Journal Staff

QC 20141208</p

Contrastive Learning of Sentence Embeddings from Scratch

Contrastive learning has been the dominant approach to train state-of-the-art sentence embeddings. Previous studies have typically learned sentence embeddings either through the use of human-annotated natural language inference (NLI) data or via large-scale unlabeled sentences in an unsupervised manner. However, even in the case of unlabeled data, their acquisition presents challenges in certain domains due to various reasons. To address these issues, we present SynCSE, a contrastive learning framework that trains sentence embeddings with synthesized data. Specifically, we explore utilizing large language models to synthesize the required data samples for contrastive learning, including (1) producing positive and negative annotations given unlabeled sentences (SynCSE-partial), and (2) generating sentences along with their corresponding annotations from scratch (SynCSE-scratch). Experimental results on sentence similarity and reranking tasks indicate that both SynCSE-partial and SynCSE-scratch greatly outperform unsupervised baselines, and SynCSE-partial even achieves comparable performance to the supervised models in most settings.Comment: Preprin

No. 14: The Impact of Proximity to Wet Markets and Supermarkets on Household Dietary Diversity in Nanjing City, China

Existing studies suggest that despite the proliferation of supermarkets, traditional wet markets have persisted in many countries and have been playing an important role in people’s daily food access. Yet, studies investigating the issue of food access and its influences on food security have mainly focused on food deserts and the proximity to supermarkets, with limited focus on wet markets and other food outlets. This study investigates the influence of the proximity to wet markets and supermarkets on urban household dietary diversity in Nanjing. Based on the data collected through a citywide survey in 2015 and the map data of wet markets and supermarkets, the Poisson regression model was deployed to examine the correlations between geographical proximity to supermarkets and wet markets and household dietary diversity. The results show that the coefficients for the distance to the nearest wet market are not statistically significant. Although the coefficients for the distance to nearest supermarket are statistically significant, they were too minor to be of practical importance. We argue, however, that the insignificant correlations reflect exactly the high physical accessibility to food outlets and the extensive spatially dense food supply network constituted by wet markets, supermarkets and small food stores in Nanjing. This is verified by the survey data that more than 90% of households purchased fresh food items within their neighbourhoods or in walking distance. In addition to the densely distributed food outlets, various other factors contributed to the non-significant influence of the distance to the nearest wet market and supermarket, including the many small food stores within or close to residential communities, the prevalence of three-generation extended households and high household income. This study highlights the importance of allowing mixed land use for food outlets with residential land and integrating wet markets into urban infrastructure planning

Latent Jailbreak: A Test Suite for Evaluating Both Text Safety and Output Robustness of Large Language Models

Considerable research efforts have been devoted to ensuring that large language models (LLMs) align with human values and generate safe text. However, an excessive focus on sensitivity to certain topics can compromise the model's robustness in following instructions, thereby impacting its overall performance in completing tasks. Previous benchmarks for jailbreaking LLMs have primarily focused on evaluating the safety of the models without considering their robustness. In this paper, we propose a benchmark that assesses both the safety and robustness of LLMs, emphasizing the need for a balanced approach. To comprehensively study text safety and output robustness, we introduce a latent jailbreak prompt dataset, each involving malicious instruction embedding. Specifically, we instruct the model to complete a regular task, such as translation, with the text to be translated containing malicious instructions. To further analyze safety and robustness, we design a hierarchical annotation framework. We present a systematic analysis of the safety and robustness of LLMs regarding the position of explicit normal instructions, word replacements (verbs in explicit normal instructions, target groups in malicious instructions, cue words for explicit normal instructions), and instruction replacements (different explicit normal instructions). Our results demonstrate that current LLMs not only prioritize certain instruction verbs but also exhibit varying jailbreak rates for different instruction verbs in explicit normal instructions. Code and data are available at https://github.com/qiuhuachuan/latent-jailbreak.Comment: Code and data are available at https://github.com/qiuhuachuan/latent-jailbrea

Synergistic enhancement of cancer therapy using a combination of docetaxel and photothermal ablation induced by single-walled carbon nanotubes

Lei Wang1, Mingyue Zhang1, Nan Zhang1, Jinjin Shi1, Hongling Zhang1, Min Li1, Chao Lu2, Zhenzhong Zhang1 1School of Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, People&amp;rsquo;s Republic of China; 2University of Maryland, College Park, MD, USA Background: Single-walled carbon nanotubes (SWNT) are poorly soluble in water, so their applications are limited. Therefore, aqueous solutions of SWNT, designed by noncovalent functionalization and without toxicity, are required for biomedical applications. Methods: In this study, we conjugated docetaxel with SWNT via p-p accumulation and used a surfactant to functionalize SWNT noncovalently. The SWNT were then conjugated with docetaxel (DTX-SWNT) and linked with NGR (Asn-Gly-Arg) peptide, which targets tumor angiogenesis, to obtain a water-soluble and tumor-targeting SWNT-NGR-DTX drug delivery system. Results: SWNT-NGR-DTX showed higher efficacy than docetaxel in suppressing tumor growth in a cultured PC3 cell line in vitro and in a murine S180 cancer model. Tumor volumes in the S180 mouse model decreased considerably under near-infrared radiation compared with the control group. Conclusion: The SWNT-NGR-DTX drug delivery system may be promising for high treatment efficacy with minimal side effects in future cancer therapy. Keywords: single-walled carbon nanotubes, docetaxel, NGR peptide, tumor-targeting, near-infrared radiatio

Relaxin inhibits 177 Lu-EDTMP associated cell death in osteosarcoma cells through notch-1 pathway

177Lu-EDTMP (Ethylenediamine tetramethylene phosphonic acid) is the most used radioactive agent for pain palliation in bone cancer patients. The present study aims to study the impact of relaxin-2 on the 177Lu-EDTMP associated cell toxicity and death in osteosarcoma cells. MG63 and Saos-2 cells were cultured with 177Lu-EDTMP (37 MBq) for 24 h with and without pretreatment of recombinant relaxin 2 (RLXH2) for 12 h and 24 h. 177Lu-EDTMP associated cellular deterioration and death was determined by LDH, MTT, and trypan blue dye assays. ELISA-based kit was used to determine apoptotic DNA fragmentation. Western blotting was used to determine expression levels of apoptotic-related signalling pathway proteins like bcl2, poly(ADP-ribose) polymerase (PARP), and MAPK (mitogen-activated protein kinase). Our results found that RLXH2 counters 177Lu-EDTMP associated cellular toxicity. Similarly, RLXH2 was able to counter 177Lu-EDTMP induced cell death in a concentration and time-dependent manner. Furthermore, it was found that RLXH2 treatment prevents apoptosis in 177Lu-EDTMP challenged cells through activation of the notch-1 pathway in a concentration- and time-dependent manner. We reported that RLXH2 significantly declined cellular toxicity and apoptosis associated with 177Lu-EDTMP in MG63 and Saos-2 cells through the notch-1 pathway