The Individual and the State: Stories of Assassins in Early Imperial China

In my thesis I try to give a new reading about the stories of assassins in the Han dynasty. I relate the assassins’ figures to Han scholar’s thoughts and read these stories as a kind of new relationship between the individual and the state. My research includes the translations of all the Chinese texts I used – many of them are not translated before, and my readings about these texts. By analyzing these stories, we may get the point that assassins shown as a category emerged during the early empires and this emerging category reflect a new conception of an individual’s role in history, namely that any given individual can have influence over the fate of the empire

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond

Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) have emerged as a noteworthy innovation in natural language processing (NLP), exhibiting impressive achievements across various classic NLP tasks. However, the question of whether LLMs can effectively address the task of logical reasoning, which requires gradual cognitive inference similar to human intelligence, remains unanswered. To this end, we aim to bridge this gap and provide comprehensive evaluations in this paper. Firstly, to offer systematic evaluations, we select fifteen typical logical reasoning datasets and organize them into deductive, inductive, abductive and mixed-form reasoning settings. Considering the comprehensiveness of evaluations, we include three representative LLMs (i.e., text-davinci-003, ChatGPT and BARD) and evaluate them on all selected datasets under zero-shot, one-shot and three-shot settings. Secondly, different from previous evaluations relying only on simple metrics (e.g., accuracy), we propose fine-level evaluations from objective and subjective manners, covering both answers and explanations. Additionally, to uncover the logical flaws of LLMs, problematic cases will be attributed to five error types from two dimensions, i.e., evidence selection process and reasoning process. Thirdly, to avoid the influences of knowledge bias and purely focus on benchmarking the logical reasoning capability of LLMs, we propose a new dataset with neutral content. It contains 3,000 samples and covers deductive, inductive and abductive settings. Based on the in-depth evaluations, this paper finally forms a general evaluation scheme of logical reasoning capability from six dimensions. It reflects the pros and cons of LLMs and gives guiding directions for future works.Comment: 14 pages, 11 figure

SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a novel visual GUI agent -- SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challenge in developing visual GUI agents: GUI grounding -- the capacity to accurately locate screen elements based on instructions. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. Along with the efforts above, we have also created ScreenSpot, the first realistic GUI grounding benchmark that encompasses mobile, desktop, and web environments. After pre-training, SeeClick demonstrates significant improvement in ScreenSpot over various baselines. Moreover, comprehensive evaluations on three widely used benchmarks consistently support our finding that advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks. The model, data and code are available at https://github.com/njucckevin/SeeClick

Metabolic reprogramming of three major nutrients in platinum-resistant ovarian cancer

Metabolic reprogramming is a phenomenon in which cancer cells alter their metabolic pathways to support their uncontrolled growth and survival. Platinum-based chemotherapy resistance is associated with changes in glucose metabolism, amino acid metabolism, fatty acid metabolism, and tricarboxylic acid cycle. These changes lead to the creation of metabolic intermediates that can provide precursors for the biosynthesis of cellular components and help maintain cellular energy homeostasis. This article reviews the research progress of the metabolic reprogramming mechanism of platinumbased chemotherapy resistance caused by three major nutrients in ovarian cancer

Vitamin B12 Levels in Methamphetamine Addicts

Objective: It has been established that reduced vitamin B12 serum levels are associated with cognitive decline and mental illness. The chronic use of methamphetamine (MA), which is a highly addictive drug, can induce cognitive impairment and psychopathological symptoms. There are few studies addressing the association of MA with vitamin B12 serum levels. This study examined whether the serum levels of B12 are associated with MA addiction.Methods: Serum vitamin B12, homocysteine (Hcy), glucose and triglyceride concentrations were measured in 123 MA addicts and 108 controls. In addition, data were collected on their age, marital status, level of education and Body Mass Index (BMI) for all participants. In the patient group, the data for each subject were collected using the Fagerstrom Test for Nicotine Dependence (FTND), the Alcohol Use Disorders Identification Test (AUDIT), and a drug use history, which included the age of onset, total duration of MA use, the number of relapses and addiction severity.Results: Our results showed that MA addicts had lower vitamin B12 levels (p < 0.05) than those of healthy controls, but Hcy levels were not significantly different between the two groups (p > 0.05). Serum B12 levels were negatively correlated with the number of relapses in the MA group. Furthermore, binary logistics regression analysis indicated that the B12 was an influencing factor contributing to addiction severity.Conclusion: The findings of this study suggest that some MA addicts might have vitamin B12 deficiency, and serum B12 levels may be involved in the prognosis of MA addiction

Knowledge mapping of exosomes in metabolic diseases: a bibliometric analysis (2007-2022)

BackgroundResearch on exosomes in metabolic diseases has been gaining attention, but a comprehensive and objective report on the current state of research is lacking. This study aimed to conduct a bibliometric analysis of publications on “exosomes in metabolic diseases” to analyze the current status and trends of research using visualization methods.MethodsThe web of science core collection was searched for publications on exosomes in metabolic diseases from 2007 to 2022. Three software packages, VOSviewer, CiteSpace, and R package “bibliometrix” were used for the bibliometric analysis.ResultsA total of 532 papers were analyzed, authored by 29,705 researchers from 46 countries/regions and 923 institutions, published in 310 academic journals. The number of publications related to exosomes in metabolic diseases is gradually increasing. China and the United States were the most productive countries, while Ciber Centro de Investigacion Biomedica en Red was the most active institution. The International Journal of Molecular Sciences published the most relevant studies, and Plos One received the most citations. Khalyfa, Abdelnaby published the most papers and Thery, C was the most cited. The ten most co-cited references were considered as the knowledge base. After analysis, the most common keywords were microRNAs, biomarkers, insulin resistance, expression, and obesity. Applying basic research related on exosomes in metabolic diseases to clinical diagnosis and treatment is a research hotspot and trend.ConclusionThis study provides a comprehensive summary of research trends and developments in exosomes in metabolic diseases through bibliometrics. The information points out the research frontiers and hot directions in recent years and will provide a reference for researchers in this field

Mg-Based Micromotors with Motion Responsive to Dual Stimuli

Mg-based micromotors have emerged as an extremely attractive artificial micro/nanodevice, but suffered from uncontrollable propulsion and limited motion lifetime, restricting the fulfillment of complex tasks. Here, we have demonstrated Mg-based micromotors composed of Mg microspheres asymmetrically coated with Pt and temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) hydrogel layers in sequence. They can implement different motion behaviors stemming from the driving mechanism transformation when encountering catalyzed substrates such as H2O2 and respond to both H2O2 concentration and temperature in aqueous environment. The as-constructed Mg-based micromotors are self-propelled by Pt-catalyzed H2O2 decomposition following the self-consuming Mg-H2O reaction. In this case, they could further generate bilateral bubbles and thus demonstrate unique self-limitation motion like hovering when the phase transformation of PNIPAM is triggered by decreasing temperature or when the H2O2 concentration after permeating across the PNIPAM hydrogel layer is high enough to facilitate bubble nucleation. Our work for the first time provides a stimuli-induced “hovering” strategy for self-propelled micromotors, which endows Mg-based micromotors with an intelligent response to the surroundings besides the significant extension of their motion lifetime

Fusing topology contexts and logical rules in language models for knowledge graph completion

Knowledge graph completion (KGC) aims to infer missing facts based on the observed ones, which is significant for many downstream applications. Given the success of deep learning and pre-trained language models (LMs), some LM-based methods are proposed for the KGC task. However, most of them focus on modeling the text of fact triples and ignore the deeper semantic information (e.g., topology contexts and logical rules) that is significant for KG modeling. For such a reason, we propose a unified framework FTL-LM to Fuse Topology contexts and Logical rules in Language Models for KGC, which mainly contains a novel path-based method for topology contexts learning and a variational expectation–maximization (EM) algorithm for soft logical rule distilling. The former utilizes a heterogeneous random-walk to generate topology paths and further reasoning paths that can represent topology contexts implicitly and can be modeled by a LM explicitly. The strategies of mask language modeling and contrastive path learning are introduced to model these topology contexts. The latter implicitly fuses logical rules by a variational EM algorithm with two LMs. Specifically, in the E-step, the triple LM is updated under the supervision of observed triples and valid hidden triples verified by the fixed rule LM. And in the M-step, we fix the triple LM and fine-tune the rule LM to update logical rules. Experiments on three common KGC datasets demonstrate the superiority of the proposed FTL-LM, e.g., it achieves 2.1% and 3.1% Hits@10 improvement over the state-of-the-art LM-based model LP-BERT in the WN18RR and FB15k-237, respectively.Agency for Science, Technology and Research (A*STAR)This research work is supported by the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funding Scheme (Project #A18A2b0046). This work was also supported by National Key Research and Development Program of China (2020AAA0108800), National Natural Science Foundation of China (62137002, 61937001, 62192781, 62176209, 62176207, 62106190, and 62250009), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Consulting research project of Chinese academy of engineering ‘‘The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China’’, ‘‘LENOVO-XJTU’’ Intelligent Industry Joint Laboratory Project, CCFLenovo Blue Ocean Research Fund, Project of China Knowledge Centre for Engineering Science and Technology, the Fundamental Research Funds for the Central Universities (xzy022021048, xhj032021013-02, xpt012022033)

Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning

Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks.Comment: Accepted by SIGIR 202

Thermal degradation of the green-emitting SrSi2O 2N2:Eu2+ phosphor for solid state lighting

A phase pure SrSi2O2N2:Eu2+ green phosphor was synthesized by a solid state reaction through careful control of the Sr:Si ratio in the starting powder consisting of SrCO3, Si3N4 and Eu2O3. The thermal degradation of the phosphor was investigated by baking it at high temperatures for 2 h. The surface states of the samples before and after baking were analyzed by SEM, HRTEM, XPS, TGA/DTA, and high temperature in situ X-ray diffraction. The results showed that the thermal degradation became intense when the temperature was higher than 500°C, and the degradation was caused by the formation of SrSiO3 on the particle surface and the oxidation of Eu2+ to Eu3+. It is suggested that the thermal stability can be enhanced by achieving high crystallinity as well as high phase purity of the phosphor. This journal is ? the Partner Organisations 2014