NormAUG: Normalization-guided Augmentation for Domain Generalization

Deep learning has made significant advancements in supervised learning. However, models trained in this setting often face challenges due to domain shift between training and test sets, resulting in a significant drop in performance during testing. To address this issue, several domain generalization methods have been developed to learn robust and domain-invariant features from multiple training domains that can generalize well to unseen test domains. Data augmentation plays a crucial role in achieving this goal by enhancing the diversity of the training data. In this paper, inspired by the observation that normalizing an image with different statistics generated by different batches with various domains can perturb its feature, we propose a simple yet effective method called NormAUG (Normalization-guided Augmentation). Our method includes two paths: the main path and the auxiliary (augmented) path. During training, the auxiliary path includes multiple sub-paths, each corresponding to batch normalization for a single domain or a random combination of multiple domains. This introduces diverse information at the feature level and improves the generalization of the main path. Moreover, our NormAUG method effectively reduces the existing upper boundary for generalization based on theoretical perspectives. During the test stage, we leverage an ensemble strategy to combine the predictions from the auxiliary path of our model, further boosting performance. Extensive experiments are conducted on multiple benchmark datasets to validate the effectiveness of our proposed method.Comment: Accepted by IEEE Transactions on Image Processing (TIP

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Most traditional AI safety research has approached AI models as machines and centered on algorithm-focused attacks developed by security experts. As large language models (LLMs) become increasingly common and competent, non-expert users can also impose risks during daily interactions. This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety. Specifically, we study how to persuade LLMs to jailbreak them. First, we propose a persuasion taxonomy derived from decades of social science research. Then, we apply the taxonomy to automatically generate interpretable persuasive adversarial prompts (PAP) to jailbreak LLMs. Results show that persuasion significantly increases the jailbreak performance across all risk categories: PAP consistently achieves an attack success rate of over $92\%$ on Llama 2-7b Chat, GPT-3.5, and GPT-4 in $10$ trials, surpassing recent algorithm-focused attacks. On the defense side, we explore various mechanisms against PAP and, found a significant gap in existing defenses, and advocate for more fundamental mitigation for highly interactive LLMsComment: 14 pages of the main text, qualitative examples of jailbreaks may be harmful in natur

Optimal power control in cognitive satellite terrestrial networks with imperfect channel state information

To address the spectrum scarcity in future satellite communications, employing the cognitive technique in the satellite systems is considered as a promising candidate, which leads to an advanced architecture known as cognitive satellite terrestrial networks. Power control is a significant research challenge in cognitive satellite terrestrial networks, especially when the perfect channel state information (CSI) of satellite or terrestrial links is unavailable. In this context, we investigate the impact of imperfect CSI of both desired satellite link and harmful terrestrial interference link on the power control scheme in cognitive satellite terrestrial networks. By adopting a pilot-based channel estimation of satellite link and a back-off interference power constraint of terrestrial interference link, a novel power control scheme is presented to maximize the outage capacity of the satellite user while guaranteeing the communication quality of primary terrestrial user. Extensive numerical results quantitatively demonstrate the effect of various system parameters on the proposed power control scheme in cognitive satellite terrestrial networks with imperfect CSI

Autologous Skin Fibroblast-Based PLGA Nanoparticles for Treating Multiorgan Fibrosis

Fibrotic diseases remain a substantial health burden with few therapeutic approaches. A hallmark of fibrosis is the aberrant activation and accumulation of myofibroblasts, which is caused by excessive profibrotic cytokines. Conventional anticytokine therapies fail to undergo clinical trials, as simply blocking a single or several antifibrotic cytokines cannot abrogate the profibrotic microenvironment. Here, biomimetic nanoparticles based on autologous skin fibroblasts are customized as decoys to neutralize multiple fibroblast-targeted cytokines. By fusing the skin fibroblast membrane onto poly(lactic-co-glycolic) acid cores, these nanoparticles, termed fibroblast membrane-camouflaged nanoparticles (FNPs), are shown to effectively scavenge various profibrotic cytokines, including transforming growth factor-beta, interleukin (IL)-11, IL-13, and IL-17, thereby modulating the profibrotic microenvironment. FNPs are sequentially prepared into multiple formulations for different administration routines. As a proof-of-concept, in three independent animal models with various organ fibrosis (lung fibrosis, liver fibrosis, and heart fibrosis), FNPs effectively reduce the accumulation of myofibroblasts, and the formation of fibrotic tissue, concomitantly restoring organ function and indicating that FNPs are a potential broad-spectrum therapy for fibrosis management.Peer reviewe

Changes of Adult Population Health Status in China from 2003 to 2008

Objectives: The purpose of this study was to examine the change in health status of China’s adult population between the years of 2003 and 2008 due to rapid economic growth and medical system improvement. Methods: Data from the third and fourth Chinese national health services surveys covering 141,927 residents in 2003 and 136,371 residents in 2008 who were aged.18 years were analyzed. Results: Chinese respondents in 2008 were more likely to report disease than in 2003. Smoking slightly decreased among men and women, and regular exercise showed much improvement. Stratified analyses revealed significant subpopulation disparities in rate ratios for 2008/2003 in the presence of chronic disease, with greater increases among women, elderly, the Han nationality, unmarried and widow, illiterate, rural, and regions east of China than other groups. Conclusions: Chinese adults in 2008 had worse health status than in 2003 in terms of presence of chronic disease. China’s reform of health care will face more complex challenges in coming years from the deteriorating health status in Chinese adults

The Detection of Yarn Roll’s Margin in Complex Background

Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm

Large π-Conjugated Chromophores Derived from Tetrathiafulvalene

A large π-conjugated chromophore composed of two dipyrido[3,2-a:2′,3′-c]phenazine units directly fused to the central tetrathiafulvalene core has been prepared as a bridging ligand and its strong binding ability to Ru2+ to form a new dinuclear complex is presented. The electronic absorption and luminescence spectra and the electrochemical behavior of the free ligand and the Ru2+ complex have been investigated in detail. The free ligand shows a very strong band in the UV region consistent with ligand-centered π–π* transitions and an intense broad band in the visible region that corresponds to an intramolecular charge-transfer (ILCT) transition. Upon coordination, a metal-to-ligand charge-transfer band appears at 22520 cm−1, and the ILCT band is bathochromically shifted by 1620 cm−1. These electrochemically amphoteric chromophores have also been characterized by spectro-electrochemical methods. The oxidized radical species of the free ligand show a strong tendency to undergo aggregation, in which long-distance attractive interactions overcome the electrostatic repulsion. Moreover, these two new chromophores reveal an ILCT fluorescence with large solvent-dependent Stokes shifts and quantum efficiencies of 0.052 for the free ligand and 0.016 for its dinuclear Ru2+ complex in CH2Cl2

Improving the Detection Ability of Inductive Micro-Sensor for Non-Ferromagnetic Wear Debris

The inductive debris sensor has been studied because of its wide application prospects in mechanical health monitoring. In order to ensure a high-precision detection performance, a comprehensive method to improve the detection sensitivity and detection ability of the inductive sensor for non-ferromagnetic metal debris is proposed. Based on the characteristics of the eddy current inside the metal, the change of the coil impedance caused by the metal debris is increased by enhancing the magnetic field strength and selecting the optimal excitation frequency. The impedance detection method involving inductance and resistance parameters is used to improve the detection limit of non-ferromagnetic metal debris. The experimental results verify that the magnetic field in the detection region can be enhanced by adding a silicon steel strip (paramagnetic material) in the central hole of the coil, thereby greatly improving the detection sensitivity of the inductive sensor, and the concentrated distribution of the magnetic field avoids the double-peak signals generated by a single particle. The characteristics of the signal amplitude of non-ferromagnetic debris with excitation frequency are studied. Higher inductance, resistance amplitudes, and signal-to-noise ratio (SNR) can be obtained by using a high-frequency alternating current. Compared with inductance parameter detection, resistance parameter detection can detect smaller non-ferromagnetic debris. Combining the detection results of the inductance and resistance parameters can effectively improve the sensor’s ability to detect non-ferromagnetic debris

HPLC fingerprint and total antioxidant capacity of the extracts from the aerial part of Paeonia lactiflora Pall

ABSTRACTHigh-performance liquid chromatography (HPLC) fingerprinting is a reliable quality control method for traditional Chinese medicine (TCM). As a research method combining fingerprint and pharmacodynamics of TCM, the TCM spectral effect relationship is widely used in the quality control of TCM and its preparations, which can better reflect the intrinsic quality of TCM and is of great significance for the research on the material basis of TCM efficacy. In this study, an HPLC fingerprint of the extracts from the aerial part of Paeonia lactiflora Pall (APPE) was first established. Then, the HPLC fingerprint of APPE was evaluated by stoichiometry analysis methods such as similarity analysis, hierarchical clustering analysis, and principal component analysis. At the same time, the spectral effect relationship between the HPLC pattern and the total antioxidant capacity of APPE was analyzed by Partial Least Squares Regression (PLSR). The results showed that the similarity correlation coefficient of the 12 batches was ≥0.994, and the total antioxidant capacity was stable. In addition, PLSR analysis showed that 13 chemical components were closely related to the antioxidant activity of APPE