The Mechanism of Crowd Stampede Based on Case Statistics through SNA Method

Stampede is a concern of urban pubic security management. The current academic research focus is the identification of risk factors of trampling accidents and determination of correlation patterns and accident-causing mechanisms among stampede elements in order to effectively obtain the influencing factors of stampede and clarify the transmission routes of stampede risk factors. Previous index cases were scrutinized and analyzed in 78 typical stampedes from 2010 - 2019 based on "pedestrian-equipment-environment-management" framework, and 17 influencing factors of stampede by adopting a conceptual coding method were obscured. Then, the degree centrality, intermediate centrality and respective weights of the influencing factors were calculated based on the social network analysis (SNA) method. The influencing level of the factors was signified, and the transmission mechanism of risk in the system network was determined. The results reveal that the degree centrality and weight with conspicuous features of over-density of crowds, pedestrian swarming and falling, and insufficient on-site transactions contribute the most. This finding indicates that these factors play a relatively major role in the stampede system. Furthermore, the intermediate centrality of insufficient on-site transactions is the top factor, meaning that this factor has a strong controlling force in the incident system and considerably influences other factors. This study shows that the SNA method is feasible in analyzing the mechanism of stampede incidents, simultaneously addressing the shortcomings of the linear statistical model of factors and providing theoretical support for comprehensive control of crowd risk

Modality-invariant and Specific Prompting for Multimodal Human Perception Understanding

Understanding human perceptions presents a formidable multimodal challenge for computers, encompassing aspects such as sentiment tendencies and sense of humor. While various methods have recently been introduced to extract modality-invariant and specific information from diverse modalities, with the goal of enhancing the efficacy of multimodal learning, few works emphasize this aspect in large language models. In this paper, we introduce a novel multimodal prompt strategy tailored for tuning large language models. Our method assesses the correlation among different modalities and isolates the modality-invariant and specific components, which are then utilized for prompt tuning. This approach enables large language models to efficiently and effectively assimilate information from various modalities. Furthermore, our strategy is designed with scalability in mind, allowing the integration of features from any modality into pretrained large language models. Experimental results on public datasets demonstrate that our proposed method significantly improves performance compared to previous methods

Data Engineering for Scaling Language Models to 128K Context

We study the continual pretraining recipe for scaling language models' context lengths to 128K, with a focus on data engineering. We hypothesize that long context modeling, in particular \textit{the ability to utilize information at arbitrary input locations}, is a capability that is mostly already acquired through large-scale pretraining, and that this capability can be readily extended to contexts substantially longer than seen during training~(e.g., 4K to 128K) through lightweight continual pretraining on appropriate data mixture. We investigate the \textit{quantity} and \textit{quality} of the data for continual pretraining: (1) for quantity, we show that 500 million to 5 billion tokens are enough to enable the model to retrieve information anywhere within the 128K context; (2) for quality, our results equally emphasize \textit{domain balance} and \textit{length upsampling}. Concretely, we find that naively upsampling longer data on certain domains like books, a common practice of existing work, gives suboptimal performance, and that a balanced domain mixture is important. We demonstrate that continual pretraining of the full model on 1B-5B tokens of such data is an effective and affordable strategy for scaling the context length of language models to 128K. Our recipe outperforms strong open-source long-context models and closes the gap to frontier models like GPT-4 128K.Comment: Code at https://github.com/FranxYao/Long-Context-Data-Engineerin

Memory-Inspired Temporal Prompt Interaction for Text-Image Classification

In recent years, large-scale pre-trained multimodal models (LMM) generally emerge to integrate the vision and language modalities, achieving considerable success in various natural language processing and computer vision tasks. The growing size of LMMs, however, results in a significant computational cost for fine-tuning these models for downstream tasks. Hence, prompt-based interaction strategy is studied to align modalities more efficiently. In this contex, we propose a novel prompt-based multimodal interaction strategy inspired by human memory strategy, namely Memory-Inspired Temporal Prompt Interaction (MITP). Our proposed method involves in two stages as in human memory strategy: the acquiring stage, and the consolidation and activation stage. We utilize temporal prompts on intermediate layers to imitate the acquiring stage, leverage similarity-based prompt interaction to imitate memory consolidation, and employ prompt generation strategy to imitate memory activation. The main strength of our paper is that we interact the prompt vectors on intermediate layers to leverage sufficient information exchange between modalities, with compressed trainable parameters and memory usage. We achieve competitive results on several datasets with relatively small memory usage and 2.0M of trainable parameters (about 1% of the pre-trained foundation model)

A novel bacterium-like particles platform displaying antigens by new anchoring proteins induces efficacious immune responses

Bacterium-like particles (BLP) are the peptidoglycan skeleton particles of lactic acid bacteria, which have high safety, mucosal delivery efficiency, and adjuvant effect. It has been widely used in recent years in the development of vaccines. Existing anchoring proteins for BLP surfaces are few in number, so screening and characterization of new anchoring proteins are necessary. In this research, we created the OACD (C-terminal domain of Escherichia coli outer membrane protein A) to serve as an anchoring protein on the surface of BLP produced by the immunomodulatory bacteria Levilactobacillus brevis 23017. We used red fluorescent protein (RFP) to demonstrate the novel surface display system’s effectiveness, stability, and ability to be adapted to a wide range of lactic acid bacteria. Furthermore, this study employed this surface display method to develop a novel vaccine (called COB17) by using the multi-epitope antigen of Clostridium perfringens as the model antigen. The vaccine can induce more than 50% protection rate against C. perfringens type A challenge in mice immunized with a single dose and has been tested through three routes. The vaccine yields protection rates of 75% for subcutaneous, 50% for intranasal, and 75% for oral immunization. Additionally, it elicits a strong mucosal immune response, markedly increasing levels of specific IgG, high-affinity IgG, specific IgA, and SIgA antibodies. Additionally, we used protein anchors (PA) and OACD simultaneous to show several antigens on the BLP surface. The discovery of novel BLP anchoring proteins may expand the possibilities for creating mucosal immunity subunit vaccines. Additionally, it may work in concert with PA to provide concepts for the creation of multivalent or multiple vaccines that may be used in clinical practice to treat complex illnesses

The use of Panax notoginseng saponins injections after intravenous thrombolysis in acute ischemic stroke: a systematic review and meta-analysis

BackgroundAs a bioactive metabolite preparation widely used in acute ischemic stroke (AIS), the efficacy and safety of Panax notoginseng saponins injections (PNSI) in patients with AIS after intravenous thrombolysis remain to be evaluated.MethodsThis study included randomized controlled trials published before 26 April 2024 in 8 databases. AIS patients who received intravenous thrombolysis were included. The control group receiving conventional treatment and the treatment group receiving additional PNSI. Primary outcomes were selected as mortality, disability, and adverse events. Secondary outcomes were selected as all-cause mortality, improvement of neurological deficit, quality of life, and cerebral injury indicators. The revised Cochrane Risk of Bias tool was used to assess risk of bias. Risk ratio (RR) and mean differences (MD) were calculated for binary variables and continuous variables, respectively, based on a 95% confidence interval (CI).ResultsA total of 20 trials involving 1,856 participants were included. None of them reported mortality or disability. There was no significant difference in the adverse events [RR: 1.04; 95% CI: 0.60 to 1.81] and hemorrhagic transformation [RR: 0.99; 95% CI: 0.36 to 2.70] between the two groups. Compared to the control group, the treatment group had a better effect in neurological improvement assessed by National Institutes of Health Stroke Scale [MD: −2.91; 95% CI: −4.76 to −1.06], a better effect in activities of daily living changes in Barthel Index [MD: 9.37; 95% CI: 1.86 to 16.88], and a lower serum neuron-specific enolase level [MD: −2.08; 95% CI: −2.67 to −1.49].ConclusionFor AIS patients undergoing intravenous thrombolysis, the use of PNSI improved neurological deficits and enhanced activity of daily living in the short term without increasing the occurrence rate of adverse events. However, due to the moderate to very low certainty of evidence, it is advisable to conduct high-quality clinical trials to validate the findings of this study.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=466851, Identifier CRD4202346685

Yi: Open Foundation Models by 01.AI

We introduce the Yi model family, a series of language and multimodal models that demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and 34B pretrained language models, then we extend them to chat models, 200K long context models, depth-upscaled models, and vision-language models. Our base models achieve strong performance on a wide range of benchmarks like MMLU, and our finetuned chat models deliver strong human preference rate on major evaluation platforms like AlpacaEval and Chatbot Arena. Building upon our scalable super-computing infrastructure and the classical transformer architecture, we attribute the performance of Yi models primarily to its data quality resulting from our data-engineering efforts. For pretraining, we construct 3.1 trillion tokens of English and Chinese corpora using a cascaded data deduplication and quality filtering pipeline. For finetuning, we polish a small scale (less than 10K) instruction dataset over multiple iterations such that every single instance has been verified directly by our machine learning engineers. For vision-language, we combine the chat language model with a vision transformer encoder and train the model to align visual representations to the semantic space of the language model. We further extend the context length to 200K through lightweight continual pretraining and demonstrate strong needle-in-a-haystack retrieval performance. We show that extending the depth of the pretrained checkpoint through continual pretraining further improves performance. We believe that given our current results, continuing to scale up model parameters using thoroughly optimized data will lead to even stronger frontier models

A Novel Tri-Coordination Zinc Complex Functionalized Silicotungstate with ROS Catalytic Ability and Anti-Tumor Cells Activity

Reactive oxygen species (ROS) can be used as an effective method to treat tumors. Artificial oxidase has received increasing attention as a catalyst for ROS generation in fields ranging from bioinorganic chemistry to pharmaceutical chemistry. In this study, an artificial oxidase based on a binuclear zinc complex and Keggin-type silicotungstate [Zn2(4,4′-bpy)(Phen)2][SiW12O40] (ZSW) (4,4′-bpy = 4,4′-bipyridine; Phen = 1,10-phenanthroline) was synthesized and structurally featured in terms of its X-ray photoelectron spectrum (XPS), bond valence sum (Σs) calculation, IR spectra, and single-crystal X-ray diffraction (SXRD). ZSW is an ionic compound in which the cation is a binuclear zinc complex [Zn2(4,4′-bpy)(Phen)2]4+ and the anion is a α-Keggin-type silicotungstate [SiW12O40]4– cluster. Notably, the Zn ions in the [Zn2(4,4′-bpy)(Phen)2] exist in tri-coordination, which was first obtained in polyoxometalate (POM) chemistry. It was also demonstrated that ZSW is capable of efficiently catalyzing the production of ROS, which, according to the computational calculations, may be due to the synergistic action of zinc complexes and POM building blocks. Furthermore, ZSW exhibited inhibition ability toward ROS-sensitive tumor cells, such as PC12 cells

A Novel Tri-Coordination Zinc Complex Functionalized Silicotungstate with ROS Catalytic Ability and Anti-Tumor Cells Activity

Reactive oxygen species (ROS) can be used as an effective method to treat tumors. Artificial oxidase has received increasing attention as a catalyst for ROS generation in fields ranging from bioinorganic chemistry to pharmaceutical chemistry. In this study, an artificial oxidase based on a binuclear zinc complex and Keggin-type silicotungstate [Zn2(4,4′-bpy)(Phen)2][SiW12O40] (ZSW) (4,4′-bpy = 4,4′-bipyridine; Phen = 1,10-phenanthroline) was synthesized and structurally featured in terms of its X-ray photoelectron spectrum (XPS), bond valence sum (Σs) calculation, IR spectra, and single-crystal X-ray diffraction (SXRD). ZSW is an ionic compound in which the cation is a binuclear zinc complex [Zn2(4,4′-bpy)(Phen)2]4+ and the anion is a α-Keggin-type silicotungstate [SiW12O40]4– cluster. Notably, the Zn ions in the [Zn2(4,4′-bpy)(Phen)2] exist in tri-coordination, which was first obtained in polyoxometalate (POM) chemistry. It was also demonstrated that ZSW is capable of efficiently catalyzing the production of ROS, which, according to the computational calculations, may be due to the synergistic action of zinc complexes and POM building blocks. Furthermore, ZSW exhibited inhibition ability toward ROS-sensitive tumor cells, such as PC12 cells

Solid fuel derived PM2.5 induced oxidative stress and according cytotoxicity in A549 cells: The evidence and potential neutralization by green tea

PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) is a well-known cytotoxic pollutant that capable to induce severe intracellular oxidative stress while the underlying mechanisms remain unclear. Herein, 4 types of PM2.5 derived from solid fuel burning were selected as stimuli in A549 cells exposure model to evaluate their effects on oxidative stress and inflammatory responses. Although resulting in different responses in cell viability, all PM2.5 exhibited over 50 % higher oxidative stress than control group, expression as intracellular reactive oxygen species, malondialdehyde and superoxide dismutase levels. The Pearson’s correlation results indicated that cations (e.g., Ca2+), heavy metals (e.g., Cr and Pb), nPAHs (nitro-polycyclic aromatic hydrocarbons, e.g., 6-nitrochrysene) and oPAHs (oxygenated PAHs, e.g., 9-fluorenone) were the main functioning toxics (r > 0.6). A key finding was the dual-directional regulation function of ECG (epicatechin gallate), that is, it could either increase the low A549 cell viabilities in coal combustion PM2.5 group or reduce them in charcoal PM2.5 group (P < 0.05). The dual-directional effects were likely because ECG can activate Nrf2 oxidation signaling pathway then inhibit the inflammatory signaling pathway NF-κB accordingly. Therefore, evidences indicated cytotoxicity of solid fuel derived PM2.5 were mainly caused by oxidative stress, which was proved to be reversed by green tea, providing a potential therapy method to PM2.5 and other hazards