A multi-demand negotiation model based on fuzzy rules elicited via psychological experiments

This paper proposes a multi-demand negotiation model that takes the effect of human users’ psychological characteristics into consideration. Specifically, in our model each negotiating agent's preference over its demands can be changed, according to human users’ attitudes to risk, patience and regret, during the course of a negotiation. And the change of preference structures is determined by fuzzy logic rules, which are elicited through our psychological experiments. The applicability of our model is illustrated by using our model to solve a problem of political negotiation between two countries. Moreover, we do lots of theoretical and empirical analyses to reveal some insights into our model. In addition, to compare our model with existing ones, we make a survey on fuzzy logic based negotiation, and discuss the similarities and differences between our negotiation model and various consensus models

Distilling Large Vision-Language Model with Out-of-Distribution Generalizability

Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models, is a promising direction towards the solution. This paper investigates the distillation of visual representations in large teacher vision-language models into lightweight student models using a small- or mid-scale dataset. Notably, this study focuses on open-vocabulary out-of-distribution (OOD) generalization, a challenging problem that has been overlooked in previous model distillation literature. We propose two principles from vision and language modality perspectives to enhance student's OOD generalization: (1) by better imitating teacher's visual representation space, and carefully promoting better coherence in vision-language alignment with the teacher; (2) by enriching the teacher's language representations with informative and finegrained semantic attributes to effectively distinguish between different labels. We propose several metrics and conduct extensive experiments to investigate their techniques. The results demonstrate significant improvements in zero-shot and few-shot student performance on open-vocabulary out-of-distribution classification, highlighting the effectiveness of our proposed approaches. Our code will be released at https://github.com/xuanlinli17/large_vlm_distillation_oo

PartSLIP: Low-Shot Part Segmentation for 3D Point Clouds via Pretrained Image-Language Models

Generalizable 3D part segmentation is important but challenging in vision and robotics. Training deep models via conventional supervised methods requires large-scale 3D datasets with fine-grained part annotations, which are costly to collect. This paper explores an alternative way for low-shot part segmentation of 3D point clouds by leveraging a pretrained image-language model, GLIP, which achieves superior performance on open-vocabulary 2D detection. We transfer the rich knowledge from 2D to 3D through GLIP-based part detection on point cloud rendering and a novel 2D-to-3D label lifting algorithm. We also utilize multi-view 3D priors and few-shot prompt tuning to boost performance significantly. Extensive evaluation on PartNet and PartNet-Mobility datasets shows that our method enables excellent zero-shot 3D part segmentation. Our few-shot version not only outperforms existing few-shot approaches by a large margin but also achieves highly competitive results compared to the fully supervised counterpart. Furthermore, we demonstrate that our method can be directly applied to iPhone-scanned point clouds without significant domain gaps.Comment: CVPR 2023, project page: https://colin97.github.io/PartSLIP_page

Elevated Serum Human Epididymis Protein 4 Is Associated With Disease Activity and Systemic Involvements in Primary Sjögren’s Syndrome

BackgroundWe aimed to investigate the clinical utility of human epididymis protein 4, a tumor biomarker being widely utilized in clinical practice in the diagnosis of ovarian cancer, in primary Sjögren’s Syndrome (pSS).MethodsA total of 109 pSS patients and 113 healthy controls (HCs) were included in the study. HE4 were determined by Roche Cobas E601 electrochemical luminescence analyzer. Clinical and laboratory findings were reviewed, and the relationships between HE4 and clinical parameters were determined by Spearman’s correlation test. The European league against rheumatism Sjögren’s syndrome disease activity index (ESSDAI) was utilized to evaluate disease activity.FindingsThe levels of HE4 were significantly elevated in patients with pSS compared to HCs (103.65 pmol/L vs. 46.52 pmol/L, p<0.001). The levels of HE4 were positively correlated with ESSDAI scores (r=0.462, p<0.001). Significant positive correlations between the levels of HE4 with pulmonary involvements (r=0.442, p<0.001) and renal involvements (r=0.320, p=0.001) were observed. Receiver operating curve (ROC) analysis revealed an optimal cut-off value of 104.90 pmol/L and 128.05 pmol/L for distinguishing patients with pulmonary and renal involvements, with the areas under the ROC curve (AUCs) of 0.778 (95%CI 0.685-0.870, p<0.001) and 0.768 (95%CI 0.646-0.891, p=0.001), respectively. Among patients with pulmonary involvement, the levels of HE4 were positively correlated with the semiquantitative HRCT grade (r=0.417, p=0.016), and negatively correlated with the percentage of forced vital capacity (FVC) (r= -0.460, p=0.047) and diffusing capacity of the lung for carbon monoxide (DLco) (r= -0.623, p=0.004). For patients with renal involvement, HE4 was positively correlated with creatinine (r=0.588, p=0.021) and negatively correlated with estimated glomerular filtration rate (r= -0.599, p=0.030).ConclusionsOur findings demonstrated a novel role of HE4 in clinical stratification of pSS, suggesting that introducing HE4 to the current pSS test panel may provide additional diagnostic value, particularly in evaluating disease activity and pulmonary/renal involvements

A gene-based recessive diplotype exome scan discovers \u3cem\u3eFGF6\u3c/em\u3e, a novel hepcidin-regulating iron-metabolism gene

Standard analyses applied to genome-wide association data are well designed to detect additive effects of moderate strength. However, the power for standard genome-wide association study (GWAS) analyses to identify effects from recessive diplotypes is not typically high. We proposed and conducted a gene-based compound heterozygosity test to reveal additional genes underlying complex diseases. With this approach applied to iron overload, a strong association signal was identified between the fibroblast growth factor–encoding gene, FGF6, and hemochromatosis in the central Wisconsin population. Functional validation showed that fibroblast growth factor 6 protein (FGF-6) regulates iron homeostasis and induces transcriptional regulation of hepcidin. Moreover, specific identified FGF6variants differentially impact iron metabolism. In addition, FGF6 downregulation correlated with iron-metabolism dysfunction in systemic sclerosis and cancer cells. Using the recessive diplotype approach revealed a novel susceptibility hemochromatosis gene and has extended our understanding of the mechanisms involved in iron metabolism

Molecular basis of USP7 inhibition by selective small-molecule inhibitors

Ubiquitination controls the stability of most cellular proteins, and its deregulation contributes to human diseases including cancer. Deubiquitinases remove ubiquitin from proteins, and their inhibition can induce the degradation of selected proteins, potentially including otherwise 'undruggable' targets. For example, the inhibition of ubiquitin-specific protease 7 (USP7) results in the degradation of the oncogenic E3 ligase MDM2, and leads to re-activation of the tumour suppressor p53 in various cancers. Here we report that two compounds, FT671 and FT827, inhibit USP7 with high affinity and specificity in vitro and within human cells. Co-crystal structures reveal that both compounds target a dynamic pocket near the catalytic centre of the auto-inhibited apo form of USP7, which differs from other USP deubiquitinases. Consistent with USP7 target engagement in cells, FT671 destabilizes USP7 substrates including MDM2, increases levels of p53, and results in the transcription of p53 target genes, induction of the tumour suppressor p21, and inhibition of tumour growth in mice