Evade ChatGPT Detectors via A Single Space

ChatGPT brings revolutionary social value but also raises concerns about the misuse of AI-generated text. Consequently, an important question is how to detect whether texts are generated by ChatGPT or by human. Existing detectors are built upon the assumption that there are distributional gaps between human-generated and AI-generated text. These gaps are typically identified using statistical information or classifiers. Our research challenges the distributional gap assumption in detectors. We find that detectors do not effectively discriminate the semantic and stylistic gaps between human-generated and AI-generated text. Instead, the "subtle differences", such as an extra space, become crucial for detection. Based on this discovery, we propose the SpaceInfi strategy to evade detection. Experiments demonstrate the effectiveness of this strategy across multiple benchmarks and detectors. We also provide a theoretical explanation for why SpaceInfi is successful in evading perplexity-based detection. And we empirically show that a phenomenon called token mutation causes the evasion for language model-based detectors. Our findings offer new insights and challenges for understanding and constructing more applicable ChatGPT detectors

AO-DETR: Anti-Overlapping DETR for X-Ray Prohibited Items Detection

Prohibited item detection in X-ray images is one of the most essential and highly effective methods widely employed in various security inspection scenarios. Considering the significant overlapping phenomenon in X-ray prohibited item images, we propose an Anti-Overlapping DETR (AO-DETR) based on one of the state-of-the-art general object detectors, DINO. Specifically, to address the feature coupling issue caused by overlapping phenomena, we introduce the Category-Specific One-to-One Assignment (CSA) strategy to constrain category-specific object queries in predicting prohibited items of fixed categories, which can enhance their ability to extract features specific to prohibited items of a particular category from the overlapping foreground-background features. To address the edge blurring problem caused by overlapping phenomena, we propose the Look Forward Densely (LFD) scheme, which improves the localization accuracy of reference boxes in mid-to-high-level decoder layers and enhances the ability to locate blurry edges of the final layer. Similar to DINO, our AO-DETR provides two different versions with distinct backbones, tailored to meet diverse application requirements. Extensive experiments on the PIXray and OPIXray datasets demonstrate that the proposed method surpasses the state-of-the-art object detectors, indicating its potential applications in the field of prohibited item detection. The source code will be released at https://github.com/Limingyuan001/AO-DETR-test

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

Background Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. Methods We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. Results Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. Conclusions Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders.Peer reviewe

Performance of a prestressed efficiently prefabricated beam-column connection

Analyzing the seismic performance and flexural capacity of beam-column joints is crucial in structural design phase. The purpose of this paper is to investigate the seismic performance and flexural capacity of precast prestressed efficiently fabricated frame (PPEFF) joints. Reverse cyclic load tests and flexural capacity analysis are conducted. The damage modes, hysteresis curves, skeleton curves, stiffness degradation, ductility, and energy dissipation capacity of five PPEFF joint specimens with different reinforcement rates of the energy-dissipating bars and shear reinforcement are obtained. The results show that the damage pattern of the specimen is ideal, i.e., the plastic hinge region at the end of the beam is severely damaged, whereas the remainder of the beam is slightly damaged. Increasing the reinforcement rate of the energy-consuming steel bars enhances the load capacity, energy dissipation capacity, and initial stiffness of the joint but reduces the ductility performance. The maximum change in ductility was 5.31 for the reinforcement rate of energy-consuming steel bars ranging from 0.38% to 0.59%. In addition, the flexural capacity of the PPEFF joint is evaluated, considering the influence of the shear steel on the yielding and ultimate states. An equation of the flexural capacity is derived. A good agreement is observed between the experimental and calculation results, verifying the correctness of the proposed flexural capacity equation

Multiple reports on the causal relationship between various chronic pain and gut microbiota: a two-sample Mendelian randomization study

BackgroundPrevious evidence suggests a link between gut microbiota and chronic pain, but the causal relationship is not yet fully understood.MethodsWe categorized gut microbiota based on phylum, class, order, family, and genus levels and gathered pain-related information from the UKB and FinnGen GWAS project. Then, we conducted MR analysis to explore the potential causal relationship between gut microbiota and chronic pain at 12 specific locations.ResultsWe have discovered a direct connection between genetic susceptibility in the gut microbiota (gut metabolites) and pain experienced at 12 specific locations. Notably, Serotonin (5-HT) and Glycine were found to be associated with a higher risk of pain in the extremities. On the other hand, certain microbial families and orders were found to have a protective effect against migraines. Specifically, the family Bifidobacteriaceae (IVW, FDR p = 0.013) was associated with a lower risk of migraines. Furthermore, the genus Oxalobacter (IVW, FDR p = 0.044) was found to be linked to an increased risk of low back pain. Importantly, these associations remained significant even after applying the Benjamini-Hochberg correction test. Our analysis did not find any heterogeneity in the data (p > 0.05), as confirmed by the Cochrane’s Q-test. Additionally, both the MR-Egger and MR-PRESSO tests indicated no significant evidence of horizontal pleiotropy (p > 0.05).ConclusionOur MR analysis demonstrated a causal relationship between the gut microbiota and pain, highlighting its potential significance in advancing our understanding of the underlying mechanisms and clinical implications of microbiota-mediated pain

Employee-Organization Relationships and Team Performance: Role of Team Collective Efficacy

Besides the previous social relationship perspective of employee-organization relationship (EOR) research, this study takes the social cognitive perspective to explore the role of team collective efficacy in mediating the relationship between EORs and team performance. This study further contends that team cohesion moderates the positive relationship between collective efficacy and team performance, thereby moderating the indirect relationship between EORs and team performance through collective efficacy. Data analyses of 231 teams in Study 1 and 63 teams in Study 2 support the hypotheses. Therefore, this study provides theoretical contributions to the EOR literature by introducing a new perspective at the team level and to the social cognitive literature by discussing a boundary condition of the effect of collective efficacy on team performance

mTOR inhibition improves the immunomodulatory properties of human bone marrow mesenchymal stem cells by inducing COX-2 and PGE2

Abstract Background Bone marrow mesenchymal stem cells (MSCs) are promising candidates for the treatment of various inflammatory disorders due to their profound immunomodulatory properties. However, the immunosuppressive capacity of MSCs needs activation by an inflammatory microenvironment, which may negatively impact the therapeutic effect because of increased immunogenicity. Here we explore the role of mammalian target of rapamycin (mTOR) signaling on the immunosuppressive capacity of MSCs, and its impact on immunogenicity in the inflammatory microenvironment. Methods Human bone marrow MSCs were cocultured with activated human peripheral blood mononuclear cells, CD4+ T cells, and mouse splenocytes to evaluate the immunosuppressive function. Immunosuppressive factors were assessed by quantitative real-time polymerase chain reaction (PCR), Western blot, and enzyme-linked immunosorbent assay (ELISA). The expression of major histocompatibility complex (MHC) was detected by flow cytometry. Short hairpin (sh)RNA was used to downregulate tuberous sclerosis complex (TSC)2, TSC1, and cyclooxygenase (COX)-2 in MSCs. Results Inhibition of mTOR signaling using rapamycin enhanced the immunosuppressive functions of MSCs, while prolonged exposure to rapamycin did not. The enhancement of the immunosuppressive function was independent of the inflammatory microenvironment, and occurred mainly through the upregulation of COX-2 and prostaglandin-E2 (PGE2) expression. Furthermore, mTOR inhibition did not impact the immunogenicity of MSCs. However, the upregulated expression of MHC class II molecules by interferon (IFN)-γ was attenuated by mTOR inhibition, whereas TSC2 knockdown had the opposite effect. Conclusions These results reveal that the mTOR signaling pathway regulates MSC immunobiology, and short-term exposure to rapamycin could be a novel approach to improve the MSC-based therapeutic effect

Glia maturation factor-γ is required for initiation and maintenance of hematopoietic stem and progenitor cells

Abstract Background In vertebrates, hematopoietic stem and progenitor cells (HSPCs) emerge from hemogenic endothelium in the floor of the dorsal aorta and subsequently migrate to secondary niches where they expand and differentiate into committed lineages. Glia maturation factor γ (gmfg) is a key regulator of actin dynamics that was shown to be highly expressed in hematopoietic tissue. Our goal is to investigate the role and mechanism of gmfg in embryonic HSPC development. Methods In-depth bioinformatics analysis of our published RNA-seq data identified gmfg as a cogent candidate gene implicated in HSPC development. Loss and gain-of-function strategies were applied to study the biological function of gmfg. Whole-mount in situ hybridization, confocal microscopy, flow cytometry, and western blotting were used to evaluate changes in the number of various hematopoietic cells and expression levels of cell proliferation, cell apoptosis and hematopoietic-related markers. RNA-seq was performed to screen signaling pathways responsible for gmfg deficiency-induced defects in HSPC initiation. The effect of gmfg on YAP sublocalization was assessed in vitro by utilizing HUVEC cell line. Results We took advantage of zebrafish embryos to illustrate that loss of gmfg impaired HSPC initiation and maintenance. In gmfg-deficient embryos, the number of hemogenic endothelium and HSPCs was significantly reduced, with the accompanying decreased number of erythrocytes, myelocytes and lymphocytes. We found that blood flow modulates gmfg expression and gmfg overexpression could partially rescue the reduction of HSPCs in the absence of blood flow. Assays in zebrafish and HUVEC showed that gmfg deficiency suppressed the activity of YAP, a well-established blood flow mediator, by preventing its shuttling from cytoplasm to nucleus. During HSPC initiation, loss of gmfg resulted in Notch inactivation and the induction of Notch intracellular domain could partially restore the HSPC loss in gmfg-deficient embryos. Conclusions We conclude that gmfg mediates blood flow-induced HSPC maintenance via regulation of YAP, and contributes to HSPC initiation through the modulation of Notch signaling. Our findings reveal a brand-new aspect of gmfg function and highlight a novel mechanism for embryonic HSPC development

Machine learning for differentiating between pancreatobiliary-type and intestinal-type periampullary carcinomas based on CT imaging and clinical findings.

PurposeTo develop a diagnostic model for distinguishing pancreatobiliary-type and intestinal-type periampullary adenocarcinomas using preoperative contrast-enhanced computed tomography (CT) findings combined with clinical characteristics.MethodsThis retrospective study included 140 patients with periampullary adenocarcinoma who underwent preoperative enhanced CT, including pancreaticobiliary (N = 100) and intestinal (N = 40) types. They were randomly assigned to the training or internal validation set in an 8:2 ratio. Additionally, an independent external cohort of 28 patients was enrolled. Various CT features of the periampullary region were evaluated and data from clinical and laboratory tests were collected. Five machine learning classifiers were developed to identify the histologic type of periampullary adenocarcinoma, including logistic regression, random forest, multi-layer perceptron, light gradient boosting, and eXtreme gradient boosting (XGBoost).ResultsAll machine learning classifiers except multi-layer perceptron used achieved good performance in distinguishing pancreatobiliary-type and intestinal-type adenocarcinomas, with the area under the curve (AUC) ranging from 0.75 to 0.98. The AUC values of the XGBoost classifier in the training set, internal validation set and external validation set are 0.98, 0.89 and 0.84 respectively. The enhancement degree of tumor, the growth pattern of tumor, and carbohydrate antigen 19-9 were the most important factors in the model.ConclusionMachine learning models combining CT with clinical features can serve as a noninvasive tool to differentiate the histological subtypes of periampullary adenocarcinoma, in particular using the XGBoost classifier