Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training

In this paper, we study the \textit{graph condensation} problem by compressing the large, complex graph into a concise, synthetic representation that preserves the most essential and discriminative information of structure and features. We seminally propose the concept of Shock Absorber (a type of perturbation) that enhances the robustness and stability of the original graphs against changes in an adversarial training fashion. Concretely, (I) we forcibly match the gradients between pre-selected graph neural networks (GNNs) trained on a synthetic, simplified graph and the original training graph at regularly spaced intervals. (II) Before each update synthetic graph point, a Shock Absorber serves as a gradient attacker to maximize the distance between the synthetic dataset and the original graph by selectively perturbing the parts that are underrepresented or insufficiently informative. We iteratively repeat the above two processes (I and II) in an adversarial training fashion to maintain the highly-informative context without losing correlation with the original dataset. More importantly, our shock absorber and the synthesized graph parallelly share the backward process in a free training manner. Compared to the original adversarial training, it introduces almost no additional time overhead. We validate our framework across 8 datasets (3 graph and 5 node classification datasets) and achieve prominent results: for example, on Cora, Citeseer and Ogbn-Arxiv, we can gain nearly 1.13% to 5.03% improvements compare with SOTA models. Moreover, our algorithm adds only about 0.2% to 2.2% additional time overhead over Flicker, Citeseer and Ogbn-Arxiv. Compared to the general adversarial training, our approach improves time efficiency by nearly 4-fold

Electrochemical Parameter Identification for Lithium-ion Battery Sources in Self-Sustained Transportation Energy Systems

Lithium-ion battery (LIB) sources have played an essential role in self-sustained transportation energy systems and have been widely deployed in the last few years. To realize reliable battery maintenance, identifying its electrochemical parameters is necessary. However, the battery model contains many parameters while the measurable states are only the current and voltage, inducing the identification inherently an ill-conditioned problem. A parameter identification approach is proposed, including the experiment, model, and algorithm. Electrochemical parameters are first grouped manually based on the physical properties and assigned to two sequenced tests for identification. The two tests named the quasi-static test and the dynamic test, are compressed on time for practical implementation. Proper optimization models and a sensitivity-oriented stepwise (SSO) optimization algorithm are developed to search for the optimal parameters efficiently. Typically, the Sobol method is applied to conduct the sensitivity analysis. Based on the sensitivity indexes, the SSO algorithm can decouple the mixed impacts of different parameters during the identification. For validation, numerical experiments on a typical NCM811 battery at different life stages are conducted. The proposed approach saves about half the time finding the proper parameter value. The identification accuracy of crucial parameters related to battery degradation can exceed 95\%. Case study results indicate that the identified parameters can not only improve the accuracy of the battery model but also be used as the indicator of the battery SOH

Calibrating LLM-Based Evaluator

Recent advancements in large language models (LLMs) on language modeling and emergent capabilities make them a promising reference-free evaluator of natural language generation quality, and a competent alternative to human evaluation. However, hindered by the closed-source or high computational demand to host and tune, there is a lack of practice to further calibrate an off-the-shelf LLM-based evaluator towards better human alignment. In this work, we propose AutoCalibrate, a multi-stage, gradient-free approach to automatically calibrate and align an LLM-based evaluator toward human preference. Instead of explicitly modeling human preferences, we first implicitly encompass them within a set of human labels. Then, an initial set of scoring criteria is drafted by the language model itself, leveraging in-context learning on different few-shot examples. To further calibrate this set of criteria, we select the best performers and re-draft them with self-refinement. Our experiments on multiple text quality evaluation datasets illustrate a significant improvement in correlation with expert evaluation through calibration. Our comprehensive qualitative analysis conveys insightful intuitions and observations on the essence of effective scoring criteria.Comment: 22 pages,11 figure

Drug-eluting stents for coronary artery disease in the perspective of bibliometric analysis

Drug-eluting stents (DES) play a crucial role in treating coronary artery disease (CAD) by preventing restenosis. These stents are coated with drug carriers that release antiproliferative drugs within the vessel. Over the past two decades, DES have been employed in clinical practice using various materials, polymers, and drug types. Despite optimizations in their design and materials to enhance biocompatibility and antithrombotic properties, evaluating their long-term efficacy and safety necessitates improved clinical follow-up and monitoring. To delineate future research directions, this study employs a bibliometric analysis approach. We comprehensively surveyed two decades' worth of literature on DES for CAD using the Web of Science Core Collection (WOSCC). Out of 5,778 articles, we meticulously screened them based on predefined inclusion and exclusion criteria. Subsequently, we conducted an in-depth analysis encompassing annual publication trends, authorship affiliations, journal affiliations, keywords, and more. Employing tools such as Excel 2021, CiteSpace 6.2R3, VOSviewer 1.6.19, and Pajek 5.17, we harnessed bibliometric methods to derive insights from this corpus. Analysis of annual publication data indicates a recent stabilisation or even a downward trend in research output in this area. The United States emerged as the leading contributor, with Columbia University and CRF at the forefront in both publication output and citation impact. The most cited document pertained to standardized definitions for clinical endpoints in coronary stent trials. Our author analysis identifies Patrick W. Serruys as the most prolific contributor, underscoring a dynamic exchange of knowledge within the field.Moreover, the dual chart overlay illustrates a close interrelation between journals in the “Medicine,” “Medical,” and “Clinical” domains and those in “Health,” “Nursing,” and “Medicine.” Frequently recurring keywords in this research landscape include DES coronary artery disease, percutaneous coronary intervention, implantation, and restenosis. This study presents a comprehensive panorama encompassing countries, research institutions, journals, keyword distributions, and contributions within the realm of DES therapy for CAD. By highlighting keywords exhibiting recent surges in frequency, we elucidate current research hotspots and frontiers, thereby furnishing novel insights to guide future researchers in this evolving field

Safety and efficacy of a novel double-lumen tracheal tube in neonates with RDS: A prospective cohort study

BackgroundThe purpose of this study was to assess the safety and efficacy of a new double-lumen tracheal tube for neonates, with a conventional tracheal tube as a control.MethodNewborns with respiratory distress syndrome (RDS) requiring endotracheal intubation admitted to the tertiary neonatal intensive care unit (NICU) of Qujing Maternal and Child Healthcare Hospital in Yunnan Province between March 2021 and May 2022 were enrolled in this prospective cohort study. Outcome indicators related to effectiveness included mainly the number of intubations, duration of ventilation, duration of oxygenation, and length of stay; safety indicators included any clinical adverse effects during and after intubation. Appropriate stratified and subgroup analyses were performed according to the purpose of intubation, gestational age, and whether the drug was administered via endotracheal tube.ResultA total of 101 neonates were included and divided into two groups based on the choice of tracheal tube: the conventional (n = 50) and new (n = 51) tracheal tube groups. There was no statistical difference between the two groups in terms of adverse effects during and after intubation (p > 0.05). In neonates who were mechanically ventilated without endotracheal surfactant therapy or newborns receiving InSurE technique followed by non-invasive ventilation, no significant differences were found between the two groups regarding any of the efficacy indicators (p > 0.05). However, for neonates on invasive mechanical ventilation, the new tracheal tube allowed for a significant reduction in the duration of mechanical ventilation (96.50[74.00, 144.00] vs. 121.00[96.00, 196.50] hours, p = 0.037) and total ventilation (205.71 ± 80.24 vs. 277.56 ± 117.84 h, p = 0.027), when used as a route for endotracheal drug delivery. Further analysis was performed according to gestational age for newborns requiring intratracheal surfactant administration during mechanical ventilation, and the data showed that for preterm infants, the new tracheal tube not only shortened the duration of mechanical ventilation (101.75 ± 39.72 vs. 155.50 ± 51.49 h, p = 0.026) and total ventilation (216.00 ± 81.60 vs. 351.50 ± 113.79 h, p = 0.010), but also demonstrated significant advantages in reducing the duration of oxygen therapy (9.75 ± 6.02 vs. 17.33 ± 8.43 days, p = 0.042); however, there was no statistical difference in efficacy outcomes between the two groups in full-term infants (p > 0.05).ConclusionThe efficacy and safety of this new tracheal tube are promising in neonates with RDS, especially those requiring surfactant administration via a tracheal tube during mechanical ventilation. Given the limitations of this study, however, the clinical feasibility of this catheter needs to be further confirmed in prospective randomized trials with larger sample sizes.Clinical Trial Registrationhttp://www.chictr.org.cn/showproj.aspx?proj=12207

Hippo pathway in intestinal diseases: focusing on ferroptosis

The incidence of intestinal diseases, such as inflammatory bowel disease, gastric cancer, and colorectal cancer, has steadily increased over the past decades. The Hippo pathway is involved in cell proliferation, tissue and organ damage, energy metabolism, tumor formation, and other physiologic processes. Ferroptosis is a form of programmed cell death characterized by the accumulation of iron and lipid peroxides. The Hippo pathway and ferroptosis are associated with various intestinal diseases; however, the crosstalk between them is unclear. This review elaborates on the current research on the Hippo pathway and ferroptosis in the context of intestinal diseases. We summarized the connection between the Hippo pathway and ferroptosis to elucidate the underlying mechanism by which these pathways influence intestinal diseases. We speculate that a mutual regulatory mechanism exists between the Hippo pathway and ferroptosis and these two pathways interact in several ways to regulate intestinal diseases

Enhancing Logical Reasoning of Large Language Models through Logic-Driven Data Augmentation

Combining large language models with logical reasoning enhance their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges to gathering reliable data from web for building comprehensive training datasets, subsequently affecting the performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logic structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into texts to create augmented data. Notably, our methodology is architecture-agnostic and enhances generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and fine-tuning discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as logical reasoning reading comprehension, textual entailment, and natural language inference. Furthermore, our method ranked first on the ReClor leaderboard \url{https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347}. The source code and data are publicly available \url{https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning}.Comment: Accepted for oral presentation at the LLM@IJCAI 2023 non-archival symposiu

Effects of lipids with different oxidation levels on protein degradation and biogenic amines formation in Sichuan-style sausages

ABS T R A C T We evaluated the effects of different oxidation levels of lipids on protein degradation and biogenic amines (BAs) formation during Sichuan-style sausages processing. Lipids with varying degrees of oxidation were obtained through storage at different temperatures and added as raw materials of Sichuan-style sausages, followed by the analyses of lipid oxidation, protein degradation, biogenic amine content, and other indicators. During the pro-cessing, with increasing degree of lipid oxidation, the contents of peroxide value (POV), thiobarbituric acid reactive substances (TBARs), protein degradation index (PI), amino acid nitrogen (AAN), free amino acids (FAAs), and BAs increased. Based on the protein electrophoresis results, the higher the oxidation degree of pig backfat, the higher degree of sarcoplasmic protein oxidation, and the greater myofibril protein degradation. Pearson correlation revealed that lipid oxidation, protein degradation, and BAs content correlated significantly (P < 0.05).Peer reviewe