predictive nomograms in HER2-negative advanced gastric cancer

PurposeTo explore the predictive value of multiple immune-inflammatory biomarkers including serum VEGFA and systemic immune-inflammation index (SII) in HER2-negative advanced gastric cancer (AGC) and establish nomograms for predicting the first-line chemotherapeutic efficacy, progression-free survival (PFS) and overall survival (OS) of patients with this fatal disease.MethodsFrom November 2017 to April 2022, 102 and 34 patients with a diagnosis of HER2-negative AGC at the First Affiliated Hospital of Bengbu Medical College were enrolled as development and validation cohorts, respectively. Univariate and multivariate analyses were performed to evaluate the clinical value of the candidate indicators. The variables were screened using LASSO regression analysis. Predictive models were developed using significant predictors and are displayed as nomograms.ResultsBaseline VEGFA expression was significantly higher in HER2-negative AGC patients than in nonneoplastic patients and was associated with malignant serous effusion and therapeutic efficacy (all p<0.001). Multivariate analysis indicated that VEGFA was an independent predictor for first-line therapeutic efficacy and PFS (both p<0.01) and SII was an independent predictor for first-line PFS and OS (both p<0.05) in HER2-negative AGC patients. The therapeutic efficacy model had an R2 of 0.37, a Brier score of 0.15, and a Harrell’s C-index of 0.82 in the development cohort and 0.90 in the validation cohort. The decision curve analysis indicated that the model added more net benefits than VEGFA assessment alone. The PFS/OS models had Harrell’s C-indexes of 0.71/0.69 in the development cohort and 0.71/0.62 in the validation cohort.ConclusionThe established nomograms integrating serum VEGFA/SII and commonly available baseline characteristics provided satisfactory performance in predicting the therapeutic efficacy and prognosis of HER2-negative AGC patients

Entire Peroxidation Reaction System of Myeloperoxidase Correlates with Progressive Low-Density Lipoprotein Modifications via Reactive Aldehydes in Atherosclerotic Patients with Hypertension

Background/Aims: Reactive oxygen species (ROS) contribute to the dysfunction of serum lipoproteins, which triggers lipid metabolism abnormalities in the development of atherosclerosis and hypertension. Myeloperoxidase (MPO) is involved in ROS modifications, triggering lipid peroxidation and aldehyde formation. However, the relationship between the entirety of the MPO reaction system and oxidative modification of serum lipoproteins in atherosclerotic patients with hypertension remains unclear. Methods: We measured MPO activity (peroxidation and chlorination), 4-hydroxynonenal-modified low-density lipoprotein (HNE-LDL), malondialdehyde-modified low-density lipoprotein (MDA-LDL), H2O2, reduced glutathione (GSH), and oxidized glutathione (GSSG) using a corresponding commercial kit in atherosclerotic patients with hypertension and healthy participants. We used Spearman’s correlation analysis to investigate the correlation between MPO activity and the levels of these oxidative and anti-oxidative stress-related indices and performed response surface regression to investigate the relationship between the MPO reaction system and the levels of HNE-LDL, MDA-LDL, and the GSH/GSSG ratio. Results: Our results showed no association between the levels of MPO peroxidation activity, MPO chlorination activity, H2O2, and Cl- and those of HNE-LDL, MDA-LDL, GSH, and GSSG, and the GSH/GSSG ratio in healthy participants. In addition, no effects of the peroxidation reaction system of MPO (PRSM) and the chlorination reaction system of MPO (CRSM) on GSH/GSSG were found in this investigation. However, we found that the PRSM rather than the CRSM correlated with progressive low-density lipoprotein (LDL) modifications by HNE-LDL and MDA-LDL in atherosclerotic patients with hypertension. Conclusion: The PRSM rather than the CRSM correlated with progressive LDL modifications via reactive aldehydes in atherosclerotic patients with hypertension. Further investigation is warranted to evaluate whether the PRSM may serve as a potential index for monitoring LDL function in atherosclerosis and hypertension

Research on electric vehicle load forecasting based on travel data

Due to the rapid promotion of electric vehicles, large-scale charging behavior of electric vehicles brings a large number of time and space highly random charging load, which will have a great impact on the safe operation of distribution network. This paper proposes a planning method of electric vehicle charging station based on travel data. Firstly, the didi trip data is processed and mined to get the trip matrix and other information. Then, the electric vehicle charging load forecasting model is established based on the established unit mileage power consumption model and charging model, and the charging demand distribution information is predicted by Monte Carlo method. Finally, the simulation analysis is carried out based on the trip data of some areas of a city, which shows the effectiveness of the established model feasibility