BadPrompt: Backdoor Attacks on Continuous Prompts

The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available at https://github.com/papersPapers/BadPrompt.Comment: Accepted at NeurIPS 202

A data-driven model to quantify the impact of river discharge on tide-river dynamics in the Yangtze River estuary

Understanding the role of river discharge on tide-river dynamics is of essential importance for sustainable water management (flood control, salt intrusion, and navigation) in estuarine environments. It is well known that river discharge impacts fundamental tide-river dynamics, especially in terms of subtidal (residual water levels) and tidal properties (amplitudes and phases for different tidal constituents). However, the quantification of the impact of river discharge on tide-river dynamics is challenging due to the complex interactions of barotropic tides with channel geometry, bottom friction, and river discharge. In this study, we propose a data-driven model to quantify the impact of river discharge on tide-river dynamics, using water level time series data collected through long-term observations along an estuary with substantial variations in river discharge. The proposed model has a physically-based structure representing the tide-river interaction, and can be used to predict water level using river discharge as the sole predictor. The satisfactory correspondence of the model outputs with measurements at six gauging stations along the Yangtze River estuary suggest that the proposed model can serve as a powerful instrument to quantify the impacts of river discharge on tide-river dynamics (including time-varying tidal properties and tidal distortion), and separate the contribution made by riverine and tidal forcing on water level. The proposed approach is very efficient and can be applied to other estuaries showing considerable impacts of river discharge on tide-river dynamics.info:eu-repo/semantics/publishedVersio

Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery

Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems

A hybrid active contour segmentation method for myocardial D-SPECT images

Ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a three-dimensional myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artefacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighbourhood centre. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy (RSF) model and local image fitting energy (LIF) model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation

Recombinant human thioredoxin-1 promotes neurogenesis and facilitates cognitive recovery following cerebral ischemia in mice

AbstractCerebral ischemia (CI) can induce loss of hippocampal neurons, causing cognitive dysfunction such as learning and memory deficits. In adult mammals, the hippocampal dentate gyrus contains neural stem cells (NSCs) that continuously generate newborn neurons and integrate into the pre-existing networks throughout life, which may ameliorate cognitive dysfunction following CI. Recent studies have demonstrated that recombinant human thioredoxin-1 (rhTrx-1) could promote proliferation of human adipose tissue-derived mesenchymal stem cells and angiogenesis. To investigate whether rhTrx-1 also regulates hippocampal neurogenesis following CI and its underlying mechanisms, adult mice were subjected to bilateral common carotid arteries occlusion (BCCAO) to induce CI and treated with rhTrx-1 before reperfusion. Mice treated with rhTrx-1 showed shortened escape latencies in Morris water maze by 30 days and improvements in spatial memory demonstrated by probe trial test. Enhanced NSCs proliferation was observed at day 14, indicated by BrdU and Ki67 immunostaining. Doublecortin (DCX)+ cells were also significantly increased following rhTrx-1 treatment. Despite increases in BrdU+/NeuN+ cells by day 30, the double-labeling to total BrdU+ ratio was not affected by rhTrx-1 treatment. The promotive effects of rhTrx-1 on NSCs proliferation and differentiation were further confirmed in in vitro assays. Western blot revealed increased ERK1/2 phosphorylation after rhTrx-1 treatment, and the ERK inhibitor U0126 abrogated the effects of rhTrx-1 on NSCs proliferation. These results provide initial evidence that rhTrx-1 effects neurogenesis through the ERK signaling pathway and are beneficial for improving spatial learning and memory in adult mice following global CI

Do radioiodine-avid lymph nodes from differentiated thyroid cancer on the initial posttherapy scan need repeated 131I therapy?

BackgroundResidual/recurrent lymph node metastase (LNM) is often found after differentiated thyroid cancer (DTC) surgery. This study aimed to investigate whether patients complicated with radioiodine-avid (131I+) lymph nodes from DTC on the initial posttherapy scan (PTS) need repeated 131I therapy.MethodsFrom June 2013 to August 2022, DTC patients with 131I+ lymph nodes on the initial PTS who received at least two cycles of 131I therapy were retrospectively enrolled. They were divided into a complete response (CR) group and an incomplete response (IR) group according to their response to the initial 131I therapy based on the 2015 American Thyroid Association (ATA) guidelines.ResultsA total of 170 DTC patients with 131I+ lymph nodes on the initial PTS were included; 42/170 (24.7%) patients were classified into the CR group and 128/170 (75.9%) were classified into the IR group according to their response to the initial 131I therapy. None of the 42 CR patients had disease progression at the subsequent follow-up, and 37/170 (21.8%) IR patients improved after repeated therapy. Univariate analysis showed that N stage (P=0.002), stimulated thyroglobulin (sTg) level before initial 131I therapy (P<0.001), LNM size (P<0.001), number of total residual/recurrent LNM (P=0.021), radioiodine-nonavid (131I-) LNM (P=0.002) and ultrasound features (P<0.001) were related to the initial treatment response. On multivariate analysis, sTg level (OR=1.186, P<0.001) and LNM size (OR=1.533, P=0.004) were independent risk factors for IR after initial 131I therapy. The optimal sTg level and LNM size cutoff value for predicting the treatment response after initial 131I therapy were 18.2 µg/l and 5mm.ConclusionThis study suggested that approximately one-quarter of patients with 131I+ lymph nodes on initial PTS, especially those with N0 or N1a stage, lower sTg level, smaller LNM size, ≤2 residual/recurrent LNMs, negative ultrasound features and no 131I- LNM, remain stable after one cycle of 131I therapy and do not need repeated therapy

Robust Nuclear Spin Polarization via Ground-State Level Anti-Crossing of Boron Vacancy Defects in Hexagonal Boron Nitride

Nuclear spin polarization plays a crucial role in quantum information processing and quantum sensing. In this work, we demonstrate a robust and efficient method for nuclear spin polarization with boron vacancy ($\mathrm{V_B^-}$) defects in hexagonal boron nitride (h-BN) using ground-state level anti-crossing (GSLAC). We show that GSLAC-assisted nuclear polarization can be achieved with significantly lower laser power than excited-state level anti-crossing, making the process experimentally more viable. Furthermore, we have demonstrated direct optical readout of nuclear spins for $\mathrm{V_B^-}$ in h-BN. Our findings suggest that GSLAC is a promising technique for the precise control and manipulation of nuclear spins in $\mathrm{V_B^-}$ defects in h-BN.Comment: 6 pages, 4 figure

Prevalence of Childhood Atopic Dermatitis: An Urban and Rural Community-Based Study in Shanghai, China

Background: Atopic dermatitis (AD) is a common inflammatory and chronically relapsing disorder with increasing prevalence. However, little is known about its prevalence in Shanghai, the top metropolitan of China. This study will estimate and compare the prevalence of AD in urban and rural areas in representative samples of 3 to 6-year-old children in Shanghai. Methodology/Principal Findings: A descriptive cross-sectional study was performed. Pre-school children were obtained by cluster sampling from 8 communities in different districts in Shanghai. The main instrument was the core questionnaire module for AD used in the U.K. Working Party’s study. All the data were statistically analyzed by EpiData 3.1 and SPSS16.0. A total of 10436 children completed the study satisfactorily, with a response rate of 95.8%. The prevalence of AD in 3 to 6-year-old children was 8.3 % (Male: 8.5%, Female: 8.2%). The prevalence in urban areas of Shanghai was gradiently and significantly higher than that in rural areas. The highest prevalence was in the core urban area (10.2 % in Xuhui Tianping) vs. the lowest far from the urban areas (4.6 % in Chongming Baozhen). Conclusions/Significance: The prevalence of AD was 8.3 % (95%CI: 7.6%–9.1%) in children aged 3 to 6 in Shanghai. Th

From concept to action: a united, holistic and One Health approach to respond to the climate change crisis

It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided