Efficient search of general and-or keyword queries in XML data

Master'sMASTER OF SCIENC

Experimental Study of Ultralight (<300 kg/m 3

A type of ultralight (<300 kg/m3) foamed concrete (FC), which can be used as a new energy-conservation and environmental-protection building material and is particularly suitable for the thermal-insulation engineering of building external walls, was produced. The influences of different mixing amounts of fly ash, fly ash activator, WC (WC) ratio, and foaming agent (FA) on the compressive strength of FC were reported. The experimental study indicated that (1) the addition of fly ash reduced the strength of the FC and that the appropriate mixing amount of fly ash in this ultralight FC system should not exceed 45%; (2) with the increasing of fly ash activator, the strength of the FC sample is notably enhanced and the appropriate mixing amount of fly ash activator is 2.5%; (3) the optimized proportion of WC ratio is 0.45, and the FC that was produced according to this proportion has relatively high compressive strength; (4) by increasing the mixing amount of FA, the compressive strength of the FC notably decreases, and the optimal mixing amount of FA in this experiment is 3.5%

ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval

Diffusion models show promising generation capability for a variety of data. Despite their high generation quality, the inference for diffusion models is still time-consuming due to the numerous sampling iterations required. To accelerate the inference, we propose ReDi, a simple yet learning-free Retrieval-based Diffusion sampling framework. From a precomputed knowledge base, ReDi retrieves a trajectory similar to the partially generated trajectory at an early stage of generation, skips a large portion of intermediate steps, and continues sampling from a later step in the retrieved trajectory. We theoretically prove that the generation performance of ReDi is guaranteed. Our experiments demonstrate that ReDi improves the model inference efficiency by 2x speedup. Furthermore, ReDi is able to generalize well in zero-shot cross-domain image generation such as image stylization.Comment: ICML 202

Factors Controlling Spatial Variation of Iodine Species in Groundwater of the Datong Basin, Northern China

AbstractTo better understand the distribution of iodine speciation composition and the controlling factors in groundwater from the Datong basin, hydrochemical studies were conducted. Total iodine concentrations in groundwater ranges from 6.2 to 1380μg/L, with the mean value of 243μg/L. Speciation of iodine in groundwater is mainly controlled by redox potential. Under reducing conditions, iodide is the dominant dissolved species, while in sub-oxic and oxic conditions, iodate is the major species, with a lower proportion of iodide. The evident existence of organic iodine in several groundwater samples may be related to anthropogenic activities

Epidemiological and virological characteristics of pandemic influenza A (H1N1) 2009 in school outbreaks in China

Background: During the 2009 pandemic influenza H1N1 (2009) virus (pH1N1) outbreak, school students were at an increased risk of infection by the pH1N1 virus. However, the estimation of the attack rate showed significant variability. Methods: Two school outbreaks were investigated in this study. A questionnaire was designed to collect information by interview. Throat samples were collected from all the subjects in this study 6 times and sero samples 3 times to confirm the infection and to determine viral shedding. Data analysis was performed using the software STATA 9.0. Findings: The attack rate of the pH1N1 outbreak was 58.3% for the primary school, and 52.9% for the middle school. The asymptomatic infection rates of the two schools were 35.8% and 37.6% respectively. Peak virus shedding occurred on the day of ARI symptoms onset, followed by a steady decrease over subsequent days (p = 0.026). No difference was found either in viral shedding or HI titer between the symptomatic and the asymptomatic infectious groups. Conclusions: School children were found to be at a high risk of infection by the novel virus. This may be because of a heightened risk of transmission owing to increased mixing at boarding school, or a lack of immunity owing to socioeconomic status. We conclude that asymptomatically infectious cases may play an important role in transmission of the pH1N1 virus

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

Large language models (LLMs) have notably enhanced the fluency and diversity of machine-generated text. However, this progress also presents a significant challenge in detecting the origin of a given text, and current research on detection methods lags behind the rapid evolution of LLMs. Conventional training-based methods have limitations in flexibility, particularly when adapting to new domains, and they often lack explanatory power. To address this gap, we propose a novel training-free detection strategy called Divergent N-Gram Analysis (DNA-GPT). Given a text, we first truncate it in the middle and then use only the preceding portion as input to the LLMs to regenerate the new remaining parts. By analyzing the differences between the original and new remaining parts through N-gram analysis in black-box or probability divergence in white-box, we can clearly illustrate significant discrepancies between machine-generated and human-written text. We conducted extensive experiments on the most advanced LLMs from OpenAI, including text-davinci-003, GPT-3.5-turbo, and GPT-4, as well as open-source models such as GPT-NeoX-20B and LLaMa-13B. Results show that our zero-shot approach exhibits state-of-the-art performance in distinguishing between human and GPT-generated text on four English and one German dataset, outperforming OpenAI's own classifier, which is trained on millions of text. Additionally, our methods provide reasonable explanations and evidence to support our claim, which is a unique feature of explainable detection. Our method is also robust under the revised text attack and can additionally solve model sourcing. Codes are available at https://github.com/Xianjun-Yang/DNA-GPT

Weak-to-Strong Jailbreaking on Large Language Models

Large language models (LLMs) are vulnerable to jailbreak attacks - resulting in harmful, unethical, or biased text generations. However, existing jailbreaking methods are computationally costly. In this paper, we propose the weak-to-strong jailbreaking attack, an efficient method to attack aligned LLMs to produce harmful text. Our key intuition is based on the observation that jailbroken and aligned models only differ in their initial decoding distributions. The weak-to-strong attack's key technical insight is using two smaller models (a safe and an unsafe one) to adversarially modify a significantly larger safe model's decoding probabilities. We evaluate the weak-to-strong attack on 5 diverse LLMs from 3 organizations. The results show our method can increase the misalignment rate to over 99% on two datasets with just one forward pass per example. Our study exposes an urgent safety issue that needs to be addressed when aligning LLMs. As an initial attempt, we propose a defense strategy to protect against such attacks, but creating more advanced defenses remains challenging. The code for replicating the method is available at https://github.com/XuandongZhao/weak-to-stron