Geotechnical Analyses of Guizhou Hotel

This paper presents the geotechnical analyses of Guizhou Hotel, a thirty story (102-m high) building situated under complicated karst engineering geological conditions. Comprehensive geotechnical investigations prior to the design included the bore hole sampling, insitu ultra-sound velocity testing, groundwater well-pumping, and laboratory testing for rock strength. Based on the field and laboratory data, the bedrocks within the construction site (about 20,000 m2) was divided into four engineering geological units Ia, Ib, Ic, and Id, ranging from simple engineering geological condition (Ia) to very complicated geological condition (Id). Different subgrade bearing capacities were selected for the four units based on field and laboratory test results. Manually dug shaft foundations with different geometric shapes and sizes were considered for different units. Subgrade distress such as excess weathering and groundwater seepage were treated through construction measures. The field monitoring data during and after the construction indicated that very little overall and differential settlement had occurred for the structure and the geotechnical design for this high-rise building under the complicated karst geology had been a success

Stochastic interest model driven by compound Poisson process and Brownian motion with applications in life contingencies

In this paper, we introduce a class of stochastic interest model driven by a compound Poisson process and a Brownian motion, in which the jumping times of force of interest obeys compound Poisson process and the continuous tiny fluctuations are described by Brownian motion, and the adjustment in each jump of interest force is assumed to be random. Based on the proposed interest model, we discuss the expected discounted function, the validity of the model and actuarial present values of life annuities and life insurances under different parameters and distribution settings. Ournumerical results show actuarial values could be sensitive to the parameters and distribution settings,which shows the importance of introducing this kind interest model

POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation

Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data, prompting research into unsupervised methods. Unsupervised neural machine translation (UNMT) methods, including back-translation, transfer learning, and pivot-based translation, offer practical solutions for LRL translation, but they are hindered by issues like synthetic data noise, language bias, and error propagation, which can potentially be mitigated by Large Language Models (LLMs). LLMs have advanced NMT with in-context learning (ICL) and supervised fine-tuning methods, but insufficient training data results in poor performance in LRLs. We argue that LLMs can mitigate the linguistic noise with auxiliary languages to improve translations in LRLs. In this paper, we propose Probability-driven Meta-graph Prompter (POMP), a novel approach employing a dynamic, sampling-based graph of multiple auxiliary languages to enhance LLMs' translation capabilities for LRLs. POMP involves constructing a directed acyclic meta-graph for each source language, from which we dynamically sample multiple paths to prompt LLMs to mitigate the linguistic noise and improve translations during training. We use the BLEURT metric to evaluate the translations and back-propagate rewards, estimated by scores, to update the probabilities of auxiliary languages in the paths. Our experiments show significant improvements in the translation quality of three LRLs, demonstrating the effectiveness of our approach

The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called \textit{Diversity-Promoting Collaborative Metric Learning} (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a \textit{diversity control regularization} term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML

Extraction and purification of antioxidative flavonoids from Chionanthus retusa leaf

In this work, flavonoids from the leaves of Chionanthus retusa were extracted using alcohol, and the extraction yield was optimized by single-factor and orthogonal experiments. Then, the extracted solution with flavonoids was purified via macroporous resin by elution with different concentrations of ethanol. The antioxidative activity of total flavonoid in purified extracted solution was evaluated by detecting its ability to scavenge DPPH free radicals. The results demonstrated that ethanol with a concentration of 60%, ultrasonic power of 140 W, liquid–solid ratio of 25:1 ml g−1, and water-bath temperature of 80°C were the optimal conditions for the extraction of total flavonoids from C. retusa leaf, achieving a yield of 121.28 mg g−1. After purification by macroporous resin using different concentrations of ethanol, the highest content of total flavonoids (88.51%) in the extracted solution can be obtained with the 50% ethanol eluant. The results of scavenging DPPH free radicals suggest that the purified flavonoids in the 50% ethanol eluant had the best antioxidant capacity over the flavonoids in other ethanol eluants. In addition, it is confirmed the antioxidant capacity of the extractives was associated with the content of total flavonoids and kinds of flavonoids. These results may provide a feasible pathway to make full use of total flavonoids from C. retusa leaf

Detection Transformer with Stable Matching

This paper is concerned with the matching stability problem across different decoder layers in DEtection TRansformers (DETR). We point out that the unstable matching in DETR is caused by a multi-optimization path problem, which is highlighted by the one-to-one matching design in DETR. To address this problem, we show that the most important design is to use and only use positional metrics (like IOU) to supervise classification scores of positive examples. Under the principle, we propose two simple yet effective modifications by integrating positional metrics to DETR's classification loss and matching cost, named position-supervised loss and position-modulated cost. We verify our methods on several DETR variants. Our methods show consistent improvements over baselines. By integrating our methods with DINO, we achieve 50.4 and 51.5 AP on the COCO detection benchmark using ResNet-50 backbones under 12 epochs and 24 epochs training settings, achieving a new record under the same setting. We achieve 63.8 AP on COCO detection test-dev with a Swin-Large backbone. Our code will be made available at https://github.com/IDEA-Research/Stable-DINO.Comment: SOTA detector. Project page: https://github.com/IDEA-Research/Stable-DIN

LLaVA-Grounding: Grounded Visual Chat with Large Multimodal Models

With the recent significant advancements in large multi-modal models (LMMs), the importance of their grounding capability in visual chat is increasingly recognized. Despite recent efforts to enable LMMs to support grounding, their capabilities for grounding and chat are usually separate, and their chat performance drops dramatically when asked to ground. The problem is the lack of a dataset for grounded visual chat (GVC). Existing grounding datasets only contain short captions. To address this issue, we have created GVC data that allows for the combination of grounding and chat capabilities. To better evaluate the GVC capabilities, we have introduced a benchmark called Grounding-Bench. Additionally, we have proposed a model design that can support GVC and various types of visual prompts by connecting segmentation models with language models. Experimental results demonstrate that our model outperforms other LMMs on Grounding-Bench. Furthermore, our model achieves competitive performance on classic grounding benchmarks like RefCOCO/+/g and Flickr30K Entities. Our code will be released at https://github.com/UX-Decoder/LLaVA-Grounding

Charge redistribution, charge order and plasmon in La2−x_{2-x}Srx_{x}CuO4_{4}/La2_{2}CuO4_{4} superlattices

Interfacial superconductors have the potential to revolutionize electronics, quantum computing, and fundamental physics due to their enhanced superconducting properties and ability to create new types of superconductors. The emergence of superconductivity at the interface of La$_{2-x}$Sr$_{x}$CuO$_{4}$/La$_{2}$CuO$_{4}$ (LSCO/LCO), with a T$_c$ enhancement of $\sim$ 10 K compared to the La$_{2-x}$Sr$_{x}$CuO$_{4}$ bulk single crystals, provides an exciting opportunity to study quantum phenomena in reduced dimensions. To investigate the carrier distribution and excitations in interfacial superconductors, we combine O K-edge resonant inelastic X-ray scattering and atomic-resolved scanning transmission electron microscopy measurements to study La$_{2-x}$Sr$_{x}$CuO$_{4}$/La$_{2}$CuO$_{4}$ superlattices (x=0.15, 0.45) and bulk La$_{1.55}$Sr$_{0.45}$CuO$_{4}$ films. We find direct evidence of charge redistribution, charge order and plasmon in LSCO/LCO superlattices. Notably, the observed behaviors of charge order and plasmon deviate from the anticipated properties of individual constituents or the average doping level of the superlattice. Instead, they conform harmoniously to the effective doping, a critical parameter governed by the T$_c$ of interfacial superconductors.Comment: 8 pages, 5 figure

Recognize Anything: A Strong Image Tagging Model

We present the Recognize Anything Model (RAM): a strong foundation model for image tagging. RAM can recognize any common category with high accuracy. RAM introduces a new paradigm for image tagging, leveraging large-scale image-text pairs for training instead of manual annotations. The development of RAM comprises four key steps. Firstly, annotation-free image tags are obtained at scale through automatic text semantic parsing. Subsequently, a preliminary model is trained for automatic annotation by unifying the caption and tagging tasks, supervised by the original texts and parsed tags, respectively. Thirdly, a data engine is employed to generate additional annotations and clean incorrect ones. Lastly, the model is retrained with the processed data and fine-tuned using a smaller but higher-quality dataset. We evaluate the tagging capabilities of RAM on numerous benchmarks and observe impressive zero-shot performance, significantly outperforming CLIP and BLIP. Remarkably, RAM even surpasses the fully supervised manners and exhibits competitive performance with the Google API. We are releasing the RAM at \url{https://recognize-anything.github.io/} to foster the advancements of large models in computer vision

Preparation of a nano emodin transfersome and study on its anti-obesity mechanism in adipose tissue of diet-induced obese rats

OBJECTIVE: To describe the preparation of nano emodin transfersome (NET) and investigate its effect on mRNA expression of adipose triglyceride lipase (ATGL) and G0/G1 switch gene 2 (G0S2) in adipose tissue of diet-induced obese rats. METHODS: NET was prepared by film-ultrasonic dispersion method. The effects of emodin components at different ratios on encapsulation efficiency were investigated.The NET envelopment rate was determined by ultraviolet spectrophotometry. The particle size and Zeta potential of NET were evaluated by Zetasizer analyzer. Sixty male SD rats were assigned to groups randomly. After 8-week treatment, body weight, wet weight of visceral fat and the percentage of body fat (PBF) were measured. Fasting blood glucose and serum lipid levels were determined. The adipose tissue section was HE stained, and the cellular diameter and quantity of adipocytes were evaluated by light microscopy. The mRNA expression of ATGL and G0S2 from the peri-renal fat tissue was assayed by RT-PCR. RESULTS: The appropriate formulation was deoxycholic acid sodium salt vs. phospholipids 1:8, cholesterol vs. phospholipids 1:3, vitamin Evs. phospholipids 1:20, and emodin vs. phospholipid 1:6. Zeta potential was −15.11 mV, and the particle size was 292.2 nm. The mean encapsulation efficiency was (69.35 ± 0.25)%. Compared with the obese model group, body weight, wet weight of visceral fat, PBF and mRNA expression of G0S2 from peri-renal fat tissue were decreased significantly after NET treatment (all P < 0.05), while high-density lipoprotein cholesterol (HDL-C), the diameter of adipocytes and mRNA expression of ATGL from peri-renal fat tissue were increased significantly (all P < 0.05). CONCLUSION: The preparation method is simple and reasonable. NET with negative electricity was small and uniform in particle size, with high encapsulation efficiency and stability. NET could reduce body weight and adipocyte size, and this effect was associated with the up-regulation of ATGL, down-regulation of G0S2 expression in the adipose tissue, and improved insulin sensitivity